MSc — 2027 entry Electronic Engineering

Expected prior learning: It is helpful, but not essential, to have taken module EEE2040 – Communications Networks (EEE2040), or to have equivalent learning. Module purpose:  This module deals with the three important processing stages of modern digital communication systems which are source coding for signal compression, channel error control coding for robust transmission and modulation for efficient digital interface with the available channel. The module is designed to provide basic-to-intermediate scale introduction of the subject at the UG level and the learning developed in this module can be enhanced further at relevant MEng / MSc level modules (EEEM017, EEEM030, EEEM031)

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Radio frequency (RF) and microwave engineers require proficiency in a specific set of skills to in electronic circuitry that does not exist for other typical applications. Therefore, a good grounding in the electronics associated with RF and microwave devices and important underlying essential fundamentals are delivered for any form of RF or microwave engineering. The module will be important for other RF related modules such as EEEM044 RF System and Circuit Design and EEEM064 Microwave Design Techniques

Expected prior learning:  Students should have an interest in materials and devices Module purpose:  Nanotechnology promises new strong and light materials, faster electronic devices which consume less energy and have enhanced functionality. Nanotechnology and nanomaterials are everywhere; it is on the nanometer scale that many of the well-known descriptions and properties of materials breakdown and where an understanding of quantum effects becomes vital.  New materials such as graphene and carbon nanotubes with outstanding physical and electronic properties have emerged. This module will introduce quantum engineering and new nanomaterials as well as showing how developments have led to unprecedented ability to see and manipulate atoms and materials on the nanoscale. The module builds upon prior study of electronic materials delivered in Year 2 of the undergraduate programme such as EEE2040 Electronic and Photonics Devices, EEE2045 Electrical Science II. This module facilitates future advanced master level leaning in advanced and modern electronic materials and devices such as in module EEEM022 Nanoelectronics and Devices.

Expected prior/parallel learning:   BEng-level understanding of digital telecommunications systems. MEng students might have partly acquired this through study of EEE3006 – Digital Communications. Module purpose: Satellite communications are an important component of modern telecommunication systems. This module provides the student with an overall understanding of satellite communication systems, technologies and techniques and equips him/her with the design tools to enter employment in the sector.   The main goal of this module is to design a satellite communication link to fix or mobile users given a specified quality of service (QoS). To do so, one needs to take into account both telecommunication and satellite related parameters. In this module, you will learn about the satellite payload and how it operates, what is in the earth station and what are the characteristics of the satellite to earth link. In particular, we clearly state the differences in design compared to the terrestrial communication systems We also study in some details the modern satellite networks and constellations. [EEE3006]: This module uses several techniques addressed in digital communications module such as coding and modulation. [EEEM032]: This module is a prerequisite for the advanced satellite communication techniques. [EEEM009],[EEEM059]: Even though other satellite applications such as earth observation or satellites for navigation and positioning are not addressed in this module, however, all such application will eventually need to stablish a communication line with earth. In this sense, this module is closely related to modules on space avionics and navigation, guidance and control.

Expected prior learning:  None. Module purpose:  To inform students as to the importance of renewable energy in the energy mix required for generation within nations. This is now becoming law in developed countries following the Kyoto agreement and green energy obligations. Students will learn as to the various energy generation options available from power scavengers for handheld calculators to energy generation to power the world’s energy need. Furthermore, an appreciation for the need for energy storage at all power levels will be discussed, with special emphasis on the materials requirements. Students will be able to examine next generation materials being proposed for meeting world's demand based on green energy.  

In this module, students  will learn about the different key concepts and elements comprising a modern wireless communication system, e.g. signal, transmitter, receiver, wireless channel, as well as understand the primary objectives of communication systems, i.e. reliability and sprectrum efficiency. They will do so by engaging with the various types of teaching materials presented to them as well as participating in our MATLAB lab sessions where they will be given the opportunity to analyse and simulate radio propagation behaviours/coverage as well as to model and simulate key aspects of a simple wireless communication system.

