The course covers following topics. Linear spaces and linear operators. Bases, subspaces, eigenvalues and eigenvectors, canonical forms. Linear differential and difference equations. Mathematical representations: state equations, transfer functions, impulse response, matrix fraction and polynomial descriptions. System-theoretic concepts: causality, controllability, observability, realizations, canonical decomposition, stability, introduction to optimal control and the Kalman filter.

Control design concepts for linear multivariable systems, System Modeling, Design of feedback controllers, Full Order Observer design, Internal Stability, Transfer functions and closed loop stability, Closed Loop Design Specifications, Sensitivity to Plant Model Uncertainty, The Bode Loop Shaping, Multivariable (MIMO) Closed Loop system representations, Observer Based Compensator, Multivariable Integral Control, Feasibility for MIMO systems, Bias Estimation & Integral Control, LQR Controller, Kalman filter as an optimal estimator, Time Domain Design Limitations, Frequency Domain Design Specifications, Robust Stability, MIMO Generalization of the Stability Robustness, Singular Values and Feedback Control, Classical properties of LQ regulators, The MIMO Root Locus.

Topics include probability axioms, sigma algebras, random vectors, expectation, probability distributions and densities, Poisson and Wiener processes, stationary processes, autocorrelation, spectral density, effects of filtering, linear least-squares estimation, and convergence of random sequences.

Introduction to digital signal processing of continuous and discrete signals. The family of Fourier Transforms including the Discrete Fourier Transform (DFT). Development of the Fast Fourier Transform (FFT). Signal sampling and reconstruction. Design and analysis of digital filters. Correlation and spectral estimation. Estimators of second order properties of random processes: nonparametric and model-based techniques of spectral estimation.

This course coversSampling, Pulse Code Modulation, Pulse Amplitude Modulation, Pulse Position Modulation, Time Division Multiplexing, Digital Pass band Modulation: Gram Schmidt Orthagonalization Procedure, Geometric representation of signals , Noise: Additive Gaussian white and colored noise, multiplicative noise , Receivers: Optimal Receiver in AWGN (ML Receiver), sub-optimal Receivers, Matched Filter Receiver, Receiver Performance, Performance comparison of modulation schemes, Probability of Error, Coherent modulation schemes: BFSK, BPSK, Binary Amplitude shift keying: BASK, M-ary modulation schemes, Performance comparison of modulation schemes in AWGN, Non-Coherent Digital Modulation Schemes, Non-Coherent ML Receiver, Non-Coherent BPSK, Non-Coherent BFSK, Non-Coherent BASK, Synchronization and Symbol Timing Recovery, Digital Communication through band limited channels: ISI, Optimum receiver with ISI, Equalization .

This course covers Overview of wireless communication, Cellular concepts & cellular standards, Channel Modeling - Path loss & Large-scale propagation, Channel Modeling – Small Scale Fading, Modulation techniques, Error Correction Coding, Multiple Access (MA) Techniques , Spread spectrum (SS), Direct Sequence Spread spectrum (DSSS), Frequency Hopping Spread spectrum (FHSS), Direct Sequence Code Division Multiple Access (DS-CDMA).

The goal of software engineering is to control the quality of software by following engineering principles during development. In the practical aspect of this course, the use of object-oriented programming, design patterns, refactoring and extreme programing will be discussed. As a graduate level course, the other aspect of this course focuses on automatic techniques that analyze software artifacts and thus facilitate the engineering process. Through course projects, students will acquire hands-on experience on analyzing software.

The course covers following topics. Vectors, Coulomb’s Law, Electric Field, Gauss’s Law, Scalar Potential, Conductors in Electrostatic Fields, Electrostatic Energy, Electric Multipoles, Boundary Conditions at Surface Discontinuity, Electrostatics in the presence of Matter, Special Methods in Electrostatics, Electric Currents, Ampere’s Law, Magnetic Induction, Integral form of ampere’s law, Vector Potential, faraday’s Law of Induction, Magnetic Energy, Magnetic Multipoles, magnetism in the presence of matter, Maxwell’s Euations, and Scalar and Vector Potentials.

The course covers following topics. Plane waves, Polarization, Laws of Reflection and Refraction, Energy Relations, Waveguides (Fields in Bounded Regions), Circuits and Transmission Lines.

