Hardware and software approaches to ILP, dynamic, speculative, VLIW, and superscalar execution models. Examples and case studies. Dynamic branch prediction techniques. Memory hierarchy, cache and virtual memory, cache coherence, memory system performance. Parallel architectures models, coherence protocols, and interconnection networks. The students are expected to carry out research projects in related field of studies.

Introduction to parallel processing architecture, sequential, parallel, pipelined, and dataflow architectures. Parallel program models. Basic parallel programming techniques, problem decomposition, assignment, orchestration, and mapping. Examples and case studies of static, semistatic, and dynamic application parallelism. Performance: evaluation, scalability, and workload selection.

Introduction to message passing multiprocessor systems. Message communication models and their correctness. Message passing system architecture & languages. Architectural support for message passing. Processor time allocation. Inter module message communication. Real time applications of message passing systems. Future trends and new technologies. The students are expected to carry out research projects in related field of studies. Prerequisite: COE 442 or equivalent.

Introduction to Hard-RT, Soft-RT and Firm-RT heterogeneous systems. Network heterogeneous computing: design issues, architecture, programming paradigm, environment, and Middleware Technologies. Applications and case studies. The students are expected to carry out research projects in related fields of study.

Fundamental concepts in the theory of reliable computer systems Design. Hardware and software reliability techniques. Evaluation of fault-tolerant computer systems. The practices of reliable system design. Case studies. Fault-tolerant multiprocessor design. The students are expected to carry out research projects in related field of studies. Prerequisite: COE 308 or equivalent.

Basics of conventional CPU architectures, their extensions for single instruction multiple data processing (SIMD) generalization of single instruction multiple thread processing (SIMT) in modern GPUs. GPU architecture basics in terms of functional units. CUDA programming model. Architecture specific details (like memory access coalescing, shared memory usage, GPU thread scheduling) that effect program performance. OpenCL/OpenACC which can be used for programming both CPUs and GPUs in a generic manner. Different architecture-aware optimization techniques relevant to both CUDA and OpenCL. Application development examples in well-known GPU computing scenarios.

Pre-Requisites: COE502

Advanced topics selected from current issues in Computer Architecture and High-Performance Computing.

Introduction to the key elements of robotics programming. Key skills in implementing robotic software for real time systems. Robot programming methods using ROS (Robot Operating System) including creating ROS service servers, implementing ROS nodes, communication between ROS nodes, ROS data structures. Simulation of robotics systems, such as SLAM using ROS frameworks. Hands-on experience on using ROS for programming ground robots and UAVs.

Principles of algebraic graph theory. Ad hoc networks: IoT and sensor network enabling technologies, Machine to Machine (M2M) routing protocols, Network topology, connectivity maintenance, fault-tolerance and coverage> Autonomous multi-robot systems formations, maintenance and control. Building a network of robots to achieve a common goal, e.g., cooperative Simultaneous Localization and Mapping (SLAM). Case studies.

Pre-Requisites: AE581

Integration of computational intelligence with advanced sensors and actuators for robotic systems perception. Computer vision, robot perception and place recognition, feature extraction, vision object tracking and obstacle avoidance. Data acquisition and control, robotic system integration. Basic topics in micro electro‐mechanical systems (MEMS) and smart materials sensing and actuation. Basic introduction to microcontrollerbased embedded systems, I/O interfacing, task scheduling, and other related topics.

Impacts of digitalization and Industry 4.0 on business and industry. Smart measurement systems: Sensors and actuators: working principles, classifications, performance, characteristics. IoT systems design and architecture: elements of IoT system, potentials, constrains, technologies and applications. Industrial IoT: Process Control Systems (PCS), Industrial Control Systems (ICS); WirelessHART, ISA100, SCADA Architecture. Artificial Intelligence, Data Analytics and Visualization, Data Storage and Transfer. Management challenges and Business Models for digital transformation. Case studies in different disciplines.

Introduction to smart systems. Sensors and actuators: working principles, classifications, performance, characteristics, interfacing with feedback control, and data acquisition. Embedded systems: types, architectures, memory management, and interfacing. Concurrency: software and hardware interrupts, timers. Embedded operating systems: components, considerations, configuration, and resource management. Embedded systems integration and programming, profiling and code optimization. Power management and energy harvesting.

Introduction to security principles and technologies related to the Internet of Things (IoT) and its components: devices, operating systems, sensors, data storage, networking and communication protocols, and system services. IoT vulnerabilities, attacks and mitigation techniques. Hands-on and case studies.