This module offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on its applications in modern communication systems. By taking this module, the student builds a strong foundation of AI/ML starting from the mathematical basics (linear algebra, probability, optimization) and the main algorithms and models (e.g. Decision Trees, Support Vector Machines, Deep Neural Networks, etc.) to the modern generative models, and reinforcement learning. Through the coursework, the student will gain hands-on experience with key AI/ML models applied to modern communication systems. By the end of the module, the student will not only understand the theory of machine learning but also develop the practical skills needed to implement and evaluate models for real-world applications in communications.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: EEE3033 – RF and Microwave Fundamentals, or equivalent learning. Module purpose: Advanced communications systems and radar operate at RF and microwave frequencies. The design principles and circuit operation, underlying these systems, are quite different from those of electronics used in signal processing at baseband frequencies. This module will cover the key elements of RF and microwave system design as well as analysis concepts for a range of commonly used active circuits, including: oscillators, frequency synthesisers, amplifiers and mixers. The module will also cover the circuit design and operation of non-linear devices used in active circuits together with deployment considerations. This module will include and build on many of the concepts studied in EEE3033 RF and Microwave Fundamentals and address further advanced features of non linear RF devices and system optimisation. It is complementary to EEEM064 Microwave Design Techniques and also EEEM006 Antennas and Propagation.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Expected prior learning:  None specifically advised. Module purpose:  Earth and planetary observation with remote sensing data is playing a key role in the present understanding of natural phenomena, prevention of disasters, resources monitoring, comprehension of origins of life. Through a series of lectures, seminars, open discussions and “thinking breaks” in class, the module aims to give an introduction to the scientific principles of remote sensing – both passive and active – as carried out by spacecraft. Remote sensing is discussed in terms of instrumentation, missions, products and applications. IMPORTANT: The Second assessment pattern (Written Exam) is only applicable to the MSc Short Course Students.

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

Expected prior/parallel learning:  Students should have an interest in materials and devices; it would be of benefit to have studied EEE3037 Nanoscience and Nanotechnology. Module purpose:  Nanoelectronics represent the ultimate in advanced electronic device design and operation. At the nanoscale the quantum nature of matter is evident in the operation of faster, energy efficient devices with greater functionality. New materials, such as graphene and new molecular electronic materials, offer unique electronic properties which continually emerge in parallel to advances in device architecture and performance aligned to the International Roadmap for Devices and Systems. This module will introduce some of the key concepts in low dimensional mesoscopic science and engineering, and molecular electronics and associated devices. The module builds on materials seen earlier in the undergraduate programme such as in EEE2042 Electronic and Photonic Devices, EEE2045 Electrical Science II, EEE3037 Nanoscience and Nanotechnology and EEE3041 Semiconductor Devices and Optoelectronics.

Expected prior/parallel learning: This module covers advanced topics on satellite communications and networks, following the Satellite Communication fundamentals (EEEM031). An alternate module containing suitable prior learning is, Space System Design (EEE3040). Module purpose: This module covers advanced topics on satellite communications and networks. These networks are an important part of global information infrastructure providing broadcasting, mobile and broadband services to millions of homes and offices as well as disaster relieves and emergency communications services.

Developing high-performance energy storage devices such as lithium-ion batteries could greatly promote the development of portable electronics, vehicle electrification and smart grid, alleviate environmental pollution and reduce our dependence on fossil fuels, addressing the “grand challenges” in the sustainability and resilience of environments. This module aims to introduce fundamental scientific, technological or engineering principles and technology applications of batteries used in different electrical systems. Students will learn as to the battery types, battery parts and how to test/monitor a battery. Battery performance requirement by electric vehicles, smart grids, next-generation electronics and electrical systems will also be covered in this module. The discussion of new materials goes beyond that found in EEE3037 Nanoscience and Nanotechnology.

Wireless networks (wide area cellular / satellite, local area residential / business, and ad-hoc / mesh) are an essential part of modern day communication / computing facilities. The network deployment, planning and management are the key issues ¿ all the network components must conform to the given hardware and software specifications in order that they can be interconnected and work successfully for the intended Quality of Service (QoS). This module introduces essential concepts about the network planning, network deployment and network management for a successful running of the network with a focus on software defined networking and virtualisation.

Expected prior learning:  Module EEE2042 – Electronic and Photonic Devices, or equivalent learning, is advisory but not required. Module purpose:  Semiconductor devices and optoelectronics play a major technology enabling amongst other things the internet. The course is given via a series of lectures and aims to give a background to the interaction of light with key photonic materials and devices. This module will introduce students to modern energy efficient electronic and photonic devices concentrating on the fundamental science of operation, device structure and characteristics.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: None. Module purpose: Antennas and the propagation of radio on the physical layer (PHY) are a fundamental aspect of communications, space and radar as well as any other device that will radiate electromagnetic waves over an air interface. In order for wireless devices to operate and comply with suitable standards, it is important that they use a suitable antenna design, while also modelling the propagation environment the device may encounter is important for purposes of testing the radio transceiver, thus knowledge of propagation modelling is required to achieve this.  

