EET | Master of Technology in Computer Technology
Courses id Courses Title
[0 - 1 - 4]
Digital Systems Lab
[0 - 1 - 4]
Computer Networks Lab
Simulation and hardware experiments on different aspects of computer communication networks. Network traffic generation and analysis, differentiated service queues, network of queues using discrete event simulations.
[0 - 0 - 6]
IEC Laboratory-I
Introduction to Cadence, Learning Cadence design framework and Virtuoso environment, Design with Virtuoso schematic editor, Layouts, Learning and applying Synopsys and Xilinx tools, Circuit simulation and SPICE.
[0 - 0 - 12]
Major Project Part-I
[3 - 0 - 0]
Software Fundamentals for Computer Technology
Introduction, data structures for combinatorial optimization: heaps, union-find, Fibonacci heaps, dynamic trees, dynamic graph structure; Asymptotic analysis; Divide & conquer and graph algorithms: Graph search: Breadth first, depth first, topological sorting, Fast Fourier Transform, Matrix Multiplication, Shortest path algorithms; Additional Data Structures: Suffix trees & string matching, Splay trees & amortized analysis; Advanced algorithmic design techniques: Dynamic programming (edit distance, chains of matrix multiplication, etc.), Network flow and its use for solving problems; Linear and integer programming, NP-completeness, Randomized algorithms (hashing & global minimum cut), Approximation Algorithms; Object oriented Software design, Design of Dependable Software.
[3 - 0 - 0]
Computer Architecture
Instruction set design, pipelining, memory hierarchy design, parallelism in various forms, warehouse scale computers, specific topics such as Vector, SIMD, GPU architectures, Embedded Systems, VLIW, EPIC, Multi-core architectures.
[0 - 0 - 24]
Major Project Part-II
[3 - 0 - 0]
Special Topics in Computers-I
[1 - 0 - 0]
Special Module in Computers
[3 - 0 - 0]
Human & Machine Speech Communication
Overview of human and machine speech communication: Applications; Speech signal measurement and representation. Speech science topics: Speech production and phonetics: Speech production mechanism; Articulatory and acoustic phonetics; Speech production model; International Phonetic Alphabet; Phonetic transcription; Hearing and perception. Speech signal analysis: Time domain analysis; Spectrum domain analysis; Spectrogram; Cepstrum domain analysis; Pitch estimation; Voicing analysis; Linear prediction analysis. Engineering applications: Speech coding; Speech quality assessment: Subjective and objective evaluation of quality; Automatic speech recognition: HMM; Language models; Keyword spotting; Text-to-speech synthesis: Concatenative and HMM speech synthesis; Prosody modification.|The course will include audio demonstrations and require students to do practical exercises with recorded speech signals. An isolated word speech recognizer using open source resources shall be designed.
[3 - 0 - 0]
Coding Theory
Measure of information, Source coding, Communication channel models, Channel Capacity and coding, Linear Block codes, Low Density Parity Check (LDPC) Codes, Bounds on minimum distance, Cyclic codes, BCH codes, Reed Solomon Codes, Convolutional codes, Trellis coded Modulation, Viterbi decoding, Turbo codes, Introduction to Space-Time Codes and Introduction to Cryptography. If time permits, LDPC/Turbo codes in the wireless standards. There are no laboratory or design activities involved with this course.
[3 - 0 - 0]
Signal Theory
Discrete random variables (Bernoulli, binomial, Poisson, geometric, negative binomial, etc.) and their properties like PDF, CDF, MGF.|Continuous random variables: Gaussian, multivariate Gaussian; whitening of the Gaussian random vector; complex Gaussian random vector, circularity; Rayleigh and Rician; exponential; chi-squared; gamma.|Signal spaces: convergence and continuity; linear spaces, inner product spaces; basis, Gram-Scmidt orthogonalization.|Stochastic convergence, law of large numbers, central limit theorem.|Random processes: stationarity; mean, correlation, and covariance functions, WSS random process; autocorrelation and cross-correlation functions; transmission of a random process through a linear filter; power spectral density; white random process; Gaussian process; Poisson process.
[3 - 0 - 0]
Digital Communications
Review of random variables and random process, signal space concepts, Common modulated signals and their power spectral densities, Optimum receivers for Gaussian channels, Coherent and non-cohrerent receivers and their performance (evaluating BER performance through software tools), Basics of Information theory, source and channel coding, capacity of channels, band-limited channels and ISI, multicarrier and spread-spectrum signaling, multiple access techniques.
[3 - 0 - 0]
Basic Information Theory
Introduction to entropy, relative entropy, mutual information, fundamental inequalities like Jensens inequality and log sum inequality. Proof of asymptotic equipartition property and its usage in data compression. Study of entropy rates of the stochastic process following Markov chains. Study of data compression: Kraft inequality and optimal source coding. Channel capacity: symmetric channels, channel coding theorem, Fanos inequality, feedback capacity. Differential entropy. The Gaussian channel: bandlimited channels, channels with colored noise, Gaussian channels with feedback. Detailed study of the rate-distortion theory: rate distortion function, strongly typical sequences, computation of channel capacity. Joint source channel coding/separation theorem. There are no laboratory or design activities involved with this course.