Introduction to network applications, discrete random variables, continuous random variables, characteristic functions. Introduction to stochastic processes. Discrete-time Markov chains, continuous time Markov chains. Introduction to queuing theory, M/M/1 and derivative queues, and M/G/1 queues. Burke’s theorem. Jackson’s theorem: open and closed network of queues. Applications to computer networks and case studies.

Introduction to distributed computing and systems: processes, tasks, threads, and abstraction. Architectural models of distributed systems and their design issues. Mutual exclusion, condition variables, and atomic instructions. Time and synchronization. Distributed algorithms and their models of execution. Consensus and leader election protocols. Distributed file systems. Distributed ledgers. Scalability-enabling techniques: partitioning and replication. CAP theorem. Locking and transactions: 2PL and 2PC. ACID transactions Programming constructs and techniques: sockets programming, remote procedure calls. Fault-tolerance. Logging and crash recovery. Examples of distributed systems: DNS and CND. Hands-on sessions.

Data privacy: definition and terminologies. Difference between data security and privacy. Data privacy attacks. Data privacy laws and regulations. Privacy risk and impact assessment. Privacy engineering, management, and evaluation. Data anonymization. Statistical privacy. Differential privacy. Cryptographic privacy. Homomorphic encryption. Secure multi-party computation. Secure data outsourcing. Data hiding and steganography. Anonymous networks. Trusted execution environment. Applications of privacy preserving technologies in computer systems and applications.

Digital logic: logic gates, combinational digital circuits. Computer organization: Instruction set architecture. Quantum gates. Quantum circuits. Quantum architecture. Quantum Programming. Quantum Compilers. Qubit control and measurement. Quantum Benchmarking.

Combinatorial optimization. Quantum optimization. Variational quantum algorithms. Quantum noise models. Quantum error-correction: . Quantum fault-tolerance. Quantum communication protocols. Quantum key exchange. Quantum Internet.

Pre-Requisites: PHYS510

Review of Computer networks layering concepts and quality of service requirements. Physical Layer, Data Link Layer; ARQ Strategies; Analysis of ARQ Strategies. Multi-access communication. Network Layer. Routing in Data Networks. Flow and Congestion Control. Transport Layer. Application Layer: peer-to-peer networking, Content Distribution networks. Studying a number of classic and current papers on these subjects. Case studies.

Local and Metropolitan Area Networks classes, standards, and network architectures. Physical layer for LANs and MANs. Introduction to basic Queueing Models. Multiple access techniques and protocols for advanced Local and Metropolitan Area Networks. Design issues, and performance modeling and analysis. Interworking and network management for LAN and MAN. Case studies including Gigabit/Terabit Ethernet, Gigabit WiFi, G/EPONs, etc. Emerging LAN and MAN technologies.

Exploring issues with the current Internet architecture. Introduction of the concept of Information-Centric Networking (ICN) and how it addresses those issues. Components of ICN such as caching, data-naming, routing and forwarding, and security. ICN proposed architectures such as Named-Data Networks (NDN), Network of Information (NetInf), Data-Oriented Network Architecture (DONA), and Publish-Subscribe Internet Routing Paradigm (PSIRP).

Introduction to radio frequency propagation models. The physical layer for advanced mobile systems. Cellular configurations and interference mitigation and coordination methods. Multiple access techniques for wireless networks. Wireless network architecture. Mobility solutions for mobile networks (Mobile-IP, Session Initiation Protocol, mobile-Stream Control Transport Protocol, etc.). Quality of service, reliability, and security in the mobile computing environment. 5th generation wireless networks. Case studies include Wireless Personal Area Networks (e.g. Bluetooth, Zigbee, etc.), Wireless Local Area Networks (e.g. 802.11n, 802.11ac, etc.) Wireless Metropolitan and Wide Area Networks (e.g. WiMAX-2, LTE/LTE-Advanced, 5G, etc.).

Introduction to the most recent advanced Mobile Ad hoc Networks (MANETs) routing protocols. The course will cover all the issues that are related to design protocols such as scheduling, capacity, medium access, QoS, topology control, and mobility tracking. In addition, modeling techniques as well as delay models will be covered using Linear Programming.

