This course is designed for graduate students with information security and computer engineering backgrounds. This will be 3 credit hours course. This course will include lectures, assignments, mid & final terms and labs.
Goal: The goal of this course is to familiarize information security and computer engineering graduate students with the strategies on how to scan, test, hack and secure their own systems. The study environment gives student in-depth knowledge and experience with the current essential security systems. Course will begin by understanding how perimeter defenses work and then leads into scanning and attacking networks (no real network is harmed). Students then learn how intruders escalate privileges and what steps can be taken to secure a system. Students will also learn about intrusion detection, policy creation, social engineering, DDoS Attacks, buffer overflows and virus creation. The last part of the course will deal with network management topics particularly SNMP v3 plus tools like Radmin and RMON.
Advanced Software engineering (SE) is about cutting-edge research topics in the development and application of processes and tools for managing the complexities inherent in creating high quality software systems of the modern day such as Cloud Computing and Smart Phone Application Development. It introduces the fundamental software engineering concepts and terminology and aims to give students both a theoretical and a practical foundation.
This course is designed for graduate students with electrical and computer engineering backgrounds. This will be a 3 credit hours course for the 11 weeks summer semester. The course will include lectures, assignments, and mid & final exams.
The goal of this course is to familiarize electrical and communication engineering graduate students with the basic building blocks of a communication system transceiver from a physical layer perspective, and to give an in-depth understanding of how to design these blocks in an optimal manner. Precisely speaking, the course will primarily cover the design of
This course is designed for graduate students with Electrical and Computer Engineering backgrounds. This will be 3 credit hours course for 15 weeks semester. This course will include lecture, Assignments, mid & final terms and Term projects.
Goal: The goal of this course is to familiarize electrical/microelectronics, mechanical, material and Computer engineering graduate students with the strategies to design MEMS (MicroElectroMechanical Systems) devices and introduce micromachining (microfabrication) technologies to fabricate MEMS. This course provides an opportunity to learn a complete MEMS design methodology and design flow with hands-on experience on MEMSPro-CAD tool developed by SoftMEMS Inc (USA). The participants will also learn how to prototype MEMS devices using commercially available micromachining technologies.
Introduction to methods used in the real time operation and control of power systems as well as to the hardware and software technology of energy management systems (EMS).
Software engineering is the application of a systematic, disciplined, quantifiable approach to the design, development, operation, and maintenance of software. Statistical Software Engineering is discipline of software engineering that plays vital role in entire development lifecycle of a software project. Statistical analysis allows software engineers and software companies to make important decisions about projects. Being able to identify trends in the data or statistics of successful projects that might seem relevant to a business can help determine if a project is right for them. Software Companies use data to make decisions about big projects all of the time. Statistics are factual and are used to eliminate the guessing within different phases of software development. Statistical analysis plays important role in estimations, measurements, planning and quality control process areas of a software project. Statistical analysis includes Descriptive Statistics, Hypothesis testing, Empirical Methods, Estimations, Correlation and Regression Analysis on different datasets of software engineering processes.
The candidate participating in the course must have knowledge about software development life cycle and software engineering.
The cognitive radios (CR) have been a topic of intense researchsincethevery first coining of the terminology in 1998 by a JosephMitolaIII. As perceived by him such a radio is intelligent enough so as to sense and adapt to the changes in its environment. One of the reasons for the popularity of this concept is the need of the future wireless devices to communicate in an innovative way where the efficient way of spectrum access can be promised. Software defined radios (SDR), a parallel technology, providedsupport for the realization of this concept. However the conventional techniques employed in the SDR were reworked to meet the philosophical requirements of CR. These include the improved ways of spectrum sensing, adaptive modulation and coding, multirate DSP, blind receiver designing, modulation recognition, arbitrary symbol rate synchronizations and others. In addition to this the regulatory issues associated with the technology remains a key part of learning. In this course some fundamentals of the digital communication will also be covered so as to complete the picture as much as possible. Starting from the physical layer we will move to MAC layer and will address the associated problems.