The course covers following topics. Euler-Lagrange formulation; Hamilton-Jacobi approach; Pontryagin's minimum principle; Systems with quadratic performance index; Second variation and neighboring extremals; Singular solutions; numerical solution techniques.

The course covers following topics. Introduction to the analysis and design of nonlinear systems and nonlinear control systems. Stability analysis using Lyapunov, input-output and asymptotic methods. Design of stabilizing controllers using a variety of methods: linearization, absolute stability theory, vibrational control, sliding modes and feedback linearization.

This course covers Vector Kinematics, Quaternion and Matrix Analysis of Kinematics, Geodesy & Rigid Body Dynamics, Review of Aerodynamics, Static Stability Analysis, Review of Classical and Modern Control Theory, Nonlinear Aircraft Model, Linear Models and Stability Derivatives, Simulation of Aircraft Dynamics, Trim point, Calculations & Numerical Linearization, Aircraft Rigid Body Modes, Handling Qualities, Stability Augmentation, Control Augmentation, Autopilots.

Introduction to Lebesgue and Hardy functional spaces, linear operators and norms; time and frequency domain representations of linear systems, internal stability, performance measures and their limitations; model reduction and approximation by balanced realization; classical method of robustness in frequency domain, Bode's gain and phase relations, sensitivity functions; different explicit models of system uncertainty, unstructured uncertainty and small gain theorem, robust stability and robust performance; structured uncertainty and mu-synthesis; H-2 and H-infinity optimal control; H-infinity loop shaping; Gap metrics, nu-gap metrics and extended loop-shaping design;

Dynamic System Models, Signal Measures, lyapunov Stabilitry, I/O stability, Adaptive parameter Estimation, Adaptive statye feedback control, Continuous time MRAC, discrete-time MRAC, Indirect Adpative Control, Multivaraiable Adaptive Control.

Fuzzy Sets, Fuzzy rules and reasoning, Fuzzy inference systems, leastv squares methods for system ID, derivative based optimization, derivavtive free optimization, Adaptive Networks, Supervised learning NN's, Learning from reinforcement, unsupervised learning, Neuro fuzzy interfaces, data clustering algorithms, neurofuzzy control.

Introduction to System ID, Models, Review of linear systems, Review of probability, random variables, Stochastic processes, Response of linear systems to random inputs, Propagation of statistics, Introduction to Kalman Filtering, Least Sq Estimation method, Time domain methods, Freq. domain methods, Introduction to PEM, Regression analysis, Application of PEM to aircraft system ID, Experiment design, Maximum Likelihood Estimation methods, Maximum Likelihood Estimation methods, Subspace methods.

This course will be an introduction to the basic neural network architectures and learning rules. Emphasis will be placed on the mathematical analysis of networks and learning rules, and on the application of neural networks to certain engineering problems in pattern recognition, signal processing and control systems. The course will incorporate necessary background material (such as linear algebra, optimization and stability), while including extensive coverage of performance learning, like the Widrow-Hoff rule and backpropagation. Several enhancements of backpropagation, such as the conjugate gradient and Levenberg-Marquardt variations, will be discussed. Simple building blocks will be used to explain associative and competitive networks, including feature maps, learning vector quantization, and adaptive resonance theory. Recurrent associative memory networks, such as the Hopfield network, will also be presented.

This course we covers Microprocessors (DSP/controllers), Real time operating systems Vx-works, Android and RT-Linux), Multi-thread programming , Scheduling schemes, Programming Communication buses (MLT-ST-1553, ARINC 429, CAN and MODBUS) , general design considerations /techniques and simulation of test systems via Hardware-In-The-Loop methodology, Embedded systems / Avionics system Components Communication Buses / Direct link, Real time operating systems User requirement generation. Design and development considerations.

Review of Random Prtocesses & Linear Systems, Modeling of physical systems by stochastic differential & difference equations, Analysis of systems whose inputsd are stochastic processes, Spectral factorization, parametric optimization,. Minimum variance control, State estimation of continous-time and discrete-time systems, Linear stochastic control theory.

