Purpose and Objectives: Introduction, presentation and discussion of the fundamental laws and axioms of electrical engineering and computer science and engineering. The course aims to introduce students to the basic theories on which the two above fields are based and to give them the opportunity to present and develop their own ideas for solving various problems faced by this scientific subject. During the course, an introduction is made to subjects such as fundamental knowledge of engineering, fundamental quantities and units of measurement, history and development of electrical engineering, introduction to signal processing, analog and digital quantities, sources of electrical energy and simple circuit connections, electricity production in Cyprus, electrical installations, grounding of electrical devices and circuits, electric shock, introduction to electrical circuits and simple connections, statistics, statistical signal analysis. Presentations are also made by all students on selected subjects of their own interests.

Learning Outcomes: Introduction to the basic principles of electrical engineering, biotechnology in combination with basic principles of Engineering and Computer Science, and to problems and challenges faced by the sector. Understanding of the basic principles and axioms of electrical engineering and how to create, manage, transfer and utilize energy. History of electrical engineering, basic electrical circuits, electrical energy production, electrical installations, grounding and statistics.

Course Content: Presentations can be made on various topics (some are given below): 1) Electric and autonomous cars; 2) Renewable energy sources; 3) Solar energy, renewable energy sources; 4) The big questions for the G5; 5) The evolution of cars and the consequences on the environment; 6) Arduino, uses and applications; 7) Facial processing and analysis for emotion detection; 8) Virtual reality; 9) Information management through computer science and the internet; 10) Quantum computer; 11) Medicine and technology; 12) Telemedicine, data transfer, storage, compression, processing, analysis; 13) Signal and image processing for medical assessment; 14) The operating room of the future.

Purpose and Objectives: Develop the theory of Electrical and Electronic Circuit Analysis (with the help of characteristic examples) in a systematic and simple way, so that the general problems of Electrical and Electronics can be understood and approached in an easy and correct way.

Learning Outcomes: Understand the various basic areas of Electrical and Electronics, the basic knowledge of which is essential for a Computer Engineer, both to operate in various Engineering environments and to communicate better with Electrical Engineer colleagues whenever it is necessary.

Course Content: Ohm's Law, Voltage Divider, Current Divider. Kirchhoff’s Laws (Current and Voltage Law). Theories and Circuit Analysis. Electric Power and Maximum Power Transfer. Construction and Operation of Diode, Bipolar Contact Transistor, Field Effect Transistors (JFET, MOSFET and new types). Polarization, Polarity stability, Switching characteristics, propagation delays Enhancement. Gate circuits and topologies (BJT, NMOS, CMOS and newer types): ideal and real gate transport characteristic, input-output levels. Basic families of logic circuits. Operational Amplifiers. Ideal Operational Amplifier. Simple Applications Comparator, filters. Converters from Digital to Analog (DAC) and from Analog to Digital (ADC), Sampling System (SAH).

Purpose and Objectives: The goal is to provide students with fundamental knowledge in digital systems, focusing on their analysis, specification, operation, design, and implementation. It also aims to provide knowledge in digital design using design automation tools and hardware description languages, along with complementary knowledge of the basic concepts of digital design through a combination of lectures, exercises, and labs.

Learning Outcomes: The course deals with the analysis and design of digital systems. Specifically, it covers number systems, Boolean algebra, and the minimization of logical functions. It includes digital gates, flip-flops, combinational and sequential digital circuits, types of registers, and logical and mathematical digital units. An introduction is given to the hardware description and development language Verilog, as well as to the automation tools for digital design used in the implementation of digital circuits in Very Large Scale Integration (VLSI) circuits.

Course Content: The course is based on the fundamental elements of logic circuit design and has the following main objectives: Introduction to the design of combinational and sequential digital circuits. Use of the hardware description language Verilog for modeling, simulation, and implementation of these circuits. Implementation of digital systems on programmable logic devices (CPLDs and FPGAs). Gaining experience in the full cycle of the design, simulation, and implementation process of digital systems using modern computer tools through laboratory exercises. The following topics/chapters are covered: Number Systems (Digital systems, Binary numbers, Conversion of numbers to other base formats, Complements, Signed binary numbers, Registers, and binary logic). Boolean Algebra(Definitions, basic theorems and properties, Boolean functions, Normal and standard forms, Logic gates, Digital integrated circuits, digital logic families (TTL, ECL, MOS, and complementary MOS-CMOS), levels of integration). Minimization of Logical Functions (Karnaugh map method, Rules for the maps, Karnaugh maps for four and five variables, Simplification of sum-of-products, Implementation with NAND and NOR gates, Don't care conditions, XOR function, and error detection and correction circuits). Combinational Logic (Analysis and design process of combinational circuits, Binary adders and subtractors, Binary multipliers, Size comparators, Encoders/Decoders, Multiplexers). Modern Sequential Logic (Latches and Flip-Flops, Analysis of sequential circuits with clock, Minimization and state encoding, Size comparators, Encoders and Decoders, Design process with Flip-Flops). Registers, Counters, Memories, and Programmable Logic (Registers/Shift registers, Ripple counters, Synchronous counters, Other types of counters, Read-only memory (ROM), Random access memory (RAM), Memory decoding, Error detection and correction, PLA, PAL, Sequential PLDs (SPLD), CPLD, FPGA). Asynchronous Sequential Logic (Analysis process, Circuits with latches, Design process, Minimization of state and flow tables, State encoding to avoid race conditions, Hazards). Hardware Description Languages (HDL) and Design Automation Tools (EDA) (Basic concepts, Verilog for combinational circuits, Verilog for sequential circuits, Register transfer level (RTL) design, Finite state machines (FSM), Top-down analysis and design, Circuit simulation and verification, Logic synthesis, Placement and routing, Technology mapping and element libraries, Fully customized integrated circuits (full-custom), Semi-custom standard cells, and gate-array integrated circuits).

Purpose and Objectives: To give a broad insight into basic notions of discrete mathematics in relation to mathematical logic, boole algebra and graph theory, which can become tools for better understanding and solving related computer science applications.

Learning Outcomes: Acquisition of additional significant background for use in computer science applications.

Course Content: Mathematical logic, propositions, quantifiers, arguments, Introduction to proofs, Sequences, mathematical induction principles, recursion theory, Sets theory, Relations, functions, equivalence relations, Boole algebra and applications, Combinations and permutations, Algorithms complexity and efficiency, Numeral systems, Graphs, paths, algorithms, trees, spannning tree.

Purpose and Objectives: This course is an introduction to the various phases of compiling a programming language. Reference is made to the main concepts of programming languages in order to illustrate the steps followed by a compiler to convert a source program into machine language. The course therefore deals with the verbal, syntactic, semantic analysis of an original programming language and its conversion into intermediate code and final language. The flex and bison tools are used for the practical application and development of small speech analysts and editorial analysts respectively.

Learning Outcomes: Upon completion of the course, successful students shall be able to: Explain how analysis, regular expressions and languages work with the application of word analysts. Explain syntax analysis, top-down and bottom-up analysis, the use of syntactic analysis. Explore how semantic analysis and intermediate code work. Explain issues related to memory organization and execution environment (runtime environment), register allocation and final code generation and optimization. Implement a simple compiler for the MIPS processor. This will be implemented through the C language and the Flex and Bison tools.

Course Content: Week 1: Introduction, Programming Languages, Compilers, Necessity and historical overview, Compiler types and related tools, Basic compiler structure. Week 2: Standard Languages, Genitive and Cognitive Models, Grammar, Chomsky Hierarchy, Identifiers, Normal Languages, Regular Expressions, Finite Auto. Week 3: Verbal Analysis, Transition Charts, Speech Analyzer Manufacturing. Week 4: Contextual Languages, Conflicting Languages, Contextual Language Representation Ways. Week 5: Editorial analysis, bottom-up analyst. Week 6: Error recovery, use of compilers to create editorial scripts. Week 7: Mid-term examination  Week 8: Structural Analysis, Ancillary Concepts, FIRST and FOLLOW Calculation, Upward and Downward Structural Analyzers, Upward Structural Analysis. Week 9: Semantic Analysis, Static Semantics, Dynamic Semantics, Semantic Check, Formula Check, Formula Extraction, Categorical Grammar, Symbol Table. Week 10: Intermediate Code Generation, Intermediate Language Forms, Quadratic Language, Operating, Operators. Week 11: Intermediate Code Generation, A Simple Four-Generation Plan, Arithmetic Expressions, Logical Expressions, Simple Commands, Advanced Command, If Command, While Command, Call Subroutines. Week 12: Final Code Generation. Week 13: Code Optimization, Code Optimization Techniques.

Purpose and objectives: Learning how to use Systems Analysis and Design tools for developing Information Systems. Learning the Phases and Activities of Systems Analysis and Design and how to follow them in developing systems. Theoretical and practical knowledge in the areas of System Management, the role, duties and responsibilities of the Systems Analyst, how the teams should be formed and organized. Understanding the Systems Development Life Cycle and its stages. Developing a feasibility study for the system to be developed.

Learning Outcomes: Upon the successful completion of the course, the attendee should be able to Analyze and Design an Information System following the Phases and Activities of Analysis.

Course Content: Introduction to various Systems Analysis and Design methodologies and terminology to be used throughout the course. The Phases and Activities used for analysing and developing an Information System. Teaching and practical training in the fields of project management, communication, information gathering and feasibility study. SECTIONS - Chapters: (1) Who is involved in Systems Analysis and Design, (2) Phases and Activities of Systems Analysis and Design, (3) Developing Information Systems, (4) Project Management / Feasibility Study, (5) System Analysis, (6) Gathering and analyzing the new system requirements, (7) Data Modelling, (8) Process Modelling, (9) The New System proposal, (10) System Design, (11) Case Tools (12) Designing the Human-Computer Interface.

