B1.BM2.4 (Informatics); B1.VM4.8 (Programming); B1.VM4.2 (Introduction to IT)
Course Objectives
This course allows student to become familiar with standard algorithms which are used in projects of enterprise information system development, web application development and some specific application development. Course gives student a feel for algorithms and data structures as a central part of what it is to be a computer scientist. Students will find out the fact that there are often several algorithms for some problem, and one algorithm may be better than another or one algorithm better in some circumstances and another better in others. During the course student will manage to implement medium sized programs based on a range of standard algorithms and data structures and develop or making appropriate use of libraries.
Learning Outcomes
Understand the parameters of an algorithm complexity.
Know standard algorithm s of quick sorting, graph and tree processing algorithms, simple social network analysis algorithms.
Design and implement medium sized programs based on a range of standard algorithms and data structures and develop or making appropriate use of libraries.
Choose the appropriate data structure and algorithm design method for a specified application.
Syllabus
Algorithm complexity, characteristics.
Data sorting algorithms.
List, Stack, Queue processing algorithms and their application.
The main goal - to prepare students for using and developing present-day database control systems in science and industry
Learning Outcomes
By the end of the course the students will know: • concepts of using relation model in science and industry; • fundamentals of development of the data structure in science and industry. The students will be able to: • apply methods of database design in science and industry. The students will have the experience in: • apply methods of database design in science and industry.
Syllabus
Topic 1 Development of the Database Concept Topic 2 Design of the Information Model in Science and Industry Topic 3 Applying OLTP in Science and Industry Topic 4 Applying Relational OLAP in Science and Industry Topic 5 Applying Big Data in Science and Industry
Labs
Lab 1 Design of the Information Model for the Specified Domain Lab 2 Creating Data Structure Using OLTP Lab 3 Creating Model Using OLAP Lab 4 Creating Model Using Big Data Approach
Projects
Database design in science and industry
Assessment
Exam
Resources
1. Chudinov I.L. Databases: study aid / I.L. Chudinov, V.V. Osipova; Tomsk Polytechnic University. – Tomsk: TPU Publisher, 2011. – 144 p. 2. Date C. J. Introduction to Database Systems. – 8th Edition. – Addison-Wesley Longman, 2006. – 1328 p. 3. Database Systems: Design, Implementation, & Management. – 11th Edition. – Course Technology, 2014. – 784 p. 4. Kudyba S. Big Data, Mining, and Analytics: Components of Strategic Decision Making. – Auerbach Publications, 2014 – 325 p.
This course aims to provide students with a fundamental knowledge of computer hardware and computer systems, with an emphasis on system design and performance. The module concentrates on the principles underlying systems organization, issues in computer system design, and contrasting implementations of modern systems.
Learning Outcomes
Understand Architecture of modern computing processes and systems knowledge; Understand different processor architectures and system-level design processes; Parallel programming language usage appropriateness analysis knowledge; Develop systems programming skills in the content of computer system design and organization
Syllabus
Current course provides computer architecture general concepts, architecture classification, modern techniques analysis methods, parallel programs parallelism mathematical analysis methods and modern processor systems overview in accordance with the criteria applicable to parallel systems.
Labs
Parallel computer systems efficiency estimation Computer system processing speed calculation Operative systems stochastic network models design Computer system characteristics calculations using stochastic network model Computer system modules optimal choice Computer system processes speed optimal choice
Projects
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Assessment
Exam
Resources
1. Clements, Alan. Principles of Computer Hardware (Fourth Edition). OUP UK – 2006 – p.672 2. Hennessy, John; Patterson, David. Computer Architecture: A Quantitative Approach (Fifth Edition). Morgan Kaufmann – 2011 – p.856 3. Randal E. Bryant, David R. O'Hallaron. Computer Systems: A Programmer's Perspective, 2nd Edition. Pearson – 2010 – p.959
Modern issues of informatics and computer science, Intelligent systems, Decision making theory
Course Objectives
The main goal - to prepare students for research and design activity.
Learning Outcomes
To study the methods of computing intelligence; the methods of its application to solution of practical problems; to develop software implementation of neural networks, evolutionary algorithms and fuzzy systems. By the end of the course the students will know: modern techniques and approaches of computing intelligence, methods of its application to solution of practical problems of artificial cognition and management. The students will be able to: set the problem and develop algorithms for its solution using methods of computing intelligence, carry out the analysis of research literature, carry out the analysis of received solutions. The students will have the experience in: application technologies of methods of computing intelligence for solving practical problems, skills in development and testing of artificial neural networks, evolutionary algorithms and fuzzy systems in one of the highlevel programming languages.
Compulsory 1. S. Osovsky. 2002. Neural networks for information processing. – М.: Finansy i statistika, 344p. 2. Luke S. Essentials of Metaheuristics. A Set of Undergraduate Lecture Notes. September, 2009. Text: http://cs.gmu.edu/~sean/book/metaheuristics/. Russian translation:http://qai.narod.ru/GA/metaheuristics.html. 3. V. G. Spitsyn, Y. R. Tsoy. 2007. Knowledge representation in information systems: handbook. – Tomsk: TPU publishing, 160p. 4. I. Z. Batyrshin, A. A. Nedosekin, A. A. Stetsko, V. B. Tarasov, A. V. Yazenin, N. G. Yarushkina. 2006. Theory and practice of fuzzy hybrid systems /Edited by N.G. Yarushkina. – М.: Phyzmatlit. Further reading 1. S. Khaikin. 2006. Neural networks: full course. – М.: Williams, 995p. 2. V. G. Redjko. 2003. Evolutionary cybernetics. М. – Nauka, 156p. (Informatics: infinite possibilities and possible limits). 3. V. G. Redjko. 2005. Evolution, neural networks, intelligence. Models and concepts of evolutionary cybernetics. Moscow: URSS. Software and Internet resources 1. Neural Computation Journal. http://www.mitpressjournals.org/loi/neco. 2. Evolutionary Computation Journal. http://www.mitpressjournals.org/loi/evco. 3. EJ «Neuroinformatics». http://www.niisi.ru/iont/ni/Journal/. 4. EJ «Fuzzy systems and soft computing». http://fuzzy.tversu.ru/. 5. Neural Networks Research Group. http://nn.cs.utexas.edu/ 6. Evolutionary Complexity Research Group (EPlex). 48 http://eplex.cs.ucf.edu/ 7. Russian association of neuroinformatics. http://ni.iont.ru/ 8. Russian association of artificial intelligence. http://www.raai.org/ 9.Russian association of fuzzy systems and soft computing. http://www.ransmv.narod.ru/
1. Ability and willingness to specialist research activities in the development, testing and diagnostic products and technologies, as well as means of technological equipment of modern automated production, created with the use of advanced information technologies of world level. 2. Preparing graduates for the operation and maintenance of modern high-tech line of automated production with high efficiency, the implementation of environmental protection requirements and the rules of industrial safety.
Learning Outcomes
1. Apply deep scientific, mathematical and engineering knowledge for the creation and processing of new materials. 2. Develop processes, design and use of new equipment and tools for processing materials and products that are competitive on the world market of engineering production. 3. Conduct theoretical and experimental research in the field of advanced materials processing technologies, nanotechnology, new materials in a complex and uncertain environment. 4. Implement, operate and maintain the modern high-tech automated production lines, to ensure their high efficiency, observe the rules of health and safety in the production of machine-building, to perform on the environmental protection requirements. 5. Actively speak a foreign language at a level that allows you to work in a foreign environment, to develop the documentation to present and defend the results of innovative engineering. 6. Effectively work individually, as a member and leader of the group, consisting of specialists from different disciplines and levels, to demonstrate responsibility for results and willingness to follow the corporate culture of the organization. 7. Self-learning and continuously improve their qualifications during the whole period of professional activity.
Syllabus
1. Introduction. 1.1. Basic concepts and definitions. 1.2. Management principles in automatic systems. 1.3. Classification of automatic control systems. 2. Linear continuous model and the characteristics of the automatic control systems. 2.1. Laplace transform and its properties. 2.2. Differential equations and transfer functions. 2.3. Temporal characteristics. 2.4. The frequency characteristics. 2.5. Characteristics of standard units. 2.6. The transformation of block diagrams. 2.7. Description of the systems in the state space. 3. Analysis of the basic properties of linear continuous automatic control systems. 3.1. The concept of stability control systems. The general condition of stability. 3.2. The algebraic Hurwitz stability criterion. 3.3. Frequency Nyquist stability criterion. 3.4. Evaluation of the accuracy of regulation in the steady state. 3.5. Direct evaluation of the quality of transients. 3.6. Indirect evaluation of the quality of transients on the roots of the characteristic equation of the system. 3.7. Indirect evaluation of the quality of transients on the frequency characteristics of the system. 3.8. Sensitivity Evaluation systems. Invariant systems. 3.9. The analysis of linear stochastic systems with stationary random actions. 3.10. Fundamentals of systems analysis in the state space. 4. Objectives and methods of synthesis of linear continuous automatic control systems. 4.1. Purpose and types of corrective devices. 4.2. The problems solved by using corrective devices. 4.3. Synthesis sequentially correcting device according LACHH method. 4.4. Synthesis sequentially correcting device according to the method of standard transfer characteristics. 4.5. Synthesis corrective devices meshed systems. 5. The digital automation system. 5.1. General characteristics of discrete systems. 5.2. Mathematical description of discrete systems. 5.3. Analysis and synthesis of digital systems. 6. Non-linear automatic systems. 6.1. Classification and characteristics of non-linear units. 6.2. Investigation of the stability of nonlinear systems. 6.3. The study of periodic modes of nonlinear systems by the method of harmonic balance. 6.4. Technical nonlinearities linearization. 6.5. Synthesis of non-linear position controller. 7. Fundamentals of Optimal Control Theory.
Labs
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Projects
1. Automation of production; 2. Optimization of production. 3. Determination of the dynamic characteristics and stability of ACS. 4. The use of the distribution laws in the statistical processing of test results; 5. Software design.
Assessment
Exam
Resources
1. Automatic Control Theory: a textbook for high schools / SE Dushyn [et al.]; ed. VB Yakovlev. - 3rd ed. - Moscow: Higher School, 2009. - 567 with: il. - For universities. - Bibliography: p. 563-567. - ISBN 978-5-06-006126-0. 2. The Theory of Automatic Control. Digital and non-linear automatic control systems: a tutorial / GA Belov; Chuvash State University (CSU). - Cheboksary Univ. Of the Chechen State University, 2009. - 448 p: il. . - Bibliography: p. 441-442. - ISBN 978-5-7677-1315-8. 3. The Theory of Automatic Control in the Examples and Problems with Solutions in MATLAB: Tutorial / AR Haiduk, VE Belyaev, TA Pyavchenko. - 2nd ed. - St. Petersburg. Lan, 2011. - 464 p: il. - Textbooks for schools. Special literature. . - Bibliography: p. 459 - Thematic Index: p. 460-463. - ISBN 978-5-8114-1255-6. 4. Automatic Control Theory [electronic resource]: laboratory practical / AP Zaitsev, AD Mitaenko, KV Samples; Tomsk Polytechnic University; Tomsk Polytechnic University (TPU). - Tomsk: Publishing house TPU, 2011 Part 1 - 1 computer file (pdf; 4.2 MB). - 2011. - Title from title screen. - The electronic version of the printed publication. - Access from the corporate network TPU. - System Requirements: Adobe Reader. access scheme: http://www.lib.tpu.ru/fulltext2/m/2012/m306.pdf 5. Automatic Control Theory [electronic resource]: laboratory practical / AP Zaitsev, AD Mitaenko, KV Samples; Tomsk Polytechnic University; Tomsk Polytechnic University (TPU). - Tomsk: Publishing house TPU, 2011 Part 2 - 1 computer file (pdf; 1.2 MB). - 2011. - Title from title screen. - The electronic version of the printed publication. - Access from the corporate network TPU. - System Requirements: Adobe Reader. access scheme: http://www.lib.tpu.ru/fulltext2/m/2012/m307.pdf 6. Automatic Control Fundamentals: Textbook / GG Sazonov. - Stary Oskol: TNT, 2013. - 236 p .: silt. - Bibliogr .: p. 234-235. - ISBN 978-5-94178-387-8.
The objective of the course is to develop knowledge, skills and experience in the field of automation of measurement and inspection; introduce student to the basic principles of selection of universal and special purpose instruments for measurement and inspection; measurements with coordinate measuring machine (CMM)
Learning Outcomes
Will be able to: • select probes and auxiliary devices for CMM; • calibrate probes and CMM; • perform locating of samples and various components for measurements; • carry out measurements using CMM
Syllabus
1. Means of measurement automation 2. Measuring devices for machine tools 3. Coordinate measuring machines: types, calibration, methods of measurement
Business applications development within corporate information systems
Level of study
Bachelor Degree
Workload
ECTS: 2 Total Hours: 80 Contact Hours: 40
Lectures: 12
Labs: 38
Seminars: 0
Course Code
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Semester
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Prerequisites
B1.BM2.4 (Informatics); B1.VM4.8 (Programming); B1.VM4.2 (Introduction to IT)
Course Objectives
This course allows student to become familiar in business applications developing within exploited corporate information system. Developed business application is intended for increasing the efficiency of an enterprise specific business processes. During the course student will manage to design medium sized business application that solve the particular task of logistics, financial processes. Students in practice will study developing business application for Microsoft Dynamics AX ERP system.
Learning Outcomes
1. Understand the purpose of business application development. 2. Know standard notation of business application design (DFD, UML). 3.Design and implement medium sized programs that solve the particular task of logistics, financial processes 4. Choose the appropriate data objects and algorithms for a specified business application.
Syllabus
1. Business application within corporate information systems and ERP systems. 2. Business application design based on DFD, UML. 3. Introduction on business application development for Microsoft Dynamics AX ERP system. 4. Develop business application for logistic tasks. 5. Develop business application for financial tasks.
Labs
1. Design a conceptual project of business application. 2. Design and develop a simple business application for for Microsoft Dynamics AX (DAX) ERP system. 3. Jobs, Tables, Classes, Dialogs in business application (DAX). X++ language. 5. Business application for material management. 6. Business application for accounting operation,
Projects
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Assessment
Credit test
Resources
1. The Application Research about Data Warehouse Based on ERP URL: http://link.springer.com/chapter/10.1007/978-3-642-27287-5_22 2. Managing Business Models for Business Application Development URL: http://link.springer.com/chapter/10.1007/3-540-36277-0_24 3. X++ language programming guide URL: https://msdn.microsoft.com/en-us/library/aa867122.aspx 4. Dynamics ax 2012 developer training URL: https://www.youtube.com/watch?v=31dSYe3c2nk 5. Microsoft Dynamics AX overview URL: https://www.microsoft.com/en-us/dynamics/erp-ax-overview.aspx
The main goal - to prepare students for research and design activity.
Learning Outcomes
To acquire knowledge and skills in developing, testing, debugging, deploying and maintaining computer software with the use of CALS-technologies and CASE facilities. By the end of the course the students will know: a life cycle of programs, assessment methods of quality assurance, software development technologies and CASE-facilities; methods and algorithms of object-oriented programming; methods, languages and standards of information products support at different stages of their life cycle. The students will be able to: use standard software oriented to solving scientific, project and technological problems; work efficiently in a team while developing software. The students will have the experience in: collecting, processing and presenting scientific/technical information about research results ready to be published or presented in a form of an abstract, report or lecture; taking responsibility for work results of software development.
Syllabus
Topic 1 Why is software development difficult (lectures 2, labs 2) Topic 2 Life cycle of software (lectures 4, labs 6) Topic 3 Software requirements and clients’ needs (lectures 4, labs 6) Topic 4 Overview of software development methods (lectures 2, labs 2) Topic 5 Methods of fast software development (lectures 4, labs 6) Topic 6 Object-oriented software development (lectures 4, labs 6) Topic 7 Information support facilities of software and CALS technologies (lectures 4, labs 6) Topic 8 Software testing and debugging (lectures 4, labs 6) Topic 9 Software assessment (lectures 2, labs 4) Topic 10 Software deployment and support (lectures 2, labs 4)
Compulsory 1. Orlov S. 2003. Software Development Technologies. Saint-Petersburg: Piter Publishing House 2. Miroshnichenko E. A. 2014. Programming Technology. Textbook. Tomsk: TPU Publishing House. 3. Foks J. 1989. Software Development. Moscow: Mir. 4. ISO 9000-3: ISO 9001 Quality Assurance Standards. Part 3. International Standard Organization, Geneva, 1991.
