19ECS431: EMBEDDED SYSTEMS L T P C 2 0 2 3 An embedded system is a multidisciplinary course which requires the knowledge of both hardware and software. The applications of embedded systems are enormous, few applications are line following robots, GPS systems, cameras, ATM cards etc. Embedded systems are broadly classified into three types- small, medium, and large embedded systems. The objective of this course is to provide the knowledge of hardware and software used in embedded systems. A course is also giving the insides of interfacing techniques and importance of real time operating system. Course objectives: 1. To understand purpose of Embedded systems and its building blocks. 2. To familiarize advanced 32-bit ARM architecture. 3. Understand the ASM programming. 4. Understand instruction set of ARM 7 controller. 5. Understand various peripheral interfacing techniques. Module I: Module Name (if any) Number of hours(LTP) 6 0 4 Introduction to Embedded Systems: Embedded systems vs general computing systems, history of embedded systems, classification of embedded systems, major application of embedded systems, purpose of embedded systems, elements of an embedded systems Learning Outcomes: After completion of this unit, the student will be able to: 1. Define an embedded system. 2. Explain the difference between general and embedded computing system. 3. Explain classification of embedded system. 4. Know the purpose and applications of embedded system. 5. Understand the basic elements required for embedded system. Module II: Module Name(if any) Number of hours(LTP) 6 0 4 ARM Architecture: The RISC design philosophy, ARM design philosophy, The General-Purpose Registers in the ARM Section, ARM Programmers model, The ARM Memory Map, ARM CPSR (Current Program Status Register), LPC2148 processor architecture. Learning Outcomes: After completion of this unit, the student will be able to: 1. Explain architecture of LPC2148 processor. 2. Explain ARM program model. 3. Differentiate GPR & SFR available in LPC2148. 4. Know the ARM execution modes. 5. Can demonstrate flags. Module III: Module Name(if any) Number of hours(LTP) 6 0 4 ARM Instruction Set and Assembly L anguage P rogramming: Arithmetic and Logic Instructions and Programs, Rotate and Barrel Shifter, Shift and Rotate Instructions in ARM, Branch Call and Looping in ARM, Calling Subroutine with BL, Simple ALP programs on Arithmetic & logical operations, Factorial. 288 Learning Outcomes: After completion of this unit, the student will be able to : 1. Know Structure of ASM program. 2. Understand use of program counter 3. Explain instruction set. 4. Know the addressing modes. 5. Can develop simple Assembly language programs. Module IV: Module Name(if any) Number of hours(LTP) 6 0 4 Introduction to ARM Programming: Simple C programs for application with LED, Control of LED with software and hardware timer, LCD interfacing, programming of LCD, ADC, Interfacing of LM35 temperature sensor, UART programming. Learning Outcomes: After completion of this unit, the student will be able to: 1. Explain the structure of C program. 2. Demonstrate the control of led using software and hardware timers. 3. Apply the interfacing techniques led and lcds. 4. Explain data acquisition using sensors. 5. Can program serial communication. Module V: Module Name(if any) Number of hours(LTP) 6 0 4 RTOS BASED EMBEDDED SYSTEM DESIGN: Operating system basics, types of operating systems, tasks, process and threads, multiprocessing, and multitasking, task scheduling: non-pre-emptive and pre-emptive scheduling, Task Synchronization Techniques, led blinking with free RTOs with Arduino. Learning Outcomes: After completion of this unit, the student will be able to: 1. Explain the difference between normal OS and RTOS 2. Demonstrate various operating systems and its features 3. Understand the task and its states 4. Know the different scheduling algorithms 5. Can develop simple RTOS programs Text Books(s) 1. Rajkamal, ‘Embedded system-Architecture, Programming, Design’, 3/e,Tata McGraw Hill Education,2017 (UNIT 1). 2. Muhammad Ali Mazidi, SarmadNaimi, SepehrNaimi and Janice Mazidi, ARM Assembly Language Programming & Architecture, MicroDigitalEd, 2/e 2016 (UNIT 2, 3). 3. Trevor Martin, Insider ’s Guide To Philips Arm7 Based Microcontroller, hitex(UK) Ltd., 1/e, 2005 (UNIT 4). 