Overview of current systems and standards. Performance of digital modulation in fading and inter-symbol interference; capacity of wireless channels, flat fading countermeasures-diversity, coding and interleaving, adaptive modulation; multiple antenna systems; inter-symbol interference countermeasures; equalization, multicarrier modulation, spread spectrum and RAKE receivers; multiple access

Dedicated (ASIC) Vs general-purpose chips. Programming of general-purpose processors and DSPs. VHDL design. FPGAs. ASIC design. Design for testability. Hardware/software code-sign using System C.

Introduction to detection and estimation theory with applications. Topics include: maxmum likelihood and Bayesian estimates, Kalman filtering, simple and composite hypothesis testing, and detection of signals in noise.

This is an advanced course in communication networks that builds upon the CIT-606 core course (Fundamentals of Networking) to develop understanding of fundamental networking concepts as well as state-of-the-art wireless networking architectures. The course focuses on multiple access, routing and congestion control. In addition, selected topics pertaining to cellular networks, Wireless Local Area

This course deals with the theory, applications and algorithms of convex optimization. It focuses on recognizing and solving convex optimization problems that arise in many engineering fields. It is divided into three parts; theory, applications, and algorithms. The theory part covers the basics of convex analysis and convex optimization problems such as linear programming (LP), semidefinite

The objective of this class is to help students develop the understanding and intuition necessary to apply stochastic process models to problems in engineering, science and operations research. It contains simple examples and case studies designed to build insight about the structure of stochastic processes and about the generic effect of these phenomena in real systems. The tools and methods

This course aims be a comprehensive introduction to the basic concepts and algorithms of digital processing of visual information that would be utilized in the most prominent applications such as medical imaging, remote sensing, space exploration, surveillance, gaming and entertainment, manufacturing and robotics. The course is divided into two closely-related parts: image processing and computer

This course covers numerical analysis and solution techniques for common scientific and engineering problems and provides essential foundation for important computational subject areas such as medical imaging, bioinformatics, financial modeling, to name a few. The course covers a variety of topics including numerical approximations and errors, roots of equations, systems of linear algebraic

The course is divided into two parts. The first part handles proofs and proof techniques. It revises the concepts of sets, cardinality, relations, functions, integers, rational numbers and real numbers. It also introduces some algebraic structures such as rings and fields and other structures such as trees and graphs. It also covers Automata and languages, and handles computability and complexity

The course is divided into two parts. The first part provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); Neural Networks, Decision Trees, Local Models, Model selection, Combining

This course provides a comprehensive overview on the mathematical techniques and methods used in visual computing applications. The course contains two central themes: inverse problems in image processing; and statistical visual information analysis. The first theme introduces linear and non-linear inverse problems related to imaging and their solutions. This includes regularization methods for

The capability of collecting and storing huge amounts of versatile data necessitate the development and use of new techniques and methodologies for processing and analyzing big data. This course provides a comprehensive covering of a number of technologies that are at the foundation of the Big Data movement. The Hadoop architecture and ecosystem of tools will be of special focus to this course

This course provides an introduction to machine learning and statistical data analysis. The first part of the course covers topics such as parameter estimation, hypothesis testing and regression analysis. The second part includes machine learning topics such as supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance

This course acts as an applied course where students can develop on their combined knowledge of BigData technologies (e.g. Hadoop, Spark, etc.) and Data Science (e.g. Statistics, Machine Learning, etc.) and understand how such combination is used to solve real-world applications. In addition to this main goal, the course has the additional goal of familiarizing students with the latest

This course provides an introduction to data mining concepts over structured and un-structured data with special emphasis on practical applications of this important research area. Data Mining usually involves the extraction and discovery of useful knowledge from raw data. The discovery process, also known as knowledge discovery, includes feature selection, data cleaning, and coding and entails

This course aims to provide students with the theoretical background and hands on experience of the basic techniques employed in bioinformatics. The course will focus on biological sequence data (DNA, RNA, protein) analysis and their application.