The basic hardware and software platforms for sensor networks and will address in detail several algorithmic techniques for deployment, localization, synchronization, MAC, sleep scheduling, data routing, querying processing, topology management and energy aware protocols. Hands-on experience through programming projects involving different platforms. In addition, different microcontrollers, such as Arduino will be used to interface different wireless communication transceivers with sensors.

Introduction to different types of computer networks: LANs, VLANs, InterVLAN Routing, ISL/802.1Q Trunking and WANs. STP and, PVST PVST+ protocols, in addition to ACL (Standard and Extended). IPv4 and IPv6 subnetting and routing. Multicasting, Internet Group Management Protocol (IGMP) and Multicast Listener Discovery (MLD) protocols. Distance-Vector Multicast and Protocol Independent Multicast. Network development life cycle. Network analysis and design methodology. Link topology and sizing; Routing; Reliability. Data in support of network design. Data center design and implementation. Packet tracer simulator/emulator or other simulation tools will be used heavily.

Overview of network management. Network management standards and models. Network management system design. Network configuration management. Network management protocols: SNMP and RMON. Network management tools and systems. Network management applications. SNMPv3 security engine. SNMPv3 access control. SNMPv3 authentication and encryption protocols. Security management.

Study of cloud computing principles, architectures, and actual implementations. Cloud solutions performance evaluation. Performance issues such as security, cost, usability, and utility of cloud computing solutions will be studied both theoretically and in hands-on exercises. How to construct and secure a private cloud computing environment based on open source solutions, and how to federate it with external clouds.

State-of-the-art topics from the areas of various transmission technologies. Prerequisite: Consent of the Instructor.

IoT systems design and architecture: elements of IoT system, potentials, constraints, and applications. IoT access technologies. IoT networking protocols such as 6LoWPAN. IoT application layer protocols such as MQTT and CoAP, and Wireless Personal Area Networks (WPAN) such as ZigBee. Low Power Wide Area Networks (LPWAN) such as LoRaWAN. IoT network architecture: cloud, fog, and edge layers.

Overview of Online attacks, Malware, Social engineering, Physical and Communication security, Access control techniques, Cryptography: Classical cipher, Mathematical cipher, Stream cipher, Block cipher, public key. Other information security: Steganography, Hashing, Secret sharing, Software reverse engineering & Program security, Firewalls & IP sec, Security policy & risk management, Advanced security topics.

SDN paradigm and decoupling of control-plane and data-plan. OpenFlow. Controller design and network programmability. Open source controllers: Floodlight, NOX/POX, OpenDaylight, etc. Traffic engineering using SDN (e.g. Google B4, Microsoft SWAN, and SDX). SDN virtualization: FlowVisor, Open vSwitch, and Network Function Virtualization (NFV). SDN for data centers, enterprise networks, wireless and mobile networks, and for service provider networks. Case studies.

Introduction to concepts of faults, errors, and failures. Basic concepts of dependable computing including dependability attributes, means, and validation. Stochastic modeling techniques in the context of network reliability analysis. Error detection and correction techniques. Fault tolerant topology design. The practices of reliable and fault-tolerant computer networks design. Case studies.

Pre-Req COE 520 or Consent of Instructor

Pre-Requisites: COE520 Or COE520

Network Management Standards, Models, and protocols. Network Management Applications, Tools, and Systems. Remote Monitoring and Management (RMM). Large scale wireless network management techniques and systems. Security of LANs, wireless LANs, and cellular networks. Authentication and authorization of wireless networks. Firewalls and Intrusion Detection and Prevention Systems. Study of diverse attack types and countermeasures for each of attack. Handson experiences in network security using Kali Linux. Hands-on experiences in implementing secure, manageable networks.

Pre-Requisites: COE540

Study of IoT principles, IoT applications requirements, Design issues in IoT. IoT access technologies such as 802.15.4, LoRaWAN and Sigfox. Industrial access control techniques for wireless sensors, such as ISA100 and WirelessHart. Publish/Subscribe messaging protocols such as MQTT and COAP protocols. Security and privacy issues in IoT and IoT communication protocols. Data analytics for IoT. Simulation tools will be used to demonstrate different components of the course.

Pre-Requisites: COE540

Recent advanced emerging technologies in computer networks, such as software defined networking (SDN) and network functions virtualization (NFV), information centric networking, smart grid communications, new wireless generations (6G and beyond), security aspects of new emerging technologies, etc. Students will search the literature for the state of the art of the most significant emerging technologies, explore new ideas through simulation projects and finally present their findings.