4. Shibu.K.V, “Introduction to Embedded Systems”, TataMcgraw Hill, 2009(UNIT 5). Reference Book(s) 1. Bishop C M “Pattern recognition and Machine learning", 1/e, Springer, 2006. 2. Bishop C M “Neural Networks for Pattern Recognition” Oxford University Press, 1995. Course Outcomes: 1. Identify hardware and software needed for an embedded system. 2. Demonstrate the philosophy of RISC architecture of ARM 7. 289 19EEI471: ROBOTICS AND AUTOMATION Robotics and automation is a branch of engineering that involves the design, manufacturing and operation of robots. It overlaps many fields of engineering including electronics, computer science, artificial intelligence, automation and nanotechnology. This course has its applications in industries related to aerospace, defense contractors, entertainment, manufacturing and medical research (development of prosthetic parts). Course Objectives: • To be familiar with history of robotics, technological advances and to gain insight on different types of End Effectors. • To learn about different robotic drive systems, actuators and their control. • To analyze the robotic kinematics in different degrees of freedom. • To study the principles of various sensors used in robotics • To explore industrial applications of robotics. UNIT I 9L Introduction: Historical robots, robots in science fiction, future trends of robots, definitions of robots, present application status. Robot End Effectors: Classification of end effectors, drive systems for grippers, mechanical grippers, magnetic grippers, vacuum grippers, adhesive grippers, hooks, scoops and other miscellaneous devices, active and passive grippers. Learning Outcomes: After completion of this unit, the student will be able to • list important developments of robot history and future trends of robots (L1). • classify robot end effectors (L3). • identify appropriate grippers for a given application (L2). • compare active and passive grippers (L4). • discuss merits and demerits of grippers (L2). L T P C 2 1 0 3 294 UNIT II 9L Robot Drives, Actuators and Control : Functions of drive systems, general types of control, pump classification, introduction to pneumatic systems, electrical drives, dc motors and transfer functions, stepper motor, drive mechanisms. Learning Outcomes: After completion of this unit, the student will be able to • list the functions of robot drive system (L1). • classify robot Pump mechanisms in hydraulic system (L3). • explain the principle operations of DC motor and stepper motor (L2). • discuss merits and demerits of Robot actuators (L2). • choose an apt drive mechanism for a robot application (L2). UNIT III 7L Robot Kinematics: Forward and reverse kinematics of 3 degrees of freedom robot arm, forward and reverse kinematics of a 4 degree of freedom, arm manipulator in 3-D, homogeneous transformations. Learning Outcomes: After completion of this unit, the student will be able to • define forward and reverse kinematics of a robot (L2). • contrast between forward and reverse kinematics of a robot (L4). • compare a 3 degree of freedom of robot with a 4 degree of freedom of robot (L4). • analyze the robotic Kinematics in different degrees of freedom (L4). • apply homogenous transformation in deriving kinematics of a robot (L3). UNIT IV 9L Robot Sensors: Need for sensors, types of sensors, robot vision systems, robot tactile systems, robot proximity sensors, robot speech and hearing, speech synthesis, noise command systems, speech recognition systems. Learning Outcomes: After completion of this unit, the student will be able to 295 • understand the need of sensors in robot development (L2). • classify types of sensors used in robot development (L2). • identify appropriate sensor for a given robot application (L2). • explain the principles of various Sensors used in robotics (L2). • elaborate robot vision system and speech recognition system (L2). UNIT V 9L Robot Applications: Capabilities of robots, materials handling, machine loading and unloading, machining and fettling, robot assembly, welding, future applications. Learning Outcomes: After completion of this unit, the student will be able to • list capabilities of robots (L1). • contrast between machine loading and unloading (L4). • explain different industrial applications of robotics (L2). • discuss future applications of robot (L2). Text Book: 1. S.R. Deb, Robotics Technology and Flexible Automation, TMH, 2010. References: 1. Satya Ranjan, Robotics Technology and Flexible Automation, TMH, 2001. 