In short this course will cover the basics of the cognitive communication philosophy, regulations, thecognitive / software defined radio architecture and the associated problems.
Topics covered include: fundamentals of input, display and hard copy devices , scan conversion of geometric primitives, 2D and 3D geometric transformations, clipping and windowing, scene modeling and animation, algorithms for visible and surface determination, introduction to local and global shading models, colour, and photorealistic image synthesis.
This course is aimed at enabling the students to comprehend key components of Information Systems (IS). The students will learn the skills to analyze a system in order to figure out what requirements the information system needs to fulfill. The students will thoroughly learn how to satisfy these requirements in their IS design. This will include the design of all IS components such as database interactions, I/O and its controls, communication, etc. Last part of the course will cover issues related to development and implementation of the designed IS.
This course covers the topic of complex variables, functions, residue theorem, complex derivatives, integral and series, mapping, transformation and applications.
Review of complex numbers and elementary functions, analytical functions, Integrals, series, residues and poles, application of residues. Mapping by elementary functions, conformal mapping, application of conformal mapping, transformation.
The theory and practice of modern power system protection techniques. Principles and role of the protection of power systems. Operational principles and main types of relays. Overcurrent and differential protection. Line protection with overcurrent relays, distance relays and differential relays. Protection of electric machines (generators and motors). Protection of power system components (buses, reactors and capacitors). Current and voltage transformers. Simplified methods of short-circuit calculations. Principles of digital protective relaying. Software tools for power system protection analysis.
The course on understanding computer networks using analytical, mathematical and simulation tools.
The course emphasis is on comparing systems using measurements, simulations and queueing models. Mathematical analysis of queuing systems and networks (Markovian and non-Markovian models) , introduction to queuing theory, single queues, and queuing networks. Method of system simulation, Random number generation and transformations, Event-by-event and Monte Carlo simulation, Sampling theory and traffic measurements Event and timecontrolled simulation methods, network topologies, physical transmission media, layer divisions, protocol specifications, ISO’s OSI model, TCP/IP and Internet, LAN, WAN, performance measures in simulation, Common mistakes and how to avoid them, selection of techniques and metrics, art of data presentation, summarizing measured data, comparing systems using sample data, introduction to experimental design, fractional factorial designs, introduction to simulation, common mistakes in simulations, analysis of simulation results. The techniques learnt in the course can be used to analyze and compare any type of systems including but not limited to algorithms, protocols, network or database systems.
Computer Networks, Programming in C/C++, Probability Theory
With multicore/ multiprocessor systems replacing traditional computers, parallel computing has moved from supercomputer domain to desktop, game-box & multimedia mobile devices. Hence the time is right for a paradigm shift in algorithm design course to meet these challenges. The course treats the sequential & parallel algorithm design approaches in an integrated fashion for finding highly efficient solutions to time critical problems.
This course covers the topic of robust and optimal control system in state space domain. Topics include optimal control using performance specifications and Hilbert spaces, structured singular values, μ synthesis and controller order reduction.
Review of Linear Algebra and dynamical systems, Singular values decomposition and analysis, Linear (Normed, Hilbert and Hardy) Subspace, Stability and performance of multivariable systems, performance limitation and specifications, H2 and H∞ control, loop shaping, Model uncertainty and μ synthesis, Balanced Truncation& Model Reduction, Hankel Norm Approximation, linear matrix inequalities
Linear Systems and Control (EE 6550)
Power transmission lines and transformers, synchronous machine modeling, network analysis, power system representation, load flow. Power system protection, symmetrical components, faults, stability. Power system operations including the new utility environment
This course intends to cover the application of electronics to energy conversion and power control. Topics covered include: Power switching devices (thyristor, BJT, MOSFET, IGBT); control, protection and commutation of power switching devices, AC to DC converters, controlled rectifies, AC to AC converters, single phase and three phase AC voltage controllers, cyclo converters, choppers for DC to DC power conversion, Inverters, single phase and three phase; pulse width modulation techniques, voltage control. Practical issues in the design and operation of converters. Ancillary topics covered may include magnetic components, filters and snubbers.