Pre-Requisites: COE540

Internet and web protocols and technologies. Basics of web development: frontend, backend, and full-stack. Web services and RESTful APIs. Introduction to utility computing: Cloud and Edge computing. Cloud Service-oriented architecture and microservices. The XaaS pyramid. Serverless computing. Cloud resource management. Automated deployment and operations techniques. Virtualization and containerization. Cloud data storage: block storage, object storage, and file storage. Cloud "Big data" processing : MapReduce and Hadoop, Spark, BigTable. Cloud-native applications. Security of Cloud computing.

Any state of the art topics or topics of recent interest in any areas in the computer systems and applications that may not fit well with description of previously mentioned courses. Prerequisite : Consent of the Instructor

Overview of modern digital systems, Digital system hierarchy & abstraction levels, Design and Modeling using HDL, Design optimization and performance criteria, High-Level synthesis, Digital system implementation using FPGAs.

Review of MOS transistors, modeling, scaling, sizing, physical design (layout). IC Design Styles, Combinational and sequential logic, static CMOS, Dynamic circuits, pass-transistor logic. Clocking strategies, clock skew, setup, hold & propagation delays, self-timed logic, I/O design. Design considerations of regular structures: ROM’s, PLA’s, arithmetic circuits. CAD tools used in VLSI design (schematic, layout, DFT …etc.). CMOS memory architecture, design constraints. ROM, SRAM and DRAM cells. Single and double-ended bit line sensing. Multiport register files. The course is project-oriented stressing the use of CAD tools through class projects.

Digital design styles, design representations, abstraction levels & domains, Binary Decision Diagrams, Satisfiability and Covering problems, Two-level logic synthesis and optimization: Exact and heuristic techniques, Testability properties of two-level circuits, Multi-level logic synthesis and optimization, Observability and controllability don’t care conditions, Testability properties of multilevel circuits, Synthesis of minimal delay circuits, Sequential logic synthesis: state minimization, state encoding, retiming, Technology mapping, High level synthesis: data flow and control sequencing graphs, scheduling, allocation.

Introduction to Hardware (HW) Supply Chain; Design, Manufacturing, and Test. Review of Cryptography primitives. Attacks on HW & Mitigation techniques; Physical & Invasive attacks, Sidechannel Attacks, Fault-injection Attacks, and FPGA Security. Supply Chain Attacks & Mitigation techniques; Attacks on Electronic Design Automation (EDA) Tools, HW Trojans, HW Intellectual Property (HWIP) theft, over-use, and counterfeiting, IC over-production and counterfeiting, HWbased Physically Unclonable Functions (PUFs), True Random Number Generators (TRNG), and Root of Trust, HW Watermarking, Metered-HWIPs, and Logic Locking.

Review of MOS transistors, modeling, scaling, sizing, physical design (layout), and static versus dynamic logic. MOS logic optimization of delay and area. IC Design Styles, Hardware description languages, ASIC design flows. ASIC design with HDL. ASIC library design, cell characterization, design area and delay. Standard-cell design methodology, propagation delay, design area, critical path, placement and routing of cells, design optimization and back annotation. HDL modeling, technology mapping and synthesis. ASICs test techniques, fault models, boundary scan and DFT. The course emphasizes hands on experience through the use of available design tools for the design of ASIC VLSI.

Introduction and approaches to digital system verification. Simulation versus Formal verification. Levels of hardware modeling (circuit, switch, gate, RTL, and Behavioral levels). Hardware description languages, Principle of Formal hardware modeling and verification. Model checking; binary and word-level decision diagrams, symbolic methods, Mathematical logic (First order logic, Higher Order Logic, Temporal Logic). Abstraction mechanisms for hardware verification. Automated theorem provers. Verification using Specific Calculus. Formal verification versus formal synthesis. Future trends in hardware verification.

Review of modern digital systems and their designs. Hardware description languages, ASIC design flows. Field programmable gate Arrays: Architectures, Configuration Techniques, Design Parameters and Models. FPGA design Flow. Application Domains, Custom computing machines and FPGA-based hardware accelerators. Case studies and contemporary issues in reconfigurable computing.

Basic principles and practice of digital system testing, Test Economics, Fault models, Fault simulation, Test generation for Combinational and Sequential circuits, Test compaction, Test Compression, Fault Diagnosis, Delay-fault testing, Design for testability, Boundary Scan, Built-in self-test: logic BIST and memory BIST, Testing of system-on-chip.