Brief Review Of Probability& Statistics, Detection Theory: Hypothesis Testing, Decision Criteria, Basic Concept Of Estimation: Maximum Likelihood, Maximum A Posteriori Estimator, Linear Estimation In Static Systems, Method Of Least Squares, Recursive Least Squares, Linear Dynamic System With Random Inputs, State Estimation In Discrete Time Linear Dynamic Systems, Estimation For Kinematic Model, Kalman Filter Applications, Extended Kalman Filter And Applications, Adaptive Estimation And Manoeuvring Target Tracking, Input Estimation And Manoeuvre Detection, Variable State Dimension Approach, Multiple Model Algorithms, Interacting Multiple Model Algorithms For Manoeuvring Targets, Multiple Sensor Data Fusion.

Embedded system design fundamentals as well as reconfigurable logic design and implementation using a hardware description language are covered in this course. Experiencing various micro-controllers and microprocessors, participants discover hardware, software and firmware design trade-offs, tool chains, and best practices in current embedded systems development. Real-time operating system topics will be considered to further emphasize embedded hardware-software impacts. Numerous hands-on laboratory projects are provided to reinforce lecture concepts. A final project will integrate course topics into an embedded system design based on an intellectual property (IP) core implemented in a reconfigurable logic package and driven by application code loaded from either the development platform or on-board firmware.

This course covers physical layer communications, Link layer protocols, Introduction to queuing theory, Higher layer protocols: TCP, IP and ATM, Routing algorithms, Flow control, Local Area Networks and multiple access, High performance switches and routers, Wireless Networks, Optical Networks and WDM.

Array signal processing belongs to the general domain of space-time processing as it uses multiple sensors, arranged in a specific geometric arrangement, to acquire multiple versions of a signal. These multiple versions of the signal are processed jointly to estimate the location of the signal source. For multiple signal sources, we can determine and track the locations of these sources. This course covers Basics of Array Signal Processing: Wavefields in Open Space, Spatial Signal Processing, Transmit Beamforming Arrays, Receiver Antenna Arrays, Uniform Linear Arrays: Theory of Array Signal Processing, Source Localization using Frequency Wave-number Spectrum, Narrowband and Wideband, Subspace Methods, Mutual Coupling and Correlation, Beam-forming and Main Beam Steering, Null Placement; Implementation of Array Signal Processing Systems: Signal Processing Errors, Array Element Failure, Ill-conditioned Matrices; Applications of Array Signal Processing, Arrays for RADAR Applications, Arrays for SONAR Applications, Arrays for Biomedical Applications, Arrays for Wireless Communications.

This course will be an introduction to radar signal processing covering full range of basic signal processing techniques on which all radar systems rely, including topics such as target interference models, matched filtering, waveform design, Doppler processing, and threshold detection, CFAR and target tracking. In addition, introductions are provided to the advanced topics of synthetic aperture imaging and space-time adaptive array processing.

Capacity of fading channels, Wireless Channel Modeling, Performance of Communication systems in Fading Channels, Diversity (both receive and transmit), Multiple Antennas and Space Time Coding, MIMO, Adaptive modulation and Coding, Adaptive modulation and Coding, Multicarrier modulation, Capacity regions for different Multiple Access Systems (TDMA, FDMA, SSMA), Multiuser Diversity, Ad hoc and mesh networks: physical layer view and capacity.

The primary purpose of this course is to present an overview of real-time computing. Basic concepts, terminology, and problems of real-time computing are introduced. The constraints of real-time computing are used to contrast real-time applications from applications that are not real-time. The course focuses on software solutions to real-time problems. Issues that are addressed include scheduling, specification of system requirements and design, real-time software architectures, languages and operating systems for real-time computing, real-time problems in a distributed processing system, and hardware-software interfaces.

The course covers following topics. Computational models and techniques for analyzing the time and space complexity of algorithms. The design and analysis of recursive and non-recursive algorithms for searching, sorting, set operations, graph algorithms, matrix multiplication, etc. NP-Complete problems.

An introduction to various methods of obtaining the extremum (minimum or maximum) of a non-dynamical system and the use of these methods in real-life applications. Computational methods for nonlinear optimization; unconstrained optimization. Constrained optimization; linear programming; simplex method for solving linear programs; Lagrange's conditions, the Karush-Kuhn-Tucker (KKT) conditions, Least squares, Penalty methods, Practical aspects of optimization.

