Course Overview:
This course explores the fundamental principles of data communication and the architecture of computer networks. Students will learn about network models, protocols, and standards that enable seamless communication between devices. Key topics include OSI and TCP/IP models, routing algorithms, network security, and wireless communication technologies.
Key Learning Objectives:
Understand the structure and function of network layers.
Analyze various networking protocols and their applications.
Design and implement secure and efficient network systems.
Explore the latest trends in wireless and mobile networking.
Course Overview:
This course covers the principles and technologies behind wireless and mobile communication systems. Topics include cellular networks, radio propagation, modulation techniques, and the latest advancements in 5G technology. Students will gain a deep understanding of how mobile communication systems operate and evolve.
Key Learning Objectives:
Comprehend the fundamentals of wireless communication.
Analyze the performance of different modulation and coding schemes.
Understand the architecture and protocols of cellular networks.
Explore the development and implementation of 5G technology.
Course Highlights:
Simulation exercises with wireless communication software.
Exploration of cutting-edge wireless technologies.
Projects on designing and optimizing mobile networks.
Course Overview:
This course introduces the Internet of Things (IoT) and its impact on various industries. Students will explore the IoT ecosystem, including sensors, communication protocols, data analytics, and cloud computing. Emphasis is placed on designing and implementing IoT solutions for smart environments.
Key Learning Objectives:
Understand the components and architecture of IoT systems.
Design IoT networks and select appropriate communication protocols.
Develop and deploy IoT applications using modern tools.
Analyze IoT data for insights and decision-making.
Course Highlights:
Hands-on projects with IoT development kits.
Case studies on smart cities, healthcare, and industrial IoT.
Collaboration with industry partners for real-world projects.
Course Overview:
This interdisciplinary course bridges the gap between artificial intelligence, machine learning, and civil engineering. Students will learn how AI and ML techniques can be applied to solve complex problems in civil engineering, such as structural health monitoring, predictive maintenance, and smart city planning.
Key Learning Objectives:
Understand AI and ML concepts and their relevance to civil engineering.
Develop predictive models for civil engineering applications.
Implement AI/ML algorithms to analyze and interpret engineering data.
Explore case studies on AI/ML applications in construction, transportation, and infrastructure.
Course Highlights:
Practical sessions with AI/ML tools and software.
Real-world projects on AI/ML applications in civil engineering.