An up-to-date survey of design automation techniques for digital hardware designers. Digital design languages, System level simulation. Register-transfer-level description and simulation. Gate-level simulation. Partitioning, placement and routing for printed and integrated circuits. Fault simulation and test generation. Automated documentation. Integrated design systems. Hands-on experience on an actual design automation system.

Advanced topics selected from current issues in the area of digital system design and automation. Prerequisite: Consent of the Instructor.

Research-oriented graduate course in digital forensics. The course aims to provide an extensive background suitable for those interested in conducting research in this area, as well as for those interested to learn about digital forensics in general. The course focuses on the technical issues and open problems in the area. Topics include fundamentals of digital forensics; digital forensics models; OS artifacts forensics; live and memory forensics; network forensics; mobile devices forensics; current tools and their limitations; legal and ethical issues.

Morphological Structures of robotics system. Design and analysis of motion coordination systems for robot arms, geometric and variational approaches. Robot languages and programming, effector and object levels. Trajectory planning and collision avoidance. Force sensing and compliance. Robotic vision and intelligence. Space robotics and remotely controlled robotic systems. Equivalent to SE 532 and EE 603 Prerequisite: COE 305 or equivalent.

Review of Switching Algebra, Complex Gates, Boolean Algebra, Multi-Valued Logic, Switch Network, Transient Analysis, Symmetric Functions, Unate Functions, Threshold Functions, Multiple-Output Network, Programmable Arrays, Fault Models, Test sets, Multi-Stage Networks, Sequential-Circuit Analysis, Finite-state Machines, Multiple-Pulse and Non-Pulse Circuits, Asynchronous Circuit Design. Prerequisite: COE 308 or equivalent.

Fixed point arithmetic: addition, subtraction, multiplication, division, fixed point ALUs. Floating point arithmetic: normalization, rounding, addition, subtraction, multiplication, division, floating point ALU. Modeling of Arithmetic Processors. Elementary functions. Nonconventional Number Systems. Prerequisite: COE 308 or equivalent.

Simulation of the functions of a computer system, Analytical and stochastic methods of performance, Graph models for multiprocessors and parallel processing. Performance measures. Performance evaluation techniques. Application areas. The modeling cycle. Flow analysis. Bottleneck analysis. Hierarchical modeling. Case studies.

Approaches to the simulation problem (event scheduling, process-based, etc.). Modeling and simulation of queuing systems. Probability, stochastic processes, and statistics in simulation. Random number generation. Monte Carlo methods. Building valid and credible simulation models. Output data analysis. Simulation formalisms. Software techniques for building simulators. Using contemporary tools like Matlab and SimEvents. Case studies in science and engineering

Advanced selected topics in computer systems and applications. Prerequisites: Graduate standing, Consent of the Instructor

Models, architectures, and development environments of self-adaptive software systems (SAS) for security and resiliency. Theoretical foundations, development toolkit, and evaluation of autonomous self-adaptive systems using reinforcement learning (RL). RL Algorithms including Policy-based REINFORCE, Value-based SARSA and DQN, and Hybrid RL Actor-critic and Proximal Policy Optimization, and Multi-agents RL. Assurability and robustness of self-adaptive systems against adversarial learning. Case studies of RL-based self-adaptive systems using OpenAI Gym and UCB Ray to improve security, resilience, and quality of experience of interconnected systems. Cybersecurity applications of self-adaptive systems that include risk-aware cyber mission planning, cyber deterrence, security orchestration and response, and cyber deception. Non-Cybersecurity applications of self-adaptive systems such as rescue mission planning, smart environment, gaming and smart manufacturing.

Fundamental concepts of neural computing. Terminology. Main neural networks architecture single/multilayer perceptrons, feedback(recurrent)/feedforward information flow; and their supervised/unsupervised learning models. Backpropagation, self-organizing, adaptive resonance, auto/heteroassociation neural memory models. Neurocomputing implementation, applications, performance evaluation. Literature survey of the most recent neural networks development. Equivalent to ICS 586 and EE 560 Prerequisites: Graduate standing, Consent of the Instructor.