Material Removal Processes, Automatics and Robotics, the Fundamentals of Computer Science
Course Objectives
Getting skills and knowledge in the concept of CAD/CAM in both a practical and theoretical level. Students will engage in Computer Aided Design and CAD/CAM code generation of part geometries developed. The aim of the module is to acquaint students with the issues of computer production integration with the use of CAD/CAM integrated environment.
Learning Outcomes
A student has knowledge as regards the role and significance of CAD/CAM systems in modern world. A student has knowledge as regards the utilization of particular modules of the CAD/CAM system for particular project-technological assignments. A student can design a sample 2D or 3D model in the CAD module. A student can plan and program the technology of making a part based on the previously prepared model. A student can prepare a program controlling the CNC machine tool (with the use of the CAM module) in order to make the designed object
Syllabus
The features of modern production. The definition of CAD/CAM systems; a historical development of the CAD/CAM systems. Computer technology applied during designing. Technology, the application and databases in CAD systems. Utilizing the CAD system to build similar and standardized parts. Geometric modelling in CAD systems as well as creating other models. The methodology of computer-aided work of a technologist. Designing a technology for conventional and CNC machine tools. Data processing in integrated CAD/CAM system. Geometric and technological databases. Tool libraries, the machined tools, and machining parameters. Geometric and direct files. Postprocessors. The description of LATHE and MILL technological modules. General principles of designing a tool track while preparing lathe and mill machining. Lathe machining with the use of the LATHE module. Drill and mill machining technology utilizing the MILL module of the CAM system.
Labs
3D modelling: creating parts and assemblies. CAM systems: LATHE module CAM systems: MILL module
Projects
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Assessment
Exam
Resources
Barry Leatham-Jones, Introduction to computer numerical control [ISBN: 0582290406] Thomas M. Crandell 2003, CNC machining and programming, Industrial Press New York [ISBN: 0831131187] Alejandro Reyes 2011, Beginner's Guide to SolidWorks 2011 Level I, SDC Publications [ISBN: 9781585036264] PowerMILL 2015 User guide, Delcam plc, 2014
The course Computer Graphics is designed to provide basic knowledge about Computer Graphics.
Learning Outcomes
By the end of the course the student will know: basic principles of 2D and 3D geometric operations and a range of computer graphics software, basic raster graphics algorithms for drawing 2D primitives, clipping, visible-line and visible-surface determination, visual realism.
• focus on fundamental concepts, principles and techniques; • introduce basic networking concepts, including: protocol, network architecture, reference models, layering, service, interface, multiplexing, switching, standards.
Learning Outcomes
• Preparing graduates for design activity in the development and implementation of hardware and software facilities of professional activities in accordance with the terms of reference and with the use of design automation. • Preparing graduates for self-learning and continuous professional self-improvement.
Syllabus
• Main Uses of Computer Networks • The Platform for Communications • Components of the Network • Application Layer Functionality • OSI Transport Layer (функциональность уровня транспорта) • TCP and UDP Protocols • OSI Network Layer • Routing • Addressing the Network: • OSI Data Link Layer • OSI Physical Layer • Ethernet
Labs
Network troubleshooting: • use software tools tests network connectivity and see where the problem is Introduction to Cisco IOS: • understand how to use the IOS CLI to configure and manage an IOS router Static routing: • learn how to configure the routers to route traffic across the network Dynamic routing: • learn how to configure the routers to route traffic across the network
Information systems architecture, The theory of information processes and systems
Course Objectives
Produce information system for Windows Gain a basic understanding of database architecture Develop a working knowledge of different application for database manipulation Understand database design principles
Learning Outcomes
To aquire systematic overview of control system dataware. By the end of the course the student will know: Relational Theory Relational Modeling Terminology Unified Modeling Language The students will be able to: build a conceptual model; build a logical model; normalize relational databases. The students will have the experience in: defining table relatives; designing relational databases.
Syllabus
Unit 1 Understanding and organizing Data-Past and Present Unit 2 Introducing Relational Theory Unit 3 Understanding Relational Modeling Terminology Unit 4 Understanding Data Modeling Methods—Graphical Syntax Unit 5 Introducing Object-Oriented Data Modeling Unit 6 Examining Levels of Analysis Unit 7 Building a Conceptual Model Unit 8 Building a Logical Model
Structural Materials Engineering, Metrology, Standardisation and Certification, Materials Science
Course Objectives
The objective of the course is to develop knowledge, skills and experience in the field of designing, manufacturing and application of common cutting tools
Learning Outcomes
Will be able to: • choose cutting tools, grades of cutting tool materials, optimal geometric parameters and cutting parameters; • resharp cutters, drills, core drills and milling cutters; • calculate values of the cutting tool geometric parameters; • design form cutters and broaches.
Syllabus
1. Design and Calculation of Broaches and Cutters 2. Design and Calculation of Drills, Core-Drills and Reamers 3. Design and Calculation of Milling Cutters 4. Design and Calculation of Thread Cutting Tools 5. Design and Calculation of Gear Cutting Tools 6. Cutting Tools for Automated Production
Labs
1. Geometry and Sharpening of Cutters 2. Geometry and Sharpening of Drills 3. Geometry and Sharpening of Core Drills and Reamers 4. Geometry and Sharpening of Milling Cutters
Projects
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Assessment
Exam
Resources
1. Material cutting and cutting tools : учебное пособие / С. В. Кирсанов (http://www.lib.tpu.ru/fulltext2/m/2014/m261.pdf) 2. Cutting Tool Applications. George Schneider, 2005 3. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006
The main goal - to prepare students for research and design activity.
Learning Outcomes
By the end of the course the students will know: - principles database construction. - the main data models. - the relational data management languages. - SQL relational data management language The students will be able to: - apply methods of database design for specific implementation areas; - develop object-oriented model of subject area. The students will have the experience in: - application of scientific and mathematical knowledge in solution of scientific and engineering problems in the field of informatics and computer science; - effective work both as an individual and a member of an interdisciplinary and international team in solution of innovative engineering problems; - self-study activities, continuous self-improvement in engineering, teaching activities.
Syllabus
Topic 1. Evolution of database concept (lectures 2) Topic 2. Database Analysis and design(lectures 4) Topic 3. SQL and Query Optimization (lectures 6) Topic 4. Stored Procedures, Triggers and Indexes (lectures 4) Topic 5. Transaction management and Concurrency control (lectures 4) Topic 6. Advanced Database Management Topics (lectures 6)
Labs
Lab 1. Modeling Data in the Organization (labs 4) Lab 2. Logical and Physical Database Design and Performance (labs 4) Lab 3. Data Modeling Tools and Notation (labs 4). Lab 4. SQL an Query Optimization (labs 6) Lab 5. Stored Procedures, Triggers and Indexes (labs 4) Lab 6. Data and Database Administration (labs 6) Lab 7. Object-Oriented Data Modeling (labs 4)
Projects
There will be also a semester-long project in this course
1. Thuraisingham, Bhavani. Database and Applications Security. Integrating Information Security and Data Management / B. Thuraisingham. — New York: Auerbach Publications, 2005. — 619 p.: ил. — Библиография в конце глав. — Index: p. 599-619. — ISBN 0-8493-2224-3. 2. Date. 1999. Introduction to database system: [translation from English.] /the 6th edition. Kiev; М.; St. Petersburg: «Williams» publishing, page 848. 3. T. Konnolly. 2000. Databases: design, implementation and support. Theory and practice: [translation from English.] / Т. Konnolly, К. Begg, А. Strachan. The 2nd edition. М.: «Williams» publishing, page 1120. 4. D. Khomonenko. 2002. Databases: textbook for high schools /А. D. Khomonenko, V. M. Tsygankov, M. G. Maltsev; ed. by А.D. Khomonenko. The 2nd edition – St. Petersburg: KORONA print, page 672. 5. Chudinov. 2000 Databases. Workbook. – Тomsk, 6. Educational materials of Dept. of Optimization system management.
“Material Cutting and Cutting Tools”, “Fundamentals of Mechanical Engineering”, “Metrology, Standardisation and Certification”, “Material Science”
Course Objectives
The objective of the course is to develop knowledge, skills and experience in the field of designing and manufacturing of complex cutting tools
Learning Outcomes
Will know: - terminology and basic concepts used in cutting tools manufacturing; - principle of cutting tools engineering; - trends in cutting tool engineering; - typical manifacturing routes for tool manufacturing Will be able to: design cutting tools of complex form; - design processes of cutting tool manufacturing; - compose manufacturing documentation in accordance with standard requirements.
1. Гинзбург Е.Г., Халебский Н.Т. Производство зубчатых колес. – Л.: Машиностроение, 1978. – 136 с. 2. Дибнер Л.Г., Шкурин Ю.П. Заточка спиральных сверл. – М.: Машиностроение, 1967. – 156 с. 3. Иноземцев Г.Г. Проектирование металлорежущих инструментов. – М.: Машиностроение, 1984. – 272 с. 4. Калашников С.Н. Зуборезные резцовые головки. – М.: Машиностроение, 1972. – 162 с. 5. Кирсанов С.В., Гречишников В.А., Схиртладзе А.Г., Кокарев В.И. Инструменты для обработки точных отверстий. – М.: Машиностроение, 2003. – 330 с. 6. Маслов А.Р. Приспособления для металлообрабатывающего инструмента: Справочник. – М.: Машиностроение, 1996. – 240 с. 7. Протяжки для обработки отверстий / Д.К. Маргулис, М.М. Твер- ской, В.Н. Ашихмин и др. – М. :Машиностроение, 1986. – 232 с. 8. Романов В.Ф. Расчеты зуборезных инструментов. – М.: Машино- строение, 1969. – 256 с. 9. Cutting Tool Applications. George Schneider, 2005 10. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006 11. Mechanical Technology. Material Removal Processes. Compendium. Jan Madl, 1996 12. Metal cutting (4th edition). Edward trent, Paul Wright. 2000, 464 p. 13. Metal cutting mechanics. V.P. Astakhov, 1998, 320 p. 14. Workshop practice (2nd edition). H.S.Bawa. Published by Tata McGraw Hill. 2009__
This course presents the fundamentals of digital signal and image processing with particular emphasis on problems in signal detection and image recognition. The focus of the course is a series of labs that are supported due to author’s experience in noised data processing.
Learning Outcomes
Subject Specific Intellectual Having successfully completed this course, you will be able to: • Identify the main fields of digital signal and image processing and explain the important concepts in these fields • Read critically key articles which have had significant impact on the discipline Transferable and Generic Having successfully completed this course, you will be able to: • Discuss and debate key topics and questions of interest within the discipline and to society in general • Motivate outsiders to learn more about digital signal and image processing Subject Specific Practical Having successfully completed this module, you will be able to: • Produce computational artefacts such as digital filtering, fast Fourier transform, continuous wavelet transform for the signal detection into a noised and damaged data
Syllabus
• Matlab® Software • Data Acquisition • Digital Filtering • Fourier Transform • Wavelet Transform
Labs
• MATLAB® software basics • Spectral analysis of the forest fire noise for early detection • The weak signal detection into a strong hydroacoustic noise using continuous wavelet transform • The interturn fault detection in a rotor generator using wavelet analysis of magnetic flux.
Resource type: Background textbook • J.-C. Pinoli, “Mathematical Foundations of Image Processing and Analysis,” Wiley, 2014. • T. Bose, “Digital Signal and Image Processing,” Wiley, 2011. Resource type: On-line resources • Biomedical Signal and Image Processing, MIT OpenCourseWare, HST.582J/6.555J/16.456J Course Materials, http://web.mit.edu/6.555/www/materials.html Resource type: Author’s articles Students will be provided the author's scientific papers in this field, which indexed in Scopus but article full texts are not available free. They will be expected to read in order to gain an overview of the field, and also have access to a range of textbooks which help them prepare for the assessed seminars and tests.
B1.BM2.4 (Informatics); B1.VM4.8 (Programming); B1.VM4.2 (Introduction to IT)
Course Objectives
• to provide students with a knowledge of signal representation in time and frequency domain; • to provide students with an understanding about basic properties of analog and discrete-time signals; • to develop practical skills of the discrete Fourier series; discrete Fourier transform and digital filters.
Learning Outcomes
• using of basic mathematics, science and technology for signal processing analytically and numerically; • understanding of the fundamentals and applications of discrete-time signals, including sampling, convolution, filtering, and discrete Fourier transforms; • performing the spectral analysis on real signals using Matlab software.
Syllabus
• Overview of Digital Signal Processing. • Review of Analog Signal Analysis. • Discrete-Time Signals and Systems. • Sampling and Reconstruction of Analog Signals. • z-transform; and inverse z-transform . • Discrete-Time Fourier Transform. • Discrete Fourier Series and Discrete Fourier Transform. • Representation of linear digital networks. • Digital filters.
Labs
1. Introduction to Matlab software and Signals Types Simulation. 2. Sampling signals and quantization. 3. Frequency analysis of periodic and nonperiodic signals. 4. Fourier Transform. Interpretation of the FT. Some important Fourier transforms. 5. Properties of the Fourier Transform (Linearity and scaling, modulation, convolution, Duality). 6. Discrete Fourier Transform. Derivation of DFT and inverse DFT.
Projects
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Assessment
Credit Test ( Pass/Fail)
Resources
1. R.G. Lyons: Understanding digital signal processing. 3rd ed., Prentice-Hall, 2010. 2. S.W. Smith: Digital signal processing – a practical guide for engineers and scientists. Newness, 2003. 3. Sanjit K. Mitra: Digital signal processing – a computer-based approach. McGraw-Hill, 2002. 4. A.V. Oppenheim, R.W. Schafer: Discrete-time signal processing. 3rd ed., Prentice-Hall, 2007.
1. To learn the fundamental theory about graphs and Boolean functions (definitions, theorems and their proofs) 2. To study the basic algorithms of graph theory and their modifications 3. To study basic algorithms of Boolean function minimization and of system of Boolean function minimization 4. To know applications of graph theory and Boolean functions theory
Learning Outcomes
1. Knowledge of basic definitions and theoretical results of the graph theory and Boolean function theory 2. Knowledge of basic algorithms of graph theory and their implementation 3. Skills of implementing of basic graph algorithms Skills of Boolean function minimization and of system of Boolean function minimization 4. Skills of both oral and written scientific communications
Syllabus
1. Basics of graph theory 2. Connectivity and optimal paths 3. Euler graphs and Hamiltonian graphs 4. Planarity and coloring problem 5. Basics of Boolean functions theory 6. Shannon expansion and full normal forms 7. Disjunctive normal forms 8. Boolean function minimization 9. Partial Boolean functions and systems of Boolean functions
Labs
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Projects
Projects include implementing algorithms in a programming language, delivering lectures and seminars, writing reviews of scientific papers. Any other options can be considered.
B1.VM4.2 Introduction to IT B1.VM4.9 File and database Management B1.BM3.3 Management B1.VM4.10 Software Project B1.VM4.19 Databases for science and industry
Course Objectives
• To build an understanding of the fundamental business processes and their support by IT and ERP-systems • To provide an overview of enterprise systems including their business purpose • To learn how to manage the supply chain of a company using an ERP system in simulated, real-time environment • To learn how to effectively analyze information from an ERP system to make business decisions • To gain an understanding and appreciation of the importance that leading people and managing change plays in the success of ERP implementations and long-term competitive advantage of companies • To prepare students for career opportunities in industry
Learning Outcomes
By the end of this course, you should be able to: • Demonstrate a good understanding of basic issues in Enterprise Systems, • Explain the scope of common Enterprise Systems (e.g., MM, SCM, CRM, HRM, • procurement), • Explain the challenges associated with implementing enterprise systems and their • impacts on organisations • Describe the selection, acquisition and implementation of enterprise systems • Use a leading Enterprise Systems package to support business operations • and decision-making, • Communicate and assess an organisation's readiness for enterprise system • implementation with a professional approach in written form, and • Demonstrate an ability to work independently and in a group.
