Speciality Requirements Student must complete 27 credit hours

Course Code	Course Name	Credit Hours	Prerequests
468500	Principles and Design of IoT Systems	3
This refers to the next generation of the internet (network) that enables communication between connected devices (via the Internet Protocol). These devices include gadgets, sensors, various artificial intelligence tools, and more. Course Outcomes: Upon completion of the course, the student should be able to: ? Understand how the internet in general and the Internet of Things (IoT) work. ? Understand the limitations and opportunities that wireless and mobile networks offer to the IoT. ? Use basic measurement tools to determine the real-time performance of packet-based networks. ? Analyze wireless and wired sensor networks that support the IoT.
468501	Smart Systems Architecture	3
Intelligent Systems Architecture This course introduces advanced concepts in the design and engineering of intelligent systems, as well as the testing of these designs. The first part of the course provides advanced knowledge in software engineering needed for designing intelligent systems, with a particular focus on architecture description languages for intelligent systems, modeling languages, and design decision-making. The second part covers topics related to system testing, with a special emphasis on model-based testing. The aim is to acquire knowledge of software languages and tools that facilitate system description, analysis, and testing techniques for component-based systems. Course Learning Outcomes: The main objective of this course is to equip students with a solid understanding of both the theory and practice of software engineering, and to apply functional analysis in intelligent systems. By the end of the course, students will be able to properly design intelligent systems using appropriate tools and software, as well as analyze these designs and develop suitable test cases. Through projects, students will practically apply the theoretical concepts described earlier. In addition, they will gain awareness of how design choices impact the quality of intelligent systems.
468502	The Evolution of Communications Networks	3
The course presents the main aspects of communication networks, with special emphasis on wireless networks and their various technologies, linking them to related applications. Course Learning Outcomes: The course covers the fundamentals of wireless communications, providing an overview of current and emerging wireless communication networks such as mesh networks, vehicular communications, and sensor and sensing communications. It highlights their ability to support distributed user applications and enable peer-to-peer information exchange. The second part of the course addresses the perspectives and limitations of cooperative wireless networks, such as relay networks and sensor networks for environmental monitoring. Various methodologies for this study are introduced, along with several examples illustrating practically significant networking scenarios. In general, the course provides an introduction to wireless technologies including IEEE 802.11, IEEE 802.15, and IEEE 802.16. Simulation is a key enabling technology used in a wide range of smart systems. It can be applied to model everything from call center operations, manufacturing facilities, and traffic flow, to physical phenomena (such as weather) and even the spread of diseases across populations. Emphasis is placed on general intelligent modeling skills, effective simulation programming tools, and mathematical analysis techniques to ensure that results are meaningful and accurate. In this course, students study modeling and simulation of natural processes and systems. The content includes an introduction to simulation; the concept of discrete-event simulation; elements of discrete-event simulation; Monte Carlo simulation; the simulation study life cycle; input and output data analysis; overview and time management/control; random number generation; estimation of the reliability of simulation results; simulation languages; distributed and parallel simulation; and applications of simulation in smart systems using supporting software tools. Course Learning Outcomes: ? Develop modeling skills for a variety of smart and real-world systems. ? Translate high-level models into simulation models. ? Learn how to run effective simulations to analyze real-world smart systems. ? Use simulation to improve smart systems.
468503	Modeling and Simulating Smart Systems	3
The course introduces simulation as a key enabling technology used in a wide range of smart systems. Simulation can be applied to model various real-world processes, including call center operations, manufacturing systems, traffic flow, physical phenomena (such as weather), and even the spread of diseases across populations. The course emphasizes general intelligent modeling skills, effective simulation programming tools, and mathematical analysis techniques to ensure accurate and meaningful results. In this course, students study the modeling and simulation of natural processes and systems. The content includes an introduction to simulation; the concept of discrete-event simulation; elements of discrete-event simulation; Monte Carlo simulation; the simulation study life cycle; input and output data analysis; time management and control; random number generation; estimation of the reliability of simulation results; simulation languages; distributed and parallel simulation; and applications of simulation in smart systems using supporting software tools. Course Learning Outcomes: 1. Develop modeling skills for a variety of smart and real-world systems. 2. Translate high-level models into simulation models. 3. Run effective simulations to analyze real-world smart systems. 4. Use simulation techniques to improve smart systems.