Expected prior learning: EEE3033–RF and Microwave Fundamentals, or equivalent learning. Module purpose: At Gigahertz frequencies the operating wavelength is small. Devices operating in the Gigahertz frequency range are therefore electrically large compared to the operating wavelength. In such cases, it is no longer appropriate to use traditional lumped element circuit components. The passive devices used at Gigahertz frequencies thus have to be made either using printed transmission lines (e.g. microstrip, coplanar waveguide) or waveguide. This module will present microwave design and analysis concepts for a range of commonly used passive circuits using both microstrip lines and waveguides, including: transmission lines, coupling networks, antennas and filters. This module will revisit and build on the use of the Smith chart also introduced in EEE3033 RF and Microwave Fundamentals and use it for further design applications of microstrip circuits. It is complementary to module EEEM044 RF Systems and Circuit Design and EEEM006 Antennas and Propagation.

The purpose of this module is to equip students with advanced knowledge of key concepts in modern mobile and wireless communication systems, such as 5G, while also exploring emerging concepts that are likely to shape future technologies, including 6G. By completing this module, students will gain the expertise needed to work with wireless communications technologies in industry or to pursue further academic research in this field. The module bridges foundational knowledge in wireless communications with cutting-edge techniques and promising innovations that can enhance future communication systems, fostering cognitive and conceptual connections across these domains.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.

Expected prior learning: It is helpful, but not essential, to have taken module EEE2040 – Communications Networks (EEE2040), or to have equivalent learning. Module purpose:  This module deals with the three important processing stages of modern digital communication systems which are source coding for signal compression, channel error control coding for robust transmission and modulation for efficient digital interface with the available channel. The module is designed to provide basic-to-intermediate scale introduction of the subject at the UG level and the learning developed in this module can be enhanced further at relevant MEng / MSc level modules (EEEM017, EEEM030, EEEM031)

Radio frequency (RF) and microwave engineers require proficiency in a specific set of skills to in electronic circuitry that does not exist for other typical applications. Therefore, a good grounding in the electronics associated with RF and microwave devices and important underlying essential fundamentals are delivered for any form of RF or microwave engineering. The module will be important for other RF related modules such as EEEM044 RF System and Circuit Design and EEEM064 Microwave Design Techniques

Expected prior learning:  Students should have an interest in materials and devices Module purpose:  Nanotechnology promises new strong and light materials, faster electronic devices which consume less energy and have enhanced functionality. Nanotechnology and nanomaterials are everywhere; it is on the nanometer scale that many of the well-known descriptions and properties of materials breakdown and where an understanding of quantum effects becomes vital.  New materials such as graphene and carbon nanotubes with outstanding physical and electronic properties have emerged. This module will introduce quantum engineering and new nanomaterials as well as showing how developments have led to unprecedented ability to see and manipulate atoms and materials on the nanoscale. The module builds upon prior study of electronic materials delivered in Year 2 of the undergraduate programme such as EEE2040 Electronic and Photonics Devices, EEE2045 Electrical Science II. This module facilitates future advanced master level leaning in advanced and modern electronic materials and devices such as in module EEEM022 Nanoelectronics and Devices.

Expected prior/parallel learning:   BEng-level understanding of digital telecommunications systems. MEng students might have partly acquired this through study of EEE3006 – Digital Communications. Module purpose: Satellite communications are an important component of modern telecommunication systems. This module provides the student with an overall understanding of satellite communication systems, technologies and techniques and equips him/her with the design tools to enter employment in the sector.   The main goal of this module is to design a satellite communication link to fix or mobile users given a specified quality of service (QoS). To do so, one needs to take into account both telecommunication and satellite related parameters. In this module, you will learn about the satellite payload and how it operates, what is in the earth station and what are the characteristics of the satellite to earth link. In particular, we clearly state the differences in design compared to the terrestrial communication systems We also study in some details the modern satellite networks and constellations. [EEE3006]: This module uses several techniques addressed in digital communications module such as coding and modulation. [EEEM032]: This module is a prerequisite for the advanced satellite communication techniques. [EEEM009],[EEEM059]: Even though other satellite applications such as earth observation or satellites for navigation and positioning are not addressed in this module, however, all such application will eventually need to stablish a communication line with earth. In this sense, this module is closely related to modules on space avionics and navigation, guidance and control.