Syllabus
1. Overview of ERP systems, their role in Business’ support and corporative IT-infrastructure 2. Implementation of ERP-Systems 3. Using ERP to Manage and Make Business Decisions
Labs
1. Business processes modelling 2. Introduction to ERP (MS AX 2012) 3. Customizing the ERP 4. Implementation the ERP
Projects
Individual projects to customize the ERP-system for business needs
Assessment
Exam
Resources
1. The Benefits of Implementing ERP System in Telecommunications http://www.sciencedirect.com/science/article/pii/S1877042815055020 2. Maguire, S., Ojiako, U., & Said, A. (2010). ERP implementation in Omantel: a case study. Industrial Management & Data Systems, 78-92 3. Doom, C., Milis, K., Poelmans, S., & Bloemen, E. (2010). Critical success factors for ERP Implementations in Belgian SMEs. Journal of Enterprise Information Management, 378-406 4. Ariwa, E., El-Qawasmeh, E., (2011), “A Learning Driven Model for ERP Software Selection Based on the Choquet Integral: Small and Medium Enterprises Context, Digital Enterprise and Information Systems”, Communications in Computer and Information Science, No. 194, pp. 358-371 5. Pacheco-Comer, A. A., González-Castolo, J. C., (2012), “An empirical study in selecting Enterprise Resource Planning Systems: The relation between some of the variables involve on it. Size and Investment”, Procedia Technology, Vol. 3, pp. 292-303 6. Selection Process of ERP Systems http://www.degruyter.com/view/j/bsrj.2013.34.issue-1/bsrj-2013-0004/bsrj-2013-0004.xml
Background in mathematics and general physics: Series, trigonometric functions, methods of solution of algebraic and differential equations, Integral Calculus, operations with vectors and complex numbers. Students should know such issues of these courses as electricity and magnetism, oscillations and waves, and the basic of circuit theory.
Course Objectives
The course is designed to apply operation principles of modern semiconductor devices and their characteristics to solve different problems of amplification and conversion of electric signals To form student knowledge in the field of modern electronics, basic electronic devices and circuits
Learning Outcomes
Course is designed to provide basic knowledge about modern electronics.
Syllabus
• Passive Circuit Components. • Semiconductor materials and their characteristics • p-n junction. Processes and properties. Types of junctions. • Electronics devices modeling. • Diodes. Operation and analysis. Application in power supplies. • Transistors. Bipolar and field-effect types. Characteristics and performance. • Transistor amplifiers and switches. Operation features. • Operational Amplifiers. • Thyistors and dynistors. Principle of operation and characteristics. Introduction to Boolean Algebra. • Boolean Arithmetic, Boolean Minimization via K-maps. • Digital Circuits.
Labs
• Passive RC-circuits. • Investigating of characteristics of diodes and Zener diodes. • Transistor amplifiers circuits (broadband, pulse). • Non-inverting and inverting amplifiers. • Investigating characteristics of bipolar and field-effect transistors • Power amplifiers. • Generator • Combinational logic circuits.
Projects
The term paper should be written using laboratory experiments’ results and should foresee the development of electronic article according to the requirement specification.
Informatics; Programming; Introduction to IT, Business application development within corporate information systems.
Course Objectives
The purpose of the course is to introduce to the basic principles of work in the framework of consulting projects related to automation of the industrial enterprises. The course describes the approaches to the classification of the consulting services. The place and task of consultants in consulting activities aimed to enterprise automation are considered as well. In practice classes, the problem of examination and selection of the most effective alternative to automate an enterprise process is performed. Students study tasks of designing, deploying a unified information space (on the basis of PLM-technologies) as well as issues of lifecycle management of a products by an industrial enterprises. Also the course discusses in theory and in practice, the main approaches to the integration of information systems, which are used in the majority of IT consulting projects.
Learning Outcomes
1. Understand how to analyze the activity of the enterprise. 2. Ability reasonably to choose IT solution among several. 3. Know how to develop solutions for the integration of information systems 4. To develop solutions to automate the management of the enterprise product life cycle,to apply methods to identify the causes of poor system performance and processes
Syllabus
1. Introduction to IT-consulting 2. Methods of expertise estimation in IT consulting 3. PLM-technologies and industrial enterprise 4. Integration task in IT-consulting 5. Quality management and statistics in quality.
Labs
1. Expertise with Delphi method. 2. Expertise with Saati method. 3. Introduction to product data management. Development of product item structure. 3. IT system integration based on COM and web-services. 5. Shewhart charts in process and product qulity.
Projects
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Assessment
Credit test
Resources
1. FEACO organisation [Электронный ресурс] URL:www.asconco.ru/index.php/feaco-2 2. Product Lifecycle Management for Society URL: http://www.springer.com/gp/book/9783642415005 3. Product Lifecycle Management: Towards Knowledge-Rich Enterprises URL: http://www.springer.com/gp/book/9783642357572 4. Gregor Hop, Bobbi Woolf. Templates of integration of corporate applications URL: http://www.enterpriseintegrationpatterns.com/downloads.html 5. SOAP Version 1.2 Part 1: Messaging Framework (Second Edition). URL:: http://www.w3.org/TR/soap12/ 6. Total Quality Management URL: http://asq.org/learn-about-quality/total-quality-management/overview/overview.html 7. Decision making with the analytic hierarchy process URL: http://www.colorado.edu/geography/leyk/geog_5113/readings/saaty_2008.pdf
Modern issues of informatics and computer science, Intelligent systems, Decision making theory
Course Objectives
The main goal - to prepare students for research and design activity.
Learning Outcomes
To study the methods of computing intelligence; the methods of its application to solution of practical problems; to develop software implementation of neural networks, evolutionary algorithms and fuzzy systems. By the end of the course the students will know: modern techniques and approaches of computing intelligence, methods of its application to solution of practical problems of artificial cognition and management. The students will be able to: set the problem and develop algorithms for its solution using methods of computing intelligence, carry out the analysis of research literature, carry out the analysis of received solutions. The students will have the experience in: application technologies of methods of computing intelligence for solving practical problems, skills in development and testing of artificial neural networks, evolutionary algorithms and fuzzy systems in one of the highlevel programming languages.
Compulsory 1. S. Osovsky. 2002. Neural networks for information processing. – М.: Finansy i statistika, 344p. 2. Luke S. Essentials of Metaheuristics. A Set of Undergraduate Lecture Notes. September, 2009. Text: http://cs.gmu.edu/~sean/book/metaheuristics/. Russian translation:http://qai.narod.ru/GA/metaheuristics.html. 3. V. G. Spitsyn, Y. R. Tsoy. 2007. Knowledge representation in information systems: handbook. – Tomsk: TPU publishing, 160p. 4. I. Z. Batyrshin, A. A. Nedosekin, A. A. Stetsko, V. B. Tarasov, A. V. Yazenin, N. G. Yarushkina. 2006. Theory and practice of fuzzy hybrid systems /Edited by N.G. Yarushkina. – М.: Phyzmatlit. Further reading 1. S. Khaikin. 2006. Neural networks: full course. – М.: Williams, 995p. 2. V. G. Redjko. 2003. Evolutionary cybernetics. М. – Nauka, 156p. (Informatics: infinite possibilities and possible limits). 3. V. G. Redjko. 2005. Evolution, neural networks, intelligence. Models and concepts of evolutionary cybernetics. Moscow: URSS. Software and Internet resources 1. Neural Computation Journal. http://www.mitpressjournals.org/loi/neco. 2. Evolutionary Computation Journal. http://www.mitpressjournals.org/loi/evco. 3. EJ «Neuroinformatics». http://www.niisi.ru/iont/ni/Journal/. 4. EJ «Fuzzy systems and soft computing». http://fuzzy.tversu.ru/. 5. Neural Networks Research Group. http://nn.cs.utexas.edu/ 6. Evolutionary Complexity Research Group (EPlex). 48 http://eplex.cs.ucf.edu/ 7. Russian association of neuroinformatics. http://ni.iont.ru/ 8. Russian association of artificial intelligence. http://www.raai.org/ 9.Russian association of fuzzy systems and soft computing. http://www.ransmv.narod.ru/
1. Ability and willingness to specialist research activities in the development, testing and diagnostic products and technologies, as well as means of technological equipment of modern, hydraulic system created with the use of advanced information technologies of world level. 2. Preparing graduates for the operation and maintenance of modern high-tech line of automated production with high efficiency, the implementation of environmental protection requirements and the rules of industrial safety.
Learning Outcomes
1. Implement, operate and maintain the modern high-tech automated production lines, to ensure their high efficiency, observe the rules of health and safety in the production of machine-building, to perform on the environmental protection requirements. 2. Actively speak a foreign language at a level that allows you to work in a foreign environment, to develop the documentation to present and defend the results of innovative engineering. 3. Effectively work individually, as a member and leader of the group, consisting of specialists from different disciplines and levels, to demonstrate responsibility for results and willingness to follow the corporate culture of the organization. 4. Self-learning and continuously improve their qualifications during the whole period of professional activity.
Syllabus
Chapter One Physical Characteristics of a Fluid 1.1. Introduction 1.2. Fluids and the continuum model 1.3. Basic dimensions 1.4. Characteristics of a fluid 1.4.1. Density, specific volume, specific weight, and specific gravity 1.4.2. Stress, pressure, and viscosity Chapter Two Fluid Statics and Fluids Moving as a Rigid Body 2.1. Introduction 2.2. Hydrostatic pressure at a point 2.3. The pressure field in a static liquid and manometry 2.4. Forces on submerged surfaces and the center of pressure 2.5. A fluid having a uniform acceleration Chapter three Flow Fields and the Fundamental Laws 3.1. Introduction 3.1.1. Some flow-field terminology3.1.2 the flux vector 3.2. Fundamental laws, systems, and control volumes and their mathematical significance 3.3. Conservation of mass in integral form3.4. One-dimensional channel flow 3.5. The continuity equation 3.5.1. Two alternate derivations of the continuity equation 3.6. The field equation based on newton's second law 3.7. More on the lagrangian and eulerian viewpoints-acceleration of a fluid particle as described from an eulerian viewpoint 3.7.1. The lagrangian viewpoint 3.7.2. The eulerian viewpoint Chapter four Flow of a Real Fluid, Dimensional Analysis, and Similitude 4.1. Introduction-flow of a real fluid 4.2. Why dimensional analysis? 4.3. Dimensional homogeneity and dimensional analysis Chapter five Steady, Incompressible Flow in Conduits and Pipes 5.1. Introduction 5.2 pipe entrance conditions and fully developed flow 5.3. Pressure drop in fully developed pipeflow-the friction factor and the moody diagram 5.3.1. Flow in noncircular conduits 5.4. Minor losses as a result of pipe fittings and sudden pipe contractions and expansions 5.5. Single-path pipeline problems 5.6 analytical determination of the friction factor for laminar flow 5.6.1. Analytical determination of the head-loss coefficient for a sudden expansion in a pipeline 5.7. Multipath pipeline problems and the hardy-cross method 5.7.1. Flow analysis by the hardy-cross method
Labs
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Projects
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Assessment
Exam
Resources
1. Fox, R.W. and McDonald A.T., Introduction to Fluid Mechanics, Wiley, New York, 1973. 2. Hunsacker, J.C., and Rightmire, B.G., Engineering Application of Fluid Mechanics, McGraw-Hill, New York, 1948. 3. Kenyon,R.A., Principles of Fluid Mechanics, Ronald Press, New York, 1960. 4. Kenyon,R.A., Bober, W., Fluid Mechanics, Wiley, Toronto,1980. 5. Potter, M.C. and Foss J.H., Fluid Mechanics, Ronald Press, New York, 1975. 6. Whitaker, S., Introduction to Fluid Mechanics, Prentice-hall, Englewood Cliff, N. J., 1968. 7. http://www.annualreviews.org/journal/fluid
Structural Materials Engineering, Metrology, Standardisation and Certification, Materials Science, Material Cutting and Cutting Tools
Course Objectives
The objective of the course is to acquire knowledge, skills and experience in the field of analysis and design of the manufacturing processes
Learning Outcomes
Will be able to: • choose methods of workpiece production; • assign tooling for product manufacturing; • carry out statistical analysis of machining accuracy; • perform tolerance analysis of the manufacturing processes; • carry out statistical analysis of machining accuracy; • design processes of parts production.
Syllabus
1. Introduction to Mechanical Production 2. Tolerance stack-ups and part location 3. Accuracy of manufacturing 4. Surface layer quality and material properties requirements 5. Production process effectiveness 6. Fundamentals of production process design
Labs
1. Industrial investigation of a lathe rigidity 2. Statistical analysis of machining accuracy 3. Measurement of thermal deformation of a cutter in finish turning 4. Effect of cutting parameters and diamond burnishing on surface finish 5. Analysis of the ring manufacturing accuracy
Projects
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Assessment
Exam
Resources
1. Fundamentals of Mechanical Engineering/ V. F. Skvortsov; Tomsk Polytechnic University (TPU). — Tomsk: Tomsk Polytechnic University Publishing House, 2014. (http://www.lib.tpu.ru/fulltext2/m/2014/m255.pdf) 2. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006 3. Shigley, Joseph E. Mechanical Engineering Design / J. E. Shigley, C. R. Mischke. — 6 Edition. — New York : McGraw-Hill, 2001. — 1248 p. : il. — Index: p. 1237-1248. — ISBN 0-07-365939-8.
Course is designed to provide basic knowledge about Theory of Measurement.
Learning Outcomes
This course is designed to MS students that measurements in any technical area are made in order to increase our knowledge about reality and to provide bases for decisions.
Syllabus
• Measurement quality. • Errors and accuracy. • Error models and measurement uncertainty. • Decision making. • Dimensional analysis. • Measurement tools. • Uncertainty Analysis. • Uncertainty analysis of a single indirect measurement. • Uncertainty analysis of repeated measurements. • Error sources and error reduction. The practical lessons are intended to supply simple examples of the calculation of measurement errors in technical measurements.
Labs
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Projects
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Assessment
Exam
Resources
1. S.V. Muravyov, Measurement Information Systems, Publisher: Tomsk Polytechnic University Press, 2005. 2. Connie L. Dotson, Fundamentals of Dimensional Metrology, Publisher: Delmar Cengage Learning; 5 edition, 2006. 3. Jay L. Bucher, The Metrology Handbook, Publisher: Quality Press; 2 Har/Cdr edition, 2012.
1. To learn the fundamental theory about graphs (definitions, theorems and their proofs) 2. To study the basic algorithms of graph theory and their modifications 3. To know applications of graph theory
Learning Outcomes
1. Knowledge of basic definitions and theoretical results of the graph theory 2. Knowledge of basic algorithms of graph theory and their implementation 3. Skills in modification of basic graph algorithms to solve nonstandard problems in different applications 4. Skills in both oral and written scientific communications
Syllabus
1. Basics of graph theory 2. Connectivity 3. Optimal paths 4. Location problem 5. Flows in networks 6. Covering and matching problems 7. Euler graphs 8. Hamiltonian graphs 9. Planarity 10. Coloring problem
Labs
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Projects
Projects include implementing algorithms in a programming language, delivering lectures and seminars, writing reviews of scientific papers. Any other options can be considered.
By the end of the course the student will know: • basic hydraulic drive performance; • design and function of various pumps and hydromotors; • controllers and directional control hydraulic equipment. The students will be able to: • read and make (draw) diagrams of hydraulic drives. The students will have the experience in: • on air-oil circuit development.
