EECS 690 - Implementation of Digital Communication Systems Digital communications systems are widely used in today's world. Therefore, the twin objectives of the course are a) to understand the theory behind digital communications systems, and b) to understand how to build and implement these systems in discrete-time. To achieve this, we will split our efforts evenly between a) analysis and design, and b) computer (Matlab) simulations. Primary topics include: signal space concepts, modulation, detection (demodulation) and matched filters. We will also consider the problem of synchronizing the receiver with the transmitter, which is a practical necessity in actual systems. Prereqs: EECS 360 Co-req: EECS 461
EECS 690 - Generic Programming Generic programming can be viewed as writing code that embodies the shape of data structures without commitment to specific types. A list, for example, is a list regardless of the data types it contains - head, tail and cons are all defined independently from the contents of the list. Object-oriented languages begin to realize the promise of generic programming, but much greater potential remains untapped. This course presents an overview of generic programming techniques ranging from simple classes with type parameters through techniques for writing complex functions using a few, abstract lines of code. Topics explored will include: parametric, ad hoc and structural polymorphism; design patterns as higher-order functions; and abstract notions of computation structure. Emphasis will be placed on implementation. Students will apply generic programming techniques in a series of projects related to programming language implementation. Prerequiste: EECS 368.
EECS 700 - Artificial Intelligence: Reasoning and Interpretation of High Level Descriptions of Perceptions This is an intermediate level Artificial Intelligence Seminar covering automatic computer-based reasoning about and interpretation of higher level perceptual outputs. The outputs would include natural language, its semantic structures, and 3D models of visual objects. The course focuses on reasoning carried on between these structures. The role of different kinds of logics and reasoning methods is described. The course is for both undergraduate seniors and graduate students. Ideally an undergraduate would have taken a KU course dealing with Artificial Intelligence (e.g. EECS 649) or Programming Languages (e.g. EECS 662) or both. Graduate students are required to have an undergraduate degree in an Artificial Intelligence, Computer Science, or a Cognitive Science area (e.g Linguistics, Psychology).
EECS 700 - Computational Methods in Genomics This course focuses on the computational analysis of genomes. Computational methods are studied in tandem with applied studies of genome structure, function, and evolution. Computational genomics topics include chromatin structure and function, genome architecture and evolution, roles of repeats, DNA composition analysis, and processes behind gene expression; computational-methodology topics include sequence analysis and modeling, dynamic programming, formal language and linguistic methods, Markov chains and optimization methods, information theory, and molecular modeling. Prerequisites: Introduction to Bioinformatics, or consent of instructor.
EECS 700 - Visualization Data representations, algorithms, filtering, and rendering techniques used in Scientific and Information Visualization with an emphasis on Scientific Visualization. Structured and unstructured grids. Interpolation. Contouring, volumetric rendering. Corequisite for undergraduate students: EECS 672. For graduate students EECS 672 or equivalent is recommended but not required.
EECS 700 - Intro to Communication Networks (EDWARDS CAMPUS) Comprehensive in-depth coverage to communication networks with emphasis on the Internet and the PSTN (wired and wireless). Extensive examples of protocols and algorithms will be presented at all levels, including: client/server and peer-to-peer applications; session control; transport protocols, the end-to-end arguments and end-to-end congestion control; network architecture, forwarding, routing, signalling, addressing, and traffic management; quality of service, basic queuing (basic M/M/1 and Little's law) and multimedia applications; LAN architecture, link protocols, access networks and MAC algorithms; physical media characteristics and coding; network security and information assurance; network management. Prerequisites: Basic working knowledge of computer systems, the Internet, and probability and statistics; basic programming skills. Credit may not be received for both EECS 663 and EECS 780!
EECS 800 - Advanced Electromagnetic Theory A theorem based treatment of electromagnetic theory, with applications. Topics include source modeling, equivalence concepts, Green's functions, construction of solutions, and integral equations. Applications include scattering and electromagnetic numerical techniques. Prerequisite: EECS 720 or equivalent.
EECS 800 - Special Topics: InSAR and Applications Description and analysis of processing data from synthetic-aperture radars and interferometric synthetic-aperture radars. Topics covered include SAR basics and signal properties, range and azimuth compression, signal processing algorithms, interferometry and coregistration. Prerequisite: EECS 725, 744
EECS 800 - Special Topics: Subsurface Radar This course focuses on the application of radar technologies for probing into the subsurface for the detection of buried targets and geology. The course will cover both in-situ ground-penetrating radar (GPR) and airborne/orbital sounding radars. Topics that will be covered in the course include: introduction to subsurface radars, radar range equation for the subsurface geometry; dielectric properties of rocks, soils, water, ice and mixtures; radar architectures including frequency- and time-domain systems and antennas; radar performance requirements; frequency- and time-domain radar sampling theories; forward modeling; reverse modeling including wave migration; and finally real-world terrestrial and planetary applications of subsurface radar. Prerequisites: Students should have a basic understanding of radar principles, signal processing, electromagnetic propagation and scattering, and antenna theory.
EECS 800 - Protein Bioinformatics This course emphasizes the applications of computational algorithms to main problems in protein bioinformatics and molecular biology. It consists of two parts. In the first part, we will introduce concepts in molecular biology focusing on proteins. In the second part, we will discuss a variety of topics, including protein sequence alignments, profiles, protein structure prediction, protein networks, and protein function prediction. Students will be asked to present some selected research papers. Prerequisites: Courses in probability and statistics, Introduction to Bioinformatics, Programming experience.