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Expected prior learning:  None. Module purpose:  To inform students as to the importance of renewable energy in the energy mix required for generation within nations. This is now becoming law in developed countries following the Kyoto agreement and green energy obligations. Students will learn as to the various energy generation options available from power scavengers for handheld calculators to energy generation to power the world’s energy need. Furthermore, an appreciation for the need for energy storage at all power levels will be discussed, with special emphasis on the materials requirements. Students will be able to examine next generation materials being proposed for meeting world's demand based on green energy.  

In this module, students  will learn about the different key concepts and elements comprising a modern wireless communication system, e.g. signal, transmitter, receiver, wireless channel, as well as understand the primary objectives of communication systems, i.e. reliability and sprectrum efficiency. They will do so by engaging with the various types of teaching materials presented to them as well as participating in our MATLAB lab sessions where they will be given the opportunity to analyse and simulate radio propagation behaviours/coverage as well as to model and simulate key aspects of a simple wireless communication system.

This module offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on its applications in modern communication systems. By taking this module, the student builds a strong foundation of AI/ML starting from the mathematical basics (linear algebra, probability, optimization) and the main algorithms and models (e.g. Decision Trees, Support Vector Machines, Deep Neural Networks, etc.) to the modern generative models, and reinforcement learning. Through the coursework, the student will gain hands-on experience with key AI/ML models applied to modern communication systems. By the end of the module, the student will not only understand the theory of machine learning but also develop the practical skills needed to implement and evaluate models for real-world applications in communications.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: EEE3033 – RF and Microwave Fundamentals, or equivalent learning. Module purpose: Advanced communications systems and radar operate at RF and microwave frequencies. The design principles and circuit operation, underlying these systems, are quite different from those of electronics used in signal processing at baseband frequencies. This module will cover the key elements of RF and microwave system design as well as analysis concepts for a range of commonly used active circuits, including: oscillators, frequency synthesisers, amplifiers and mixers. The module will also cover the circuit design and operation of non-linear devices used in active circuits together with deployment considerations. This module will include and build on many of the concepts studied in EEE3033 RF and Microwave Fundamentals and address further advanced features of non linear RF devices and system optimisation. It is complementary to EEEM064 Microwave Design Techniques and also EEEM006 Antennas and Propagation.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Expected prior learning:  None specifically advised. Module purpose:  Earth and planetary observation with remote sensing data is playing a key role in the present understanding of natural phenomena, prevention of disasters, resources monitoring, comprehension of origins of life. Through a series of lectures, seminars, open discussions and “thinking breaks” in class, the module aims to give an introduction to the scientific principles of remote sensing – both passive and active – as carried out by spacecraft. Remote sensing is discussed in terms of instrumentation, missions, products and applications. IMPORTANT: The Second assessment pattern (Written Exam) is only applicable to the MSc Short Course Students.

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

Expected prior/parallel learning:  Students should have an interest in materials and devices; it would be of benefit to have studied EEE3037 Nanoscience and Nanotechnology. Module purpose:  Nanoelectronics represent the ultimate in advanced electronic device design and operation. At the nanoscale the quantum nature of matter is evident in the operation of faster, energy efficient devices with greater functionality. New materials, such as graphene and new molecular electronic materials, offer unique electronic properties which continually emerge in parallel to advances in device architecture and performance aligned to the International Roadmap for Devices and Systems. This module will introduce some of the key concepts in low dimensional mesoscopic science and engineering, and molecular electronics and associated devices. The module builds on materials seen earlier in the undergraduate programme such as in EEE2042 Electronic and Photonic Devices, EEE2045 Electrical Science II, EEE3037 Nanoscience and Nanotechnology and EEE3041 Semiconductor Devices and Optoelectronics.

Expected prior/parallel learning: This module covers advanced topics on satellite communications and networks, following the Satellite Communication fundamentals (EEEM031). An alternate module containing suitable prior learning is, Space System Design (EEE3040). Module purpose: This module covers advanced topics on satellite communications and networks. These networks are an important part of global information infrastructure providing broadcasting, mobile and broadband services to millions of homes and offices as well as disaster relieves and emergency communications services.