The course is presented in three units. Foundations: the review of continuous-time and discrete-time signals, and spectral analysis; design of finite impulse response and infinite impulse response digital filters; processing of random signals. Speech processing: vocal tract models and characteristics of the speech waveform; short-time spectral analysis and synthesis ; linear predictive coding. Image processing: two dimensional signals, systems, and spectral analysis; image enhancement; image coding; image reconstruction. The laboratory experiments are closely coordinated with each unit. Throughout the course, the integration of digital signal processing concepts in a design environment is emphasized.

Theory and applications of adaptive filtering in systems and signal processing. Iterative methods of optimization and their convergence properties: transversal filters; LMS (gradient) algorithms. Adaptive Kalman filtering and least-squares algorithms. Specialized structures for implementation: e.g., least-squares lattice filters, systolic arrays. Applications to detection, noise cancelling, speech processing, and beam forming.

This is a course on bifurcations, chaos, fractals and their applications in diverse fields such as price fluctuations in the stock market, flow of data traffic on the Internet, biological rhythms, and superconducting circuits with particular emphasis on applications in automatic control and telecommunications. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors.

Characteristics of speech and image signals; important analysis and synthesis tools for multimedia signal processing including subspace methods, Bayesian networks, hidden Markov models, and factor graphs; applications to biometrics (person identification), human-computer interaction (face and gesture recognition and synthesis), and audio-visual databases (indexing and retrieval). Emphasis on a set of MATLAB machine problems providing hands-on experience.

This course introduces fundamental theory and techniques for efficient representation and processing of video signals. Topics to be covered include: introduction to video systems, Fourier analysis of video signals, properties of the human visual system, motion estimation, basic video compression techniques, video communication standards, and stereo video processing. A term-project is required in the second half of the semester.

Topics include resonators, filters, detectors, mixers, amplifiers, and microwave systems. There are student design projects for a micro-strip resonator, micro-strip low pass filter, and a high dielectric constant coaxial resonator bandpass filter based upon the Microwave Office software package and use of MathCad at student’s option. LEC

Properties of waveguides, striplines, and micro-strips. Scattering parameters. Butterworth and Chebyshev impedance transformers. Microwave couplers, cavities, and Fabry-Perot resonators. Periodic structures. Microwave filter design. Faraday rotation and non-reciprocal devices.

The course covers following topics. Theory and design of passive and active microwave components and monolithic integrated circuits including: microstrip, lumped inductors and capacitors, GaAs FETs, varactor and mixer diodes, monolithic phase shifters, attenuators, amplifiers and oscillators. Experimental characterization of the above components using network analyzer, spectrum analyzer, power and noise meter.

This course is devoted to the study of analog circuits realized in bipolar technology, with a focus on applications such as transimpedance amplifiers, and broadband amplifiers for networking and communications. The course begins with a consideration of device operation and the modeling needed to support both the hand analysis and computer simulation needed for design. Basic circuit building blocks and cascaded multistage amplifiers will be analyzed. The analysis and design of feedback circuits is a key component of the course.

The principle of operation, device physics, and analytical numerical, and circuit device models for semiconductor devices, such as bipolar junction transistors, metal-semiconductor junctions and transistors, heterostructure junctions and transistors. Selected advanced semiconductor devices, such as novel microwave devices, are also introduced. Prerequisite: SDM-I or equivalent.

Analysis, design and applications of modern analog circuits using integrated bipolar and field-effect transistor technologies. Provides the student with a working knowledge of the basic circuits used in modern analog integrated circuits and techniques for analysis and design.

Introduction to the fundamental operating principles of power conditioning circuits that are currently being used to effect power flow from ac to dc and vice versa. Emphasis is on the relationship between form and function of these circuits. Circuits discussed will include ac/dc line-cummutated converters, dc/dc converters, dc/variable frequency converters, resonant converters, and ac/ac converters. Computer simulations will be used as a part of the course work.

Principles and Elements of Instrumentation and Mesuremet Systems, Review of Random Processes, Inertial Force Sensors, Inertial Rotation Sensors, Applications of rate gyros, Coriolis Angular rate sensors,Fibre optics gyros, Ring Laser Gyros, Filtering Estimation and Aiding.