Purpose and Objectives: The Data Structures and Algorithms course is an introduction to Data Structures that govern programming and to Algorithm Analysis. The aim of the course is to understand how common computational problems can be solved efficiently in programming by using appropriate data structures and algorithms. The course is taught in Python programming language. In the laboratory, students will perform programming exercises using the Python programming language, which will aim to consolidate the theory that will be delivered in the lectures.

Learning Outcomes: Upon completion of the course, students will be able to: Describe and implement basic data structures and algorithms in Python. Implement programming techniques that are frequently encountered in solving algorithmic problems. Analyze the complexity of algorithms and estimate the computational cost of algorithms. Select appropriate data structures and algorithms to solve specific programming problems. Combine algorithms and data structures to solve complex computational problems.

Course Content: The course covers the following content: Algorithm Analysis, Python Programming and Object-Oriented Programming, Recursion, Arrays, Stacks, Queues, Linked Lists, Trees and Binary Trees, Priority Queues and Heaps, Maps, Dictionaries, Hash Maps, Sets Search Trees and AVL Trees, Splay Trees, (2,4) Trees, Sorting and Selection, Graphs and Shortest Path Algorithms.

Purpose and Objectives: The purpose of the course is to teach how a modern operating system is designed and how it operates. The double role of an operating system, as a manager of system resources and as a service provider is explained.

Learning Outcomes: Upon the successful completion of the course the attendee should be able to: Know the historical evolution of computers and operating systems, and understand how an Operating System manages the following: CPU - Processes, Main Memory, Secondary Memory - Files, Input / Output.

Course Content: The course content includes four sections: Part 1: Introduction Introduction to basic terminology and definitions. Historical evolution of computers and Operating Systems. Basic structure, operation and services of the Operating System. The Operating System as an abstraction machine: service provider, I/O handler, error handler, process and memory manager. Part 2: Process Management: A. Process Definition Process scheduling Inter-process communication Communication in Client-Server Systems. B. Threads and Multithreading Models C. CPU Scheduling Criteria and goals of process scheduling. Preemptive and non-preemptive scheduling, priorities, scheduling algorithms. D. Process synchronization Racing conditions, critical regions, mutual exclusion, inter-process communication. E. Deadlocks When and why deadlocks occur, examples and solutions. Part 3: Main and Virtual memory management A. Main memory management. Memory allocation algorithms, paging, swapping, segmentation. B. Virtual memory Paging, frame allocation, thrashing. Part 4:  Secondary Storage management A. File Management. File operations: creation, read/write, deletion. File Access methods, file management, memory allocation, integrity mechanisms. Directories/subdirectories. B. I/O Management Character code independence, device independence, efficiency, uniform treatment of devices, buffering. Disk: Seek time, latency, rotational delay. Seek time strategies and improvement, use of cache memory.

Purpose and Objectives: The course introduces the fundamental principles of algorithm design and analysis, focusing on both the practical techniques for solving computational problems and the theoretical foundations of algorithmic complexity.

Learning Outcomes: At the end of the course, students will be able to: (a) Choose correctly between different algorithms and algorithmic methodologies for solving computational problems. (b) Design and implement new algorithms for solving computational problems using various methodologies such as divide and conquer, dynamic programming, and greedy algorithms. (c) Analyze the complexity of the algorithms and estimate the computational cost of their software.

Course Content: The course covers the following topics: algorithm analysis, asymptotic analysis, recurrence relations, divide-and-conquer algorithms, dynamic programming, greedy algorithms, graph representation, graph search, minimum spanning trees, shortest paths, maximum flow, and NP-completeness.

Purpose and Objectives: The purpose of this course is to equip students with a comprehensive understanding of object-oriented programming (OOP) using the Java programming language, with a focus on both foundational principles and modern practices. The course aims to develop students’ ability to design, implement, and analyze object-oriented software systems that are robust, modular, and scalable. Through a combination of lectures, hands-on labs, and individual assignments, students will explore core concepts such as encapsulation, inheritance, and polymorphism, and learn to apply them effectively using Java. Emphasis is placed on practical problem solving, the effective use of the Java Collections Framework, proper exception handling, file I/O, and the development of graphical user interfaces. In addition, the course introduces advanced topics such as multithreading, generics, and software design patterns, preparing students for real-world development scenarios. Unified Modeling Language (UML) is used throughout the course to support object-oriented design and to strengthen students’ ability to model and communicate software architecture.

Learning Outcomes: Upon successful completion of the course, students shall be able to: (1) Understand and apply the core principles of object-oriented programming, including encapsulation, inheritance, polymorphism, and composition. (2) Design and implement modular, maintainable applications in Java using appropriate language features and object-oriented structures. (3) Work effectively with the Java Collections Framework and generics to manage and manipulate structured data. (4) Handle file input/output and exceptions to build reliable and robust applications. (5) Test and debug Java programs using appropriate tools and techniques. (6) Develop interactive programs with graphical user interfaces and event-driven behavior. (7) Understand and apply basic concepts of concurrent programming using threads. (8) Use UML diagrams to support the analysis, design, and communication of object-oriented software systems.

Course Content: The course covers fundamental and advanced concepts of object-oriented programming in Java, along with essential practices in software design and development. Java syntax, control structures, methods, arrays. Classes, objects, encapsulation, composition. Inheritance, polymorphism, abstraction. Interfaces, abstract classes, generics. Collections: lists, sets, maps, iterators, sorting. Exception handling. File input/output (I/O, NIO). Modularization and package management. Testing and debugging. Graphical user interfaces (GUI), event handling. Multithreading and concurrency. UML diagrams. Basic design patterns. Software development models

Course purpose and objectives: To learn multiprogramming and methods of synchronization and communication of processes and threads. To understand the mechanisms by which access to the various units and libraries of a Unix operating system is achieved using the programming language C and various shells, as well as the exact way in which Unix-based operating systems operate.

Learning outcomes: Basic knowledge of operation and programming of Unix-based operating systems. Knowledge of multiprogramming.

Course content: Introduction to Unix; the Unix file system; processes and threads: creation, synchronization, communication, signals; pipes; TCP/UDP sockets; mutexes; condition variables; regular expressions; shell scripts; filters.

Purpose and Objectives: Students should assimilate the necessary theoretical and practical knowledge about the various aspects of bioinformatics. Introduction to Biomechanics and Bioinformatics. Statistical significance of the alignment results. Searching databases for similar sequences, efficiency of related algorithms. Statistical analysis of DNA microarray experimental data. Special topics include, biomechanics and Biomaterials, bioprocessing and telemedicine, biomedical instrumentation, signal processing and imaging modalities. Introduction to the emerging medical device industry. Imaging with ultrasound, X-ray, CT, MRI, and positron emission tomography (PET). Role of laser beams in surgery. Endoscopy and endoscopy-assisted treatments. Cardiac devices (e.g. pacemakers). Principles of the nervous system. Understanding of the electrocardiogram, electroencephalogram and electromyogram. Principles of the nervous system. Understanding of the electrocardiogram, electroencephalogram and electromyogram. Principles of radiotherapy, telematics in health care. Therapeutic ultrasound and applications of therapies using thermal radiation. Pairwise sequence alignment and multiple sequence alignment algorithms. Protein classification and structure prediction.

Learning Outcomes: Gain comprehensive knowledge to the basic principles of biotechnology combined with basic principles of engineering in problems related to biology and medicine. Understanding the basic functions of the human body and how biological signals are generated. Apply basic engineering principles to biological signal retrieval and methods to problems in biology and medicine, the integration of engineering into biology, and new areas in industry. Introduction to bioinformatics, with pairwise sequence alignment and multi-sequence alignment algorithms. Statistical significance of alignment results. Phylogenetic prediction. Also obtain knowledge in Searching databases for similar sequences, efficiency of related algorithms. Protein classification and structure prediction. Statistical analysis of experimental DNA microarray data. Special topics include biomechanics and biomaterials bioprocessing and telemedicine biomedical instrumentation signal and image processing. Understanding of how to apply and utility of modern technologies in health care. Understanding of basic principles of standards and methods of medical data storage. Understand the principles of ultrasound, x-ray, CT, MRI, and PET imaging.

Course Content: The course is based on basic elements of Bioinformatics and has as its main objectives the following: The following chapters are covered: Module 1: Biomedical signals, systems and ultrasound basics. Module 2: Introduction to Biostatistics and Statistical Data Analysis, Biomedical Health Systems. Module 3: Health Information Systems, MRI and DICOM & PACS standard. Module 4: Medical Imaging (Processing, Analysis, Quality Assessment), technologies and applications. Digital radiography. Module 5: Human Brain, Brain Anatomy, Nervous System (CNS), X-rays. Module 6: Endoscopy, Heart and function and diagrams, Electrocardiogram, Pressure and function. Module 7: Medical Machines, EEG, Blood Pressure, Blood Pressure, Radiation devices. Module 8: Telemedicine, technologies and applications. Module 9: Biomaterials. Module 10: Biosensors, and statistical data analysis. Why do we need bionics? Module 11: 3D Printing, Introduction to molecular biology, analysis/representation. Natural diversity and variation. Module 12: Thermal Ablation, Treatment, Techniques.