Developing high-performance energy storage devices such as lithium-ion batteries could greatly promote the development of portable electronics, vehicle electrification and smart grid, alleviate environmental pollution and reduce our dependence on fossil fuels, addressing the “grand challenges” in the sustainability and resilience of environments. This module aims to introduce fundamental scientific, technological or engineering principles and technology applications of batteries used in different electrical systems. Students will learn as to the battery types, battery parts and how to test/monitor a battery. Battery performance requirement by electric vehicles, smart grids, next-generation electronics and electrical systems will also be covered in this module. The discussion of new materials goes beyond that found in EEE3037 Nanoscience and Nanotechnology.

Wireless networks (wide area cellular / satellite, local area residential / business, and ad-hoc / mesh) are an essential part of modern day communication / computing facilities. The network deployment, planning and management are the key issues ¿ all the network components must conform to the given hardware and software specifications in order that they can be interconnected and work successfully for the intended Quality of Service (QoS). This module introduces essential concepts about the network planning, network deployment and network management for a successful running of the network with a focus on software defined networking and virtualisation.

Expected prior learning:  Module EEE2042 – Electronic and Photonic Devices, or equivalent learning, is advisory but not required. Module purpose:  Semiconductor devices and optoelectronics play a major technology enabling amongst other things the internet. The course is given via a series of lectures and aims to give a background to the interaction of light with key photonic materials and devices. This module will introduce students to modern energy efficient electronic and photonic devices concentrating on the fundamental science of operation, device structure and characteristics.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: None. Module purpose: Antennas and the propagation of radio on the physical layer (PHY) are a fundamental aspect of communications, space and radar as well as any other device that will radiate electromagnetic waves over an air interface. In order for wireless devices to operate and comply with suitable standards, it is important that they use a suitable antenna design, while also modelling the propagation environment the device may encounter is important for purposes of testing the radio transceiver, thus knowledge of propagation modelling is required to achieve this.  

Expected prior learning: EEE3033–RF and Microwave Fundamentals, or equivalent learning. Module purpose: At Gigahertz frequencies the operating wavelength is small. Devices operating in the Gigahertz frequency range are therefore electrically large compared to the operating wavelength. In such cases, it is no longer appropriate to use traditional lumped element circuit components. The passive devices used at Gigahertz frequencies thus have to be made either using printed transmission lines (e.g. microstrip, coplanar waveguide) or waveguide. This module will present microwave design and analysis concepts for a range of commonly used passive circuits using both microstrip lines and waveguides, including: transmission lines, coupling networks, antennas and filters. This module will revisit and build on the use of the Smith chart also introduced in EEE3033 RF and Microwave Fundamentals and use it for further design applications of microstrip circuits. It is complementary to module EEEM044 RF Systems and Circuit Design and EEEM006 Antennas and Propagation.

The purpose of this module is to equip students with advanced knowledge of key concepts in modern mobile and wireless communication systems, such as 5G, while also exploring emerging concepts that are likely to shape future technologies, including 6G. By completing this module, students will gain the expertise needed to work with wireless communications technologies in industry or to pursue further academic research in this field. The module bridges foundational knowledge in wireless communications with cutting-edge techniques and promising innovations that can enhance future communication systems, fostering cognitive and conceptual connections across these domains.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.

Expected prior learning: It is helpful, but not essential, to have taken module EEE2040 – Communications Networks (EEE2040), or to have equivalent learning. Module purpose:  This module deals with the three important processing stages of modern digital communication systems which are source coding for signal compression, channel error control coding for robust transmission and modulation for efficient digital interface with the available channel. The module is designed to provide basic-to-intermediate scale introduction of the subject at the UG level and the learning developed in this module can be enhanced further at relevant MEng / MSc level modules (EEEM017, EEEM030, EEEM031)

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Radio frequency (RF) and microwave engineers require proficiency in a specific set of skills to in electronic circuitry that does not exist for other typical applications. Therefore, a good grounding in the electronics associated with RF and microwave devices and important underlying essential fundamentals are delivered for any form of RF or microwave engineering. The module will be important for other RF related modules such as EEEM044 RF System and Circuit Design and EEEM064 Microwave Design Techniques

Expected prior learning:  Students should have an interest in materials and devices Module purpose:  Nanotechnology promises new strong and light materials, faster electronic devices which consume less energy and have enhanced functionality. Nanotechnology and nanomaterials are everywhere; it is on the nanometer scale that many of the well-known descriptions and properties of materials breakdown and where an understanding of quantum effects becomes vital.  New materials such as graphene and carbon nanotubes with outstanding physical and electronic properties have emerged. This module will introduce quantum engineering and new nanomaterials as well as showing how developments have led to unprecedented ability to see and manipulate atoms and materials on the nanoscale. The module builds upon prior study of electronic materials delivered in Year 2 of the undergraduate programme such as EEE2040 Electronic and Photonics Devices, EEE2045 Electrical Science II. This module facilitates future advanced master level leaning in advanced and modern electronic materials and devices such as in module EEEM022 Nanoelectronics and Devices.