Electric Circuits and Network Analysis, Electronic Devices, MATLAB
This course is a blend of academic knowledge with practical experience. The detailed concepts of software quality management are presented, but the true value of the course comes from the identification, solutions and tips about how to deal with the unique challenges faced specifically by the quality managers. The course will provide in-depth coverage to various conceptual frameworks for assessing quality of a software product and organization. The key tools and processes involved in ensuring software quality will be covered. General topics include total quality, CMMI, quality measurement, software process improvement, and engineering software under statistical control. The course outlines few of the most important issues as per the IEEE, ACM and CMMI standards. Students will be encouraged to bring specific situations to be discussed. Participation in class discussions is essential. We will learn from each other's experience and perspective. Independent and out of the box thinking will be encouraged.
The course deals with data analysis on very large volume of data to help business making strategic decisions. Specifically, it considers the issues related to multidimensional data analysis and the design and implementation of data mining techniques and applications built on data warehouses. In addition the course also emphasizes the need for advance analytics in the spectrum of business intelligence in order to gain a competitive edge over business rivals. The course adopts a case study based approach, taking into consideration real life examples and success stories who have adopted advanced analytics and data mining in order to gain a cutting edge. In addition this course will use free online tools in order to analyze various data sets belonging to different application areas like telecommunications, medical science and agriculture by applying various analysis techniques taught in this course.
The course starts by introducing the big picture which includes the need for data mining and advanced analytics in the whole knowledge discovery process. It emphasizes on the differences between data mining and data warehousing. In then introduces some prerequisite statistical analysis concepts that include sampling, distributions, statistical tests and probability. The course details the significant tasks in the knowledge discovery process like preprocessing, data transformation and validation. Finally, it introduces data mining techniques such as predictive analysis in form of classification and regression, exploratory analysis and unsupervised learning such as clustering, association rule mining and other advanced topics such as temporal, spatial and text mining.
In addition to this the course will introduce various data mining tools along the way in order to provide technical insights to the implementation of these technologies. These tools include Tanagra, R and Weka.
The course involves major concepts, principles & applications for modern Computer Networks. The five layers of the Internet layered model will be covered in top-down fashion. The latest developments in networks will be addressed by considering major design & architectural aspect of Software Defined Networking.
The course focuses on the advanced topics of Cryptography and modern cryptanalytic techniques of Cryptographic primitives. The outcome will be the knowledge of analysis methods of Cryptographic algorithms.
Advanced Topics of Cryptography: Cryptographic properties of Boolean functions, S-boxes, Non-linearity, Resiliency, Correlation Immunity, Algebraic Immunity, Walsh Transform, Pseudo-randomness, Zero knowledge Protocols, Secret sharing schemes.
Cryptanalysis of Block Ciphers: Differential Cryptanalysis, Linear cryptanalysis, time memory trade off attack.
Cryptanalysis of Stream Ciphers: Linear Feedback Shift Registers, linear recurrence, Berlekamp Masssey algorithm, Combining and filtering functions, Correlation attack.
Analysis of Public key algorithms: Factorization algorithms, discreet log problem, indirect attacks on RSA, introduction to side channel attacks.
Graduate level course in nonlinear systems. It starts from the introduction to nonlinear dynamics, multiple solutions, stability analysis, bifurcation, and various techniques to analyze motions. Each topic will include examples of physical systems undergoing these phenomena. The course will also cover some control techniques including feedback linearization and sliding-mode control.
Introduction to nonlinear phenomenon: Equilibrium solutions, Periodic Solutions, Quasi-periodic solutions, Chaos.
Analysis of nonlinear systems: Bifurcation, Phase-plane techniques, Poincare maps, Numerical Methods, Tools to analyze motions, Lyapunov stability theory, feedback linearization, sliding-mode control.