Purpose and Objectives: Introduction to computer networks, focusing on the Internet. The course covers a wide range of topics, such as application layer protocols (e.g., Web, FTP, DNS), content distribution, client-server architectures and peer-to-peer architectures, data transport protocols TCP/UDP, reliable data transfer and congestion control principles, Internet congestion control, Internet routing principles, the IP protocol and network router architectures, Internet addresses, IPv4 and IPv6, wired and wireless access networks (e.g., Ethernet, DSL, WLANs), data link protocols.

Learning Outcomes: Knowledge of principles, operation and programming of computer networks with emphasis on the Internet.

Course Content: Introduction to computer networks, Internet structure, protocol layers, performance measures, basic concepts. Data link layer, local area networks. Network layer, IP protocol, routing, addresses. Transport layer, TCP/UDP protocols, congestion and flow control. Application layer, Client-Server and Peer-to-Peer architectures.

Purpose and Objectives: This course aims at the practical training and experimentation of students in computer networking problems. In particular, the course offers students the ability to connect local computer networks using switches, routers and hosts, and perform various experiments related to the TCP/IP protocol stack.

Learning Outcomes: Practical experience with concepts like addressing, subnetting, subnet masks, routing protocols (e.g., RIP, OSPF), multiple access protocols, routers, switches, hosts, as well as experience in router programming (Cisco IOS).

Course Content: Introduction to the laboratory hardware and software, the Cisco operating system (Cisco IOS). Single segment IP networks. Static routing. Dynamic routing (RIP, OSPF, BGP). UDP and TCP experiments and measurements on real networks.

Purpose and Objectives: The course provides a comprehensive introduction to the fundamentals of computer architecture, focusing on the design and behavior of computer systems. The course answers the “What” and “Why” of a computer system and focuses on the various techniques that increase its performance. The course emphasizes the relationship between architecture and software performance, as well as current trends and challenges in computer architecture research. The course teaches Instruction-level Parallelism (ILP), Data-level Parallelism (DLP), and Thread-level Parallelism (TLP). It also teaches the latest technologies such as Cloud Computing (at the architecture level) and the latest Domain-specific Accelerators, including GPU and AI accelerators. Each module will examine the most important techniques used today in existing systems as well as in research projects.

Learning Outcomes: Upon completion of the course, students will be able to: Understand and apply modern methodology for evaluating and comparing the performance of computer systems. Describe and explain the basic and advanced principles that govern the architecture of different types of modern processors and their impact on performance. Evaluate current trends and challenges in computer architecture. Answer the questions "What" and "Why" a computer system implements to increase its performance.

Course Content: The course covers the following content: Basic principles of quantitative design and analysis of computer systems. Parallelism at the instruction level, Branch Prediction, Dynamic Scheduling, Hardware Speculation. Parallelism at the data level, GPUs. Parallel systems, Multiprocessors, Shared memory and Cache coherence, Memory Consistency. Parallelism at the request level, Cloud Computing. Special Accelerators, Domain-specific Accelerators, AI and DNN Accelerators. Additional research/practical topics based on the evolution of technology (e.g. Processor Security, Quantum Computing).

Purpose and Objectives: To enable students to absorb the necessary theoretical knowledge about the various phases of software development, study the associated complexity and related problems, and identify the techniques and models available to address them.

Learning Outcomes: Upon completion of the course students shall be able to identify, distinguish and use the steps of the various phases for the analysis, specification, design and implementation of software systems, with additional knowledge of software project management.

Course Content: This course is an introduction to Software Engineering, a branch of Computer Science that deals with the theoretical approaches, methodologies and tools required to develop quality software systems within the agreed schedule and budget. The following chapters are covered: (1) Introductory concepts. (2) The software development process. (3) Software life cycle models. (4) Team organization and management of software projects. (5) CASE Tools: Computer Assisted Software Engineering (6) Testing (7) Introduction to Objects (8) Requirements (9) Specifications (10) Object-oriented analysis (11) Design (12) Implementation (13) Implementation and integration (14) Maintenance

Purpose and objectives: This course introduces the principles and practices of database systems. It covers the theoretical foundations of relational databases, practical skills for designing and querying databases, and insight into how modern database management systems (DBMS) operate.

Learning Outcomes: At the end of the course, students will be able to: (a) design an effective and efficient database. (b) execute SQL queries in a database for data insertions, updates, deletions, extraction, and analysis. (c) manage a database properly according to application requirements.

Course Content: The course provides a solid database background focusing on relational database management systems. Topics include data modeling, database design theory and methodology, entity-relationship model, normalization, data definition and manipulation languages (SQL), storage and indexing techniques, and transaction processing. The course also covers fundamentals of database management systems architecture and methods for database application development.

Purpose and Objectives: The aim of the course is to provide students with the knowledge and skills required to develop a complex web application.

Learning Outcomes: a) Students will gain knowledge of the basic design of Internet applications, covering protocols, standards, data management, multi-level architectures and security. b) They will learn how to design and implement reliable, easy to use, scalable, flexible, secure and sustainable web applications. c) They will study many academic articles that outline the systems philosophy underlying the WWW infrastructure. d) Through a series of web application exercises, students will acquire the programming knowledge and infrastructure needed to create an advanced web application.

Course Content: This course is an introduction to Internet technologies, which are now the main platform for software applications. We will cover a wide range of frameworks for developing web applications that are used in critical large-scale business applications, such as Online Social Networks, Banking, Online Stores, Online Video Services, etc. We will describe the basic principles of web applications such as protocols, standards, data management, multi-tier architectures and security. We will study how we can develop reliable, user-friendly, scalable, flexible and sustainable applications. Week 1: Introduction. Web servers and HTTP. Task: Install LAMP stack (Ubuntu 10.04LTS, Apache, MySQL, PHP / Python). Week 2-3: Interface design and application usability. HTML, CSS. Task: Configure / set up Apache web server and service a single HTML / CSS page. Week 4-6: Tissue Engineering. Model-View-Controller architectural pattern for web applications. PHP, server-side programming, sessions. Task: Set up PHP and serve a simple dynamic page. Multi-tier applications. Task: Configure MySQL database. Use PHP for dynamic pages loading data from the database. Week 7-9: Client-side programming. Javascript, AJAX, JQuery, XML. Task: Upgrading the hitherto deployed application with AJAX interactivity. Week 10: Web Application Security. OpenSSL and X.509 Public Key Certificates. Task: Convert the hitherto deployed application to HTTPS / SSL and close its security gaps.

Purpose and Objectives: The objective is to familiarize students with: 1) the latest hardware and software developments, system architecture and new programming examples (MapReduce), with emphasis on both speed performance and energy efficiency. 2) how traditional multiprocessors and multifunctional networks have been transformed into web-scale networks and clouds for ubiquitous use in the future Internet, including large-scale social networks and the Internet of Things that are rapidly emerging in recent years.

Learning Outcomes: At the end of the course students: 1) will gain deep technical insights on when and how to leverage the capabilities of modern parallel and distributed systems to serve the needs of their organizations. 2) will be able to discern what solution is the most appropriate for serving each need, depending on its specific characteristics, as well as the characteristics of the organization as a whole.

Course Content: Distributed System Models and Enabling Technologies, Computer Clusters for Scalable Computing, Virtual Machines and Virtualization of Clusters and Datacenters, Design of Cloud Computing Platforms, Service Oriented Architectures, Cloud Programming and Software Environments, Grid Computing and Resource Management, Ubiquitous Computing with Clouds and The Internet of Things.

Purpose and Objectives: Students shall apply in practice the necessary theoretical knowledge about the various stages of producing complete software systems through the development of a large system in groups.

Learning Outcomes: At the end of the course students shall develop the ability to apply practically and successfully all stages and phases of the analysis, specification, design and implementation of a large software system, along with its control and documentation, as well as practical software project management issues.

Course Content: This course is a natural follow-up to CEI 324 - Software Engineering and aims to assimilate and put to practice all theoretical approaches and methodologies used to develop quality software systems, through teamwork by developing a specific software product to serve specific needs of a customer in the local market and society, as well as the academic / research area. The work performed is monitored by the instructor through weekly meetings with each group separately (instead of lectures), during which issues related to the development process are discussed and analyzed, various related problems are presented and solved, while alternative strategies are considered. In each meeting, the teams deliver a progress report detailing the work carried-out since the previous meeting, monitoring the progress of the project based on the original schedule and Gantt chart, outlining the problems encountered and other issues to be discussed during the meeting. This document also contains a brief description of the work carried out by each member of the team, as well as the tasks to be undertaken and completed by the next meeting. During the implementation of the systems, metrics are recorded regarding the effort, cost, time, development process and software quality. Each group documents and delivers the following: Requirements Analysis Document, Technical Specifications Document, Design Document, Implementation Document, Installation and Operation Manual. Finally, the team trains the client and performs a first system maintenance / support cycle for a one-week period during which it detects and corrects any problems and errors arise, and records future extensions of the software system developed.

Purpose and Objectives: The course is an introduction of core concepts in modern artificial intelligence. We first introduce the concepts of supervised, unsupervised, and semi-supervised learning. Subsequently, we present the main biological inspiration of modern unsupervised data representation, namely Deep Learning. Further, we introduce basic supervised learning models. In addition, we elaborate on Collaborative Filtering algorithms, with emphasis on Matrix Factorization. We present the problems of multiple instance learning and semi-supervised, as they are treated in the context of the large margin framework. In all cases, we elaborate on techniques we can use to prevent overfitting, by means of appropriate use of regularization methods. Finally, we introduce reinforcement learning techniques, where agents learn optimal policies through dynamic interaction with their environment.