2. James L.Fuller, Robotics: Introduction, Programming and Projects, Maxwell Macmillan, 2000 Course Outcomes: After successful completion of the course, the student will be able to • explain the history of robotics, technological advances and many types of end effectors (L2). • acquire knowledge on different robotic drive systems, actuators and their control (L2). • understand the robotic kinematics (robotic movements, position and orientation) (L2). • select the sensors based on different applications (L4). • understand industrial applications of Robotics (L2). 296 19ECS443: Natural Language Processing L T P C 2 0 2 3 Ability to understand and interpret complex language utterances is a crucial part in design of intelligent agents. Natural language processing is the sub-field of linguistics and computer science which helps in interpreting the human language by a machine. More specifically, natural language processing is the computer understanding, analysis, manipulation, and/or generation of natural language. This course enables the students to learn Natural language processing at different levels like Morphological Level, Syntactic Level, Semantic Level, Discourse Level and Pragmatic Level. Course objectives: 1. To understand the architecture and design of Natural language processing 2. To analyse various tagging techniques 3. To adopt concepts of Context free grammars for NLP 4. To provide knowledge on semantic properties of embeddings 5. To implement and learn the applications like sentiment analysis Module I: Number of hours (LTP) 6 0 6 Introduction to natural language processing, ambiguities in language, Regular expression, words, morphology, morphology parsing, word tokenization, lemmatization & stemming, edit distance. N- grams language models, smoothing-Laplace smoothing, Good-Turing discounting, Interpolation- Backoff, and perplexity Learning Outcomes: After completion of this unit, the student will be able to: 1. acquire a basic understanding of the natural language processing (L2) 2. have the understanding of tokenization. (L2) 3. explain lemmatization and stemming (L2) 4. differentiating smoothing and Laplace smoothing(L4) 5. illustrate Good Turing discounting (L2) Module II: Number of hours (LTP) 6 0 6 Introduction, English word classes, tagsets in English, rule-based part of speech tagging, HMM part of speech tagging, transformation-based part of speech tagging, Evaluation and error analysis, Issues- tag indeterminacy and tokenization, Unknown words Learning Outcomes: After completion of this unit, the student will be able to: 1. experiment with tagging (L3) 2. make use of the part of speech tagging(L3) 3. outline the knowledge of issues of tagging (L2) 4. understand tag indeterminacy (L2) 5. make use of evolution and error analysis(L3) Module III: Module Name (if any) Number of hours (LTP) 6 0 6 Syntactic Parsing, Ambiguity, CKY parsing, Early parsing, probabilistic context free grammar, PCGFS for language modelling, Probabilistic CKY parsing of PCFGs, ways to learn rule probability, Problems with PCFGs Learning Outcomes: After completion of this unit, the student will be able to: 1. understand the problems with PCFGs (L2) 2. visualize probabilistic CKY parsing (L3) 3. experiment with context free grammar(L3) 4. apply rule of probability (L3) 309 5. define parsing (L1) Module IV: Number of hours (LTP) 6 0 6 Lexical Semantics, Vector Semantics, Words and Vectors, Cosine for measuring similarity, TF - IDF: Weighing terms in the vector, word2Vec, Visualizing Embeddings, semantic properties of embeddings, bias and embeddings, Evaluating vector models. Learning Outcomes: After completion of this unit, the student will be able to: 1. apply lexical semantics (L3) 2. understand semantic properties of embeddings (L2) 3. illustrate cosine similarity (L2) 4. differentiate between lexical and vector semantics (L2) 5. explain about how to evaluate vector models (L2) Module V: Discourse Analysis Number of hours (LTP) 6 0 6 Coreference Resolution - Text Coherence - Discourse Structure, word sense disambiguation, semantic role labelling. Machine Translation -Transfer Metaphor–Interlingua- Statistical Approaches- IBM1 model. Application of NLP: Sentiment classification, Text summarization and Factoid Question Answering Learning Outcomes: After completion of this unit, the student will be able to: 1. have a basic understanding of discourse coherence (L2) 2. have the understanding disambiguation (L2) 3. infer co-reference resolution (L2) 4. understanding sentiment classification (L2) 5. make use of NLP for text summarization (L3) Text Books(s) 1. Daniel Jurafsky, James H Martin, “Speech and Language Processing: An introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, 2/e, Prentice Hall, 2008. 