To make the most of unique combination of performance & flexibility of reconfigurable devices the digital system designer needs to consider both hardware as well as software issues at various levels of abstraction. The course, therefore, addresses these issues at RTL /HLS/ TLM levels usingVerilog &SystemC to design complex digital systems (SoPC/ NOC etc)
This course covers the concept of linear algebra, matrix computation algorithms for small and large scale systems. This course includes the computational solutions of linear curve fitting problems, eigenvalues and singular value problems. MATLAB will be used for computation and algorithms Pre-requisites: Advanced Engineering Mathematics (if required)
Fundamentals of Linear algebra, Matrix-Vector Multiplications, Singular Value Decomposition, QR factorizations, Gram-Schmidt Orthogonalization, Householder Triangularization, Least Square Problems, Conditioning & Stability, Systems of Equations, Eigenvalue Problem, Iterative Methods
This course covers the modeling of physical system into state space representation for analysis and simulation. This course focuses on modeling of inter-disciplinary systems (e.g. electrical & mechanical together) in a common modeling scheme. This will be covered with bond graph modeling technique for analytical equations, simulation in 20-sim software and MATLAB.
Introduction to Systems, Subsystems and components, Multiport Systems, basic Components Models, Bond graph modeling, 20-Sim Software and MATLAB Simulink, Automated Computer Simulations, State Space Equations, Analysis of Linear Systems, Multiport Field and Junction Structures, Transducers, Amplifiers and Instruments, Mechanical Systems with nonlinear geometry, Distributed-parameter systems, Nonlinear System simulations.
This course is a blend of academic knowledge with practical experience. The detailed concepts of software project management are presented, but the true value of the course comes from the identification, solutions and tips about how to deal with the unique challenges faced specifically by the software project managers.
The course will provide in-depth coverage to various conceptual frameworks for management of software projects. The key tools and processes involved in ensuring a successful software project will be covered. This course outlines few of the most important issues as per the IEEE, ACM and CMMI standards.
Students will be encouraged to bring specific situations to be discussed. Participation in class discussions is essential. We will learn from each other's experience and perspective. Independent and out of the box thinking will be encouraged.
Introduction to SPM, Why SPM?, Software Project Management: Classical Mistakes, Essentials of software project management, Introduction to software project management processes, Software project management process and other software engineering process, Software project life cycles, Scope and requirements management for software projects, eliciting requirements, developing, SRS specifications, Software project stakeholders, Introduction to software project contract management and legal issues in software, Software project artifacts, Planning, estimating (software estimation), scheduling, costing (software costing) for software projects <<<<< including decomposition techniques, empirical estimation models and make buy decision structures, Quality planning for software projects, Software project time and resource management, Monitoring, measuring, controlling and evaluating software projects, Software project based metrics, Managing outsourcing, Risk management for software projects, Integrated project management for software projects, Software project communication, reporting and human resource management, Distant project management, Choosing an organizational form, Software project team structures and team selection, Motivating software project teams, {Negotiation and conflict management for software projects}, Software project management and human / soft factors, Organizational support for effective software project management, Project management tools for management of software projects, {Software Reusability}, {Software project feasibility preparation}, {Financial analysis for software projects}, Software maintenance, Introduction to software economics, software management, Software project closure, termination and post performance analysis, Project Management Barriers in IT Industry of Pakistan, {Agile Estimating and Planning}.
This course is meant to provide a basic foundation for students who wish to work in the areas of wireless system design, and wireless communication research. It is primarily focused on the digital aspects of wireless communication.
Our everyday lives involve the use of DSP systems in things such as cell phone & high-speed modems; companies like Texas Instruments, Analog Devices, Lucent have introduced several DSP processors to meet the high performance demands of today’s signal processing. This course provides the know-how for the implementation & optimization of computationally intensive signal processing algorithms on these DSP processors.
