Electronic Engineering MSc

You will be provided with detailed coverage of digital filters, spectrum analysis, source coding, channel coding, baseband and bandpass digital modulation and demodulation techniques, detection theory and error analysis, spread spectrum signals and multiuser communications, wireless fading channels, channel equalization techniques, and advanced topics in 5G/6G enabling technologies.

This module provides a comprehensive and advanced understanding of Digital Twins and their industrial applications, e.g., IoT sensors and devices, data management frameworks, advanced data analytics and machine learning algorithms. You will also learn how to create digital twins and adopt them to model, simulate and manage industrial processes.

You will be provided with detailed coverage of real-time computing with respected to timeliness, reliability/safety, and environmental interface. You will be analytically and experimentally exposed to the state-of-the-art approaches to design and implement systems meeting time-deadline tasks given constrains on physical/hardware and energy resources.

This module covers issues such as sustainability goals, recognition of obligations to the society, the professional practice and a commitment to professional standards and Code(s) of Conduct relevant to their discipline (such as the Engineering Council), materials and their impact on environmental factors, waste management, ethics, EDI in a workplace. 

This module equips you with knowledge of fundamental concepts of robot manipulators, such as coordinate systems, transformations, kinematics, motion planning. You will put the knowledge of these principles into practice by modelling, simulating, programming and operating robot arms. You will gain experience of specialised software frameworks for robotic manipulation.

You will explore key machine learning (ML) methods and algorithms, applying them to real-world applications such as robotics, communications, mechatronics, electronics, and cyber-physical systems. Through practical development of artificial intelligence (AI) and ML systems, you will also engage with and critically examine theoretical challenges within AI and its engineering applications.

The module consolidates the knowledge and advanced skills gained in the preceding modules allowing you to develop and demonstrate mastery in undertaking advanced engineering projects in their future employment. The module develops advanced skills and practical experience in planning, problem solving, implementing engineering solutions, making effective written, and oral presentations.

The course is taught through a series of practical lab sessions as well as self-directed study and project-based learning.

You will be taught by an experienced teaching team with a wide range of expertise and professional experience. In addition, there are technical tutors and graduate academic assistants to support you during and after classes.

You will be assigned a personal tutor who will support you with help and advice throughout your studies.

You will be based at our leafy north London campus - mainly in the Ritterman and Hatchcroft buildings. It is equipped with industry-standard equipment in mechatronics, robotics, electronic and networking solutions.

Whether you are studying full or part-time – your course timetable will balance your study commitments on campus with time for work, life commitments and independent study.

We aim to make timetables available to students at least 2 weeks before the start of term. Some weeks are different due to how we schedule classes and arrange on campus sessions.

A typical week looks like this:

• On-campus: This includes tutor-led sessions such as seminars, lab sessions and demonstrations as well as student-led sessions for work in small groups
• Online learning: This is teaching that is delivered online using tools like MS Teams or Zoom, as well as work that you do yourself using online teaching resources
• Independent study: This is the work you do in your own time including reading and research
• Part-time study: You can also study this course part-time

Coursework and assessments

You will be assessed through a variety of tasks and assignments such as individual and group projects, coding and hardware implementations with live or recorded demos, reports, research papers, presentations, project proposals and literature reviews, logbooks and blogs.

Feedback

You'll evaluate your work, skills and knowledge and identify areas for improvement. Sometimes you'll work in groups and assess each other's progress.

Each term, you'll get regular feedback on your learning.