1. Gear and Wing Pumps 2. Eccentric Pumps 3. Hydraulic Engine Velocity Control 4. Pump Station Performance 5. Pump Station Performance 6. Switch Hydraulic Equipment 7. Controlling hydraulic Equipment 8. Testing Hydraulic Equipment
Projects
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Assessment
Exam
Resources
1. Bashta T.M. Hydraulics, hydraulic machines and hydraulic drives. - М.: Mechanical Englineering, 1970. - 424 p. 2. Bashta T.M. Hydraulic drive and hydraulic and pneumatic control systems. – М.: Mechanical Englineering, 1972. - 320 p. 3. Khurmi R. S. A Textbook Of Hydraulics Fluid Mechanics and Hydraulic Machines, 2000. - 670 p. 4. Mechanical engineering hydraulic drive. Edited by V.N. Prokofiev. – М.: Mechanical Engineering, 1978. – 495 p. 5. Pneumatic devices and systems in mechanical engineering. Reference book. Edited by E.V. Gerts. – М.: Mechanical Engineering, 1981 – 408 p. 6. Dmitriev V.N., Gradetsky V.G. Basics of pneumatic control. - М.: Mechanical Engineering, 1973. – 360 p. 7. Problem book on mechanical engineering hydraulics. Edited by I.I. Kukolevsky, L.G. Podviza. – М.: Mechanical Engineering, 1981 – 464 p. 8. Sveshnikov V.K., Usov A.A. Machine tool hydraulic drives: Reference bok. - М.: Mechanical Engineering, 1982. - 464 p. 9. Temny V.P. Basics of hydraulic control. – М.: Science, 1972. – 244 p. 10. Krauinsh P.Y., Smaylov S.A., Moyzes B.B. Hydraulics and air-oil circuit. Teaching aid. . TPU Publishing house, 2006. – 223 p. 11. Internet resource: //hydraulicspneumatics.com/
B1.VM4.2 Introduction to IT B1.VM4.9 File and database Management B1.BM3.3 Management B1.VM4.10 Software Project B1.VM4.19 Databases for science and industry
Course Objectives
Course mastering goals: training students in forming student’s knowledge, skills and experience in IT’s methods and tools to apply it for system analysis and processes’ simulation in different fields. The most important objectives of the course are: • skill of usage basic and specific knowledge in the field of modern information technologies for engineering purposes; • skill of usage basic and specific knowledge in the field of project management to perform complex engineering activity.
Learning Outcomes
By the end of this course, you should be able to: • To plan project’s performing, to develop Gantt’s diagram and to work in team • To know popular CASE-tools (Ramus, Business Studio, MS Visio) and methods (IDEF0, DFD, BPMN, UML, EPC) of formal description of business processes and enterprises • To design and simulate business-processes (from abstract to detailed levels and estimate them using Function Cost Analysis) and organizational charts by tools of modern CASE-software • To design the enterprise’ strategy (strategic maps and balanced scorecard indicators) and analyze organizational problems (Fishbone Diagram) • To develop technical task for information system according to business purposes.
Syllabus
1. Introduction to the course 2. Basics of system approach to formal description of applied field by IT-tool 3. Application of IT-methods and tools to formal description of applied field 4. Individual attestation work
Labs
1. Project of IT-complex 2. Plan to perform individual task 3. Business processes of enterprise 4. Organizational chart 5. IDEF0- and DFD-- diagrams 6. EPC-, Process- and Procedure- diagrams 7. UML- и BPMN-diagrams 8. Balanced scorecard indicators 9. Strategic map 10. Cause-Effect (Fishbone) Diagram 11. Imitational simulation and functional cost analysis of business processes 12. Technical task for information system 13. Report about the individual task
Projects
Individual attestation work – project to develop the project of information system for business purposes
Assessment
Exam
Resources
Primary literature: 1. Vichugova A.A., Vichugov V.N., Dmitrieva E.A., Tsapko G.P. Informatsionnie technologii: Uchebnoe posobie [Information technologies: A Textbook] Tomsk, Tomsk Polytechnic University, 2012. 105 p. 2. Managing Information Technology (7th Edition) Carol V. Brown, Daniel W. DeHayes, Jeffrey A. Hoffer, Wainright E. Martin, William C. Perkins - 2011 3. Complete Systems Analysis: The Workbook, the Textbook, the Answers. James Robertson. Suzanne Robertson, 2013 4. Green IT Strategies and Applications: Using Environmental Intelligence. Bhuvan Unhelkar, 2011 Supplementary literature: 1. Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development. Craig Larman, 2004 2. UML Distilled: A Brief Guide to the Standard Object Modeling Language (3rd Edition), Martin Fowler, 2003 3. Green Computing: Tools and Techniques for Saving Energy, Money, and Resources. Bud E. Smith, 2013 4. Design Patterns: Elements of Reusable Object-Oriented Software. ErichGamma, RichardHelm, RalphJohnson, and JohnVlissides (the GangOfFour), 2010 Internet resources (including the List of World Library Resources): 1. www.wikibooks.org 2. http://ru.wikipedia.org 3. www.intuit.ru 4. http://www.Business Studio.ru 5. http://guidetoaction.ru/ind_bp_tech.html 6. http://stud.lms.tpu.ru/course/view.php?id=417 7. http://www.advanta-group.ru/
Informatics, Programming, Introduction to database
Course Objectives
Provides the opportunity to design and implement the system development cycle. Course includes analysis of current systems, logical and physical systems design, program development.
Learning Outcomes
By the end of the course the students will know: • modern methods and techniques of distributed information system design; • application of tools of information system development based on DBMS; • technology JavaEE for implementation of distributed information systems. The students will be able to: • carry out innovative engineering projects on automated systems hardware and software development using modern design methods, automated design systems, best practices of development of rival devices. • develop technical knowledge and participate in hardware and software development. • select methods and develop algorithms of solution of problems of automation object management and design; • apply modern techniques for program complex development using CASE-technologies, verify quality of developed software.
Syllabus
• Topic 1 Designing information systems. (lectures 2) • Topic 2 Modern methodology to develop software (lectures 2) • Topic 3 Modeling, prototyping, and CASE tools. (lectures 4) • Topic 4 Unified Modeling Language (UML) tools and techniques including use cases, use case diagrams, class diagrams, sequence diagrams, state transition diagrams, and activity diagrams.(lectures 6) • Topic 5 Introduction to JavaEE architecture, Java Server Pages (JSP) and Java servlets, Java frameworks. (lectures 6) • Topic 6 Output and User Interface Design(lectures 2) • Topic 7 Maturity models of programming – СMMI (lectures 2)
Labs
Lab 1. Modeling, prototyping, and CASE tools (labs 6) Lab 2. Unified Modeling Language (UML) tools (labs 10) Lab 3. Introduction to JavaEE architecture(labs 4) Lab 4. Java Server Pages (JSP) (labs 2) Lab 5. Java servlets (labs 2) Lab 6. Java frameworks (labs 12).
Projects
There will be also a semester-long project in this course
• The Unified Modeling Language User Guide, Grady Booch, James Rumbaugh, Ivar Jacobson, Addison-Wesley, 2005, 475p • Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development, Craig Larman, Kindle Edition, 736p • The Complete Business Process Handbook: Body of Knowledge from Process Modeling to BPM, Mark von Rosing, Henrik von Scheel, 2014, 696p • Introduction to Business Process Mapping with IDEF0 & IDEF3, Brian Hunt, 2013,21p • Systems Analysis and Design with UML, Alan Dennis,Barbara Haley Wixom , 2012, 592p • Head First Java, 2nd Edition, Kathy Sierra, Bert Bates, 2005,688p • The Java EE 7 Tutorial: Volume 1 (5th Edition) (Java Series), Eric Jendrock, Ricardo Cervera-Navarro, 2014, 641p • Professional Java EE Design Patterns Murat Yener, Alex Theedom, 2015, 224p
In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data and machine learning. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships. You will also use side information about products and users to improve predictions.
Learning Outcomes
By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Create a collaborative filtering system. -Reduce dimensionality of data using SVD, PCA, and random projections. -Perform matrix factorization using coordinate descent. -Deploy latent factor models as a recommender system. -Handle the cold start problem using side information. -Examine a product recommendation application.
Syllabus
Module 1: Machine Learning Foundations 1. Introduction 2. Microsoft Decision Trees 3. Microsoft Clustering 4. Microsoft Native Bayes 5. Microsoft Time Series 6. Microsoft Association 7. Microsoft Sequence Clustering 8. Microsoft Neural Network 9. Microsoft Linear Regression 10. Microsoft Logistic Regression Module 2: Business Intelligence Concepts and Platform Capabilities 11. Lesson 2.1: BI Concepts 12. Lesson 2.2 : BI Platform Capabilities 13. Lesson 2. 3: Business Reporting 14. Lesson 2. 4: BI OLAP Styles Module 3 - Data Visualization and Dashboard Design 15. Lesson 3.1: Data Visualization 16. Lesson 3. 2: Data Visualization Guidelines and Pitfalls 17. Lesson 3. 3: Performance Dashboards 18. Lesson 3. 4: Dashboard Design Guidelines and Pitfalls
Labs
1. VS BI 2. Microsoft SQL Server Analysis Services (SSAS) • Microsoft Decision Trees • Microsoft Clustering • Microsoft Native Bayes • Microsoft Time Series • Microsoft Association • Microsoft Sequence Clustering • Microsoft Neural Network • Microsoft Linear Regression • Microsoft Logistic Regression 3. SQL reporting
• Consideration of multi-level approach to the transmission of data through the internet; • Getting skills of self-study the discipline and the solution of typical problems; • Getting skills to work with modern software for developing internet applications; • Assimilation of the knowledge acquired by students, as well as the formation of motivation to self-education through increased self-cognitive activity.
Learning Outcomes
• Graduates will be ready to receive information from foreign sources and use this information in the development of internet applications. • Graduates will be ready to develop internet applications using modern programming tools.
Syllabus
• Web-based programming using ASP.net • The principles of user interface design web applications • Navigation on web-application • Using themes when making web-based applications • Using a web-caching application • Using databases in ASP.net applications • RIA - applications • ASP.net ajax technology
Labs
Projects
Assessment
Credit Test ( Pass/Fail)
Resources
Background textbook: • Cross M. Developer's Guide to Web Application Security / M. Cross. — Rockland: Syngress, 2007. — 489 p.: il. • Internet and Digital Economics. Principles, Methods and Applications / edited by: E. Brousseau and N. Curien. — Cambridge: Cambridge University Press, 2007. — 796 p.: il. • Bhowmick S. Web Data Management. A Warehouse Approach / S. S. Bhowmick, S. K. Madria, W. K. Ng. — New York: Springer, 2004. — 465 p.: il. • Wai-Sing Loo A. Peer-to-Peer Computing. Building Supercomputers with Web Technologies / A. Wai-Sing Loo. — New York: Springer, 2007. — 268 p.: il. • Botnets: The Killer Web App / C. Schiller. — Rockland: Syngress, 2007. — 464 p.: il. • Zimmermann O. Perspectives on Web Services. Applying SOAP, WSDL and UDDI to Real-World Projects / O. Zimmermann, M. Tomlinson, S. Peuser. — Berlin: Springer-Verlag, 2003. — 648 p.: il. On-line resources: Web Application Development Resources // MSDN: Web devel-opment. 2015. URL: https://msdn.microsoft.com/en-us/web-app-development-msdn
This course provides knowledge on database concepts applied in Information Systems. The course also provides students with solid background in the SQL skills and techniques of DBMS Oracle 10: installation of Oracle, creating a database, tables, queries, etc. The course materials will be drawn from both classic and recent research literature.
Learning Outcomes
By the end of the course the students will know principles database construction and the main data models. The students will be able to: • develop of an Entity-Relationship model for subject domain. • build a logical database structure based on Normalization rules. • create a physical database, tables, indexes, views, etc. • develop queries with functions, joins, conditions using SQL. • create commands using Data Definition and Data Manipulation Languages. The students will have the experience in effective work both as an individual and a member of an interdisciplinary and international team in solution of innovative engineering problems.
Syllabus
Topic 1. Evolution of database concept and problems of distributed data processing (lectures 2) Topic 2. Data Structures (lectures 4) Topic 3. Entity-Relationship model and basic database normalization (lectures 6) Topic 4. Relational Algebra (lectures 2) Topic 5. Introduction to SQL (lectures 6). Topic 6. Indexing (lectures 2) Topic 7. Other models (lectures 2)
Labs
1. Conceptual Modeling (Lab 2) 2. Database Construction in Oracle DB (Lab 4) 3. Translating Conceptual Models into Relational Schemas (Lab 4) 4. Relational Algebra and SQL(Lab 4) 5. SQL DML Queries (Lab 12) 5. SQL DDL Queries (Lab 6) 6. Indexing (Lab 4 )
Projects
There will be also a semester-long project in this course
Assessment
30% - in-class tests. 30% - semester-long project. 40% - final test
Resources
1. Thuraisingham, Bhavani. Database and Applications Security. Integrating Information Security and Data Management / B. Thuraisingham. — New York: Auerbach Publications, 2005. — 619 p.: ил. — Библиография в конце глав. — Index: p. 599-619. — ISBN 0-8493-2224-3. 2. Date. 1999. Introduction to database system: [translation from English.] /the 6th edition. Kiev; М.; St. Petersburg: «Williams» publishing, page 848. 3. T. Konnolly. 2000. Databases: design, implementation and support. Theory and practice: [translation from English.] / Т. Konnolly, К. Begg, А. Strachan. The 2nd edition. М.: «Williams» publishing, page 1120. 4. D. Khomonenko. 2002. Databases: textbook for high schools /А. D. Khomonenko, V. M. Tsygankov, M. G. Maltsev; ed. by А.D. Khomonenko. The 2nd edition – St. Petersburg: KORONA print, page 672. 5. Chudinov. 2000 Databases. Workbook. – Тomsk, Educational materials of Dept. of Optimization system management.
Physics, Theoretical Mechanics, Theory of machines and mechanisms.
Course Objectives
At the end of this course, the student will be able to: • carry out force analysis of machinery through application of the principle of virtual work; • model elements of single degree of freedom systems and perform free vibration analysis of such systems; • obtain forced response of single degree of freedom systems due to harmonic forcing; • carry out free vibration analysis of multi degree of freedom systems with no damping; • design a flywheel to suit to a given speed fluctuation limit and to a specified set of supply torque-load combination in machinery.
Learning Outcomes
1. Ability to conduct force analysis of machinery through application of the principle of virtual work. 2. Capability to analyze static and dynamic equilibrium conditions in machinery and to derive the governing equation(s) of motion. 3. Ability to model equivalent inertial, elastic and damping properties of single degree of freedom systems based on energy equivalence. 4. Ability to write equation of motion of single degree of freedom systems and to derive undamped and damped natural frequency expressions. 5. Ability to identify damping and natural frequency in the free vibration response of single degree of freedom systems. 6. Ability to obtain amplitude and phase characteristics associated with the forced response of single degree of freedom systems. 7. Ability to design inertial, stiffness and damping properties for vibration isolation. 8. Ability to formulate equations of motion for a multi degree of freedom system. 9. Ability to solve equations of motion for undamped natural frequencies and associated mode shapes. 10. Ability to obtain free vibration response of a multi degree of freedom system due to a specified set of initial conditions. 11. Ability to specify flywheel characteristics for a specified limit of speed fluctuation in reference to a machine load torque and prime mover supply torque configuration over a working cycle.
Syllabus
Topic 1. Virtual work method. Topic 2. Driving torque characteristics and machine-prime mover interactions. Topic 3. Modeling and elements of vibratory systems. Topic 4. Free and forced vibrations of single degree-of-freedom systems. Topic 5. Introduction to multi degree-of-freedom systems. Topic 6. Vibration control. Topic 7. Critical speeds of shafts. Topic 8. Balancing of rotating machinery.