Introduction to Embedded and Efficient Machine Learning and Intelligence. TinyML applications and use cases. Efficient training techniques (distributed training, gradient compression and on-device transfer learning). Efficient inference techniques (model compression, pruning, quantization, efficient neural architecture search, and distillation). Application-specific model optimization techniques. TinyML frameworks, tools, and techniques. Emerging topics and research in Embedded and Efficient Machine Learning methods and applications. Hands-on experience in implementing deep learning applications on resource-constrained devices. Pre-requisite knowledge: Basic knowledge of machine learning and deep learning

Multimedia architecture and systems in ubiquitous computing devices. Time-Frequency Representation, Predictive Coding, Speech Analysis and Synthesis, Image Understanding and Modeling, Image Compression Techniques, Color Models and Color Applications, 3-D Representation, Illumination Models, Graphics Systems, MPEG Standards, Video Compression, Video Conferencing, Digital Rights Management. Distributed machine learning systems and computational challenges.

Classification of DSP Functional Units, Programmable DSP Architectures, Video Processors, Fine Grain Image Processors, Application Specific DSP Architectures, DSP Linear Array Architectures and their Synthesis, Mapping of DSP Algorithms, Algorithmic and Architectural Transformation for DSP, VLIW DSP Architectures, Multimedia Processor Architectures, Memory Architecture for DSPs, Programmability of Advanced Architectures. Prerequisite: COE 308 or equivalent.

Introduction to computer vision processing and intelligence. Vision sensing and imaging techniques. Image representation, Image processing, Feature detection and matching, Feature extraction. Image classification, object detection and tracking, semantic segmentation, motion and depth estimation. Deep learning for visual intelligence. 3D Vision. Introduction to embedded computer vision. Emerging topics and research in computer vision methods, systems and applications. Hands-on experience in building computer vision applications.

Overview of natural evolution and its application as a problem-solving tool. Genetic algorithm and its extensions. Simulated annealing and taboo search. Evolution strategies and genetic programming. Social computing. Plant-based algorithms. Neural networks. Quantum computing. Examples and applications.

Introduction to real-time systems, concurrency, and timing constraints, real-time programming: task model and specification, event loop, never-ending tasks, periodic and aperiodic tasks, thread synchronization, inter-task communication, synchronization, memory management, scheduling: ratemonotonic scheduling, EDF, resource sharing, priority inheritance, sporadic servers, multiprocessor scheduling, reliability and fault tolerance. Digital feedback control systems for example RTS, implementation strategies, sampling rate, and effect of task scheduling on control latency, case studies. Prerequisite: COE 515 or Consent of Instructor

Pre-Requisites: COE515

Graduate students are required to attend the seminars by faculty members, visiting scholars, and fellow graduate students. Additionally, each student must present at least one seminar on a timely research topic. Among other things, this course is designed to give the students an overview of research in COE, and a familiarity with research methodology, journals and professional societies in his discipline. Graded on a Pass or Fail basis.

A project on an independent study performed under the supervision of a COE faculty advisor. A written report should be submitted, presented and orally examined by a faculty committee.

Pre-Requisites: COE599*

Co-Requisites: COE 599

Introduction to massively parallel multiprocessors and their programming models. Streaming multiprocessor, SIMD and multithreading. Highly multithreaded architectures, thread-Level parallelism, resources sharing, thread scheduling, score-boarding, transparent scalability. Data dependence analysis, recurrences, races. Shared-memory, atomicity, mutual exclusion, barrier, and synchronization. Memory hierarchy optimization, locality and data placement, data reuse, loop reordering transformations, shared-memory usage, global memory bandwidth and accesses. Control-flow, SIMD, thread blocks partitioning, vector parallel reduction, tree-structured computation, serialized gathering, Predicated execution, and dynamic task queues. Applications of static, semi-static, and dynamic parallel computations: dense and sparse linear Algebra, bucket sorting, N-body simulation, and ray-tracing.

Pre-Requisites: COE501

This course is intended to allow M.S.students conduct research-related independent study. The faculty offering the course should submit a research plan to be approved by the COE Graduate Program Committee. The student is expected to deliver a public seminar and a written report on his research outcome at the end of the course. The course is graded on a Pass or Fail basis. To select adequate subject, prior arrangement with the instructor is required. Graded on a Pass or Fail basis.

The student has to undertake and complete a research topic under the supervision of a faculty member in order to probe in depth a specific problem in Computer Engineering. Graded on a Pass or Fail basis.

Pre-Requisites: COE599*

Co-Requisites: COE 599

A graduate student will arrange with a faculty member to conduct an industrial research project related to the robotics and autonomous intelligent systems field of the study. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.