Expected prior/parallel learning:   BEng-level understanding of digital telecommunications systems. MEng students might have partly acquired this through study of EEE3006 – Digital Communications. Module purpose: Satellite communications are an important component of modern telecommunication systems. This module provides the student with an overall understanding of satellite communication systems, technologies and techniques and equips him/her with the design tools to enter employment in the sector.   The main goal of this module is to design a satellite communication link to fix or mobile users given a specified quality of service (QoS). To do so, one needs to take into account both telecommunication and satellite related parameters. In this module, you will learn about the satellite payload and how it operates, what is in the earth station and what are the characteristics of the satellite to earth link. In particular, we clearly state the differences in design compared to the terrestrial communication systems We also study in some details the modern satellite networks and constellations. [EEE3006]: This module uses several techniques addressed in digital communications module such as coding and modulation. [EEEM032]: This module is a prerequisite for the advanced satellite communication techniques. [EEEM009],[EEEM059]: Even though other satellite applications such as earth observation or satellites for navigation and positioning are not addressed in this module, however, all such application will eventually need to stablish a communication line with earth. In this sense, this module is closely related to modules on space avionics and navigation, guidance and control.

Expected prior learning:  None. Module purpose:  To inform students as to the importance of renewable energy in the energy mix required for generation within nations. This is now becoming law in developed countries following the Kyoto agreement and green energy obligations. Students will learn as to the various energy generation options available from power scavengers for handheld calculators to energy generation to power the world’s energy need. Furthermore, an appreciation for the need for energy storage at all power levels will be discussed, with special emphasis on the materials requirements. Students will be able to examine next generation materials being proposed for meeting world's demand based on green energy.  

In this module, students  will learn about the different key concepts and elements comprising a modern wireless communication system, e.g. signal, transmitter, receiver, wireless channel, as well as understand the primary objectives of communication systems, i.e. reliability and sprectrum efficiency. They will do so by engaging with the various types of teaching materials presented to them as well as participating in our MATLAB lab sessions where they will be given the opportunity to analyse and simulate radio propagation behaviours/coverage as well as to model and simulate key aspects of a simple wireless communication system.

This module offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on its applications in modern communication systems. By taking this module, the student builds a strong foundation of AI/ML starting from the mathematical basics (linear algebra, probability, optimization) and the main algorithms and models (e.g. Decision Trees, Support Vector Machines, Deep Neural Networks, etc.) to the modern generative models, and reinforcement learning. Through the coursework, the student will gain hands-on experience with key AI/ML models applied to modern communication systems. By the end of the module, the student will not only understand the theory of machine learning but also develop the practical skills needed to implement and evaluate models for real-world applications in communications.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: EEE3033 – RF and Microwave Fundamentals, or equivalent learning. Module purpose: Advanced communications systems and radar operate at RF and microwave frequencies. The design principles and circuit operation, underlying these systems, are quite different from those of electronics used in signal processing at baseband frequencies. This module will cover the key elements of RF and microwave system design as well as analysis concepts for a range of commonly used active circuits, including: oscillators, frequency synthesisers, amplifiers and mixers. The module will also cover the circuit design and operation of non-linear devices used in active circuits together with deployment considerations. This module will include and build on many of the concepts studied in EEE3033 RF and Microwave Fundamentals and address further advanced features of non linear RF devices and system optimisation. It is complementary to EEEM064 Microwave Design Techniques and also EEEM006 Antennas and Propagation.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Expected prior learning:  None specifically advised. Module purpose:  Earth and planetary observation with remote sensing data is playing a key role in the present understanding of natural phenomena, prevention of disasters, resources monitoring, comprehension of origins of life. Through a series of lectures, seminars, open discussions and “thinking breaks” in class, the module aims to give an introduction to the scientific principles of remote sensing – both passive and active – as carried out by spacecraft. Remote sensing is discussed in terms of instrumentation, missions, products and applications. IMPORTANT: The Second assessment pattern (Written Exam) is only applicable to the MSc Short Course Students.