2. C. Manning, H. Schutze, “Foundations of Statistical Natural Language Processing”, MIT Press. Cambridge, MA, 1999. 3. Jacob Eisenstein, Introduction to Natural Language Processing, MIT Press, 2019. Reference Book(s) 1. Jalaj Thanaki, Python Natural Language Processing: Explore NLP with machine Learning and deep learning Techniques, Packt, 2017. Course Outcomes: 1. Understand the morphology, morphology parsing, word tokenization, lemmatization & stemming 2. Understand the concepts tag indeterminacy and tokenization 3. Apply various parsing techniques for natural language processing processors 4. Distinguish and apply lexical and vector semantics to design word embeddings 5. Design a statistical model for IBM1 and sentimental analysis 310 19ECS463: Software Testing Methodologies L T P C 2 0 2 3 This course aims to provide an understanding of basics of testing concepts and introducing the various testing techniques. It also introduces the concepts of test management and quality management. Course objectives: 1. Understand the concepts of software testing 2. Familiar with various testing methods 3. Learn the various validation activities 4. Know the concepts of test management 5. Understand the quality management concepts Module I: Introduction Number of hours (LTP) 6 0 6 Software testing definition, evaluation of software testing, software testing myths and facts, goals and model of software testing, software testing terminology, software testing life cycle, software testing methodology, verification, and validation activities. Learning Outcomes: After completion of this unit, the student will be able to: 1. Describe software testing (L2) 2. Explain model of software testing (L2) 3. Demonstrate software testing life cycle (L2) 4. Differentiate verification and validation (L2) Module II: Dynamic Testing Number of hours (LTP) 6 0 6 Black-Box testing: Boundary value analysis, equivalence class testing. White-box testing: Introduction, basis path testing, loop testing. Static testing: inspections, structured walkthroughs, technical reviews. Learning Outcomes: After completion of this unit, the student will be able to: 1. Describe various testing methods of Black-Box testing (L2) 2. Describe various testing methods of White-Box testing (L2) 3. Present the process of inspections (L2) 4. Illustrate the technical reviews (L2) Module III: Validation Activities Number of hours (LTP) 6 0 6 Unit validation testing, integration testing, function testing, system testing, accepting testing. Regression Testing: Objectives of regression testing, regression testing types, regression testing techniques. Learning Outcomes: After completion of this unit, the student will be able to: 1. Illustrate working of unit testing with an example (L3) 2. Describe the performance of integration testing (L2) 3. Explain the concepts of regression testing (L2) Module IV: Test Management Number of hours (LTP) 6 0 6 Test organization, structure of testing group, test planning, detailed test design and test specifications. Efficient test suite management: Introduction, minimizing the test suite and its benefits, defining test suite minimization problem, test suite prioritization, types of test case prioritization, prioritization techniques. Learning Outcomes: 332 After completion of this unit, the student will be able to: 1. Demonstrate the concepts of test management (L2) 2. Develop test design and test specifications (L3) 3. Apply the efficient test suite management (L3) 4. List the types of test case prioritization (L4) 5. Classify prioritization techniques (L4) Module V: Software Quality Management Number of hours (LTP) 6 0 6 Software quality, quality cost, quality control and quality assurance, quality management, QM and project management, quality factors, methods of quality management, software quality metrics, SQA models. Learning Outcomes: After completion of this unit, the student will be able to: 1. Illustrate working of software quality management (L3) 2. List the quality factors required to provide the quality (L4) 3. List the methods of quality management (L4) 4. Explain the working of quality metrics (L2) 5. Summarize working of SQA models (L2) Text Books(s) 1. Naresh Chauhan, Software Testing: Principles and Practices, 1/e, Oxford University Press, 2010 Reference Book(s) 1. William E. Perry, Effective Methods for Software Testing, 3/e, Wiley, 2006. 