Introduction to navigation science, coordinate frames and transformations, review of relavenyt concepts from systems theory and random processes, discrete linear and nonlinear kalmna filtering techniques, the global positioning system, inertial navigation, navigation examples and case studies.

Tactical Missile Guidance: Proportional navigation; Important closed-form solutions and their utility; Method of Adjoints: Analysis of missile guidance systems using adjoints; Noise Analysis: Simulating noise, stochastic adjoints; Monte Carlo results; Proportional Navigation and Miss Distance: Useful design relationships for rapid guidance system sizing; Digital Noise Filters: Digital noise filter properties and system performance; Advanced Guidance Laws: Deriving optimal guidance laws without optimal control theory; Kalman Filters and the Homing Loop: Combining Kalman filtering and optimal guidance and optimal guidance techniques; Endoatmospheric Ballistic Targets: Speed, Re-entry angle, Ballistic coefficient; Extended Kalman Filtering: Performance comparison of linear, linearized, and extended Kalman filters; Other Forms of Tactical Guidance and Tactical Zones: Beam rider, command to line-of-sight guidance plus drag and acceleration factors; Strategic Considerations: Gravitation and it’s impact on performance; Boosters: Using the rocket equation and an introduction to gravity turn steering; Lambert Guidance; Miscellaneous Topics and T4 Guidance: Gravity compensation, pulsed and burnout guidance; predictor-corrector method; Radome Slope Estimation: Dither signals and bandpass filtering.

The course covers, Entropy rates of stochastic processes. Maximum entropy and Burg's Theorem. Kolmogorov complexity. Information theory and statistics. Stein's Lemma. AEP. Network information theory. Slepian-Wolf Theorem. Broadcast channel. Multiple access channel capacity. Optimal investment and information theory. Universal portfolios and universal data compression.

Course will explore current techniques for the analysis of visual data (primarily color images). In the first part of the course we will examine the physics and geometry of image formation, including the design of cameras and the study of color sensing in the human eye. In each case we will look at the underlying mathematical models for these phenomena. In the second part of the course we will focus on algorithms to extract useful information from images. This includes detection of reliable interest points for applications such as image alignment, stereo and instance recognition; robust representations of images for recognition; and principles for grouping and segmentation. Time permitting we will look at some additional topics at the end of the course.

The course covers following topics. Fundamentals of software testing; Test generation using finite state models, Statecharts, Timed automata, Constraint Logic, Petri nets, Z, Combinatorial design, and others; Test adequacy assessment using black box and white box criteria; Industrial applications of model based testing. Students will be required to form small teams of two or three, preferably interdisciplinary, and make in-class presentations based on a selected topic in model based testing. The work of each team will be reviewed by the instructor and other teams.

Antenna concepts, linear wire antennas, linear arrays, aperture and horn antennas, printed-circuit radiators, frequency-independent antennas, and measurement techniques.

This course presents advanced techniques applicable to the design of RF amplifiers and oscillators and emphasizes advanced theory and design techniques. Considerable emphasis is placed on microstrip implementation of UHF and microwave circuits. In the latter part of the course, commercially available computer-aided design and analysis software packages are introduced and used to complete the second design problem.

Current topics of interest in control systems. This course may be repeated for credit.

Current topics of interest in communications. This course may be repeated for credit.

Current topics of interest in microwave engineering. This course may be repeated for credit.

Theory of linear algebra, Eigen values, Eigen vectors, orthogonality. Vector calculus, Gauss’s divergence theorem, Stokes’s theorem, Cartesian tensors, Variational calculus, Linear programming, Constrained and unconstrained Optimization, Integral Transforms (Laplace, Fourier, Mellin, Hankel, Z-Transform), Numerical Integration are introduced.

Introduce the concept of numbers and their properties with specific emphasis to their use in cryptology. This course is intended to serve as prerequisite for more advanced courses in cryptography and cryptanalysis.

The course will cover classical concepts such as network architecture, switching, routing, congestion control, and quality-of-service, and discuss recent developments in these areas. The course will also cover new developments in networking such as network measurements, network management, overlay networking and peer-to-peer systems, network security, and new network architectures. The course will emphasize a system-oriented and empirical view of Internet architecture.