Introduction to DSP system, Overview of Embedded & Real time Systems, Embedded System Development Life Cycle using DSP, Overview of DSP Algorithms, Digital Signal Processors Architectures, Optimizing DSP Software, Real-Time Operating Systems for DSP, Testing & Debugging DSP systems, Embedded DSP software design using Multicore System on a Chip (SoC) Architectures, Future trends of DSP software technology.
The course focuses on the security of computer systems at various layers of operating system & application software. The course includes the security aspects of contemporary advanced operating systems and database systems.
Security problem in computing: Meaning, Characteristics, Attacks & Defenses, Program Security; secure programs, non-malicious program errors, malicious code, targeted code, covert channels, Controls against program threats. Protection in general purpose operating systems: Object & methods protection, memory & address protection, Access control to objects, file protection mechanism & user authentication.
Trusted Operating Systems: Meaning, security policies, models of security, designing TOS, Assurance in TOS, Database & Data Mining Security: concepts, requirements, reliability, integrity & sensitivity of data, inference, multilevel security & data mining security
Programming for numerical calculations, round-off error, approximation and interpolation, numerical quadrature, and solution of ordinary differential equations. Iterative solution of systems of nonlinear equations, evaluation of eigenvalues and eigenvectors of matrices, applications to simple partial differential equations. Practice on the computer.
Numerical Analysis, By Burden & Faires, Fifth Edition
Information Representation, Binary number system and codes, Introduction to Boolean Algebra, Logic Gates and Special Functions, Logic reduction techniques, Logic reduction techniques continued, Don’t Cares, NAND and NOR implementations, Combinational Logic Design concepts, Design methodology, HDL introduction, Code Converters, Encoders/Decoders, Combinational Logic Building Blocks: multiplexers, demultiplexers, arithmetic circuits, Combinational Circuit Design, Delays, Transient Operation, Hazards, Sequential Logic Circuit Fundamentals, Flip Flops, characteristic tables, Sequential Circuit Analysis and Design Techniques and HDL representation, Sequential Logic Building Blocks, Registers and counters, Programmable Logic Devices; ROM, PAL, PLD and FPGAs, Design of Large Complex Circuits (e.g. Digital Computer), Separation of Data and Control path, Data path design, pipelined data path, the ALU, Control path design, State machine design, Computer Architecture concepts, Instruction Set Architectures, CPU designs; CISC and RISC, Computer Memory Organization; RAM, CACHE, Virtual Memory, Bulk Storage, Input Output devices and Communication buses, PCI, DMA.
Continuous and Discrete time Signals, exponential and sinusoidal signals, unit step Functions, continuous discrete time systems and their properties. Response of LTI systems, Convolutional integral and summation, Properties of LTI systems, Difference and differential system representations of causal systems. Response of LTI systems to Complex exponentials, Fourier series representation and its properties (continuous and discrete time), Filtering concepts and difference equation representations of filters. Fourier representation of Aperiodic and periodic signals, properties of Continuous time Fourier transform, Characterization of systems by linear constant coefficient differential equations. Discrete time Fourier transform of periodic and non-periodic signals, Properties of Discrete time Fourier transform, Characterization of systems by linear constant coefficient differential equations. Sampling Techniques, Sampling Theorem, Interpolation and Decimation, Aliasing and Signal Reconstruction from its samples. Laplace Transform, ROC, Inverse Laplace Transform, Properties of Laplace Transform, System function algebra and block diagram representation. Z-transform, ROC, Inverse z-transform, properties of z-transform, analysis of LTI systems using z-transform.
This course is an introduction to probability and random processes. The material covered is of central importance to many fields within electrical engineering and computer science including communications theory, communications networks and statistical signal processing.
Introduction to probability, finite sample spaces, conditional probability and independence, one dimensional random variables, functions of random variables, two and higher dimensional random variables, further characterization of random variables, The Poisson and other discrete random variables, some important continuous random variables, The Moment generating functions, sums of random variables and central limit theorem.