Labs
Lab 1. Study of the dynamic of single-mass model of the elastic system. Lab 2. Amplitude-phase frequency response of single-mass model of the elastic Lab 3. Spectral analysis of the dynamic of single-mass model of the elastic Lab 4. Study of the dynamic model of single-mass model of the elastic system in block diagram form. Lab 5. Study of the dynamic of tow-mass model of the elastic system. Lab 6. Study of the dynamic of multi degree-of-freedom of the elastic system. Lab 7. Study methods of vibration control. Lab 8. Balancing of rotating machinery.
to acquire knowledge, skills and experience in the field of machine shops design, layout of the basic (technological) equipment at manufacturing divisions and auxiliary services
Learning Outcomes
After completion of the ”Machine Shops Design” course the student is to: know • Basic principles of organizing production departments and shops; • Techniques of choosing shop structure and organizational forms of its basic divisions; • The content of technical, organizational, economic and social problems solved at designing; • Main principles of designing shops; • Structure and functions of all services of the auxiliary system; • Sequence of developing a technical design assignment. be able to: • Calculate labour input of annual processing of all products in the shop depending on manufacture seriality; • Calculate the batch size; • Arrange the shop depending on manufacture seriality; • Calculate the required quantity of equipment, floor space of the shop and its divisions; • Carry out a rational layout of the equipment in the shop when building a new premise and reconstructing the old one; • Calculate the required floor space and carry out a layout of the auxiliary services of the shop and factory; • Choose a type of the building and its arrangement depending on the requirements for the accuracy of live parts, technical characteristics of assembled mechanisms and manufacture seriality; • Choose cutting tools, tip grades, optimum geometrical parametres and cutting mode; • Develop tasks for building, sanitary and power sections; • Develop a general plan of the factory; • Make a feasibility report of the project. master: • Calculation of the required quantity of equipment, floor space of the shop and its divisions; • Performance of a rational layout of the equipment in the shop when building a new premise and reconstructing the old one; • auxiliary system design.
Syllabus
1. Main goals, principles and sequence of design. The role of reconstructing and modernizing existing machine-assembling manufactures. The content and stages of manufacturing processes. Main principles of organizing manufacturing divisions. Seriality of manufacture. Project design works. General requirements for a layout of divisions and shops. Contents of technical design assignment. Sequence of designing. Making technical design assignment. Sequence of design and building. Use of computer-aided design (CAD) systems for designing divisions and shops. The contents of technical, organizational, economic and social problems solved at design. Main design principles. 2. Design of shop floor production. Raw data for design. Defining the structure of basic (technological) equipment. Calculation of total labour input of the annual program of all products for flow-line and non-line production. Applying a method of the reduced program for medium-size and small-scale production. Working mode (working time) arrangement of the equipment. Calculation of batch size. Calculation of machine tools quantity and floor space. Workplace organization. A layout of the basic (technological) equipment at manufacturing divisions. 3. Design of auxiliary system. Structure of auxiliary system. Storehouse and transport service in mechanical assembly production. Tool management, repair and maintenance service. Product quality inspection service. Labour safety service. Management and production planning service. 4. Developing a general layout of the factory and project feasibility study. Types and arrangements of buildings for mechanical assembly production. The general layout of a factory (enterprise). A project feasibility study.
Labs
Labs: № 1: Drawing a shop floor layout not to scale № 2: Performing a shop floor layout to scale № 3: Analysing a shop floor layout № 4: Performing a shop floor layout to scale conforming to design norms Seminars: № 1: Calculating labour input to process the annual programme of all parts in the shop № 2: Calculating equipment quantity and production floor space № 3: Designing a storehouse, transport and a tool management service № 4: Designing a management and production planning service
Projects
Assessment
Exam
Resources
1. Kozlov V.N., Pichugova I.L., Machine shops design: study aid / Tomsk Polytechnic University. – Tomsk: TPU Publishing House, 2013. – 132 p. 2. Козлов В.Н. Проектирование механосборочных цехов. Учебное пособие.– Томск, Изд. ТПУ, 2009 г. – 144 с. 3. Вороненко В.П. Проектирование механосборочных цехов: учебник\ В.П. Вороненко, Ю.М. Соломенцев, А.Г. Схиртладзе. 2-е издание, стер. – М.: Дрофа, 2006. – 380 с.: ил. 4. Проектирование механосборочных цехов. Методические указания и индивидуальные задания для студентов ИнЭО, обучающихся по направлению 150700 «Машиностроение», профиль «Технология, оборудование и автоматизация машиностроительных производств», 2015/ Сост. В.Н. Козлов. – Томск, Изд. ТПУ, 2015 г. – 45 с. 5. Королёва Н.И. Организация производства на предприятии: учебное пособие. – Томск, Изд. ТПУ, 2005 г. – 156 с. 6. Machine Shops Design. Methodological instructions for self-study designed for students enrolled in the Bachelor Degree program 150700 “Mechanical engineering” / developed by V.N. Kozlov. – Tomsk: Tomsk Polytechnic University Publishing House, 2012. – 28 p. 7. Machine Shops Design. Methodological instructions for laboratory works designed for students enrolled in the Bachelor Degree program 150700 “Mechanical engineering” / developed by V.N. Kozlov. – Tomsk: Tomsk Polytechnic University Publishing House, 2013. – 20 p..
Mathematical Methods of Experimental Data Processing
Level of study
Master Degree
Workload
ECTS: 5 Total Hours: 144 Contact Hours: 72
Lectures: 36
Labs: 36
Seminars: 8
Course Code
В3.В.1.7
Semester
Winter or Summer
Prerequisites
Computer Science, Calculus
Course Objectives
Getting skills and knowledge in the field of data processing: creating mathematical models and decision-making based on experimental data
Learning Outcomes
Getting skills and knowledge in the field of data processing: creating mathematical models and decision-making based on experimental data By the end of the course the student will know: Basic statistical tool for data analysis; The students will be able to: Make decision and provide evidence based on experimental data The students will have the experience in: Performing statistical analysis using special software packages
Syllabus
Introduction to statistic Descriptive statistic Student’s t-test Correlation analysis Regression analysis Analysis of variance Design of experiment
Labs
Descriptive statistic Student’s t-test Correlation analysis Regression analysis Analysis of variance Design of experiment
Projects
Assessment
Exam
Resources
Resource type: Background textbook Larson, Ron, Elizabeth Farber, and Elizabeth Farber. Elementary statistics: Picturing the world. No. QA276. 12. L373 2009. Pearson Prentice Hall, 2009. Mann, Prem S. Introductory statistics. John Wiley & Sons, 2007. Siegel, Andrew. Practical business statistics. Academic Press, 2011.
Quantitative Reasoning and Mathematics: quantitative reasoning and mathematics will be characterized by logic, critical evaluation, analysis, synthesis generalization, modeling, and verbal, numeric, graphical, and symbolic problem solving. -Ability to model situations from a variety of settings in generalized mathematical forms; - Ability to express and manipulate mathematical information, concepts, and thoughts in verbal, numeric, graphical and symbolic form while solving a variety of problems; - Ability to solve multiple-step problems through different (inductive, deductive and symbolic) modes of reasoning; - Ability to properly use appropriate technology in the evaluation, analysis, and synthesis of information in problem-solving situations; - Ability to shift among the verbal, numeric, graphical and symbolic modes of considering relationships; - Ability to extract quantitative data from a given situation, translate the data into information in various modes, evaluate the information, abstract essential information, make logical deductions, and arrive at reasonable conclusions; - Ability to employ quantitative reasoning appropriately while applying scientific methodology to explore nature and the universe; - Ability to discern the impact of quantitative reasoning and mathematics on the sciences, society, and one's personal life.
Learning Outcomes
- The student will be able to identify the relevant parameters and relationships for real-world problems and hence guide experimental design. - The student will be able to exploit dimensional analysis to construct realistic scaled models. - Given schematic diagram of vibration system, the student will be able to write the differential equation(s) describing the dynamics of the system. - Given few fit a model to experimental data, the student will be able to choose which model best fits the data (Model fitting). - Given experimental data, the student will be able to create model and to predict other experimental results (Model interpolation). - Given a mathematical model, the student will be able to determine, what is the ‘best’ possible output (Optimization).
Syllabus
Topic 1. Mathematical Modelling. Basic definitions and concepts Topic 2. Dimensional Analysis. Topic 3. Similitude. Topic 4. Modeling with differential equations Topic 5. Mathematical processing of experimental data. Fitting Data Topic 6. Mathematical processing of experimental data. Interpolation. Topic 7. Optimization parameters of mathematic model.
Labs
Projects
Assessment
Credit test
Resources
Resource type: Background textbook Alexander A. Samarskii, Alexander P. Mikhailov. Principles of Mathematical Modelling: Ideas, Methods, Examples. - 2001 Resource type: Background textbook Nguyen V.M. Man, Ph.D., Mathematical Modeling and Simulation.-2010 Resource type: Background textbook Stephen L. Campbell, Jean-Philippe Chancelier, Ramine Nikoukhah. Modeling and Simulation in Scilab/Scicos.- 2000 Resource type: Background textbook Chen K., Giblin P., Irving A., Mathematical Explorations with MATLAB. - 2008 Resource type: On-line resources Students will be provided a list of classic papers which are available on-line which they will be expected to read in order to gain an overview of the field, and also have access to a range of textbooks which help them prepare for the assessed seminars and tests. http://stud.lms.tpu.ru/course/view.php?id=1039
Getting knowledge in the field of mathematical statistics methods: creating mathematical models and decision-making based on experimental data
Learning Outcomes
By the end of the course the student will know: Basic statistical tool for data analysis; The students will be able to: Make decision and provide evidence based on experimental data The students will have the experience in: Performing statistical analysis and interpreting its results
Syllabus
Introduction to statistic Descriptive statistic Student’s t-test Correlation analysis Regression analysis Analysis of variance
Labs
Analyses of difference Analyses of association
Projects
Assessment
Exam
Resources
Resource type: Background textbook Larson, Ron, Elizabeth Farber, and Elizabeth Farber. Elementary statistics: Picturing the world. No. QA276. 12. L373 2009. Pearson Prentice Hall, 2009. Mann, Prem S. Introductory statistics. John Wiley & Sons, 2007. Siegel, Andrew. Practical business statistics. Academic Press, 2011.
To acquire basic knowledge of set theory; To learn fundamentals of prepositional and predicate logic; To develop practical skills of logic problem solving; To acquire basic knowledge of algorithm and computability theory.
Learning Outcomes
Upon successful completion of the course, a student will be able to: Write and interpret mathematical notation and mathematical definitions; Formulate and interpret statements presented in Boolean logic. Apply truth tables and the rules of propositional and predicate calculus; Formulate short proofs using the following methods: direct proof, indirect proof, proof by contradiction, and case analysis; Explain basic concepts from Recursion Theory, including recursive and recursively enumerable sets of natural numbers, and apply them to theoretical and appropriate applied problems in logic; Explain basic concepts from Proof Theory, including languages, formulas, and deductions, and use them appropriately.
Syllabus
1. Basics of Set Theory 2. Prepositional Logic 3. Tautologies and Logical Consequence 4. Predicate Logic 5. Quantifiers 6. Basics of Computability Theory
Labs
Projects
Assessment
Credit Test (Pass/Fail)
Resources
1. W. Rautenberg. A Concise Introduction to Mathematical Logic, Springer New York, 2010. DOI: 10.1007/978-1-4419-1221-3 2. S.M. Srivastava. A Course on Mathematical Logic, Springer New York, 2008. DOI: 10.1007/978-0-387-76277-7 3. M. Ben-Ari. Mathematical Logic for Computer Science, 3rd edition, Springer London, 2012. DOI: 10.1007/978-1-4471-4129-7 4. D. Mundici. Logic: A Brief Course, Springer Milan, 2012. DOI: 10.1007/978-88-470-2361-1 5. B. Shillito. Introduction to Higher Mathematics https://www.youtube.com/playlist?list=PLZzHxk_TPOStgPtqRZ6KzmkUQBQ8TSWVX
“Metrology, Standardisation and Certification”, “Material Cutting and Cutting Tools”, “Fundamentals of Mechanical Engineering”, “Metal Cutting Machines and Workholding Devices”
Course Objectives
to acquire knowledge, skills and experience in the field of analysis and design of the manufacturing processes
Learning Outcomes
After completion of the ”Mechanical Engineering”, part 1, course the student is to: know • basic concepts of mechanical engineering production; • fundamentals of technological ensuring required machining accuracy; • principles of technological process design of typical machine building products: stepped shafts, sleeves, flanges, gear wheels and body parts; be able to • assign tooling for product manufacturing; • improve technological ability of part; apply the following methods • deigning of the processes of simple parts job-production.
Syllabus
1. Technological process design in machining of typical parts (stepped shafts, sleeves, flanges, gear wheels and body parts). 2. Analysis of dimension chain in machining. 3. Methods of technological ability improvement.
Labs
1. Machining of the internal and external surfaces. 2. Investigation of rigidness of machine-attachment-tool-work system. 3. Improvement of workpiece surface roughness. 4. Investigation of technological accuracy in turning.
Projects
Assessment
Exam
Resources
1. Fundamentals of mechanical engineering: textbook / Skvortsov V.F., TPU 2. Основы технологии машиностроения: учебное пособие / В. Ф. Сквор-цов ; Национальный исследовательский Томский политехнический университет (ТПУ). — Томск: Изд-во ТПУ, 2012. — 352 с.: ил. 3. Суслов А.Г., Дальский А.М. Научные основы технологии машиностроения. –М.: Машиностроение, 2004 4. Технология машиностроения: учебник / А. А. Маталин - 3-е изд., стер. - Санкт-Петербург : Лань, 2015 - 512 с. : ил. - (Учебники для вузов. Специальная литература). 5. Brown, J., Advanced Machining Technology Handdbook, McGraw-Hill, 1998 6. Connor, J., Six Sigma and other Continuous Improvement Tools for the Small Shop, Society of Manufacturing Engineers, 2001 7. Darbyshire, Alan. Mechanical Engineering. BTEC National Option Units / A. Darbyshire. — Oxford : Newnes, 2003. — 411 p. : ил. — Index: p. 407-411. — ISBN 0-7506-5761-8. 8. Encyclopedia of Production and Manufacturing Management/ editor P. Swamidass. — 1,065 Items. — Berlin : Springer US, 2000. 9 .Erdel, B., High-Speed Machining, Society of Manufacturing Engineers, 2003 10. Handbook of Surface Treatment and Coatings, ASME Press, 2003 11. Machinery’s Handbook, Industrial Press, revised periodically 12. Quensenberry, C.P., SPC Methods for Quality Improvement, Wiley, 1997 13. Shigley, Joseph E. Mechanical Engineering Design / J. E. Shigley, C. R. Mischke. — 6 Edition. — New York : McGraw-Hill, 2001. — 1248 p. : il. — Index: p. 1237-1248. — ISBN 0-07-365939-8. 14. Stenerson, J. and Curran, K.S., Computer Numerical Control: Operation and Programming, 2nd ed., Prentice Hall, 2000 15.Tozawa, B. Bodek, N., The Idea Generator: Quick and Easy Kaizen, PCS Press, 2001 Web links: 16. www.cours.polymtl.ca/mec4530/Anim/Menu.swf 17. https://www.asme.org/ 18. http://www.ctemag.com/ 19. http://icrank.com/ 20. www.matweb.com/ 21.www.shender4.com/eng-links.htm 22. www.thomasnet.com/ 23. www.efunda.com/home.cfm 24. www.globalspec.com/ 25. www.icademic.org/97445/Mechanical-Engineering 26. www.engcen.com/mechjobs.htm 27. www.theengineer.co.uk/ 28. www.engc.org.uk/ 29. www.engineersedge.com/ 30. www.eef.org.uk/ 31. www.researchgate.net
“Mechanical Engineering, part 1”, or “Metrology, Standardisation and Certification”, “Material Cutting and Cutting Tools”, “Fundamentals of Mechanical Engineering”, “Metal Cutting Machines and Workholding Devices”
Course Objectives
to acquire knowledge, skills and experience in the field of analysis and design of the manufacturing processes
Learning Outcomes
After completion of the ”Mechanical Engineering”, part 1, course the student is to: know • basic concepts of mechanical engineering production; • methods of electrical discharge, chemical and electrochemical processing, electrochemical grinding, ultrasonic machining, electron beam and laser processing, plasma arc, abrasive jet and abrasive flow processing; • principles and methodology of technological processes design of manufacture of parts in metal cutting by CNC machines. be able to • develop control programs for CNC lathes and CNC milling machines; • customize CNC machines for batch machining; • assign tooling for product manufacturing; apply the following methods • programming the CNC turning machines;
Syllabus
1. Methods of finishing abrasive machining (honing, superfinishing, grinding, polishing). Technological possibilities, advantages and disadvantages. 2. Electrophysical and electrochemical machining methods. Electrical discharge, electrochemical, ultrasonic, laser, electron beam, plasma, waterjet machining. Restoration of parts by gas-flame sprayng covering.