Queuing theory. Stochastic Petri nets and Markov Chains. Separable queuing networks. Priority queuing systems. Queuing networks, product forms and various solution techniques. Matrix geometric solutions to queuing theory. Bounds and approximations. Fluid analysis and diffusion processes. Evaluation studies: monitoring techniques, modeling methods and model validation. Simulations and variance reduction techniques. Application of queuing theory to computer time sharing & multi-access systems, multiprocessor systems, interconnection networks. Computer communication networks. Case studies of several distributed and network system configurations.

Pre-Requisites: COE520

Radio resource management and performance analysis in transporting homogenous/ heterogeneous traffic in wireless communication networks. Traffic characteristics, connection admission control, packet scheduling, access control, and mobility and handoff management. Cases studies on mobile wireless networks and wireless sensor networks.

Pre-Requisites: COE543

Security for contemporary wireless communication networks such as cellular networks, wireless LANs, mobile ad-hoc networks, wireless sensors, and mesh networks. Study of diverse attack types such as radio signal jamming, MAC-layer attacks, routing attacks, Sybil, Blackhole attacks, and O/S dependent attacks. Study of countermeasures and scope for each of these attacks. Light-weighted security for resource-constrained wireless devices. Secure multi-casting. Key management techniques for wireless networks.

Fundamentals concepts in multimedia systems. Resource management issues in distributed/networked multimedia systems, QoS routing and multicasting. Traffic shaping, Traffic engineering, Task and message scheduling, Internet QoS. Adaptive multimedia applications over the Internet. Storage architecture and scalable media servers. Compression techniques, synchronization techniques for multimedia. Multimedia over wireless networks. Case studies.

Pre-Requisites: COE540

A current-day system on a chip (SoC) consists of several different microprocessor subsystems together with memories and I/O interfaces. This course covers SoC design and modeling techniques with emphasis on architectural exploration, assertion-driven design and the concurrent development of hardware and embedded software. This is the ‘front end’ of the design automation tool chain.

Pre-Requisites: COE561

Embedded System Design Considerations, Classical Design Methods, co-representation, Performance Modeling, Co-design Trade-offs, Functional Decomposition, Partitioning, Design methodologies, Co-design Environments, Abstract Models, Recent Techniques in Co-design, Case Studies.

Pre-Requisites: COE561

Concepts of organizational planning related to IT Systems. The IT Planning process. Understanding information systems planning: functions, processes, information groups, subject databases. Information systems planning strategies and standards. Information needs analysis. Strategic planning of information systems. IS planning for office automation and industrial automation. Make or Buy strategy. Students should conduct a research project.

Advance selected topics in computer engineering

Advanced selected topics in computer engineering.

Advanced selected topics in computer engineering.

PhD students are required to attend Departmental seminars delivered by faculty, visiting scholars, and graduate students. Further, each PhD student should present at least one seminar on a timely research topic. The course is graded as pass or fail. To secure a passing NP grade in this course, the student should have passed the PhD Comprehensive Exam. A student registered in the Seminar Course will be assigned an IC (incomplete) grade in case he fails the PhD Comprehensive exam in that semester. The IC grade will be changed to a passing NP grade once he passes the PhD Comprehensive Exam latest by the following semester to avoid having the IC grade changed to F.

This course is intended to allow the student to conduct research in advanced problems in his PhD research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee at the academic department. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the course. Prerequisite: prior arrangement with an instructor

This course is intended to allow students to conduct research on advanced problems in their Ph.D. area of specialization. Among other things, this course is designed to give the students an overview of research in COE, and a familiarity with research methodology, journals, and professional societies in his discipline. At the end of the course, the student must deliver a public seminar to present his work and findings. The course is graded on a Pass or Fail basis. Prerequisite: Prior arrangement with an instructor

This course enables the students to submit his PhD Dissertation proposal and defend it in public. The student passes the course if the PhD Dissertation committee accepts the submitted dissertation report and upon successfully passing the Dissertation proposal public defense. The course grade can be NP, NF.

Pre-Requisites: COE699*

Co-Requisites: COE 699

This course enables the students to work on his PhD Dissertation as per the submitted dissertation proposal, submit its final report and defend it in public. The student passes the course if the PhD Dissertation committee accepts the submitted final dissertation report and upon successfully passing the Dissertation public defense. The course grade can be NP, NF or IP.

Pre-Requisites: COE711