The broad course contents are as follows:
Goal: Machine learning combines theory from different areas like Statistics, Mathematics, Engineering, and Information Technology towards building computer programs that can automatically improve their performance through experience. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Upon successful completion of the course, students will have a broad understanding of machine learning algorithms and they will be able to identify, formulate and solve machine learning problems that arise in practical applications. Students will be able to begin to conduct original research in machine learning and its use in data-driven knowledge discovery and program synthesis.
This course is ideally designed for students who are (or intend to get) involved with SE. The course covers concepts from basic to advanced level. The course not only ensures that students are able to understand SE discipline and theory formally, rather most importantly; the course ensures that students are able to deploy SE theory for solving practical problems related to SE. In order to achieve this target several case studies are incorporated within the course. The role of the instructor in this course is to ensure that students cover necessary breadth and depth of the subject within 16 weeks. The instructor of this course; at times; plays the role of a facilitator and mostly assist students (through teaching) by explaining SE concepts and their practical application in detail. This course uses top notch standards of IEEE and focuses on high quality literature from various renowned academicians from SE disciplines. This course is strongly aimed to ensure that misconceptions of students about software engineering are also stratified.
This course focuses on current research issues in wireless communication systems and networks with more focus on wireless networking issues. Topics include multiple access techniques, ad-hoc wireless networking and stochastic geometry for the analysis and design of wireless networks. A rough set of the topics to be covered is given during the first week of classes. Lectures are based on required reading from magazine and journal articles, textbook sections, or supplemental handouts. Students present current research papers to the class as part of the lectures. A term project is also part of the course requirements.
The course will have four important aspects – Lectures on course topics, Paper reviews with class discussions, research based group projects and class assignments that will be a series of 4.
The course forms the basis for evaluation & analysis of modern Information & Communication (wired/ wireless) Systems. Information theory part deals with representation of information for efficient storage & transmission. It provides information measurement& quantification framework to determine the limits on information compression & channel capacity. Coding theory part deals with issues of protection of data while passing through hostile environment. It provides techniques (Error Control Codes) that add enough redundancy in data to detect & correct information bits without overloading system.
The course has been designed to strike a balance between required Mathematics& its application to information & Communication systems.
To teach students about the advanced concepts of integral equations and their application in Engineering.
Modeling integral and integro-differential equation of real world problem.
Solution methods of integral equations using transforms and other techniques.
Layered architectures (Internet and the OSI Reference Model), Overview of networking and communication software (Sockets), Standards in networks access protocols (CSMA, etc.), Architectures and control algorithms of local-area, point-to-point, and mobile networks, Models of network interconnection, Design issues and protocols in the data link, network, and transport layers, Direct Link Networks, Encoding and Framing, Error Detection and Reliable Transmission, Ethernet and Token Ring Networks, Wireless 802.11 Networks, Packet-Switched Networks, Switching and Forwarding, Bridges and LAN Wwitches, Cell Switching (ATM), Internetworking, Internet Protocol (IP), Unicast and Multicast Routing, Global Internet, MPLS, End-to-End Protocols, UDP, TCP and RPC, Congestion Control and Network QoS, Resource Allocation and Queuing Disciplines, Congestion Control and Avoidance Mechanisms, Quality of Service, Representation of End-to-End Data, Presentation Formatting (ASN.1, etc.), Data Compression Techniques (JPEG, MPEG, MP3), Network Applications, DNS, HTTP, SMTP, etc., Overlay Networks and Peer-to-Peer Networking
This course is meant to provide a strong foundation for students who aspire to work in the areas of digital and wireless communication system design and research. The course starts with a detailed block diagram of a digital/wireless communication link, and in the following lectures an in-depth coverage will be given to each of the individual blocks at transmit and receive side.
Students with telecom as their area of specialization are highly recommended to take this course, as it provides them the fundamental knowledge base needed for their MS program.
The course will also include some MATLAB based tutorials providing students the opportunity to explore various design principles at their own. This on-hands experience will be vital for students who wish to pursue research/thesis in the area of digital/wireless communications.
