An introduction to dynamic systems and their control. Differential equation models of physical systems using transfer functions and state space models. Linearization. Initial and input response. Stability theory. Principle of feedback. Internal Model Principle. Frequencey response. Nyquist stability. Loop shaping theory. Computer aided design using MATLAB and Simulink.

An introduction to transmission lines: voltage and current waves, characteristic impedance, reflections from the load and source, transients on a transmission line, Smith's chart, impedance matching. Fundamentals of electromagnetic theory: Maxwell's equations, boundary conditions, wave equation and its solutions in lossless and lossy media. Constitutive relations and dispersion. Plane wave propagation, reflection and transmission at boundaries. Waveguides; propagating and evanescent waveguide modes and cut-off frequencies. Introduction to radiation and antennas.

Fundamentals of algorithm design and computational complexity, including: analysis of algorithms, graph algorithms, greedy algorithms, divide-and-conquer, dynamic programming, network flow, approximation algorithms, the theory of NP-completeness, and various NP-complete problems.

An introduction to electronics. Basic electronic circuits: introductory frequency-domain analysis, operational amplifiers, diodes, field-effect transistors, bipolar junction transistors, small-signal analysis, single-stage amplifiers.

Layered network architectures; overview of TCP/IP protocol suite. Introduction to sockets; introduction to application layer protocols. Peer-to-Peer Protocols: ARQ; TCP reliable stream service; flow control. Data Link Controls: Framing; PPP; HDLC. Medium access control and LANs: Aloha; Ethernet; Wireless LANs; Bridges. Packet Switching: Datagram and virtual circuit switching; Shortest path algorithms; Distance vector and link state algorithms.

An introductory course in analog and digital communication systems. Analog and digital signals. Probability and random processes. Energy and power spectral densities; bandwidth. Distortionless analog communication; amplitude, frequency and phase modulation systems; frequency division multiplexing. Sampling, quantization and pulse code modulation (PCM). Baseband digital communication; intersymbol interference (ISI); Nyquist's ISI criterion; eye diagrams. Passband digital communications; amplitude-, phase- and frequency-shift keying; signal constellations. Performance analysis of analog modulation schemes in the presence of noise. Performance analysis of PCM in noise.

This course will provide students with a grounding in optimization methods and the matrix algebra upon which they are based. The first past of the course focuses on fundamental building blocks in linear algebra and their geometric interpretation: matrices, their use to represent data and as linear operators, and the matrix decompositions (such as eigen-, spectral-, and singular-vector decompositions) that reveal structural and geometric insight. The second part of the course focuses on optimization, both unconstrained and constrained, linear and non-linear, as well as convex and nonconvex; conditions for local and global optimality, as well as basic classes of optimization problems are discussed. Applications from machine learning, signal processing, and engineering are used to illustrate the techniques developed.

This course will focus on different classes of probabilistic models and how, based on those models, one deduces actionable information from data. The course will start by reviewing basic concepts of probability including random variables and first and second-order statistics. Building from this foundation the course will then cover probabilistic models including vectors (e.g., multivariate Gaussian), temporal (e.g., stationarity and hidden Markov models), and graphical (e.g., factor graphs). On the inference side topics such as hypothesis testing, marginalization, estimation, and message passing will be covered. Applications of these tools cover a vast range of data processing domains including machine learning, communications, search, recommendation systems, finance, robotics and navigation.

State space analysis of linear systems, the matrix exponential, linearization of nonlinear systems. Structural properties of linear systems: stability, controllability, observability, stabilizability, and detectability. Pole assignment using state feedback, state estimation using observers, full-order and reduced-order observer design, design of feedback compensators using the separation principle, control design for tracking. Control design based on optimization, linear quadratic optimal control, the algebraic Riccati equation. Laboratory experiments include computer-aided design using MATLAB and the control of an inverted pendulum on a cart.

An introduction to adaptive control and reinforcement learning for discrete-time deterministic linear systems. Topics include: discrete-time state space models; stability of discrete time systems; parameter adaptation laws; error models in adaptive control; persistent excitation; controllability and pole placement; observability and observers; classical regulation in discrete-time; adaptive regulation; dynamic programming; Rescorla-Wagner model; value iteration methods; Q-learning; temporal difference learning.

This course will provide students with an overview of continuous-time and discrete-time signal processing techniques, and the analysis and design of analog and mixed-signal circuit building blocks used in modern electronic systems. Topics covered include: analysis, specification, simulation, and design of continuous-time filters with linear transconductors and op-amps; phase-domain model, noise model, and design methodology for low phase noise Phase Lock Loops and associated building blocks (VCO, phase-frequency detector, charge pump); discrete-time signal analysis using z-transform; discrete-time filter design based on switched capacitors; as well as fundamentals, architectures, building blocks, and characterization techniques for digital-to-analog and analog-to-digital converters.