468504	Security of Smart Devices and Cyber-Physical Systems	3
The course covers introductory topics in the security of Cyber-Physical Systems (CPS). It exposes students to the security fundamentals of electronic physical systems and their application to a wide range of current and future security challenges. The course focuses on several types of cyber-physical systems, such as Industrial Control Systems (ICSs). Students will learn to work with various tools and techniques used by attackers to compromise computer systems or interfere with normal operations (Ethical Hacking). They will also use tools to interact with cyber-physical systems in order to identify vulnerabilities and strengthen them. Additionally, students will learn the principles of cryptography used in smart systems. Course Learning Outcomes: ? Generalize and apply concepts of smart cyber-physical systems. ? Explain concepts related to applied cryptography, including plaintext, ciphertext, symmetric encryption, asymmetric encryption, and digital signatures. ? Explain the underlying theory of security for various cryptographic algorithms. ? Describe common network vulnerabilities and attacks, defense mechanisms against network attacks, and cryptographic protection mechanisms. ? Identify requirements and mechanisms for identification and authentication, along with potential threats to each mechanism and methods of protection. ? Explain real-time communication security requirements and issues related to web services security. ?Explain non-real-time security requirements (such as email security), and methods for ensuring privacy, source authentication, message integrity, non-repudiation, proof of submission, proof of delivery, message flow confidentiality, and anonymity.
468505	Artificial Intelligence: A Modern Approach	3
The primary objective of this course is to provide students with an understanding of some fundamental methods and algorithms in Artificial Intelligence, as well as an appreciation of how they can be applied to interesting real-world problems through a variety of examples. The course consists of three components: lectures, tutorials, and projects. The lectures cover selected core topics such as search, game playing, decision-making, and machine learning. The tutorials allow students to apply algorithms to simple ?game-like? examples. The projects provide students with the opportunity to develop a small solution in a specific area of Artificial Intelligence. Course Learning Outcomes: Students will gain an understanding of fundamental methods and algorithms in Artificial Intelligence and robotics. Students will become familiar with practical applications of these algorithms. Students will complete a hands-on project in teams.
468590	Scientific Research Methodologies	3
The Principles of Scientific Research course introduces key theoretical and philosophical foundations of scientific research as a tool for acquiring knowledge. It reviews different research methodologies across various scientific disciplines, as well as the stages and steps involved in conducting scientific research. These include identifying and analyzing research problems, understanding hypotheses (both probabilistic and non-probabilistic), and exploring data collection tools and methods along with their psychometric properties. The course also explains the concept of variables and their types, in addition to other aspects related to the implementation of scientific research. Course Learning Outcomes: ? Understand introductory concepts of scientific research. ? Identify research problems and formulate hypotheses. ? Understand variables in scientific research. ? Understand sampling methods in scientific research. ? Apply data collection tools in scientific research. ? Understand research methodologies. ? Develop and apply a scientific research proposal.
468599	Thesis	6
The Thesis Course is designed to guide students through the process of conducting independent research and producing a substantial scholarly work in their chosen field of study. This capstone experience allows students to demonstrate mastery of the knowledge, skills, and methodologies acquired throughout their academic program. The thesis course is typically undertaken in the final year of a graduate or undergraduate program, enabling students to integrate and apply their knowledge and skills in a focused research project. Students will work closely with a faculty advisor or thesis committee throughout the process, receiving guidance and mentorship to support their research efforts. Prerequisites: Completion of relevant coursework in the student?s field of study, including research methods or capstone courses. Students should have a strong foundation in academic writing, research design, and critical thinking skills. This course is open to both undergraduate and graduate students across disciplines who are pursuing a thesis or an equivalent research project as part of their degree requirements. It provides an opportunity for students to make original contributions to their field of study and to prepare for future academic or professional endeavors.

Speciality Optional Requirements Student must complete 9 credit hours

Course Code	Course Name	Credit Hours	Prerequests
468550	Machine Learning and Deep Learning	3
This course enables students to explore, implement, and develop various machine learning algorithms that allow computers to make autonomous decisions. This is achieved by analyzing available data and learning from it to handle new tasks and situations. The course covers supervised and unsupervised learning techniques, including Artificial Neural Networks, Bayesian Networks, Random Forests, Support Vector Machines, and Deep Learning, among others. Course Learning Outcomes: ? Distinguish between supervised and unsupervised learning methods. ? Evaluate appropriate techniques for solving real-world problems across different domains. ? Develop and train machine learning models using real-world data.