The students will also be assigned individual research topics for literature survey, and they will present those topics to fellow students towards the end of the course. The purpose of this graded exercise is to familiarize students with the art of literature survey and to sharpen their presentation skills.
Fundamental Problems in Information Theory Wideband and narrowband channel models Capacity of fading channels Digital modulation in wireless channels Adaptive Modulation Diversity (both receive and transmit) Multicarrier Modulation Spread spectrum, RAKE receivers, and CDMA Multiple access channels and their capacities Multiuser diversity Ad hoc and mesh networks: physical layer view and capacity
The objective of the course is to teach students Verilog as hardware description language FPGA architecture and logic Synthesis concepts Architecture of basic building blocks, adders, multipliers, shifters Converting floating-point algorithms design in Matlab to Fixed-point format.
This course is designed to introduce engineers and designers advanced digital design concepts. The students are taught a spectrum of techniques for designing and mapping of algorithms on FPGAs / ASICS using HDLs.
Introduction to Image Processing, Digital Image Fundamentals, and Image Acquisition, Image Enhancement in Spatial Domain, - Pixel Operations & Histogram Processing, - Histogram Equalization, - Histogram specification and local enhancement techniques, - Local enhancement techniques using Spatial (Mask) Filtering, Image Enhancement in Frequency Domain, - Basic Properties of Fourier Transforms, - Properties and Implementation (FFT’s), - Frequency Domain Filtering, Image Sampling, Image Restoration, - Noise models and additive noise removal, - Adaptive filtering, notch filtering and interactive restoration techniques for additive noise removal, - Degraded image restoration, - Geometric transformations, Color Imaging, Multi-resolution Processing (including Wavelet Transforms), Image Compression, - Introduction, - Error-free compression, - Predictive coding, - Transform coding, Morphological Image Processing, - Morphological Processing on Binary Images, - Morphological Processing on Grey Scale Images, Image segmentation, - Point, Line and Edge Detection, Edge Linking, and Thresholding, Water Marking and other Advanced Topics.
Introduction to Nonlinear Systems, System Trajectories, Describing Functions, Lyapunov Stability Theory, Lasalle Theorem, Lyapunov Theory for Discrete Time Systems, Circle Criterion, Passivity, Sliding Mode Control, Adaptive Control, Design applications.
Dynamic System Modeling through Differential Equations. Motivation and Concept of Laplace Transforms. Basic Transfer Function Modeling of Dynamic Systems. Frequency domain parameters of a second Order System. Bode Analysis, Root Locus Analysis, Nyquist Analysis, PID Controller Design, Lead-Lag Compensation,Discrete Time Controller Design (Z- transform based), State Space Concepts, State Feedback Design, Observer Design, Linear Quadratic Regulator.
All students taking the course will be required to submit an undertaking that during the time they are enrolled in the course they will not search the web, nor will they use any material copied from the web in their assignments and for the preparation of the final exams.
ECE 4002 C and Data Structure
ECE 6607: Computer Networks (Students must have a thorough understanding of networks, and the TCP/IP protocol suite, at the level of ECE 6607 Computer Networks course).
Advanced Engineering Mathematics
This course will focus on three parts. First part is on development of single variable optimization; second part is devoted to multi-variable optimization and final part will deal with stochastic optimization algorithms. After the completion of this course students should be able to numerically solve an engineering optimization problem using mathematical model for a physical system and computers. Students should be ready to work hard and to learn new concepts used in MATLAB and its toolboxes.
The prerequisites for this course are basic knowledge of differential calculus, Mathematics and problem solving. We will not be using very heavy mathematics. Students should be ready to work hard and to learn new concepts used in MATLAB and its toolboxes.
The pre-requisite is undergraduate level Signals and Systems Course.
To teach students about the advanced concepts covering overview, system structures, process management, threading, CPU scheduling, synchronization, deadlocks, main memory and secondary storage management, file-system implementation, I/O systems, disk scheduling and security in order to understand design and implementation methodologies of parallel and multitasking operating systems.