Learning Outcomes: The students will grasp a holistic view of the machine learning landscape. They will be able to discern among the main types of machine learning approaches, understanding their pros and cons, what types of problems they can solve each, and what one must be careful about.

Course Content: The module comprises a theoretical part and a series of laboratory exercises. THEORETICAL PART: Introduction, Elements of Machine Learning, Regression, Classification, Validation, Model Selection, Clustering, Feature Learning. LABORATORY: Solving simple classification, regression, and clustering tasks using Scikit learn.

Purpose and Objectives: This course aims to provide a thorough introduction to the foundational principles and core methodologies of cryptography and computer security. It is designed to equip students with the theoretical background and practical insights necessary to understand, implement, apply, and evaluate security practices and mechanisms in modern computing environments. The primary objective is to familiarize students with the essential concepts that underpin the design and implementation of secure systems, including both classical and modern approaches to cryptographic techniques and threat mitigation. Emphasis is given on developing the ability to reason critically about security requirements, understand threats, assess risks, and apply suitable cryptographic tools, mechanisms, and strategies to protect computing systems. In addition to the core material, the course offers a brief introduction to advanced topics such as post-quantum cryptography and homomorphic encryption, primarily to raise awareness of emerging challenges and developments in the field. The course also touches upon foundational privacy-related concepts within the broader context of security.

Learning Outcomes: Upon successful completion of the course, students will be able to: 1. Understand the fundamental principles of computer and network security, including the core security goals of confidentiality, integrity, and availability. 2. Describe and compare essential cryptographic primitives, such as symmetric and asymmetric encryption, hash functions, message authentication codes (MACs), and digital signatures. 3. Apply cryptographic protocols, including key exchange and digital certificates (PKI), within the context of secure communication, for secure system design. 4. Demonstrate awareness of selected advanced topics, such as post-quantum cryptography and homomorphic encryption. 5. Identify and analyze common security threats and attack vectors, including malware (e.g., viruses, worms, Trojan horses), spoofing and phishing, denial-of-service attacks, cross-site scripting (XSS), SQL injection attacks, and buffer overflow vulnerabilities. 6. Recognize evolving security threats and system vulnerabilities, and design secure systems/applications by selecting and applying appropriate defense strategies, including authentication, authorization, and access control. 7. Evaluate alternative cryptographic techniques and security mechanisms, considering trade-offs, performance, complexity, usability, and the value of protected assets. 8. Discuss basic privacy concerns in computer systems and understand foundational privacy-preserving practices.

Course Content: The course provides an introduction to the foundational concepts and techniques of cryptography and computer security. It begins with the basic principles and objectives of security, including confidentiality, integrity, availability, as well as authentication, non-repudiation, and accountability. It also provides an overview of security design principles and classical security models. Students are introduced to risk assessment and threat modeling techniques as a foundation for evaluating system vulnerabilities and mitigating potential attacks. Core cryptographic topics include symmetric and asymmetric encryption, hash functions, pseudorandom generators, stream ciphers, message authentication codes (MACs), and digital signatures. The course also covers key management practices and cryptographic protocols, including secure key exchange and the use of digital certificates within Public Key Infrastructures (PKI). Fundamental authentication and access control models and mechanisms are explored, along with models for authorization and identity management. The course addresses software and system vulnerabilities, including denial-of-service attacks, malware (e.g., viruses, worms, Trojans), phishing, spoofing, and common web security issues such as cross-site scripting (XSS), SQL injection, and buffer overflow attacks. Students learn secure coding practices and principles for reducing software-based risk, as well as browser security mechanisms such as the Same-Origin Policy. It also examines key defense mechanisms used in practice, including antivirus software, firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPS). Real-world security protocols such as TLS/SSL are introduced, alongside password-based authentication, password management and storage, and multi-factor authentication mechanisms. The course also explores social engineering techniques, human factors in security, and the challenges of balancing security with practicality and usability. Finally, the course includes a brief overview of selected advanced topics in cryptography, such as post-quantum cryptography, homomorphic encryption, and lightweight cryptographic methods, and introduces students to privacy-related concepts and privacy-preserving mechanisms and practices.

Purpose and Objectives: The course delves into the principles of designing and implementing distributed and networked operating systems for Machine Learning (ML). It emphasizes classical operating system design principles and describes the implementation of modern systems that form the backbone of big data processing and storage infrastructure in cloud computing. The course content includes the following topics: Virtual Memory, Process Management, and Inter-process Communication. The Unix operating system. Log-structured file systems. Distributed file systems. Virtual Machines. Topics covering the lifecycle of data analysis for ML, including data sourcing and preparation for ML, ML programming models, scalable model creation systems, and emerging topics such as ML system governance, explainability, and ethics (fairness, transparency, etc.).

Learning Outcomes: By the end of the course, students will be able to: (1) Describe the major design principles of distributed systems. (2) Understand and implement key techniques for large-scale distributed systems. (3) Comprehend the functioning of cloud computing and ML infrastructure from companies like Google, Amazon, and Facebook. (4) Read and understand research papers on computer systems engineering. (5) Implement and modify complex large-scale distributed systems for Machine Learning and evaluate their performance.

Course Content: (1) Process Management. The Unix Time Sharing System. (2) Memory Management. (3) File Systems. Virtual Machines: LFS, Xen. (4) Infrastructures for Big Data (Google): Google Filesystem, Google BigTable, Google Borg. (5) ML Lifecycle Overview. (6) Classical ML Training at Scale: Parameter Server, XGBoost. (7) Deep Learning Systems: TensorFlow, TVM. (8) System-assisted Feature Engineering and Model Selection: Cerebro, MSMS. (9) Data Sourcing and Preparation: TFDV, Snorkel. (10) ML Deployment; Clipper Online Prediction Serving System, Federated Learning.

Purpose and Objectives: This course delves into specific areas and advanced topics of Software Engineering with emphasis on three axes: 1. Software Engineering for Distributed Systems and the Cloud. 2. Process Mining. 3. Software Reuse

Learning Outcomes: (a) summarize the key principles, problems and challenges in software development for distributed systems and cloud computing; (b) consider the DevOps approach and evaluate the benefits of using it; (c) familiarize, develop and use service software (online and offline); (d) introduce and explain distributed transaction processing and software systems that use the Blockchain (e) introduce the main concepts and principles of process mining with real-world examples; (f) identify software development through reuse, and in particular scalable systems with software components.

Course Content: The course syllabus includes the following. Part A’ – Software Engineering for Distributed Systems and the Cloud. Chapter 1 - Distributed Software Engineering. Chapter 2 – Introduction to DevOps. Chapter 3 - Software Engineering and Cloud Computing. Chapter 4 - Service-oriented Software Engineering. Chapter 5 - Introduction to Blockchain Part B’ – Process Mining. Chapter 6: Introduction to Process Mining. Chapter 7: Process Modelling and Analysis. Chapter 8: Getting the Data. Part C’ – Software Reusability. Chapter 9 - Software Reuse. Chapter 10 - Component Based Software Engineering

Purpose and Objectives: The course explores the design and analysis of modern computer architecture, with an emphasis on high-performance and energy-efficient systems. Building fundamental computer architecture, students will delve into advanced topics such as multicore processing. The course also covers cutting-edge industry trends and research directions, including AI accelerators, GPU and TPU design, RISC-V, domain-specific architectures, and hardware security. Through a combination of theory, simulation tools, case studies, and discussions of scientific and research articles, students will gain a deep conceptual understanding and practical skills in evaluating and designing complex architectures that power modern computing systems.

Learning Outcomes: Upon completion of the course, students will be able to: 1) Describe and explain advanced principles governing the architecture of modern types of processors and optimizations, as well as their impact on application performance. 2) Evaluate current technologies in the field of computer architecture. 3) Understand, assess, and analyze scientific articles in the field of Computer Architecture. 4) Conduct research at an advanced undergraduate level through the development of fundamental research skills.

Course Content: Basic processor design principles, Parallelism and parallel architectures, Scalable Multi-core and Many-core Systems, Interconnection Networks and NoC (Network-on-Chip), Emerging and High-performance computing, GPU Architecture and GPGPU Programming, Specialized AI Accelerators, RISC-V Architecture and Open-ISA Revolution, Heterogeneous Computing, Accelerators and Domain-specific architecture, FPGAs in the Datacenter, Neuromorphic Computing, Quantum-Aware Architectures, In-Memory Computing, Security in architecture, Side-channel Attacks and Defenses, Trusted Execution Environments (Intel SGX, AMD SEV), Current research directions and trends, Open Hardware Movement and Hardware-Software Co-design, Approximate Computing.

Purpose and Objectives: Learning and understanding concepts, techniques, and systems related to scalable data processing using parallel relational database systems, NoSQL systems, and dataflow systems such as MapReduce and Spark.

Learning Outcomes: Students will learn about the functionality of modern systems and techniques for large-scale data management processing. Students will be able to select and utilize the appropriate tools depending on the type of analysis they want to perform and the structure of the data they want to process.

Course Content: The need to store and process massive amounts of data has led to the evolution of existing database systems while a new breed of data processing systems has emerged. This course covers a spectrum of topics from core techniques in relational data management to highly-scalable data processing using parallel database systems and distributed processing systems such as MapReduce and Spark. First, the course covers the basic principles in database query processing and optimization, including index structures, sort and join processing, query rewrites, and physical plan selection. Next, the course covers topics from parallel and distributed databases, including data partitioning and distributed join algorithms. Finally, the course covers scalable data processing systems and NoSQL databases (column, document, and key-value stores). The course material will be drawn from textbooks as well as recent research literature.