Labs
1. Design features of CNC machines. Machine control panel, software. 2. Basic principles of Fanuc system programming. 3. Coordinate system, linear interpolation, creation of a tool and a toolbar. 4. Fillets and roundness, circular interpolation. 5. Correction on cutting tool position, condition of limiting of the correction application. 6. Processing path, longitudinal and transverse cycle in rough and finish machining. 7. Customize of wire electrical discharge machine DK7725. 8. Restoration of a shaft by flame spraying.
Projects
Assessment
Exam
Resources
1. Fundamentals of mechanical engineering: textbook / Skvortsov V.F., TPU 2. Основы технологии машиностроения: учебное пособие / В. Ф. Сквор-цов ; Национальный исследовательский Томский политехнический университет (ТПУ). — Томск: Изд-во ТПУ, 2012. — 352 с.: ил. 3. Суслов А.Г., Дальский А.М. Научные основы технологии машиностроения. –М.: Машиностроение, 2004 4. Технология машиностроения: учебник / А. А. Маталин - 3-е изд., стер. - Санкт-Петербург : Лань, 2015 - 512 с. : ил. - (Учебники для вузов. Специальная литература). 5. Brown, J., Advanced Machining Technology Handdbook, McGraw-Hill, 1998 6. Connor, J., Six Sigma and other Continuous Improvement Tools for the Small Shop, Society of Manufacturing Engineers, 2001 7. Darbyshire, Alan. Mechanical Engineering. BTEC National Option Units / A. Darbyshire. — Oxford : Newnes, 2003. — 411 p. : ил. — Index: p. 407-411. — ISBN 0-7506-5761-8. 8. Encyclopedia of Production and Manufacturing Management/ editor P. Swamidass. — 1,065 Items. — Berlin : Springer US, 2000. 9 .Erdel, B., High-Speed Machining, Society of Manufacturing Engineers, 2003 10. Handbook of Surface Treatment and Coatings, ASME Press, 2003 11. Machinery’s Handbook, Industrial Press, revised periodically 12. Quensenberry, C.P., SPC Methods for Quality Improvement, Wiley, 1997 13. Shigley, Joseph E. Mechanical Engineering Design / J. E. Shigley, C. R. Mischke. — 6 Edition. — New York : McGraw-Hill, 2001. — 1248 p. : il. — Index: p. 1237-1248. — ISBN 0-07-365939-8. 14. Stenerson, J. and Curran, K.S., Computer Numerical Control: Operation and Programming, 2nd ed., Prentice Hall, 2000 15.Tozawa, B. Bodek, N., The Idea Generator: Quick and Easy Kaizen, PCS Press, 2001 Web links: 16. www.cours.polymtl.ca/mec4530/Anim/Menu.swf 17. https://www.asme.org/ 18. http://www.ctemag.com/ 19. http://icrank.com/ 20. www.matweb.com/ 21.www.shender4.com/eng-links.htm 22. www.thomasnet.com/ 23. www.efunda.com/home.cfm 24. www.globalspec.com/ 25. www.icademic.org/97445/Mechanical-Engineering 26. www.engcen.com/mechjobs.htm 27. www.theengineer.co.uk/ 28. www.engc.org.uk/ 29. www.engineersedge.com/ 30. www.eef.org.uk/ 31. www.researchgate.net
Mathematics, Physics, Theoretical Mechanics, Construction Materials Engineering, Metrology, Standardization and Certification, Engineering Mechanics
Course Objectives
Acquisition of knowledge and skills in the field of mechanical engineering measurements; determination of machining error and error of measurement of dimensions, form and position deviations; introduction to the basic principles of selection of universal and special purpose instruments for measurement and inspection.
Learning Outcomes
Will be able to: • apply basic principles of interchangeability; • carry out linear and angular measurements; • assign universal and special means of measurement; • carry out calibration of the measuring instrument on-site; • carry out inspection of form and location deviations; • design manufacturing processes and engineering inspection operations
Syllabus
1. Measuring instrument error and measurement error 2. Universal and special purpose measuring instruments 3. Verification of measuring instruments and gauges 4. Designing of manufacturing processes, measurements and inspection
Labs
1. Verification of a Vernier Caliper 2. Measurement of a Tapered Plug-Gauge on a Universal Measuring Microscope 3. Measurement of a Stepped Shaft with a Vernier Caliper and Micrometer 4. Determination of a fit by measuring the mating parts 5. Measurement of the Basic Parameters of External Thread on a toolmaker microscope 6. Measurement of a Gauge Block with a Vertical Optimeter 7. Measurement of a Limit Plug Gauge with a Minimeter
Projects
Assessment
Credit test
Resources
1. Encyclopedia of production and manufacturing management / Editor Paul M. Swamidass. Kluwer Academic Publishers, 2000. 2. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006 3. Geometric Dimensioning and Tolerancing for Mechanical Design. Gene Cogorno. Publisher: McGraw-Hill Professional. 2011
1. Be effective inter-disciplinary engineers and problem solvers. 2. Be well educated in the basic engineering sciences and fundamentals of mechanical, electrical, and computer engineering. 3. Be able to use engineering tools that will enhance their productivity. 4. Be able to design, analyze, and test “intelligent” products and processes that incorporate suitable computers, sensors, and actuators. 5. Be effective oral, written, and graphical communicators. 6. Be able to function effectively as members of multi-disciplinary teams. 7. Be prepared for a variety of engineering careers, graduate studies, and continuing education.
Learning Outcomes
students will be able • to define the problem. • to employ the basic mathematical skills needed to solve routine engineering problems. • to implement engineering solutions and techniques to solve design problems • to design mechatronic components and systems. • to select the appropriate mechatronic device for a given application • to discuss the impact of engineering on society, safety, and the environment in • relation to contemporary issues.
Syllabus
• Mechatronics; • Development of Mechatronic Systems; • Trends and demands of the world market in Mechatronic systems area and their elements.
Labs
Projects
Assessment
Credit test
Resources
1. Poduraev Y. Mechatronics: Fundamentals, Methods, Application: Proc. allowance for university students. -. 2 nd ed. - M .: Engineering, 2007. -256 p. 2. EI Yurevich Fundamentals of Robotics. Textbooks. - SPb .: BHV-Petersburg,2005. 3. Handbook of Industrial Robotics: In 2 kn. / Edited by S. Nofal; per. From Eng. -M .: Engineering 2012. 4. Kozyrev YG Industrial robots: Directory - M .: Engineering 2008. 5. Chelpakov IB, SN Kolpashnikov The tongs industrial robots: Under the general. Ed. JA Shifrin. -M .: Engineering 1999. 6. M. Shahinpur. robotics course. Trans. from English. - M .: Mir, 2010.
Structural Materials Engineering, Metrology, Standardisation and Certification, Materials Science
Course Objectives
The objective of the course is to develop knowledge, skills and experience in the field of metal cutting
Learning Outcomes
Will know: • physics of cutting; • types of chip and ways of chip management; • chip formation, machined surface workhardening; • characteristics of cutting tool wear, optimal tool life Will be able to: • rationally choose methods of machining; • choose type and grade of coolant depending on surface finish requirements and economy; • calculate cutting forces and required machine tool power; • calculate values of the cutting tool geometric parameters; • calculate cutting parameters
Syllabus
1. Basics of Cutting 2. Chip Formation 3. Heat Generation in Cutting 4. Tool life 5. Machinability 6. Cutting and Grinding Operations
Labs
1. Geometry of Cutting Tools 2. Dynomometers and Load Cells 3. Cutting Forces vs Cutting Parameters 4. Heat Generation and Build-Up Edge 5. Cutting Tool Life
Projects
Assessment
Exam
Resources
1. Material cutting and cutting tools : учебное пособие / С. В. Кирсанов (http://www.lib.tpu.ru/fulltext2/m/2014/m261.pdf) 2. Cutting Tool Applications. George Schneider, 2005 3. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006
1. Technology of Constructional Materials; 2. Metrology, Standardization and Certification; 3. Materials Science.
Course Objectives
Course objectives of the discipline “Metalworking equipment” are: 1. To train the students for professional activity; 2. To consolidate students’ knowledge, forming skills and obtaining experience in the sphere of kinematics analysis and machine tools construction in terms of rational usage in technological processes of the machine work of different purpose parts.
Learning Outcomes
By the end of the course the student will know: 1. Principles of operation of metalworking equipment; 2. Purpose of metalworking equipment; 3. Arrangement of metal–cutting machine tools (MCMT); 4. Application areas of different types of MCMT; 5. Classification of MCMT; 6. Basic engineering-and-economic performance of MCMT; 7. Construction of standard mechanisms and systems of MCMT; 8. Know metalworking equipment testing and analysis methods as well as methods of operation. The students will be able to: 1. Mount and adjust machine tools for different operations; 2. Read a kinematic scheme of any metalworking equipment, carry out analysis and synthesis of kinematics; 3. Develop layout and kinematic scheme of a special-purpose machines; 4. Select a metalworking equipment with optimal parameters for a processed detail according to its drawing. The students will have the experience in: Practical work on metal-cutting equipment (universal, machine tools with numerical control).
Syllabus
Topic 1. Technical and economic indices of machine tools (lectures 2). Topic 2. Typical mechanisms and machine tools (lectures 2, labs 4). Topic 3. Metalworking equipment for rotating bodies (lectures 2, labs 3). Topic 4. Machine tools for prismatic parts (lectures 2, labs 3). Topic 5. Machines with complex geometry (lectures 1, labs 2). Topic 6. Machine tools for grinding, electrochemical and electrophysical processing (lectures 1, labs 2). Topic 7. Machine tools with computer numerical control (CNC) (lectures 2, labs 2).
Labs
1. Drawing up the kinematic scheme of speed gear box 2. General purpose lathe adjustment for thread cutting 3. Precision test of general purpose lathe 4. Adjustment of gear milling machine for cutting gear wheels
Projects
DESIGN AND CALCULATIONS OF SPINDLE UNITS
Assessment
Exam: in writing
Resources
1. Oberg, E., F. D. Jones and H. Horton, Machinery’s Handbook, 23rd Ed., Industrial Press Inc., New York. 2. Acherkan N., Machine Tool Design, Mir Publishers, Moscow. 3. Juneja B. L. and G. S. Sekhon, Fundamentals of Metal Cutting and Machine Tools, Wiley Eastern Ltd., New Delhi. 4. Berezovsky Yu., D. Chernilevsky and M. Petrov, Machine Design, Mir Publishers, Moscow. 5. Chapman W. A. J., Workshop Technology, Edward Arnold (Publishers) Ltd., London.
Methods and technologies of social network analysis
Level of study
Bachelor Degree
Workload
ECTS: 2 Total Hours: 140 Contact Hours: 72
Lectures: 32
Labs: 40
Seminars: 0
Course Code
Semester
Prerequisites
Informatics; Programming; Introduction to IT
Course Objectives
This course provides a practical, but comprehensive introduction to the analysis of social networks. The purpose of the course is to cover the concepts, methods and data analysis techniques of social network analysis. The course begins with a general introduction to the goals and perspectives of social network analysis. It starts from practical discussion of network data, covering issues of collection, validity, visualization, and mathematical/computer representation.
Learning Outcomes
Students will obtain a thorough understanding of the main theories and common methods of social network analysis. Students will be able to use ready packages and develop their own application and carry out a social network research studies, as well as be able to interpret network analyses in a consultancy setting.
Syllabus
1. Introduction to Social Network Analysis 2. Explain how to describe and visualise networks using specialist software (NodeXL and other) 3. Explain key concepts of Social Network Analysis (e.g. Cohesion, Brokerage, Centrality etc). 4. Provide hands-on training to use software to investigate social network structure 5. Develop application to analyze data from social network
Labs
1. Introduction to SNA software 2. Social data retrieval. 3. Network data and visualisation 4. Network statistics for SNA 5. Actor-based measures (e.g. degree, centrality measures) 6. Network-based measures (e.g. centralization, degree of clustering, average path length) 7. Methods for detecting network clusters and communities 8. Algorithms of network visualization
Projects
Assessment
Exam
Resources
1. Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca. 2009. “Network Analysis in the Social Sciences.” Science 323(5916):892–95. 2. Scott, John P. 2000. Social Network Analysis: A Handbook. 2nd ed. London: Sage. 3. Jackson, Matthew O. 2008. Social and Economic Networks. Princeton: Princeton University Press. 4. Scott, John. 1988. “Social Network Analysis.” Sociology 22(1):109–27. 5. M. A. J. van Duijn & J. K. Vermunt (2006) What Is Special about Social Network Analysis? Methodology 2006; Vol. 2(1):2–6 6. Robert Hanneman and Mark Riddle (2005) Introduction to social network methods
• to provide a basic understanding of the principles of measurement and calibration of measuring instruments; • to study basic concepts and definitions, ISO Guide requirements for uncertainty determination; • to study existing examples and solutions of metrological problems.
Learning Outcomes
By the end of the course the student will know: • measuring methods and instruments; • SI measurement units; • ways of measurement results treating and obtaining reliable information about the object under investigation; • normative and legislative bases of Metrology as the foundations of quality guarantee; • measurement error reduction methods. The student will be able to: • apply different measuring methods to determine the value of a physical quantity; • treat the obtained measurement data; • estimate measurement uncertainty; • find and eliminate sources of measurement errors. The student will have the experience in: • carrying out of a measuring instrument calibration; • calculating the uncertainty of a measurement.
Syllabus
Topic 1 Measurement: main concepts Topic 2 The SI Metric Systems Topic 3 Errors and their statistical treatment Topic 4 Measurement Uncertainty Topic 5 Measurement error sources and error reduction Topic 6 Calibration Topic 7 Experimental data treatment Topic 8 Measurement Standards and Traceability
Labs
1. Dimensional analysis 2. Measurement tools classification 3. Uncertainty Analysis 4. Uncertainty analysis of a single indirect measurement 5. Uncertainty analysis of repeated measurements 6. Error sources and error reduction 7. Calibration of a measuring instrument
Projects
Assessment
Intermediate control: In-class control tests. Final control: Exam.
Resources
1. Connie L. Dotson, Fundamentals of Dimensional Metrology, Publisher: Delmar Cengage Learning; 5 edition, 2006. 2. Jay L. Bucher, The Metrology Handbook, Publisher: Quality Press; 2 Har/Cdr edition, 2012. 3. S.V. Muravyov, Measurement Information Systems, Publisher: Tomsk Polytechnic University Press, 2005.
Mathematics, Physics, Construction Materials Engineering
Course Objectives
To develop knowledge and understanding of tolerancing principles and inspection methods and instruments applied in mechanical engineering. The course also aims to develop the ability for technical communication via correct dimensioning on engineering drawings
Learning Outcomes
Will be able to: • apply basic principles of the interchangeability; • carry out linear and angular measurements; • assign universal and special measurement instruments; • carry out inspection of form and location deviations
Syllabus
1. Principles of Dimensional Tolerancing 2. Principles of Geometric Tolerancing 3. Surface Texture 4. Tolerance Analysis 5. Engineering Metrology 6. Standardisation and Certification
Labs
1. Design and Application of Vernier Instruments 2. Design and Application of Micrometers 3. Design and Application of Dial Indicators 4. Inspection of a Limit Gauge 5. Measurement of the Basic Parameters of External Thread on a toolmaker microscope 6. Surface Finish
Projects
Assessment
Exam
Resources
1. Metrology, standardisation and certification: study aid / A. B. Kim; Tomsk Polytechnic University (TPU). — Tomsk: Tomsk Polytechnic University Publishing House, 2014. (http://www.lib.tpu.ru/fulltext2/m/2014/m258.pdf) 2. Manufacturing Engineering and Technology. Fifth edition. Serope Kalpakjian, Steven R. Schmid, 2006 3. Geometric Dimensioning and Tolerancing for Mechanical Design. Gene Cogorno. Publisher: McGraw-Hill Professional. 2011 4. H. Dagnall M.A. Exploring Surface Texture. Rank Taylor Hobson, 1980 5. H. Dagnall M.A. Let’s Talk Roundness. Rank Taylor Hobson, 1976
• Produce apps for Android platform devices • Gain a basic understanding of computer architecture • Develop a working knowledge of different application development tool • Understand mobile design principles • Identify need and opportunity in application markets
Learning Outcomes
Upon successful completion of this course, students will be able to: understand Android architecture and the key principles underlying its design and have a good understanding the processes that are involved in an application. Students will gain a good knowledge of how Android can achieve smooth integration with different components. Students will be able to have a knowledge of different applications available with Android and become familiar with Android development tools and user interface.