Basic concepts of digital communication. Baseband data transmission, intersymbol interference, Nyquist pulse shaping, equalization, line coding, multi-path fading, diversity. Binary and M-ary modulation schemes, synchronization. Signal space concepts, optimum receivers, coherent and noncoherent detectors. Information theory, source encoding, error control coding, block and convolutional codes.

Design issues in distributed systems: heterogeneity, security, transparency, concurrency, fault-tolerance; networking principles; request-reply protocol; remote procedure calls; distributed objects; middleware architectures; CORBA; security and authentication protocols; distributed file systems; name services; global states in distributed systems; coordination and agreement; transactions and concurrency control; distributed transactions; replication.

An Introduction to the basic theory, the fundamental algorithms, and the computational toolboxes of machine learning. The focus is on a balanced treatment of the practical and theoretical approaches, along with hands on experience with relevant software packages. Supervised learning methods covered in the course will include: the study of linear models for classification and regression, neural networks and support vector machines. Unsupervised learning methods covered in the course will include: principal component analysis, k-means clustering, and Gaussian mixture models. Theoretical topics will include: bounds on the generalization error, bias-variance tradeoffs and the Vapnik-Chervonenkis (VC) dimension. Techniques to control overfitting, including regularization and validation, will be covered.

Analysis and design of systems employing radio waves, covering both the underlying electromagnetics and the overall system performance aspects such as signal-to-noise ratios. Transmission/reception phenomena include: electromagnetic wave radiation and polarization; elementary and linear dipoles; directivity, gain, efficiency; integrated, phased-array and aperture antennas; beam-steering; Friis transmission formula and link budget. Propagation phenomena include: diffraction and wave propagation over obstacles; multipath propagation; atmospheric and ionospheric effects. Receiver design aspects include: radio receiver architectures, receiver figures of merit, noise in cascaded systems, noise figure, and noise temperature. System examples are: terrestrial communication systems; satellite communications; radar; radiometric receivers; software-defined radio.

Losses in conductors and dielectrics; RF and microwave transmission lines; transients on transmission lines; matching networks; planar transmission lines (microstrip, stripline, coplanar waveguide); design with scattering parameters; 3- and 4-port RF devices (power dividers/combiners, couplers, isolators & circulators); coupled lines and devices; microwave active circuits (RF amplifiers, mixers, and receiver front ends); RF and microwave filters. The hands-on laboratories engage students in the design, simulation, fabrication, and test of practical passive and active microwave circuits using industry-standard RF/microwave simulation tools and measurement systems.

The human visual interface is rapidly evolving with the emergence of smart glasses, AR/VR wearable display, and autonomous vehicles. This course examines the photonic devices and integrated systems that underline such technologies, and how they are shaped by human visual perception and acuity. Advanced integrated photonic systems in optical display and sensing will be deconstructed and the underlying fundamental concepts studied. Topics include introduction to: heads up and wearable display, optical lidar, optical fiber, waveguide circuits, holography, optical switches, light sources (LED, laser), detectors and imaging sensors.

Review of MOSFET semiconductor device equations. Noise in electronic devices. Review of single-stage amplifiers and frequency response, including noise analysis. Basic CMOS op amp. Op amp compensation. Advanced op amp circuits: telescopic and folded-cascode op amps. Fully-differential op amps. Common mode feedback.

An introductory course in digital filtering and applications. Introduction to real world signal processing. Review of sampling and quantization of signals. Introduction to the discrete Fourier transform and its properties. The fast Fourier transform. Fourier analysis of signals using the discrete Fourier transform. Structures for discrete-time systems. Design and realization of digital filters: finite and infinite impulse response filters. DSP applications in areas such as communications, multimedia, video coding, human computer interaction and medicine.

The introduction to VLSI fabrication techniques, integrated circuit designs and advanced semiconductor devices will give a proper perspective of the past, present and future trends in the VLSI industry. Following the evolution of MOS and bipolar devices, digital and analog CMOS, BiCMOS, deep submicron CMOS, SOI-CMOS, RF-CMOS and HV-CMOS technologies will be studied. Special attention will be given to the physical scaling limits such as short channel effects. In addition, CAD tools and design methodology for the development of advanced semiconductor devices and integrated circuits will be introduced in the laboratory environment. These include the simulation of device fabrication, device characteristics, device modeling, circuit layout, design verification. Finally, advanced technology such as GaN HEMTs, graphene devices, carbon nano-tube devices, power devices, heterojunctions, InP and GaSb HBTs will also be studied.