Purpose and Objectives: The course aims to emphasize in specific areas and advanced topics of Integrated Circuit Design and Testing such as: The design flow and implementation of hardware circuits and systems as well as the analysis and comparative evaluation of the various alternative circuit design architectures. Optimization techniques and their practical application for digital circuits and systems hardware design. Testing and testability of ICs, the valuation of ICs production lines (yield), the economics of ICs testing etc. Fault Models and faults detection as well design for testability (DFT) in ICs.

Learning Outcomes: The Learning outcomes of this course are: Evaluation and application of the various alternatives of digital systems hardware designs architectures design strategies and implementations for designing high speed, low-power and low-integration area digital circuits and systems. Comparative evaluation and application of optimization techniques at all levels of the design flow. Acquiring extensive experience in designing, optimizing and implementing hardware digital circuits and systems using VHDL through weekly mini lab exercises homework. Analysis and evaluation of various integrated circuits testing techniques, taking in consideration economic aspects of ICs design and implementations. Use and practical application of techniques for fault modeling and detection at logic level in ICs and techniques for design for testability (DFT).

Course Content: Basic and advanced techniques for developing, designing and testing integrated circuits. Digital design optimization techniques in terms of various designs parameters (speed, power, area) as well as practical approaches in testing the designed Integrated Circuits. Analysis of digital hardware design flow, design levels and implementations, Top/Down and Bottom/Up hardware design approach with examples. Timing and optimizations, Pipelined Digital Design for high throughput, Parallel Digital Design for low area consumption, Parallel, Algorithmic Loop Unfolded and Pipelined Digital Designs for low area consumption, Testbenches for hardware designs simulations, Challenges in 90nm Digital Hardware Design.  Digital Hardware Design with hardware description language VHDL. Extensive case study, analysis and optimized redesign of hardware designed cryptographic algorithm: Pipelined SHA-1 Hash function. Introduction to the need for testing, Failures, Defects and Fault Models, Stuck-at Faults, Simple and multiple stuck-at faults, Faults coverage, Bridging Faults, Logic fault simulation, Serial, parallel, deductive and concurrent fault simulators, Test patterns generation, Algebraic and algorithmic methods for test patterns generation, Design for testability, Observation and control points, Pseudoexhaustive testing, Scan path design technique.

Purpose and Objectives: Deep Learning 1 is the first part of a two-course sequence on deep learning, focusing on core concepts and techniques. This module covers the foundations of Deep Learning, enabling students to understand how to build and train neural networks effectively. Students will learn about fundamental neural network architectures, the principle of representation learning, training and optimization methods, and introductory knowledge of Convolutional and Recurrent Neural Networks. By the end of the course, students will be prepared with a solid base for more advanced deep learning topics in the subsequent module (Deep Learning 2) and for leading successful machine learning projects.

Learning Outcomes: By the end of the course, students will be able to: Describe and classify the fundamental design principles of deep learning systems and neural network architectures. Implement and use widely accepted techniques for constructing and training deep neural networks (including parameter tuning and basic regularization). Summarize and critically evaluate research articles on core deep learning topics. Apply deep learning methods to carry out a complete project, from problem formulation to model implementation and evaluation, on a practical task.

Course Content: Introduction to Deep Learning: Overview of deep learning history, significance, and typical use cases. Neural Network Basics: Perceptrons, activation functions, multilayer feed-forward networks, and the backpropagation algorithm. Fundamentals of Representation Learning: The principle of learning data representations (features) automatically, contrast with traditional feature engineering. Training Deep Neural Networks: Optimization basics for deep learning – stochastic gradient descent (SGD) and its variants; loss functions; practical considerations for training stability. Regularization Techniques: Strategies to prevent overfitting in neural networks (e.g., L2 regularization, dropout, early stopping) and their impact on model generalization. Optimization and Tuning Methods: Hyperparameter tuning (learning rates, batch size, etc.), initialization strategies, and normalization techniques such as batch normalization to speed up training. Convolutional Neural Networks (CNNs): Principles of convolutional layers and pooling, classic CNN architectures for image recognition, and understanding how CNNs extract spatial features. Recurrent Neural Networks (RNNs): Sequence modeling with RNNs, vanishing/exploding gradient issues, and an introduction to gated units (LSTM/GRU) for handling long-term dependencies.

Purpose and Objectives: Deep Learning 2 is the second part of the deep learning series, concentrating on advanced topics and state-of-the-art techniques in the field. Building on the foundation from Deep Learning 1, this module introduces students to cutting-edge neural network architectures and methodologies. Students will explore modern developments such as transformer models and self-attention mechanisms, diffusion-based generative models, and other advanced generative architectures. The course also covers sophisticated regularization and optimization strategies, approaches for training large-scale models, and examines current state-of-the-art applications of deep learning in industry and research. The objective is to enable students to understand and apply these advanced techniques, preparing them to tackle contemporary research problems and to design innovative deep learning solutions.

Learning Outcomes: By the end of the course, students will be able to: Describe advanced deep learning architectures (including transformers and other novel network structures) and explain the principles behind their design and functionality. Apply and implement state-of-the-art deep learning techniques, such as generative models and advanced regularization methods, to solve complex problems. Critically evaluate modern deep learning research publications, and effectively present and discuss the findings of advanced research articles in the field. Develop and adapt deep learning solutions for large-scale or cutting-edge applications, demonstrating the ability to handle the practical challenges of training and deploying complex models.

Course Content: Transformer Models and Self-Attention: In-depth study of transformer architecture and self-attention mechanism; transformer-based models for sequence learning (e.g., the "Attention is All You Need" framework, BERT/GPT family models). Generative Deep Networks: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs); principles of deep generative modeling and examples of how these models synthesize data. Diffusion Models: Introduction to diffusion-based generative models and related approaches for high-quality data generation (e.g., Denoising Diffusion Probabilistic Models and their applications in image/audio synthesis). Advanced Regularization Techniques: Cutting-edge methods to improve generalization and robustness in deep learning (beyond basics) – for example, data augmentation strategies, adversarial training for robustness, and other recent regularization approaches suited for large models. State-of-the-Art Applications: Exploration of current frontiers and applications of deep learning, such as large language models in NLP, advanced computer vision systems, deep learning in reinforcement learning and other domains. Discussion of ethical considerations and interpretability as relevant to these cutting-edge applications.

Purpose and Objectives: This course focuses on the key concepts and practices involved in the rigorous execution of a data science project. Students will be introduced to the data science process and lifecycles typically undertaken for the realisation of a data science project. Students will also become familiar with the processes for collecting, manipulating and cleaning data, while gaining experience in assessing the quality of data. Relational databases, SQL, and other database paradigms, such as NoSQL, are covered as a way of storing and accessing data. Students will be introduced to data distributions and statistical analysis in data science, including correlation, confidence intervals and inferential statistics, and how to use them in a programming environment. Finally, students will gain experience with different machine learning problem domains (e.g., supervised, unsupervised, and reinforcement learning) and develop an appreciation of machine learning models available, in addition to ‘hands-on’ experience for designing, developing, and deploying appropriate data pipelines that integrate these models. Best approaches and ethics in data science will also be addressed. On completing this course, students will have developed a strong foundation of applications-focused knowledge and practical skills stretching across the entire data science pipeline; they will be able to design, develop, deploy, and evaluate their own data science solutions.

Learning Outcomes: By the end of the course, students will be able to: 1) Distinguish between different types of data that are generated in science, engineering and design, and employ strategies for ensuring data quality. 2) Retrieve data from a variety of different data sources in a variety of different formats. 3) Apply inferential statistics and statistical procedures to test hypotheses about features and relationships within data sets. 4) Use appropriate visualisations to present and explore data sets. 5) Use databases, both relational and of other paradigms, to store and query data. 6) Implement supervised and unsupervised machine learning algorithms. 7) Identify the ethical concerns regarding the provenance of data, the privacy of individuals, and the impact data analytics can have on society, and apply topics from the code of ethics of a professional data protection body.

Course Content: Week 1: Introduction to data science, data science lifecycles and paradigms. Week 2: Data collection, exploratory data analysis, data visualisation. Week 3: Structured and unstructured data, relational databases. Week 4: Database normalisation, Structured Query Language, Database APIs, NoSQL. Week 5: Data processing and data integrity. Week 6: Probabilities, central limit theorem, confidence intervals, covariance, correlation. Week 7: Inferential statistics (parametrics and non-parametric). Week 8: Machine Learning introduction and supervised learning (regression). Week 9: Supervised learning (classification). Week 10: Unsupervised learning (clustering, dimensionality reduction). Week 11: Data science ethics, standards/laws/guidelines on data. Week 12: Data science models deployment and evolution. Week 13: Project presentations

Purpose and Objectives: Understand the function and characteristics of semiconductors for specific applications. The student should fully analyze the basic circuits with diodes and transistors.

Learning Outcomes: Acquire comprehensive knowledge on the analysis, operation, design and implementation of circuits with diodes, and transistors.

Course Content: 1) Semiconductor diodes 2) Bipolar junction transistors 3) Field effect transistors 4) Bias and stability 5) Small signal amplifiers 6) Differential Amplifiers 7) Frequency response.