2. Paul C. Jorgensen, Software Testing: A Craftsman's Approach, 3/e, Auerbach publication, 2015. Course Outcomes: 1. Understanding software testing life cycle (L2) 2. Design test cases for Black-Box testing and White-Box testing (L4) 3. Analyze the performance of integration testing (L4) 4. Use the concepts of test management (L2) 5. Apply software quality management principles (L3) 333 19EHS 403 : Organizational Behaviour L T P C 3 0 0 3 Module I: Number of hours(LTP) 9 0 0 Introduction; Definition of Organization Behaviour and Historical development, Environmental Context (Information Technology and Globalization), Diversity and Ethics, Design and Cultural, Reward Systems. The Individual: Foundation of individual behaviour, Ability Module II: Module Name(if any) Number of hours(LTP) 9 0 0 Learning: Definition, Theories of Learning, Individual Decision Making, classical conditioning, operant conditioning, social Making, learning theory, continuous and intermittent reinforcement. Perception: Definition, Factors influencing perception, attribution theory, selective perception, projection, stereotyping, Halo effect. Module III: Module Name(if any) Number of hours(LTP) 9 0 0 Motivation: Maslow’s Hierarchy of Needs, Mc. Gregory’s theory X and Y, Herzberg’s motivation Hygiene theory, David Mc Cleland three needs theory, Victor vroom’s expectancy theory of motivation. Module IV: Module Name(if any) Number of hours(LTP) 9 0 0 Values and attitudes: Definitions – values, Attitudes: Types of values, job satisfaction, job involvement, professional Ethics, Organizational commitment, cognitive dissonance. Conflict Management: Definition of conflict, functional and dysfunctional conflict, stages of Conflict process. Module V: Module Name(if any) Number of hours(LTP) 9 0 0 Leadership: Definition, Behavioural theories – Blake and Mouton managerial grid, Contingency theories – heresy - Blanchard’s situational theory, Leadership styles – characteristics, Transactional, transformation leaders. The Organization: Mechanistic and Organic structures, Minitberg’s basic elements of organization, Organizational Designs and Employee behaviour, organization development – quality of work life (QWL) Text Books(s) 1. Stephen P Robbins -Organizational Behaviour, Pearson Education Publications,ISBN– 81– 7808–561-5, 9th Edn. 2012. 2. Fred Luthans -Organizational Behaviour, Mc Graw Hill International Edition,ISBN–0–07– 20412–1, 11th Edn. 2006. Reference Book(s) 1. Hellriegel, Srocum and woodman, Thompson Learning -Organization Behaviour, Prentice Hall India, 9th Edition -2001. 2. Aswathappa -Organizational Behavior, Himalaya Publishers. 2001. 3. VSP Rao and others -Organizational Behaviour, Konark Publishers 2002. 4. Organizational Behaviour- (Human behaviour at work) John Newstron / Keith Davis 9th Edition 2002. 5. Paul Henry and Kenneth H. Blanchard -Management of Organizational Behaviors, Prentice Hall of India, 1996. 334 19ECS491 : Project Phase I L T P C 0 0 2 1 Project Phase I intended to train the students to identify a problem of practical significance related to i) Software design process ii) Research in specific domain iii) Application/ software development The student is encouraged to study of literature based on the guidance received by a project supervisor and identify a specific problem and works for a solution. At the end he is expected to submit a report based on his findings. The project can be done as a group consisting maximum of four persons. 19ECS49 3 : Industrial Training /Internship /Research Projects in - National Laboratories or Academic Institutions L T P C 0 0 0 1 This course is designed to expose the students to industrial practices or working on research problems. The student is expected to correlate his theoretical knowledge gained all the way to the industrial needs and or solving practical/ research problems for the benefit of the humanity. The student goes through the training during the summer after his pre-final year. He has to maintain a dairy of findings he experienced and submit a detailed report after completion of the training. 335