Provides an overview of the fundamental principles and clinical applications of neuromodulation. Topics include (i) overview of the human nervous system & neural oscillations, (ii) introduction to electrical-neural interfaces, (iii) fundamentals of neural recording, neural stimulation & signal processing as well as (iv) instrumentation and clinical applications of commonly used neuromodalities including Electroencephalography (EEG), Deep brain stimulation (DBS), Transcranial magnetic stimulation (TMS) and Functional electrical stimulation (FES).

The collaborative software development process. Software requirements elicitation and specifications. Software design techniques. Techniques for developing large software systems. Software testing, quality assurance, documentation, and maintenance. Open-source software and web application design.​

Generation, transmission and the significance of bioelectricity in neural networks of the brain. Topics covered include: (i) Basic features of neural systems. (ii) Ionic transport mechanisms in cellular membranes. (iii) Propagation of electricity in neural cables. (iv) Extracellular electric fields. (v) Neural networks, neuroplasticity and biological clocks. (vi) Learning and memory in artificial neural networks. Laboratory experiences include: (a) Biological measurements of body surface potentials (EEG and EMG). (b) Experiments on computer models of generation and propagation of neuronal electrical activities. (c) Investigation of learning in artificial neural networks.

Physical acoustics, acoustic measurements, electroacoustic transducers, and physiological acoustics. Speech processing, speech recognition algorithms and signal processing by the auditory system. Engineering aspects of acoustic design. Electrical models of acoustic systems. Noise, noise-induced hearing loss, and noise control. Introduction to vision and other modalities. Musical and psychoacoustics.

Modern technologies in the biosciences generate tremendous amounts of biological data ranging from genomic sequences to protein structures to gene expression. Biocomputations are the computer algorithms used to reveal the hidden patterns within this data. Course topics include basic concepts in molecular cell biology, pairwise sequence alignment, multiple sequence alignment, fast alignment algorithms, deep learning approaches, phylogentic prediction, structure-based computational methods, gene finding and annotation.

Fundamental techniques for programming computer systems, with an emphasis on obtaining good performance. Topics covered include: how to measure and understand program and execution and behaviour, how to get the most out of an optimizing compiler, how memory is allocated and managed, and how to exploit caches and the memory hierarchy. Furthermore, current trends in multicore, multithreaded and data parallel hardware, and how to exploit parallelism in their programs will be covered.

Review of sampling and discrete-time signals in one or more dimensions; linear shift-invariant systems; the Z-transform; the discrete-time Fourier transform; the discrete Fourier transform and computationally efficient implementations (fast Fourier transforms); general orthogonal representations; wavelet bases; discrete-time filters: finite and infinite impulse response filters; fixed-point implementations and finite word-length effects; multidimensional filters and multidimensional signal processing. Illustrative applications are drawn from audio and biomedical signal processing, communication systems, and image and video signal processing.

This course will cover the fundamentals of protocols for packet switching networks with emphasis on Internet type of networks including the following topics: the Internetworking concept and architectural model; data link layer (Ethernet and PPP); service interface; Internet addresses; address resolution protocol; Internet protocol (connectionless datagram delivery); routing IP datagrams; Internet control message protocol (error and control messages); subnet and supernet address extensions; ping program; traceroute program; user datagram protocol; reliable stream transport service (TCP); the socket interface; routing (GGP, EGP, IP, OSPF, HELLO); Internet multicasting; domain name system; applications such as HTTP, electronic mail, and SNMP; Internet security and firewall design; Ipv6, RSVP, flows, and ISIP.

Topics in the engineering area of multimedia systems with particular emphasis on the theory, design features, performance, complexity analysis, optimization and application of multimedia engineering technologies. Topics include sound/audio, image and video characterization, compression, source entropy and hybrid coding, transform coding, wavelet-based coding, motion estimation, JPEG coding, digital video coding, MPEG-1/2 coding, content-based processing, and MPEG-7.

Electric drives comprise electric machines (i.e. motors/generators) together with power electronic actuation to enable the control of mechanical motion. Topics include electro-mechanical mechanisms for torque production relevant to rotating machines, speed-torque diagrams, DC machine analysis, dynamics and torque/speed/position control, introduction to space vectors and their application to motion control of synchronous machines and stepper motors. Steady state and variable speed operation of the induction machine using constant flux control is also covered.