Purpose and Objectives: The objectives of this course are: 1) Students to gain hands-on experience in digital circuit design and in usage of integrated ICs both in educational boards and with CAD tools in FPGAs. 2) Students to acquire practical knowledge and to practice the necessary design approaches for designing systems such as reusability, hierarchical design, abstraction levels, etc. 3) Students should acquire practical knowledge and practice simulation techniques and debugging, during the design phase. 4) The study and understanding of the basic functional principles of Digital Systems and Circuits, their parameters, their design constraints and the various choices and technologies for their implementation and design. 5) The use and understanding of the structure and utilization of FPGA devices, the schematic description of combinational and sequential logic circuits, as well as the comparison to the description of digital circuits and systems with hardware languages (HDL). 6) Implementation in educational boards as well as with CAD tools in FPGAs, while understanding the basic operation principles, of combinational and sequential logic design, as well as their optimal design and the various implementation parameters. 7) Analysis, design and implementation of more complex designs like counters (synchronous and asynchronous) and ALUs both in educational boards and in FPGAs with CAD tools.

Learning outcomes: The objectives of this course are: 1) Students to gain hands-on experience in digital circuit design and in usage of integrated ICs both in educational boards and in FPGAs with CAD tools. 2) To acquire practical knowledge and to practice the necessary design approaches for designing systems such as reusability, hierarchical design, abstraction levels, etc. 3) Students should acquire practical knowledge and practice simulation techniques and debugging during the design phase. 4) The study and understanding of the basic functional principles of Digital Systems and Circuits, their parameters, their design constraints and the various choices and technologies for their implementation and design. 5) The use and understanding of the structure and utilization of FPGA devices, the schematic description of combinational and sequential logic circuits. 6) Implementation in educational boards as well as in FPGAs with CAD tools, while understanding the basic operational principles, of combinational and sequential logic design, as well as their optimal design and the various implementation parameters. 7) Analysis, design and implementation of complex systems like counters (synchronous and asynchronous) and ALUs both in educational boards and in FPGAs with CAD tools.

Course Content: Introductory course to lab equipment, familiarization with basic logic gates and design and implementation of combinational circuits with SSI and MSI ICs. Design and implementation of Multiplexers, Encoders, Decoders, Demultiplexers, Comparators, Adders etc, Familiarization with Flip Flops: S-R flıp flop, D flıp flop, J-K flıp flop. Preset and Clear functıons. Design and implementation of digital sequential circuits with SSI and MSI ICs in educational breadboards as well as Synchronous and Asynchronous Counters. Introductory course to: design software ALTERA Quartus II, Programmable logic devices and implementations of digital circuits on FPGAs, FPGSs vs ICs, Schematic Description vs HDL Description, Simulation and Debugging in Altera Quartus II, FPGA Downloading. FPGA implementation of combinational circuits: Adders, Comparators, Multiplexers, Encoders/Decoders, Hierarchical and Cascade Design. Design and FPGA implementation of sequential circuits: S-R flıp flop, D-Type flıp flop, J-K flıp flop, Abstraction levels and reusability. Asynchronous and Synchronous counters. Hierarchical design and FPGA implementation of Arithmetic and Logic Unit (ALU). Advantages and disadvantages of levels of abstraction: comparison of ALU alternative designs.

Purpose and Objectives: Students should assimilate the necessary theoretical and practical knowledge about the various phases in digital processing of one-dimensional and two-dimensional digital signals. Design and analysis of digital systems and their applications / implementations using programming. The course is offered through a combination of lectures, exercises and programming workshops.

Learning Outcomes: Gain comprehensive knowledge of signal analysis, specifications, operation, design, analysis and programming of digital systems. Also obtain knowledge of discrete-time systems and properties, continuous and discrete Fourier transform and applications. Introduction to the design of finite and infinity digital filters (FIR and IIR) impulse response.

Course Content: The course is based on the basic elements of the analysis and processing of digital signals and systems, offered in the prerequisite course, and has as its main objectives the following: Recapitulation on signals, systems, sinusoidal sampling. Aliansing and folding. Sampling theorem. Discrete-time systems and properties. Impulse response. Convergence. Application in MATLAB environment. Series, Fourier transform and properties. System Frequency Response. Finite and Infinite Impact Response Systems (FIR, IIR). Applications in MATLAB environment. Frequency response of FIR and IIR systems. Linear phase. Frequency sampling. Discrete Fourier transform. Applications in MATLAB environment. Fourier discrete transform properties and applications. Signal spectrum analysis. Circular convolution. Introduction to digital filters. Design of FIR filters using windows. Applications in MATLAB environment. Design of FIR filters by frequency sampling. Z-Transform. Implementation in MATLAB environment. Design of IIR filters. Implementation of discrete time systems. The following chapters are covered: 1. Sampling of sinusoids signals. Sampling theorem. 2. Discrete-time systems and their properties. 3. Fourier Series and Fourier Transform and Properties. Finite and Infinite convolution (Response) Systems (FIR, IIR). 4. Frequency sampling. Discrete Fourier transform. Circular convolution. 5. Introduction to digital filters. Design of FIR filters using windows and frequency sampling. 6. Transformation Z. 7. Design of IIR filters.

Purpose and Objectives: Understanding the basic properties of light, optical waveguides, and more generally the basic concepts of photonics.

Learning Outcomes: Acquire basic knowledge of photonics with regard to the visual and the possibility of further understanding applications in engineering science.

Course content: 1. Introduction to Photonics and Optical Communication Overview of photonics and the applications in different areas such as information technology and communications, healthcare, life sciences, optical sensing, lighting, energy and manufacturing. The evolution of light-wave systems will be discussed before the basics of optical communications systems will be introduced. 2. Nature of light and the production of EM radiation for photonics applications i) Wave Nature of Light Light waves in homogeneous media. Refractive index. Group velocity and group index. Magnetic field, Irradiance and Poynting vector. Snell's law and total internal reflection. Fresnel's equations. Multiple interference and optical resonators. Temporal and spatial coherence. Diffraction principles. Polarisation. Methods to define the characteristics of light mathematically (Stokes parameters, Jones vectors & matrices) and how to determine these characteristics. ii) Optical sources and lasers Semiconductor science and materials. Semiconductor concepts and energy bands. Direct and indirect band-gap semiconductors: E-k diagrams. P-n junction principles and band diagram. Principles of light emission and amplification in lasers, lasers (pulsed and CW), light emitting diodes (LEDs), semiconductor lasers (edge emitting lasers and VCSELs). Steady-state rate equation. Single frequency solid state lasers. Optical amplifiers. 3. Optical waveguides The propagation of light in optical waveguides. Dielectric waveguides and optical fibres. Symmetric planar dielectric slab waveguide. Modal and waveguide dispersion in the planar waveguide. Step index fibre. Numerical aperture. Dispersion in single mode fibres. Bit-rate, dispersion and optical non-linearities, Electrical and optical bandwidth. Graded index optical fibre. Attenuation in optical fibres-light absorption and scattering. Fibre manufacture. 4. Optical detectors and receivers The detection of light and the demodulation of light including photoconductors, photodiodes and receiver systems. Principle of the p-n junction photodiode. External photocurrent. Absorption coefficient and photodiode materials. Quantum efficiency and responsivity. The p-i-n, avalanche and heterojunction photodiodes. Phototransistors. Photoconductive detectors and photoconductive gain. Noise in photodetectors. Generic system issues: sources of noise and signal-to-noise ratio, limitations on temporal response and effective bandwidth. 5. Imparting information onto EM radiation & communication techniques i) Basic modulation principles Polarisation and modulation of light. Light propagation in anisotropic media: birefringence. Birefringent optical devices. Optical activity and circular birefringence. Electro-optic effects. Integrated optical modulators. Acousto-optic modulator. Magneto-optic effects. Non-linear optics and second harmonic generation. ii) Modulation Acousto-optic and electro-optic techniques, LED switching, analogue and digital techniques using lasers, AM, FM, phase modulation techniques 6. Applications for light-wave systems A summary of important concepts of digital communication including base band and broadband digital transmission, bit error rate, bit group error rate and time division multiplexing (TDM) and wavelength division multiplexing (WDM). Trends and new directions in photonic applications i) Noise and detection Noise arising from the properties of fibres, transmitters, receivers and amplifiers as well as the determination of the bit error rate. ii) Optical MUX and DEMUX The operating principle of multiplexers and demultiplexers. Different optical devices, essential to optical networks, optical amplifiers, polarisation control devices, optical isolators, optical filters and diffraction gratings, modulators and switches. iii) Optical systems design Design process for a point-to-point optical links.

Purpose and Objectives: Its general objective is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer Science and Engineering. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods, and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Science and Engineering aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Engineering.

Learning Outcomes: By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Science, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. Have developed sufficient individual, academic research skills to fulfill their respective obligations in the Computer Science and Engineering curriculum

Course Content: Computer Science and Engineering Degree - what does it mean? The Cyprus University of Technology - identity. Take notes. Types of learning. Bibliographic references, paraphrases, plagiarism. Write a summary. Library use skills. Dictionary skills (monolingual, bilingual). Career in Computer Engineering. Familiarity with textiles from Computer Science. Digital knowledge. The Information Age. Graphical User Interfaces. Multimedia. Ducts. The Internet. Communication systems. Robotics.

Purpose and Objectives: General objective of the second part of the special course on Computer Engineers is to enable students to communicate competently at a B1-B2 level of the Common European Framework of Reference (CEFR) for Languages on issues related to students’ social and student life (university, curriculum, field of study), as well as on issues related to their future professional careers (occupations, short CV, etc.). Computer Assisted Language Learning (CALL) and culture awareness development are done through communicative situations deriving from authentic-like communication situations students may experience in their lives, during their studies and professional careers. Language competence is acquired through the use of text types, scenarios, and roles which promote the production and understanding of spoken and written language related to the topics covered. The course is based on post communicative era method, a student-centered, blended learning approach, and aims to familiarize students with authentic reading material related to general topics such as university student life, learning styles and language learning, academic topics such as how to study effectively, take notes and use the appropriate reference style and to specific topics related to the field of Electrical Engineering and Computer Science and Engineering. The program builds on contemporary learning theories such as constructivism and social constructivism and student-centered teaching methods and develops both student language skills and transferable 21st century skills such as communication, use of new information and communication technologies, collaboration, creativity and innovation, critical thinking and intercultural awareness as well as skills such as autonomous and lifelong learning. In particular, the course in Computer Science and Engineering aims to make students capable of managing English Bibliography, as well as understanding and editing material related to Computer Science and Engineering.