[3 - 0 - 0]
Telecommunication Switching and Transmission
Wireline access circuits, long haul circuits, signaling, switching exchanges, analysis of telecom switching networks, teletraffic engineering, management protocols, multi-service telecom protocols and networks.
[3 - 0 - 0]
Optoelectronic Instrumentation
Introduction to test and measuring instruments, instrumentation amplifier, chopper stabilized amplifier, analog signal processing: active filter, A/D, D/A converters, integrated, transimpedance and low impedance pre-amplifiers design, sample & hold circuits, multiplexer, peak detector, zero crossing detector etc., digital design: PALs, FPGA, signal analyzer: superheterodyne spectrum analyzer, DFT and FFT analyzer, digital filters and computer interface, microcontrollers: introduction to microcontroller and applications such as 8031, optical post, in-line and pre-amplifiers, noise figure, optoelectronic circuits: transmitter and receiver design, OTDR, optical spectrum analyzer, sensors: fiber optic and radiation types, distributed sensors, fiber optic smart structure, display devices.
[3 - 0 - 0]
MOS VLSI design
Digital integrated circuit design perspective. Basic static and dynamic MOS logic families. Sequential Circuits. Power dissipation and delay in circuits. Arithmetic Building blocks, ALU. Timing Issues in synchronous design. Interconnect Parasitics.
[3 - 0 - 0]
Analog Integrated Circuits
Introduction to MOSFETs, Single stage amplifiers, Biasing circuits, Voltage and Current reference circuits, Feedback analysis, Multistage amplifiers, Mismatch and noise analysis, Differential amplifiers, High speed and low noise amplifiers, Output stage amplifiers, Oscillators.
[3 - 0 - 0]
Introduction to Machine Learning
Introduction to Machine intelligence and learning; linear learning models; Artificial Neural Networks: Single Layer Networks, LTUs, Capacity of a Single Layer LTU, Nonlinear Dichotomies, Multilayer Networks, Growth networks, Backpropagation and some variants; Support Vector Machines: Origin, Formulation of the L1 norm SVM, Solution methods (SMO, etc.), L2 norm SVM, Regression, Variants of the SVM; Complexity: Origin, Notion of the VC dimension, Derivation for an LTU, PAC learning, bounds, VC dimension for SVMS, Learning low complexity machines - Structural Risk Minimisation; Unsupervised learning: PCA, KPCA; Clustering: Origin, Exposition with some selected methods; Feature Selection: Origin, Filter and Wrapper methods, State of the art - FCBF, Relief, etc; Semi-supervised learning: introduction; Assignments/Short project on these topics.
[3 - 0 - 0]
Computer Communication Networks
Theory/Lecture: Review of data communication techniques, basic networking concepts, layered network and protocol concepts, quality of service, motivations for cross-layer protocol design. Motivations for performance analysis, forward error correction and re-transmission performances, Markov and semi-Markov processes, Littles theorem, M/M/m/k, M/G/1 systems, priority queueing, network of queues, network traffic behavior. Concepts and analysis of multi-access protocols; contention-free and contention multi-access protocols. Basic graph theoretic concepts, routing algorithms and analysis.|Suggested lab Course content:|Laboratory: Simulation and hardware experiments on different aspects of computer communication networks. Network traffic generation and analysis, differentiated service queues, network of queues using discrete event simulations.
[3 - 0 - 0]
Embedded Systems and Applications
Introduction to embedded system. Architectural Issues: CISC, RISC, DSP Architectures.|Component Interfacing, Software for Embedded Systems : Program Design and Optimisation techniques, O.S for Embedded Systems, Real-time Issues. Designing Embedded Systems : Design Issues, Hardware- Software Co-design, Use of UML. Embedded Control Applications, Networked Embedded Systems : Distributed Embedded Architectures, Protocol Design issues, wireless network. Embedded Multimedia and Telecommunication Applications: Digital Camera, Digital TV, Set-top Box, Voice and Video telephony.
[3 - 0 - 0]
Computer Vision
Link between Computer Vision, Computer Graphics, Image Processing and related fields; feature extraction; camera models; multi-view geometry; applications of Computer Vision in day-to-day life.
[3 - 0 - 0]
Adaptive and Learning Control
Introduction to adaptive control, Review of Lyapunov stability theory, Direct and indirect adaptive control, Model reference adaptive control, Parameter convergence, persistence of excitation, Adaptive backstepping, Adaptive control of nonlinear systems, Composite adaptation, Neural Network-based control, Repetitive learning control, Reinforcement learning-based control, Predictive control, Robust adaptive control.
[3 - 0 - 0]
Telecommunication Technologies
Types of Data Networks, types of access and edge networks, core networks, OSS/NMS and Telecom Management network (TMN), Teletraffic Theory and Network analysis.
[3 - 0 - 0]
Computational Neuroscience
Fundamentals of brain anatomy and physiology, signals of brain, Brain signal recording and imaging techniques, Human experimentation study design, Processing the X-D neural data, Machine learning approaches, Graph theory and neural networks, Multivariate pattern analysis in 4D Imaging data, Statistical inferences, student projects and presentations.
[0 - 1 - 4]
Software Lab
Experiments related to the following topics: advanced data structures and algorithms, compilers, GUI, component-based software design, distributed and web based applications, UML, firmware, database applications.
Block II, IIT Delhi Main Rd, IIT Campus, Hauz Khas, New Delhi, Delhi 110016