Introductory graduate courses

For more detailed descriptions, syllabi and schedule go to ECE graduate courses page.

  • EC501: Dynamic Systems Theory. Introduction to analytical concepts and examples of dynamic systems and control. Mathematical description and state-space formulation of dynamic systems: discrete and continuous time, non-linear and linear. Concepts of controllability, reachability, observability. Eigenvector and transform analysis of linear time invariant systems. Modeling, canonical forms for linear systems, pole shifting, computer control, hands-on experience with the design of controllers and observers, introduction to stability. Prereq: familiarity with differential equations and matrices.
  • EC504: Advanced Data Structures. Review of basic data structures and Java syntax. Data abstraction and object-oriented design in the context of high-level languages and databases. Design implementation from the perspective of data structure efficiency and distributed control. Tailoring priority queues, balanced search trees, and graph algorithms to real-world problems, such as network routing, database management, and transaction processing.
  • EC505: Stochastic Processes, Detection and Estimation. Introduction to discrete and continuous-time random processes. Correlation and power spectral density functions; linear systems ms driven by random processes. Optimum detection and estimation. Applications of Poisson and discrete Markov processes.
  • EC508: Process Control. Integrated study of process control and modern control theory. Includes process modeling and simulation, analysis of linear and nonlinear dynamics, actuator and measurement evaluation and selection, control structure design for single and multiple variable systems, and control algorithm design. Examples drawn from a variety of process control applications, including welding, sheet metal fabrication, crystal growth, and plasma processes. Students cannot receive credit for both SC508 and MN508.
  • EC514: Simulation. Modeling of discrete event systems and their analysis through simulation. Systems considered include, but are not limited to, manufacturing systems, computer-communication networks, and computer systems. Simulating random environments and output analysis in such contexts. A simulation language is introduced and is the main tool for simulation experimentation. Meets with ENG ME 514; students may not receive credit for both.
  • EC515: Digital Communication. Introductory graduate level course with a focus on signal processing in communication systems. The topics covered include signal detection and design, intersymbol interference, equalization, decision feedback equalization, sequential and block encoders, viterbi decoding.
  • EC516: Digital Signal Processing. Advanced structures and techniques for digital signal processing and their properties in relation to application requirements such as real-time, low-bandwidth, and low-power operation. Optimal FIR filter design; time-dependent Fourier transform and filter banks; Hilbert transform relations; cepstral analysis and deconvolution; parametric signal modeling; multidimensional signal processing; multirate signal processing.
  • EC517: Introduction to Information Theory. Discrete memoryless stationary sources and channels; Information measures on discrete and continuous alphabets and their properties: entropy, conditional entropy, relative entropy, mutual information, differential entropy; Elementary constrained convex optimization; Fundamental information inequalities: data-processing, and Fano’s; Block source coding with outage: weak law of large numbers, entropically typical sequences and typical sets, asymptotic equipartition property; Block channel coding with and without cost constraints: jointly typical sequences, channel capacity, random coding, Shannon’s channel coding theorem, introduction to practical linear block codes; Rate-distortion theory: Shannon’s block source coding theorem relative to a fidelity criterion; Source and channel coding for Gaussian sources and channels and parallel Gaussian sources and channels (water-filling and reverse water-filling); Shannon’s source-channel separation theorem for point-to-point communication; Lossless data compression: Kraft’s inequality, Shannon’s lossless source coding theorem, variable-length source codes including Huffman, Shannon-Fano-Elias, and Arithmetic codes; Applications; Mini course-project.
  • EC520: Digital Image Processing and Communication. Review of signals and systems in multiple dimensions. Sampling of still images. Quantization of image intensities. Human visual system. Image color spaces. Image models and transformations. Image enhancement and restoration. Image analysis. Image compression fundamentals. Image compression standards (JPEG, JPEG-2000). Homework will include Matlab assignments.
  • EC524: Optimization Theory and Methods. Introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies, and underlying mathematical structures. Classical optimization theory as well as recent advances in the field. Topics include modeling issues and formulations, simplex method, duality theory, sensitivity analysis, large-scale optimization, integer programming, interior-point methods, non-linear programming optimality conditions, gradient methods, and conjugate direction methods. Applications are considered; case studies included. Extensive paradigms from production planning and scheduling in manufacturing systems. Other illustrative applications include fleet management, air traffic flow management, optimal routing in communication networks, and optimal portfolio selection. Meets with ENGSE524. Students may not receive credit for both.
  • EC541: Computer Communication Networks. Basic delay and blocking models for computer communications: M/M/1 queue; Jackson networks and loss networks; analysis of MAC protocols; flow control for data traffic; TCP and active queueing mechanisms for congestion control; traffic shaping and network calculus; packet switch architectures and scheduling algorithms; routing algorithms; flow assignment and fairness.
  • EC544: Networking the Physical World. Considers the evolution of embedded network sensing systems with the introduction of wireless network connectivity. Key themes are computing optimized for resource constrained (cost, energy, memory and storage space) applications and sensing interfaces to connect to the physical world. Studies current technology for networked embedded network sensors including protocol standards. A laboratory component of the course introduces students to the unique characteristics of distributed sensor motes including programming, reliable communication, sensing modalities, calibration, and application development. Meets with ENGME544. Students may not receive credit for both.
  • EC561: Error-Control Codes. Introduction to codes for error detection and correction in communication and computation channels, linear algebra over finite fields, bounds, Shannon’s Theorem, perfect and quasi-perfect codes, probability of error detection, Hamming, BCH, MDS, Reed-Solomon, and non-linear codes. Application of codes to error detection/correction in communication channels, computer memories, processors, and multiprocessor systems. Data compression and data reconciliation by error-detecting or error-correcting codes.
  • EC569: Introduction to Subsurface Imaging. Introduction to subsurface imaging using electromagnetic, optical, X-ray, and acoustic waves. Transverse and axial imaging using localized probes (confocal scanning, time of flight, and interferometric techniques). Multiview tomographic imaging: computed axial tomography, diffraction tomography, diffuse optical tomography, electrical impedance tomography, and magnetic resonance imaging. Image reconstruction and inverse problems. Hyperspectral and multisensor imaging.
The statistical  theory of pattern recognition, including both parametric and nonparametric  approaches to classification. Covers classification with likelihood functions  and general discriminant function, density estimation, supervised and  unsupervised learning, decision trees, feature reduction, performance estimation, and classification using sequential and contextual information, including Markov and hidden Markov models.