Learning Outcomes:   By the end of the course, students shall be able to: Derive communicative circumstances, scenarios and roles through authentic situations of their social and student life (university, building access guidelines, their field of study), as well as topics related to their future career (workplace, occupations) industry, duties). Communicate with sufficient level B1-B2 proficiency of the Common European Framework of Reference (CEFR) for the languages (production and comprehension of oral and written language) on the above topics, through relevant and relevant textual types, scripts, and roles. Understand English-language written texts on Computer Science, Engineering, various new technologies, and begin to develop the ability to critically analyze these texts. Have developed oral comprehension skills and adequate attendance of English language lectures and seminars by foreign visitors to the Department. Have developed sufficient individual academic research skills to fulfill their respective obligations in the Computer Science and Engineering curriculum

Course Content: Computer architecture; Computer applications; Data Security; Computer Support; Peripheral devices; Websites; Artificial Intelligence; Hackers and viruses

Course Objective: Upon successful completion of the course students are expected to be able to: know and describe what is a cell, tissue, organ and what are the main cellular functions of a living organism; distinguish similarities and differences between different biological systems; convert nucleic acid sequences to another nucleic acid and / or protein sequences. Recognize and explain the importance of Evolutionary Theory for understanding all the life sciences. Know about the interdisciplinary link between Life Sciences and Computer Science and Engineering.

Learning Outcomes: Gaining basic knowledge in the major life sciences modules with emphasis on the creation of nuclear knowledge and skills for future study by biological students with applications in engineering science.

Course Content: The following chapters are covered: The beginning of life, features of life. Chemistry of life. Enzymes and metabolism. Cell, Photosynthesis, Cellular respiration, Tissues and organs. Expression of genetic material. Cell cycle (mitosis – meiosis). Development and differentiation. Infectious diseases. Genetic diseases, Cancer. Genetics – Genetics Exercises. Evolution and natural selection. Synthetic biology.

Purpose and Objectives: The purpose of the course is to assimilate basic concepts of the infinite and integral calculus of real functions of a variable and apply them to problems of engineering science.

Learning Outcomes: To develop a good general background so that students can then deepen their knowledge in science and engineering.

Course Content: Functions. Composition of functions. Inverse functions. Limits. Continuity theorems of continuous functions. The derivative function definition of the derivative function, derivatives of trigonometric functions. Differentials. Applications of the derivative (maxima and minima, concavity, L’hospital’s rule). Integration. The indefinite and definite integral. Methods of integration. Applications of the definite integral (areas, volumes, lengths of plane curves, etc.). Complex numbers. Differential equations of first order. Sequences of real numbers. Convergence of a sequence. Infinite series. Convergence tests (the integral test, the comparison, ratio and root tests). Alternating series. Absolute and conditional convergence. Power series. Radius and interval of convergence. Maclaurin & Taylor series. Applications using Maclaurin series (sums, limits, solution of differential equations in series forms, etc.).

Purpose and Objectives: The main objective of the course is to ensure a broad introduction to physics at the academic level and to develop students' natural intuition in macroworld problems. The specific objectives of the course are: (1) Providing students knowledge of Newtonian Engineering. Students will acquire several problem solving skills while developing their physical intuition. (2) Introduction to the Basic Principles of Thermodynamics so that students are thoroughly familiar with key concepts such as heat, temperature, thermal properties and the three thermodynamic axioms.

Learning Outcomes:   Upon successful completion of the course, students shall: (1) be able to apply the theory to solve any Neutron Mechanics and Thermodynamics problem. (2) be convinced that physics is important and directly related to what we see around us. (3) develop physical intuition and ability to build problem solving models.

Course Content: Kinematic, Newton's Laws, Projectile motions, Energy, Energy Conservation. Momentum and Collisions. Solid body rotation, rotating reference systems. Solid body movement. Gravitational attraction. Oscillations. Mechanics of fluids. Wave motion. Temperature. Ideal Gases, First Thermodynamic Law, Kinetic Gas Theory, Thermal Engines, Entropy, Second Thermodynamic Law, Third Thermodynamic Law.

Purpose and Objectives: To understand the importance and role of Calculus and Differential Equations in all the sciences and especially in Electrical Engineering, Computer Engineering and Computer Science, as well as to become fluent in the various of techniques for solving problems in these areas.

Learning Outcomes: It is expected that the student shall be able to: (1) have full knowledge of the theoretical foundations of the subject (that is to be able to state and reproduce the definitions, the notation, the properties, the theorems), (2) translate the relations between different variable quantities in a specific problem into mathematical terms, (3) construct and solve the appropriate Differential Equation that governs a specific problem, (4) investigate the true meaning of the solution and (5) determine whether it is acceptable based on the fundamental laws of Physics, (6) define functions of many variables, graphically represent them, calculate their limits and determine whether they are continuous, (7) calculate partial derivatives of functions of many variables, and apply them to solve maximization and minimization problems, (8) calculate multiple integrals and use them to find areas and volumes, calculate the mean, find moments, centrifugal and surface areas.

Course Content: (i) Differential Equations: (1) Basic characteristics of differential equations (DE). (2) Simple DE of first and second order, applications (Newton’s equations.) (3) General study of linear DE. (4) Linear DE with variable coefficients, method of power series (applications to Bessel and Legendre equations). (ii) Calculus: (1) Functions of several variables. (2) Partial derivatives. (3) Applications of partial derivatives. (4) Multiple integrals. (5) Applications of integrals.

Course Purpose and Objectives: To give a broad knowledge as to basic concepts of linear algebra in relation to matrices and solutions of linear systems as well as basic complex analysis, which can become tools of better understanding and solving related engineering applications.

Learning Outcomes: Upon successful completion of the course, the student will have the knowledge and skills required to: 1) Solve linear systems using various methods: Gauss elimination, Cramer rule, inverse use; 2) Calculate the determinant and the inverse of a table; 3) Define the fundamental areas of a table, its dimension, its eigenvalues and their corresponding eigenvectors; 4) Specify the table representing a linear representation on a given basis, as well as its kernel and image; 5) Operate with complex numbers, converts complex numbers into polar form; 6) Calculate boundaries, decide on the continuation of complex functions; 7) Define complex representations of functions such as power lines, Laurent series, compute residues; 8) Calculate complex integrals using residues.

Course Content: Matrix algebra: definitions, properties, special matrices, Determinants, Sarrus rule, cofactors, row reduction, Inverse matrix, systems of matrix equations, Systems of linear equations, Cramer’s rule, Gaussian reduction, matrix method, Vectors: definitions, properties, Linear spaces: subspaces, linear Independence, basis, rank and nullity, Linear transformations, The eigenvalue problem: eigenvalues, eigenvectors, quadratic forms, Linear algebra applications, Singular value decomposition, Complex numbers: algebra, geometry, coordinate systems, Complex functions: definitions, limits, differentiation, power series functions, integration, Contour integrals, residuals.

Purpose and Objectives: The course aims to enrich students' knowledge in the organization of computer hardware (microprocessors and memory structures) and computation (Turing machines, finite state machines, normal languages, frameless grammars). Introduction to modular programming. Presentation of the process of development of functional and procedural software and introduction of the basic principles programming and program design using the C language. A general introduction of the C language focusing on data types, control structures, iterative structures, functions, tables, alphanumeric sequences, pointers, and recursion. The course includes a number of programming exercises and a semester paper.

Learning outcomes: Upon successful completion of the course, the student should: (1) Know the historical overview of the evolution of computers. (2) Know the organization of computer hardware. (3) Be aware of the different methodologies and their differences in programming as well as the generations of programming languages.

Course content: Topics to be considered include: Algorithms, C commands such as if, if/else, while, do/while, for, etc. as well as functions, various libraries, header files, random numbers, storage classes and scope rules, recall, tables and fonts, multidimensional tables, indexes, income/expense formatting and file editing.

Purpose and Objectives: This course aims to further enrich students’ knowledge in object-oriented programming with C++. The course teaches C++ starting from simple concepts such as classes, objects, inheritance, I/O flow classes, reference to C++ types, markers, struct, etc. The concepts of complexity and overload of operators are also taught. MODULES-CAPITALS: Classes, derivative classes, input/output classes, substructs, pointers, inheritance, function and operator overload, complexity.

Learning Outcomes: After the successful completion of the course, the learner should: Know the difference between object-oriented and structured programming. Be aware of concepts such as classes, objects-member functions, and member data. Know the differences of access identifiers (public, private, protected) and what is constructor and destructor. Be able to write the classes in a different file to create a project by separating the programs into interface, implementation and client for reusability purposes. Be aware of the concepts like encapsulation, inheritance, polymorphism and virtual methods. Can solve problems using control commands, with pointers, fonts, files, tables vectors, and functions. Know how to use most of the libraries available in C++.

Course Content: The topics to be examined include: Classes and all general concepts of object-oriented programming such as encapsulation, inheritance, polymorphism, and virtual methods. All C++ commands, such as control commands, functions, various libraries, random numbers, storage classes and scope rules, recursion, tables and fonts, multi-dimensional tables, pointers, format input/output and files.

