[Computers As Components, Third Edition: Principles Of Embedded Computing System Design (The Morgan K

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Objectives: Familiarization with fundamental concepts and principles of distributed systems in both breadth and depth. Development of capabilities of designing, analyzing and programming distributed systems.

Content: Basic concepts and principles of distributed systems. Communication, processes and synchronization. Naming. Distributed file systems and distributed operating systems. Security and cryptography in distributed systems. Distributed shared memory and its consistency. Fault-tolerance. Distributed algorithms and distributed programming. Design and development of applications in distributed environments. Case-studies of specific distributed systems (eg. PlanetLab). Practical exposition with programming project or programming exercises.

Computers as Components, Third Edition: Principles of Embedded Computing System Design (The Morgan K

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Objectives: To provide students with an advance introduction to the foundations and principles of the world wide web. To examine selected topics in web technologies from the recent research literature.

Objectives: The course will cover the basic principles, concepts and contemporary techniaues in using Software Architectures as the driving paradigm for the design and development of modern software systems. The role of Software Architectures in the broader field of Software Engineering will be addressed, with emphasis on the important paradigm of software reuse.

Content: TBasic concepts. Design of a software architecture. Connectors. Modelling. Visual Representation. Design patterns for software architectures. Analysis and implementation. Non functional properties. Security and trust. Standards. The human factor. Domain-specific Software Engineering.

Objectives: This course covers specialized topics in Artificial Intelligence, such as modeling and solving constraint satisfaction and knowledge representation problems, symbolic learning, learning with various forms of neural networks, including deep learning and reinforcement learning.

Objectives: Students will learn: (a) current methodology for performance evaluation and comparison of computer systems; (b) basic and advanced concepts in the organization of modern microprocessors; and (c) current trends in the computer architecture area. Also, with the use of different tools, presented in the course, students will perform research projects in certain computer architecture topics.

Content: Performance evaluation and comparison, as well as benchmarking programs; Basic microarchitecture concepts of modern processors; Pipelining, instruction-level parallelism, prediction, speculation, memory hierarchy, and static/dynamic instruction scheduling; Examples of modern processors; Current research projects in the area of computer architecture.

Objectives: Understanding (at a graduate level) of the basic concepts and matters regarding Computer Networks and the Internet. Familiarization with modern views of Computer Networks and exposure to the related open research problems.

Content: Introduction to Internet and Networking Technologies. TCP/IP suite of protocols, Quality of Service (QoS), New Networking Architectures. Protocols and Standards (e.g. DiffServ, IPv6, MPLS). Network Performance Evaluation (e.g. queueing theory, and simulation tools). Traffic Modeling and Traffic Engineering. Congestion Control and Resource Allocation. Network Design and Optimization.

Content: Binary image processing, intensity transformations, the discrete Fourier transform, linear and nonlinear filtering, image compression, image analysis, basic principles of video processing. Basic principles of 3Dgraphics: polygonal representations, transformations, local and world coordinate system, scene graph, camera and field of view specification, orthographic and perspective projection, clipping in 2D & 3D, polygon rasterization, back face elimination, visible surface determination with the Z-byffer method and Binary Space Partitioning Trees, local illumination - flat, Phong & Gouraud shading, real-time graphics, applications.

Objectives: Introduction to fundamental concepts, applications and limitations of mobile computing. Familiarization with practical applications and research topics of current interest in the field of Mobile Computing.

Content: Introduction (wireless technologies, architectures, applications, limitations). Software architectures for mobile computing. Theoretical models for mobile computing. Support for information recovery. Information Management. Dynamic redirection of computations. Indicative applications and open problems.

Objectives: Study in depth of the technologies of Electronic Commerce. Introduction to the software technology of client/server systems of e-Commerce and to Business Models of e-Commerce.

Content: Game structure and design, computer animation, movement and deformation, interactive cameras, visual simulation of physically-basedmodels, special effects using particle systems, collision detection, articulated characters, navigation and other behavioural models for autonomous characters.

