Courses Conducting

Introduction to low (micro/macro) and high level languages — features and positions within the computer system. Definition of HLLs of syntax and semantics. Data types, control structures, concurrency, declarations, procedures. Recursion and recursive definitions. Procedural and data abstraction. Critique of major programming languages features and design issues (e.g., power, efficiency, security, modularity, readability). Examples from major realms of current programming languages — imperative (block structured, object oriented), declarative (function, logic) paradigms.

Introduction to computer networking, the ISO/OSI protocol stack, LAN, MAN, and WAN. Physical layer: transmission media (wireline and wireless); data signaling, modulation, and coding; multiplexing. Data link layer: error/flow control; MAC protocols; wireless IEEE 802.11 protocols. Network layer: subnet switching and routing protocols; congestion control and QoS. ISO vs. TCP/IP Internet stacks. Introduction to network security. Application layer protocols, e.g., DNS, E-mail.

Advanced topics on wireless sensor networks (WSNs) technology, with focus on significant components of WSNs: protocols, applications, typologies, deployment, sensed data manipulation, mobile ad-hoc wireless communication, security. Students will be exposed to most recent research development in the field.

Major types of artificial neural networks. Fuzzy logic theory and fuzzy systems construction. Genetic algorithms and evolutionary computing. Intelligent systems and engineering applications. Comparative study of the soft computing paradigm as a problem-solving methodology.

Biological neurons and their modeling. Overview of single/multilayer feedforward/back ANN models and learning rules, single layer perceptron, multi-layer feedforward error backpropagation, recurrent associative memory, Hopfield model, multilayer ANNs for clustering & competitive learning, Kohonen SOFM, Learning Vector Quantization, Adaptive Resonance Theory model. ANN applications (projects).

Design of smart & secure wireless sensor networks (SS-WSNs) toward the future era of IoT. WSN technology and protocols covered. Neural network modeling introduced for intelligent components. Major WSN security topics and protocols addressed. Discusses challenges including limited computational/power budgets, wireless medium vulnerabilities. Covers critical SS-WSN military and civil applications.

Artificial neural networks, with emphasis on multilayer feedback networks, self-organizing networks, and Hopfield-style networks. Learning algorithms. Introduction to fuzzy systems and evolutionary computing. Engineering applications of soft computing.

Biological neurons and modeling. Single/multilayer ANN models, backpropagation, Hopfield model, Kohonen SOFM, LVQ, counterpropagation, Adaptive Resonance Theory model. ANN applications (projects). Graduate-level depth and rigor.

Advanced topics on wireless sensor networks technology, focus on protocols, applications, typologies, deployment, data manipulation, mobile ad-hoc wireless communication, security. Students present literature papers on most recent research developments in the field.