Purpose and Objectives: To give a wide knowledge to engineers as to basic concepts on signals and systems as well as the connected transformations, which can become tools for better understanding and solving related applications.

Learning Outcomes: Acquisition of additional significant background for use in engineering applications.

Course Content: Introduction to signals, Introduction to systems, Fourier expansion and series, Fourier transform and applications, Fourier transform of discrete signals, Laplace transform and applications, z transform.

Purpose and Objectives: The course presents in detail the basic operating structure of computer systems with a single core processor and introduces computer engineering. This course answers the question "How" a computer system is implemented and focuses on the physical implementation and the actual details of the hardware that implement architectural concepts/techniques. It presents the basic building blocks of a computer system: arithmetic and logic units, control units, memory system, input/output system, data bus. It presents the RISC Central Processing Unit (CPU) using the MIPS architecture and the RISC ISA (Instruction Set Architecture) as a basis. It presents computer arithmetic, pipelining, the memory hierarchy (Main Memory, Cache, Virtual Memory). And finally, it presents the input/output units of a computer (Storage, Networks, Peripherals).

Learning Outcomes: Upon completion of the course, students will be able to: i) Describe the basic building blocks of a computer, such as the Central Processing Unit (CPU), memory, input/output devices, and how these components interact to perform calculations. ii) Program in Assembly language, which is a symbolic representation of machine code. iii) Describe the various levels of memory in a computer, such as cache, main memory, and secondary storage, and how these levels work together to provide fast and efficient access to data. iv) Describe the various technologies used to connect peripheral devices, such as keyboards, mice, monitors, and printers, to the computer. v) Answer the question "How" is a computer system implemented and operates in hardware.

Course Content: The course covers the following content: Abstraction and Computer Technology; Machine Language, Instructions, MIPS; Computer Arithmetic, Integer and Floating Point Numbers, Addition, Subtraction, Multiplication, Division; Computer Performance Analysis; Central Processing Unit (CPU), Datapath and Control; Pipelining, Data, Structural and Control Hazards; Memory Hierarchy, Main Memory, Cache Memory, Virtual Memory; Input/Output Devices, Permanent Storage, Interfacing with CPU and Other Peripherals.

Purpose and Objectives: The course provides a broad and systematic introduction to the basic principles and advanced concepts of Probability Theory and Random Processes, with particular emphasis on the tools and applications needed in the field of Electrical Engineering and Computer Science and Engineering.

Learning Outcomes: Understand and apply Probability Theory concepts to solve practical and theoretical engineering problems. Use of theoretical tools to explore the properties of random variables. Analysis of random processes and their application in many different areas such as signal processing in communication systems, study of system reliability, automatic control, etc.

Course Content: (1) Basic concepts-probability axioms, Introduction, Sample Space, Events, Mathematical Probability, Probability Axioms, Conditional Probability, Independence. (2) One-dimensional Random Variables, Random variables, Distribution function, Discrete random variables, continuous random variables, functions of random variables, mean value, dispersion. (3) Important Random Variables and their Properties. Discrete: Bernoulli, Binomial, Poisson, Geometric. Continuous: Uniform, Normal, Exponential, Gamma. Central Limit Theorem. (4) Multidimensional Random Variables Two-dimensional random variables, covariance and correlation coefficient. (5) Conditional probabilities and independent random variables. (6) Introduction to stochastic processes Random Signals, Mean Correlation and auto-covariance Functions. Poisson's process. (7) Autocorrelation and Power spectral density. Gauss stochastic process. Noise: Shot Noise, Thermal Noise, White Noise. (8) Response of linear systems to stochastic inputs.

Purpose and Objectives: This course extends into the field of electric power systems based on the course EEN320. It focuses on the use of computational methods for the analysis of electric power systems in steady-state as well as during symmetrical and asymmetrical faults. It proceeds to provide the fundamental understanding of protection devices operation and selection.

Learning Outcomes: On completion of this course, students should be able to: Understand the fundamental computational methods to analyze the steady-state and under-fault operation of electric power systems; Understand and analyze the basic power transfer limits in electric power systems; and, Understand the fundamentals of protection devices and protection selection.

Course Content: The course consists of these parts: 1. Revision of power engineering fundamentals (per-unit, line/transformer/generator/load models) 2. Fundamentals of power system operation (Surge impedance loading, lossless line, efficiency, loadability, maximum power over line, P-δ and P-V characteristics, Ferranti effect) 3. Power flow analysis (nodal admittance matrix, NR-method, fast decoupled, DC power flow) 4. Unbalanced operation (symmetrical components) 5. Fault analysis (modeling line/transformer/generator during fault, solid faults) 6.              Protection fundamentals (fundamentals, structure, overcurrent, distance, differential, digital relays, protection design and coordination).

Purpose and Objectives: This course provides an advanced understanding of power electronic systems, covering power semiconductor devices, converter topologies, circuit analysis, and practical applications. Building on prior knowledge of electronics and circuit theory, students will develop the ability to analyse, design, and optimise power electronic circuits. A problem-based learning approach, combined with simulations, enables students to apply theoretical concepts to practical scenarios, solve complex engineering problems, and validate circuit designs. Through a blend of theoretical analysis, problem solving, and hands-on application, students will gain a comprehensive perspective on power electronics, preparing them for both industry and research roles. With a strong emphasis on analytical thinking and simulation-driven design, this course equips students with the expertise needed to tackle real-world engineering challenges in power electronics. Objectives: Introduce students to the fundamental concepts and techniques used in modern power electronics. Provide insight into the selection and application of semiconductor power devices in real-world systems. Familiarise students with typical power conversion circuits and their role in electrical energy processing. Encourage the use of simulation tools as part of the design and analysis process for power electronic circuits. Expose students to the challenges and considerations in designing efficient and reliable power converters. Provide an overview of emerging trends and technologies shaping the future of power electronics.

Learning Outcomes: By the end of this course, students will be able to: 1. Evaluate the properties, operation, and performance trade-offs of power semiconductor devices, including diodes, MOSFETs, IGBTs, and wide bandgap semiconductors. 2. Apply the principles of power semiconductor switches to design and optimise power electronic circuits. 3. Analyse different power conversion topologies (AC/DC, DC/DC, DC/AC, AC/AC, and active filters) and assess their efficiency and performance in various applications. 4. Interpret and generate waveforms and current characteristics to assess the behaviour of power electronic circuits. 5. Use advanced simulation tools (e.g., SPICE-based modelling) to design, verify, and optimise power electronic devices or systems. 6. Solve complex engineering problems that integrates theoretical concepts with practical implementation. 7. Design and assess power electronic circuits for renewable energy applications, particularly in photovoltaic and wind turbine systems. 8. Investigate and discuss emerging trends and advancements in power electronics, including novel semiconductor technologies and system architectures.

Course Content: Semiconductor Power Switches, Power semiconductor materials and properties, Operation and characteristics and performance metrics for switching devices, The ideal power switch and fundamentals of Power Semiconductor Devices, Power Diodes and Rectifiers, MOSFETs for Power Electronics, Insulated Gate Bipolar Transistors (IGBTs), Wide Bandgap Semiconductors Inverter Topology and Function, Five Basic Power Electronics Circuits (AC / DC, DC / DC, DC / AC, AC / AC and Active Filters, Generation and description of relevant waveforms and currents. Power Circuit Analysis, Spice modelling, simulation and analysis of power electronic circuits Applications, Limited presentation of applications in Photovoltaics and wind turbine systems New Trends, Descriptively new trends and new systems in Power Electronics.

Purpose and Objectives: This course is designed to provide engineering students with an in-depth and critical understanding of renewable energy technologies, with a strong focus on photovoltaic and wind energy systems. The course aims to develop the ability to critically assess, design, and implement renewable energy systems, while addressing real-world engineering constraints and sustainability considerations. The main objectives are to: Deepen students' theoretical and practical understanding of key renewable energy technologies. Develop critical thinking and design capabilities related to the analysis and integration of renewable energy systems. Enable students to interpret and apply complex engineering principles for the development of efficient and sustainable energy solutions. Foster the ability to evaluate system performance, considering both technical and environmental factors. Expose students to modern energy storage technologies and hybrid system configurations.

Learning Outcomes: Upon successful completion of the course, students will be able to: 1. Acquire in-depth knowledge of the analysis, design, and implementation of photovoltaic and wind turbine systems. 2. Critically assess the operation, performance, and integration of PV and wind energy systems. 3. Develop technical specifications and conduct system-level assessments under varying conditions. 4. Design and optimize renewable energy systems using advanced engineering methods. 5. Use simulation and design tools to model and analyse system behaviours. 6.              Evaluate and improve the performance of PV and wind systems. 7.           Compare and select appropriate energy storage solutions for integration with renewable systems. 8. Propose innovative solutions to enhance the functionality and resilience of renewable energy systems, in line with emerging trends.

Course Content: Photovoltaic Power Systems, The photovoltaic effect and electrical energy conversion principles. PV panels: construction, electrical characteristics, and array configurations. Design and implementation of photovoltaic arrays. Electronic converters and their role in PV systems. Maximum Power Point Tracking (MPPT) techniques. Wind Power Systems, Wind energy fundamentals and wind turbine types. Power extraction principles and efficiency considerations. Converter systems for wind turbines. MPPT strategies for wind power applications. Electrical machines for wind applications Other Renewable Energy Technologies and Energy Storage, Overview of additional renewable technologies (e.g., small hydroelectric systems). Battery technologies and other electric storage systems.