Content: Support for parallel program execution, parallel architectures, different types of multiprocessor interconnection networks, compilation of parallel programs, and performance analysis of various parallel applications.
Prerequisites: Undergraduate course equivalent to the CS420 (Computer Architecture) or undergraduate course equivalent to the CS605 (Advanced Computer Architecture I) or the consent of the instructor.

Objectives: This course goes beyond the basics of digital image synthesis, looking at issues such as photo-realistic rendering, modeling and animation. A big component for this are the creation of realistic and detailed models as well as the faithful simulation of light transport. We will see how these can be applied to virtual and augmented reality. Students will acquire both the theoretical foundations as well as practical skills since a significant part of the course is the student project.

Content: Modeling, parametric and implicit surfaces, camera specification, projections of primitives. Graphics Pipeline. Local and global illumination, shadows, ray tracing and radiosity. Real-time rendering of large environments. Acceleration techniques.

Objectives: Introduction to wireless networks (mobile/local/cellular/Ad-hoc/Sensor) with an emphasis on the fundamental concepts and principles of the technologies which are important for the design, application, evaluation and development of these systems. The course will also cover new architectures and topologies, existing and proposed standards, as well as open research issues.

Content: Wireless environment, Interference and other problems in wireless communications, basic principles of wireless local and metropolitan area networks, and cellular wireless networks. Newer architectures and technologies of wireless networks and wireless communication (e.g., ad-hoc and sensor networks, VANETS). Resource management techniques, Next Generation wireless networks of 3rd, 4th and 5th generation (LTE, 4G, 5G, 6G), design and planning of wireless networks, protocols for wireless and mobile networks. Internet of Things (IoTs), new trends in Wireless Communication, such as Programmable Wireless Environments and Intelligent Surfaces.

Content: A review of embedded system processors. Organization of embedded systems: CPUs, RAM, ROM, buses, peripherals, sensors, actuators, interfacing. Examples of widely used processors buses and peripherals. Interfacing with peripherals: sampling, interrupts, advantages and disadvantages. Process distribution between hardware and software. Tools for the development of embedded systems and real-time operating systems. Hands-on experience with the development and implementation of embedded systems.

Objectives: The objective of this course is to examine the main computer science principles that lie behind Google and other search engines. To this end, the course will focus on basic and advanced techniques for text-based information systems: efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; text classification and clustering. The course will also focus on Web search including crawling, link-based algorithms, and Web metadata.

Content: Introduction to Information Retrieval. Boolean Retrieval.Text encoding: tokenisation, stemming, lemmatisation, stop words, phrases. Dictionaries and Tolerant retrieval. Index Construction and Compression. Scoring and Term Weighting. Vector Space Retrieval. Evaluation in information retrieval. Relevance feedback/query expansion. Text classification and Naive Bayes. Vector Space Classification. Flat andHierarchical Clustering.Web Search Basics.Web crawling and indexes. Link Analysis.

Objectives: Introduction of the fundamental principles, algorithms and techniques that support the development and implementation of data mining systems leading in the extraction of knowledge.

Content: Data Warehouse and OLAP Technology for Data Mining. Data Processing. Data Mining Primitives, Languages, and System Architectures. Concept Description: Characterization and Comparison. Mining Association Rules in Large Databases. Classification and Prediction. Cluster Analysis. Mining Complex Types of Data. Applications and Trends in Data Mining.

Content: Historical introduction. Review of Classical Logic. Abduction and induction. Knowledge representation and knowledge. Reasoning about Actions and Change. Application of Computational Logic. Declarative Programming. Autonomous Agents. Knowledge-based Robotics. Intelligent Information Integration.

Content: Formal methods for system specification and analysis. Concurrent systems and interleaving and partial-order semantics. Transition systems and Kripke structures. Temporal logic (linear and branching). Automatic verification and model-checking. Process algebras: syntax, semantics, equivalence relations and axiom systems. Real-time system analysis (timed automata, timed process algebras and timed temporal logic). The tools SPIN and Concurrency Workbench.