Syllabus
Unit 1: App Interfaces Unit 2: Adding Logic To mobile Applications Unit 3: Persisting Data & Networking Unit 4: Compiling and Submitting Your App
Labs
Projects
Assessment
Exam
Resources
1. Jonathan McCallister. Mobile Apps Made Simple: The Ultimate Guide to Quickly Creating, Designing and Utilizing Mobile Apps for Your Business - 2nd Edition. 2013 2. Jeff McWherter, Scott Gowell. Professional Mobile Application Development 1st Edition. 2012 3. Bill Phillips, Chris Stewart, Brian Hardy, Kristin Marsicano. Android Programming: The Big Nerd Ranch Guide (2nd Edition). 2015 4. Dawn Griffiths, David Griffiths. Head First Android Development 1st Edition.2015 5. Brian Fling . Mobile Design and Development: Practical concepts and techniques for creating mobile sites and web apps (Animal Guide). 2009. 6. Leigh Williamson, Roland Barcia, Omkar Chandgadkar, Ashish Mathur, Soma Ray. Enterprise Class Mobile Application Development: A Complete Lifecycle Approach for Producing Mobile Apps (developerWorks Series).2015 7. Joseph Annuzzi Jr.,Lauren Darcey,Shane Conder. Introduction to Android Application Development: Android Essentials 8. Android Design Principles – http://developer.android.com/design/get-started/principles.html 9. Android app development - http://www.adobe.com/devnet/devices/android.html
Basic principles of the multimedia, information networking and multimedia networks will be studied
Learning Outcomes
By the end of the course the student will know: specific multimedia data transferring and corresponding problems solving. will be able to: work with WireShark, VLC and OptiView Link Analyzer. It aims to give students an in-depth view of advanced features of this software and makes special emphasis on multimedia networks.
Syllabus
1. Multimedia basics 2. Best-effort Internet support for distributed multimedia traffic requirements 3. Enhancing the TCP/IP protocol stack to support functional requirements of distributed multimedia applications 4. Introduction to RTP 5. RTP data transfer protocol 6. RTP control protocol 7. Media capture, playout, and timing 8. Lip synchronization 9. Error concealment 10. Error correction 11. Congestion control
Labs
1. Analyzing multimedia networking 2. Overview course “Multimedia Networking” in other universityes
Basic principles of the multimedia, creating each of the elements of multimedia and group creation of multimedia project for maximum effect.
Learning Outcomes
By the end of the course the student will know: basic principles of the multimedia, specific multimedia data types, multimedia hardware and software. will be able to: work with different multimedia software during creating own multimedia project. It aims to give students an in-depth view of advanced features of this software and makes special emphasis on multimedia.
Syllabus
1. What Is Multimedia? 2. Text 3. Images 4. Sound 5. Animation 6. Video 7. Making Multimedia 8. Multimedia Skills 9. Planning and Costing 10. Designing and Producing 11. Content and Talent 12. The Internet and Multimedia 13. Designing for the World Wide Web 14. Delivering
Labs
1. Creation own multimedia application
Projects
Assessment
Exam
Resources
Multimedia:Making It Work. Eighth Edition. Tay Vaughan, 2011
The main goal - to prepare students for research and design activity.
Learning Outcomes
To give systematic overview of modern models of biologic and artificial neural networks, to study techniques for their application in information processing and image recognition. By the end of the course the students will know: modern models of biologic and artificial neural networks, application techniques for information processing and image recognition. The students will be able to: set problems and develop algorithms for their solution to to develop software implementation of neural networks for processing statistic and video images. The students will have the experience in: application technologies for mathematical methods and in neural network processing of large volumes of space time data.
Syllabus
Topic 1 Biologic and artificial neural networks (lectures 2) Topic 2 One-layered and multi-layered perceptrons (lectures 2, labs 2) Topic 3 Networks on the basis of radial basis function (lectures2, labs 4) Topic 4 Support vectors machines (lectures 4, labs 2) Topic 5 Analysis of key components (lectures 2, labs 4) Topic 6 Kohonen's self-organisation maps (lectures 2, labs 2) Topic 7 Biologically probable models of neural networks (lectures 2, labs 2)
Compulsory 1. S. Khaikin. 2006. Neural networks: full course. Moscow.: Williams, 1104p. 2. S. Osovsky. 2007. Neural networks for information processing. Moscow.: Finansy i statistika, 345p. 3. V. G. Spitsyn, Y. R. Tsoy. 2008. Knowledge representation in information systems: handbook. Tomsk: TPU publishing, 152p. 4. Arbib M. A. 2003. The Handbook of Brain Theory and Neural Networks Oxford Cambridge, Massachusetts, USA: MIT Press Inc., 1301p. Further reading 1. Bishop C. M. 2005. Neural Networks for Pattern Recognition. New York, USA: Oxford University Press Inc., 251p. 2. Acharya T., Ray A. K. 2005. Image Processing. Principles and Applications, Hoboken, New Jersey, USA: John Wiley & Sons, Inc., 451p. 3. Duda R. O., Hart P. E., Stork D. G. 2001. Pattern Classification. USA: John Wiley & Sons, Inc., 654p. 4. Y. B. Kazanovich. 2010. Theory of time correlation and models of segmentation of visual information in brain (overview) // Мmathematical biology and bioinformatics. v. 5. # 1, pp. 43-97. Software and Internet resources 1. Software for laboratory works in Visual Studio 2010. http://raai.org/ – Russian association of neural networks. 2. http://www.niisi.ru/iont/ni – Russian association of neuroinformatics. 3. EJ «Neuroinformatics». http://www.niisi.ru/iont/ni/Journal/. 4. Neural Computation Journal. http://www.mitpressjournals.org/loi/neco. 5. http://www.elsevier.com/wps/find/journaldescription. cws_home/505628/description 6. The Journal of Neuroscience. http://www.jneurosci.org/ 7. EJ «Pattern Recognition and Image Analysis» http://www.maik.ru/cgiperl/ 1. journal.pl?name=patrec&page=main
Mathematical Analysis, Linear Algebra and Analytic Geometry, Algorithms. Chapters: "Integration", "Linear and non-linear equations", "Differential Equations", "Matrix calculus”.
Course Objectives
to provide students with a theoretical foundations and methods of computational mathematics; to provide students with a knowledge of statement typical mathematical problems and numerical methods of solving them; to develop practical skills of the construction numerical algorithms and error estimates for the numerical solution.
Learning Outcomes
To carry out theoretical and experimental research, including search and study of the scientific and technical information, mathematical modeling, experiment, analysis, and interpretation of data. To present modern numerical approaches to classical problems in mathematics, science and engineering whose numerical solutions are essential. To emphasize algorithm development and programming and application to realistic engineering problems
Syllabus
Introduction and subject Computational errors Numerical Integration Numerical solution of Nonlinear Equations and Set Equations Numerical Linear Algebra Numerical solution of ordinary differential equations Approximation of Data and Functions
Labs
1. Errors of computing and algorithms. 2. Numerical Integration: Quadrature Formulas, Mid-point, Trapezoidal and Simpson's rules. 3. Numerical solution of Nonlinear Equations: Bisection method, Secant method, Newton-Raphson’s method. 4. Numerical Linear Algebra: Direct methods, Gauss elimination, Numerical factorizations. Iterative methods, Jacobi method, Gauss-Seidel method. 5. Numerical solution of ordinary differential equations: Euler method, Runge-Kutta methods for equations and set equations ordinary differential equations. 6. Approximation problems: Spline interpolation, Least square approximation, Prediction.
Projects
Assessment
Credit Test
Resources
1. Dukkipati, R.V., Numerical Methods, New Age International Publishers (P) Ltd., New Delhi, India, 2010. 2. Ansari, K. A. Ansari, Introduction to Numerical Methods using MathCAD 14, Publisher: Schroff Development Corporation, 2008. 3. Corless, R.M., & Fillion, N. A Graduate Introduction to Numerical Methods: From the Viewpoint of Backward Error Analysis. New York, NY: Springer-Verlag, 2014. 4. Chapra, S. C and Canale, R. P., Numerical Methods for Engineers, 5th Edition, Tata McGraw-Hill, New Delhi, 2007. 5. Ward Cheney, David Kincaid, Numerical Mathematics and Computing, 7th international edition , Cengage Learning, 2013,
1. Understand fundamentals of programming such as variables, conditional execution, methods etc 2. Understand fundamentals of object-oriented programming in C#, including defining classes, invoking methods, using class libraries, etc. 3. Have the ability to write a computer program to solve specified problems.
Learning Outcomes
After completing this course student will have a thorough knowledge about, and be able to use: • Compose object-orientated solutions for problem solving • Evaluate and apply the concepts of inheritance and polymorphism amongst classes • Develop computer programs using the advanced concepts of multithreading and exception handling • Manipulate classes provided in the programming API and incorporate them into computing solutions
Syllabus
1. Introduction to C# 2. Expressions, Types, and Variables 3. Control Statements - Selection, Loops 4. Methods and Namespaces 5. Classes. Inheritance and Polymorphism 6. Properties and Indexers 7. Structs 8. Interfaces 9. Delegates and Events 10. Exception Handling. Attributes 11. Enums 12. Overloading operators 13. Encapsulation 14. Generic Collections 15. Types. Nullable types
Labs
1. Program structure 2. Conditionals and Loops 3. Arrays 4. Methods 5. Classes and Objects 6. Collections 7. Inheritance
Projects
Final Test Project
Assessment
Credit Test
Resources
Students will be provided a list of classic papers which are available on-line which they will be expected to read in order to gain an overview of the field, and also have access to a range of textbooks which help them prepare for the assessed seminars and tests.
• acquaintance to appointment, properties, architecture and bases of functioning of modern operating systems; • obtaining skills of work with Microsoft Windows and Unix OS.
Learning Outcomes
To aquire systematic overview of modern operating systems, to study perspectives for development of operating systems. By the end of the course the student will know: • principles of modern operating systems and features of their application; • the appointment, functions and structure of the OS; • characteristics of modern general-purpose operating system, advantages and disadvantages of individual families OS Microsoft Windows and Unix; • methods for configuration and administration of the new OS; The students will be able to: • configure specific configuration of operating systems; • work in Windows XP, Windows 7 and Unix/Linux; • analyze and reasonably choose the OS depending on the task and the resources available; • install, configure and administer the simplest cases, the new OS; • work with different operating systems and administrating. The students will have the experience in: operating systems configuration.
Syllabus
• Operating Systems: Definition And Evolution • OS architectural features and classification • Processes control • Storage Management • Files and file systems
Labs
• First view of the basics of Unix and interactive work • Practical skills in using GNU. Making utility to build the project • Simple programs development using the basics of multithreaded programming, streams synchronization using different media • Software development using inter-process communication.
Projects
Assessment
Exam
Resources
• Operating Systems,Theory and practice, Alexander V. Zamyatin, 2011 • Modern Operating Systems (3th edition), Andrew S. Tanenbaum
Modern issues of informatics and computer science, Intelligent systems, Decision making theory
Course Objectives
The main goal - to prepare students for research and design activity.
Learning Outcomes
To study Parallel Computing a student should: Know: modern tendencies of development of parallel computing and technology; basics of creation of parallel programs for shared memory systmes; principles of functioning and technologies of parallel computing; basic parallel constructs that control over a parallel program execution. Be able to: apply mathematical methods, theory of algorithms, theory of programming to solve different tasks; develop parallel programs using the OpenMP, MPI, OpenCL, CUDA technologies; evaluate the performance of parallel programs and increase their efficiency. transform sequential programs into their parallel implementation; Have skills of: designing of parallel algorithms; designing of parallel programs; optimizing of parallel algorithms; optimizing of parallel programs;
Syllabus
Topic 1 Introduction to parallel and distributed computing (lectures 2) Topic 2 Technologies of parallel computing for multi-core processors (lectures 2, labs 4) Topic 3 Technologies of parallel computing for GPU (lectures 4, labs 4) Topic 4 Technologies of distributed computing (lectures 4, labs 4) Topic 5 Hybrid computing methods (lectures 4, labs 4)
Labs
Lab 1 OpenMP model for multi-thread processing Lab 2 CUDA technologies Lab 3 OpenCL model for heterogeneous computing Lab 4 MPI technology for distributed processing Lab 5 Project (Team Work)
Projects
Assessment
Credit test
Resources
Compulsory 1. Hoffman S., Lienhart R. Informatik im focus. OpenMP: - Berlin: Springer, 164 s. 2. M Snir, SW Otto, S Huss-Lederman, DW Walker, J (1998) MPI—The Complete Reference: Volume 1, The MPI Core. MIT Press, Cambridge, MA. 3. Aoyama, Yukiya; Nakano, Jun (1999) RS/6000 SP: Practical MPI Programming 4. Quinn Michael J, Parallel Programming in C with MPI and OpenMP McGraw-Hill Inc. 2004. 5. R. Chandra, R. Menon, L. Dagum, D. Kohr, D. Maydan, J. McDonald, Parallel Programming in OpenMP. Morgan Kaufmann. 6. Chapman B., Jost G., van der Pas R. Using OpenMP portable shared memory parallel programming., Cambridge, MA, MIT Press, 2008. – 353 p. Software and Internet resources 1. NVIDIA official site. CUDA Technology. URL: http://www.nvidia.com 2. MPI official web-page. Message Passing Interface Forum. URL: http://mpi-forum.org 3. OpenMP official page. The OpenMP API specification. URL: http://openmp.org 4. OpenCL official page. OpenCL – The open standard for parallel programming of heterogeneous systems. URL: http://www.khronos.org/opencl/
To teach students to process remote sensing data to solve application tasks using ERDAS Imagine software.
Learning Outcomes
By the end of the course the student will know: Methods and algorithms of the remote sensing data processing. The students will be able to: To solve tasks of remote sensing data interpretation. The students will have the experience in: using systems of remote sensing data processing.
Syllabus
Labs
Topic 1 Remote Sensing Basics. Data obtaining, earthexplorer.usgs.gov Internet service basics (labs 6) Topic 2 ERDAS Imagine basics. Image preparation methods: Layer stack, Spatial resolution enhancement of image (labs 4) Topic 3 Preprocessing methods: map rectification (labs 6) Topic 4 Unsupervised Classification of Landsat images using ISODATA (labs 8) Topic 5 Supervised Classification of Landsat images using Maximum Likelihood Classification Method (labs 8)
The concept of Total Quality Measurement occupies an important place in the decision-making process under uncertainty, whether the problem is one faced in business, in government, in sciences, or just in one's own everyday personal life. Most decisions are made in the face of uncertainty. In a broad sense TQM theory can be understood as a mathematical model for the all business processes. The course provides numerous worked examples and exercises to help build the important skills necessary for problem solving. Goals: To tell about problems which have led to TQM occurrence, the purposes of their creation, and also about various TQM models; To acquaint with principles of TQM design according to ISO 9001:2015 standard; To give the information on TQM role for everyone and that it can give to everyone.
Learning Outcomes
to develop the skills in using the statistical principles. to apply the knowledge of statistical principles in the Industry practice.