Introduction of the basic notions of confidentiality, integrity, availability; authentication models; protection models; security kernels; secure programming; audit; intrusion detection and response; operational security issues; physical security issues; personnel security; policy formation and enforcement; access controls; information flow; legal and social issues; identification and authentication in local and distributed systems; classification and trust modeling; risk assessment.

Introduce the concept of Applied Cryptography along with mathematical details/description and practical systems. The course is intended to make the student understand the role of cryptography in information security and give them background knowledge for further studies and research in the field of cryptology. The course will serve as a perquisite course for Cryptanalysis.

The course is intended to discuss general concepts, methods and algorithms used for cryptanalysis. The construction of classical, stream, block and number theoretic ciphers are discussed from cryptanalysis point of view. During course established practices used in cryptanalysis will be discussed in detail and latest research in the cryptanalysis field will be introduced for extending research if any. The outcome of the course is intended to familiarize students with algorithmic and implementation weakness of cryptographic algorithms which may lead to exploitation and their countermeasures.

This course is concerned with fundamental principles of computer security as applied to management. It covers privacy concerns, secrecy issues, operational security, physical security, hardware security, software security, communications security, and data security. This course is designed and developed to cover the 10 domains in the Information Security Common Body of Knowledge. They include: Security Management Practices, Security Architecture and Models, Business Continuity Planning (BCP) and Disaster Recovery Planning (DRP), Law, Investigations, and Ethics, Physical Security, Operations Security, Access Control Systems and Methodology, Cryptography, Telecommunications, Network, and Internet Security.

This course covers both fundamentals and advanced topics in operating system (OS) security. We will study OS level mechanisms and policies and how they relate to mitigating and defending against real-world attacks on computer systems, including self-propagating worms, large-scale botnets, and advanced malware. Basic OS security techniques such as logging, system call auditing, address space randomization, memory protection, virtual machine introspection (VMI) will be discussed. Recent advanced techniques such as host-based intrusion detections, system randomization, vulnerability fingerprinting, and virtualization will also be introduced.

This course will begin by first establishing the definition of cloud computing, then describing the various service delivery models of a cloud computing architecture, and the ways in which clouds can be deployed as public, private, hybrid, and community clouds, followed by a much deeper review of the security and privacy issues related to cloud computing environments. We will examine cloud computing models, look into the threat model and security issues related to data and computation outsourcing, and explore practical applications of secure cloud computing. Using the confidentiality, integrity, and availability of data (CIA) model we will examine the threats and security implications to befall poorly established and maintained cloud computing environment. Audit approaches and methodologies for assessing internal control exposures within cloud computing environments will also be fully discussed and examined.

This course covers a broad range of topics related to parallel and distributed computing, including parallel and distributed architectures and systems, parallel and distributed programming paradigms, parallel algorithms, and scientific and other applications of parallel and distributed computing. In lecture/discussion sections, students examine both classic results as well as recent research in the field. The lab portion of the course includes programming projects using different programming paradigms, and students will have the opportunity to examine one course topic in depth through an open-ended project of their own choosing. Course topics may include: multi-core, SMP, MMP, client-server, clusters, clouds, grids, peer-to-peer systems, GPU computing, scheduling, scalability, resource discovery and allocation, fault tolerance, security, parallel I/0, sockets, threads, message passing, MPI, RPC, distributed shared memory, data parallel languages, MapReduce, parallel debugging, and applications of parallel and distributed computing.

This course covers security and privacy issues in wireless networks and systems, such as cellular networks, wireless LANs, wireless PANs, mobile ad hoc networks, vehicular networks, satellite networks, wireless mesh networks, sensor networks and RFID systems. Security problems of MAC and especially upper layers will be emphasized. Attacks and proposed solutions at several layers, authentication, key distribution and key management, secure routing, selfish and malicious behaviors, and secure group communication are analyzed for applicable wireless network types. A short overview of cryptography and wireless networking principles will be given at the beginning of the course.

The knowledge of computer and network forensics has become essential in securing today's network-centric computing environment. This new course will give the students both the fundamental knowledge and hands-on practice on computer and network forensics. The added exposure to forensics will enhance the marketability of our students and serve the students who carry the skills and knowledge forward into their future careers.