468551	Cyber-Physical System	3
Cyber-Physical Systems (CPS) involve the integration and interaction between computational processes and the physical environment. CPS combines physical process dynamics with communication, computation, networking, and system analysis technologies. Applications include traffic control, safety systems, advanced automotive systems, smart buildings, environmental monitoring, and smart energy management. Course Learning Outcomes: ? Understand CPS architectures and infrastructure. ? Study wireless sensor networks (WSNs): architectures, protocols, and technologies. ? Understand RFID systems and components. ? Explore routing, MAC protocols, data aggregation, and clustering in WSNs. ? Analyze collision avoidance and communication protocols. ? Understand sensing-actuation interaction in CPS. ? Apply optimization and performance evaluation techniques in smart systems.
468552	Big Data Analysis	3
This course provides a practical introduction to designing and building data processing systems on Google Cloud Platform. Students will learn to design data pipelines, analyze structured and unstructured data, and implement machine learning solutions. Course Learning Outcomes: ? Design and build data processing systems on cloud platforms. ? Utilize unstructured data using Spark APIs. ? Process batch and streaming data using scalable pipelines. ? Extract, transform, clean, and validate data (ETL). ? Design data architectures and pipelines. ? Build and maintain machine learning and statistical models. ? Query datasets, visualize results, and generate reports.
468553	Cloud Computing	3
This course introduces cloud computing technologies that have transformed IT infrastructures and business operations. It covers cloud ecosystems and real-world applications. Course Learning Outcomes: ? Understand cloud computing concepts and ecosystems. ? Learn technologies such as AWS, Azure, Google Cloud, OpenStack, and vSphere. ? Understand service models: IaaS, PaaS, SaaS. ? Gain hands-on experience in deploying, configuring, and managing cloud systems.
468554	Adaptive Systems	3
This course covers key concepts in adaptive systems, including control theory, system self-regulation, cooperation, competition, and self-organization. Course Learning Outcomes: ? Understand adaptive processes in real-world systems. ? Learn theoretical and practical tools for adaptive systems. ? Explore adaptive algorithms and models. ? Apply adaptive techniques in solving problems through programming projects
468555	Software Quality Analysis	3
This course focuses on functional and non-functional properties of component-based software systems, including reliability and performance analysis, and advanced UML modeling techniques. Course Learning Outcomes: ? Develop skills in modeling and analyzing software systems. ? Use tools that support software quality analysis. ? Effectively utilize software engineering tools
468556	Smart Systems Management	3
This course aims to equip students with skills required to manage smart system projects, including planning, analysis, risk management, communication, leadership, and time management. Course Learning Outcomes: ? Develop planning and analytical skills. ? Manage risks in projects. ? Improve communication and leadership skills. ? Manage time effectively in project environments.
468557	Smart Systems Applications	3
This course explores real-world applications of smart systems by analyzing different domains and studying how intelligent applications improve their performance and environments. 468558 ? Robotics (3 Credit Hours) This course teaches students how to design, build, and program robots for various applications. It covers robot components, control systems, algorithms, and mechanical design. Course Learning Outcomes: ? Understand robot components: processors, controllers, sensors, actuators, and communication systems. ? Design different types of mobile robots. ? Explore robotics applications such as navigation, mapping, AI, and autonomous systems. ? Build robots for specific environments and applications.
468558	Robotics	3
This course teaches students how to design, build, and program robots for various applications. It covers robot components, control systems, algorithms, and mechanical design. Course Learning Outcomes: ? Understand robot components: processors, controllers, sensors, actuators, and communication systems. ? Design different types of mobile robots. ? Explore robotics applications such as navigation, mapping, AI, and autonomous systems. ? Build robots for specific environments and applications.
468559	Embedded Systems Programming	3
This course covers embedded system development, resource-aware programming, multithreading, inter-process communication, and debugging. Course Learning Outcomes: ? Develop real-time system programming skills. ? Build fault-tolerant systems. ? Detect and fix system errors effectively.
468560	Block Chain Technology	3
This course introduces blockchain as a distributed system technology, covering distributed consensus, CAP theorem, Byzantine fault tolerance, and modern consensus mechanisms. Course Learning Outcomes: ? Understand blockchain concepts, benefits, and limitations. ? Differentiate blockchain from other technologies. ? Apply blockchain concepts in case studies and system design. ? Evaluate when to use blockchain in real systems
468561	Intelligent Transportation Systems	3
This course covers smart transportation and mobility systems that use ICT technologies to improve safety, efficiency, and sustainability. Course Learning Outcomes: ? Understand challenges in intelligent transportation systems. ? Explore research areas and applications in smart cities. ? Apply data analysis techniques in transportation systems.