Syllabus
• Concept of Quality • Evolution of Quality • Deming's axioms and 14 Principles • TQM Tools • System of Quality Measurement • The Process Approach as Basis of TQM System • Statistics, statistical hypothesis. • Fitting criteria, correlations. • Linear parametrization. • Quality Function Deployment (QFD)
This subject provides students with the ability to use statistical tools to characterize the reliability of an item; the working knowledge to determine the reliability of a system and suggest approaches to enhancing system reliability; the ability to select appropriate reliability validation methods.
Learning Outcomes
Upon successful completion of the course, a student will be able to: compute system reliability; model reliability by various life distributions; estimate reliability by product testing; analyze life data for estimating reliability; apply the appropriate methodologies and tools for enhancing reliability of components and systems; specify life test plans for reliability validation.
Syllabus
1. Basics of Reliability Theory: Reliability Measures 2. Lifetime Distributions 3. Reliability of Series Systems 4. Redundancy: “hot”, “warm”, “cold” 5. Majority Voting Systems, k-out-of-n systems 6. Reliability of Repairable Systems, Renewal Processes 7. Steady State Availability 8. Reliability Simulation 9. Reliability Parameters Estimation
Labs
Projects
Assessment
Written Exam
Resources
1. Case Studies in Reliability and Maintenance, edited by R. Wallace, D.N. Blischke. John Wiley & Sons, Inc., Hoboken, New Jersey, 2003. DOI: 10.1002/0471393002 2. B. Epstein, I. Weissman. Mathematical Models for Systems Reliability, Chapman and Hall/CRC, Boca Raton, Florida, 2008. 3. M. Finkelstein. Failure Rate Modelling for Reliability and Risk, Springer London, 2008. DOI: 10.1007/978-1-84800-986-8 4. W. Marshall, I. Olkin. Life Distributions: Structure of Nonparametric, Semiparametric, and Parametric Families, Springer New York, 2007. DOI: 10.1007/978-0-387-68477-2 5. Mathematical and Statistical Models and Methods in Reliability: Applications to Medicine, Finance, and Quality Control, edited by V.V. Rykov, N. Balakrishnan, M.S. Nikulin. Birkhäuser Boston, 2010. DOI: 10.1007/978-0-8176-4971-5 6. A. Birolini. Reliability Engineering: Theory and Practice, 7th edition, Springer Berlin Heidelberg, 2014. DOI: 10.1007/978-3-642-39535-2 7. S. C. Saunders. Reliability, Life Testing and the Prediction of Service Lives: For Engineers and Scientists, Springer New York, 2007. DOI: 10.1007/978-0-387-48538-6 8. Springer Handbook of Engineering Statistics, edited by Prof. Hoang Pham. Springer London, 2006. DOI: 10.1007/978-1-84628-288-1
The main goal - to prepare students for research and design activity.
Learning Outcomes
By the end of the course the students will know: basic concepts, principles and methods of simulating complex systems and how to realize their interrelation with other disciplines. different methods of modeling; main problems of modeling systems. The students will be able to: use modeling methods when they solve problems of analysis and design of systems of various kinds; analyze modeling results and assess the quality of a simulated model; optimize models; use the technologies of computer modeling with the use of dynamic modeling software Rockwell Arena, GPSS World (Simulink optionally). Students will have the experience in: carrying out research and analyzing experiments results; applying theoretical knowledge into practice designing models of real systems; solving problems with the use of simulation methods.
Syllabus
Topic 1 MODELING and SIMULATION (lectures 4) Topic 2 Queuing systems (lectures 2/labs 2) Topic 3 Single Channel Systems (lectures 4/labs 4) Topic 4 Multiple-Channel Queuing Model (lectures 4/labs 4) Topic 5 Methods and facilities of structural analysis and design (lectures 2) Topic 6 Project (labs 6)
Labs
Lab 1 Queuing systems Lab 2 Single Channel Systems Lab 3 Multiple-Channel Queuing Model Lab 4 Project (Team Work)
Projects
Assessment
Credit test
Resources
1. Zamyatina O.M. Networks Modelling and Simulation: study aid / O. M. Zamyatina; National Research Tomsk Polytechnic University (TPU). — Tomsk: SPB Graphics, 2012. — 151 p. 2. Mark M. Meerschaert. Mathematical Modeling, Fourth Edition. – Pub.: Elsevier Inc., 2013. – 363 p. 3. Frank R. Giordano, William P. Fox, Steven B. Horton. A First Course in Mathematical Modeling, Fifth Edition. – Pub.: BROOKS/Cole, 2013. – 670 p. 4. https://www.arenasimulation.com/ - Rockwell Arena 5. http://matlab.ru/tpu/ - MATLAB Installation and activation for TPU
Let student know about main process for software lifecircle and delivery
Learning Outcomes
This course highlights the importance and role of software product management, product delivery. It also provides an overview of the specialization, as well as its goals, structure, and expectations. The course explains the value of process, requirements, planning, and monitoring in producing better software. - Relate software product management to better software products - Recognize the role of a software product manager - Distinguish between different process models for organizing software production. - Gauge the applicability of process models for a software development project. - Apply the fundamentals of Agile software development and management practices.
Syllabus
Module 1: Systems Engineering and its Relevance and Benefits 1. Introduction to Systems Engineering 2. Relevance and Benefits of Systems Engineering Module 2: Software Product Management - The Discipline 1. Introduction 2. Lesson 1: Better Software 3. Lesson 2: Why Software Product Management? 4. Lesson 3: The Role of a Software Product Manager 5. Lesson 4: Specialization Overview Module 3 - Introduction to System Life Cycle. Agile Preview 1. Lesson 1: Project Success, Why Agile? 2. Lesson 2: Agile Manifesto 3. Lesson 3: Why Process? 4. Lesson 4: Why Requirements? 5. Lesson 5: Why Planning? 6. Lesson 6: Why Monitoring?
“Information systems”, “Management” or “Economics”
Course Objectives
To know and apply methods and models of system analysis to solve problem situations in different systems
Learning Outcomes
- knowledge of basic notions and definitions of system analysis; - experience of practical application of basic models of system analysis; - ability to define the structure of the system explored; - ability to determine and describe problem situation; - ability to apply different methods of goals and problems structuring; - knowledge of methods of optimal decision choice
Syllabus
1. Basic terms and notions 2. Basic models of system analysis 3. Decision-making models and methods 4. Applied models of system analysis 5. Applied methods of system analysis
Labs
1. Building a Back-Box model 2. Definition of model’ restrictions and requirements 3. Definition of Problem situation 4. Systems goals definition 5. Building of an hierarchical containsive model 6. Decision-making process (morphological analysis) 7. Decision-making process (an optimal decision choice methods)
Projects
General project based on tools and methods studied within practical seminars. A topic of the project is chosen by the student.
Assessment
Exam
Resources
Ackoff, R. (1978). The art of problem solving. New York: Wiley. K. Kendall, J. Kendall, Systems Analysis and Design. 5th edition J.S.Valachich, J.F.George, J.A.Hoffer, Essentials of Systems Analysis and Design (6th Edition) J. O’Connor, I.McDermott, The Art of systems thinking
Chemistry, Physics, Fundamentals of Mechanical Engineering
Course Objectives
Elaboration of the creative thinking, acquisition of competence in inventive problem solving
Learning Outcomes
This module provides students with the technical system evolution understanding and gives different principles for any possible technical system contradictions solving. After the module completion the student will: know • main technical system development laws and system evolution ways • basic ways to overcome system scores contradictions be able to • analyze different technical systems and inventions • demonstrate system characteristics knowledge and system fault scores apply the following methods • technical system development laws for system analysis • System Su-Field analysis in order to overcome system scores contradictions TRIZ algorithm for a new idea search.
Syllabus
1. Techniques of Creative Approach Most cutting-edge techniques for inventive problem solving. Morphological analysis and morphological tables. 2. Technical System Evolution Criteria of technical system development. The laws of technical system evolution in accordance with TRIZ. 3. The Ways to Overcome Technical Systems Contradictions Modeling in TRIZ. Su-Field analysis. Main principles to overcome contradictions. Physical, chemical effects and geometry in inventive tasks. 4. Algorithm of Inventive Problem Solving. Program Task Solving
Labs
Algorithm of Inventive Problem Solving. Program Task Solving (6 hours) Benchmarking (2 hours)
Projects
Final Credit Task
Assessment
Credit Test ( Pass/Fail)
Resources
https://portal.tpu.ru/SHARED/s/SHOB/study/disc3 http://ips1357.blogspot.ru 1. Jones J. Design Methods. John Willey & Sons, New York, Toronto, Chichester, Brisbane, 1982. 2. Edward de Bono. How to Have Creative Ideas. – London: Vermilion, McQuaig Group Inc., 2007. 3. The Theory of Inventive Problem Solving (TRIZ) Режим доступа: http://www.mazur.net/triz/
To acquire basic knowledge of probability theory; To learn fundamentals of mathematical statistics; To develop practical skills of estimating distribution parameters.
Learning Outcomes
Upon successful completion of the course, a student will be able to: compute probabilities and conditional probabilities; apply central limit theorem to describe inferences; solve basic problems in probability theory, including problems involving the binomial, geometric, exponential, Poisson, Weibull and normal distributions; represent and statistically analyze data both graphically and numerically; estimate basic population parameters and perform a basic hypothesis test; construct and interpret confidence intervals to estimate means and standard deviations for populations.
Syllabus
1. Basics of Probability Theory 2. Law of Large Numbers 3. Central Limit Theorem 4. Notable Probability Distributions 5. Fundamentals of Statistics 6. Statistical Hypothesis Testing 7. Point and Interval Estimation
Labs
1. Probability Distributions 2. Random Samples 3. Parameter Estimation
Projects
Assessment
Credit Test ( Pass/Fail)
Resources
1. F.M. Dekking et al. A Modern Introduction to Probability and Statistics: Understanding Why and How, Springer-Verlag London Limited, 2005. DOI: 10.1007/1-84628-168-7 2. A. DasGupta. Fundamentals of Probability: A First Course, Springer New York, 2010. DOI: 10.1007/978-1-4419-5780-1 3. M. Lefebvre. Applied Probability and Statistics, Springer New York, 2006. DOI: 10.1007/0-387-28505-9 4. G. Schay. Introduction to Probability with Statistical Applications, Birkhäuser Boston, 2007. DOI: 10.1007/978-0-8176-4591-5 5. J. Haigh. Probability Models, 2nd edition, Springer London, 2013. DOI: 10.1007/978-1-4471-5343-6 6. R.B. Schinazi. Probability with Statistical Applications, 2nd edition, Birkhäuser Boston, 2012. DOI: 10.1007/978-0-8176-8250-7
Mathematics, Descriptive Geometry and Engineering Graphics, Theory of Machines and Mechanisms, Metrology, Standardisation and Certification, Fundamentals of Mechanical Engineering
Course Objectives
The objective of the course is to acquire knowledge, skills and experience in the field of tolerance analysis of mechanical products
Learning Outcomes
Will be able to: • draw dimensional chains for mechanical assemblies and individual parts; • apply proper methods of tolerance analysis; • solve direct and reverse tasks of tolerance analysis; • correct engineering drawings according to tolerance analysis results.
Syllabus
1. Introduction to tolerance stack-ups and analysis 2. Dimensional chains 3. Methods of tolerance analysis
Labs
Projects
Assessment
Exam
Resources
1. Fundamentals of Mechanical Engineering/ V. F. Skvortsov; Tomsk Polytechnic University (TPU). — Tomsk: Tomsk Polytechnic University Publishing House, 2014. (http://www.lib.tpu.ru/fulltext2/m/2014/m255.pdf) ) 2. Fischer BR. Mechanical tolerance stackup and analysis. 2nd ed. Boca Raton, FL: CRC Press, 2012
• Introduction to IT (B1.VM4.2) • Programming (B1.VM4.8) • Programming technologies (B1.VM4.13) • Computer graphics (B1.VM4.21)
Course Objectives
This course is an introduction of the area of user interface design. It shows the various components of user interface design for the web, mobile and desktop applications. Good user interface design is a critical part of the success of a product. Students will be exposed to techniques that lead to better designs.
Learning Outcomes
• Learn the basic principles of effective software UX/UI design • Gain an understanding of best practices for user interface design
Syllabus
• Fundamental concepts of user interface • User interface and user experience • Design Process for Digital Products • Goal-Directed Design • Interaction Design Principles • Designing for the Desktop • Designing for Mobile and Other Devices • Designing for the Web
Labs
• Introduction to WPF and XAML • Controls • Layouts • Element binding and commands • Styles • Triggers and behaviors • Shapes, brushes, transforms • Animation • Geometries and drawings • Control templates, custom elements
Projects
User interface development using Windows Presentation Foundation (WPF) and extensible application markup language (XAML)
Assessment
Exam
Resources
• About Face: The Essentials of Interaction Design, Alan Cooper, 2015 • Jenifer Tidwell, Designing Interfaces., - 2011 • WPF 4.5 Unleashed, Adam Nathan, 2013 • Pro WPF 4.5 in C#: Windows Presentation Foundation in .NET 4.5, Matthew MacDonald, 2013 • C# 6.0 and the .NET 4.6 Framework, Andrew Troelsen, 2015 • CLR via C# (4th Edition), Jeffrey Richter, 2012
1. Installation VSAT HUGHES HN/HX 2. Installation of HN/HX terminals 3. Maintenance vsat HUGHES HN/HX 4. Legalization of VSAT
Learning Outcomes
After completing this course student will have a thorough knowledge about, and be able to use: 1. Composition of satellite communication system HUGHES HX/HN 2. Kinds of satellite terminals HX/HN 3. Structure of terminals web-interface 4. Signal flowing trough system components 5. Understanding of main processes of date transmit from/to terminal 6. Understanding of satellite terminal legalization algorithm
Syllabus
1. Introduction to satellite communication system VSAT. Composition of indoor and outdoor equipment. 2. Choice of installation place for antenna support 3. Installation of parabolic antenna 4. Installation of indoor and outdoor equipment 5. Installation of flex HF cable and power cable for VSAT 6. Antenna pointing on receive signal. 7. Cross-polarization adjusting 8. Adjusting and monitoring of remote terminal 9. Basic and Advanced user web interface 10. Algorithm of troubleshooting 11. Configuring and advance antenna pointing 12. Manual and auto-commissioning of terminals 13. Regulation of maintenance 14. Maintenance indoor equipment 15. Maintenance outdoor equipment 16. VSAT legalization scheme 17. Documents for registration of VSAT at agency of communications
Labs
1. Installation of indoor and outdoor equipment 2. Antenna pointing on receive signal. 3. Operating of HN/HX terminals 4. Basic and Advanced user web interface 5. Manual commissioning of terminals 6. Auto commissioning of terminals 7. Maintenance of equipment
Projects
Assessment
Credit Test
Resources
Students will be provided satellite communication equipment (Remote terminals, antennas, and active network operation center) for training and labs. They will be provided a list of classic papers which are available on-line which they will be expected to read in order to gain an overview of the field, and also have access to a range of textbooks which help them prepare for the assessed seminars and tests.
Instructors
Litvinov Vladimir Petrovish e-mail: litvinoff@tpu.ru mob: +7-913-850-74-50
Mathematics, Physics, Descriptive Geometry and Engineering Graphics, Theory of Machines and Mechanisms, Construction Materials, Metrology, Standardisation and Certification, Material Science, Fundamentals of Mechanical Engineering
Course Objectives
The objective of the course is to develop skills in engineering design of work-holding devices with respect to the type of manufacturing, as well as in preparing drawings of the designed equipment.
Learning Outcomes
Will be able to: • choose locating and clamping elements and devices; • perform accuracy analysis of the designed devices; • calculate forces needed for workpiece clamping; • assemble common work-holding devices.
Syllabus
1. Fundamentals of locating and work-holding 2. Basic elements of work-holding devices 3. Types of drives used in work-holding 4. Pneumatic and hydraulic drives 5. Indexing and guiding devices 6. Classification of clamping elements 7. Devices used on lathes 8. Devices used on drilling machines 9. Devices used on milling machines 10. Devices for CNC machines