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

Expected prior/parallel learning:  Students should have an interest in materials and devices; it would be of benefit to have studied EEE3037 Nanoscience and Nanotechnology. Module purpose:  Nanoelectronics represent the ultimate in advanced electronic device design and operation. At the nanoscale the quantum nature of matter is evident in the operation of faster, energy efficient devices with greater functionality. New materials, such as graphene and new molecular electronic materials, offer unique electronic properties which continually emerge in parallel to advances in device architecture and performance aligned to the International Roadmap for Devices and Systems. This module will introduce some of the key concepts in low dimensional mesoscopic science and engineering, and molecular electronics and associated devices. The module builds on materials seen earlier in the undergraduate programme such as in EEE2042 Electronic and Photonic Devices, EEE2045 Electrical Science II, EEE3037 Nanoscience and Nanotechnology and EEE3041 Semiconductor Devices and Optoelectronics.

Expected prior/parallel learning: This module covers advanced topics on satellite communications and networks, following the Satellite Communication fundamentals (EEEM031). An alternate module containing suitable prior learning is, Space System Design (EEE3040). Module purpose: This module covers advanced topics on satellite communications and networks. These networks are an important part of global information infrastructure providing broadcasting, mobile and broadband services to millions of homes and offices as well as disaster relieves and emergency communications services.

Developing high-performance energy storage devices such as lithium-ion batteries could greatly promote the development of portable electronics, vehicle electrification and smart grid, alleviate environmental pollution and reduce our dependence on fossil fuels, addressing the “grand challenges” in the sustainability and resilience of environments. This module aims to introduce fundamental scientific, technological or engineering principles and technology applications of batteries used in different electrical systems. Students will learn as to the battery types, battery parts and how to test/monitor a battery. Battery performance requirement by electric vehicles, smart grids, next-generation electronics and electrical systems will also be covered in this module. The discussion of new materials goes beyond that found in EEE3037 Nanoscience and Nanotechnology.

Wireless networks (wide area cellular / satellite, local area residential / business, and ad-hoc / mesh) are an essential part of modern day communication / computing facilities. The network deployment, planning and management are the key issues ¿ all the network components must conform to the given hardware and software specifications in order that they can be interconnected and work successfully for the intended Quality of Service (QoS). This module introduces essential concepts about the network planning, network deployment and network management for a successful running of the network with a focus on software defined networking and virtualisation.

Expected prior learning:  Module EEE2042 – Electronic and Photonic Devices, or equivalent learning, is advisory but not required. Module purpose:  Semiconductor devices and optoelectronics play a major technology enabling amongst other things the internet. The course is given via a series of lectures and aims to give a background to the interaction of light with key photonic materials and devices. This module will introduce students to modern energy efficient electronic and photonic devices concentrating on the fundamental science of operation, device structure and characteristics.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: None. Module purpose: Antennas and the propagation of radio on the physical layer (PHY) are a fundamental aspect of communications, space and radar as well as any other device that will radiate electromagnetic waves over an air interface. In order for wireless devices to operate and comply with suitable standards, it is important that they use a suitable antenna design, while also modelling the propagation environment the device may encounter is important for purposes of testing the radio transceiver, thus knowledge of propagation modelling is required to achieve this.  

Expected prior learning: EEE3033–RF and Microwave Fundamentals, or equivalent learning. Module purpose: At Gigahertz frequencies the operating wavelength is small. Devices operating in the Gigahertz frequency range are therefore electrically large compared to the operating wavelength. In such cases, it is no longer appropriate to use traditional lumped element circuit components. The passive devices used at Gigahertz frequencies thus have to be made either using printed transmission lines (e.g. microstrip, coplanar waveguide) or waveguide. This module will present microwave design and analysis concepts for a range of commonly used passive circuits using both microstrip lines and waveguides, including: transmission lines, coupling networks, antennas and filters. This module will revisit and build on the use of the Smith chart also introduced in EEE3033 RF and Microwave Fundamentals and use it for further design applications of microstrip circuits. It is complementary to module EEEM044 RF Systems and Circuit Design and EEEM006 Antennas and Propagation.

The purpose of this module is to equip students with advanced knowledge of key concepts in modern mobile and wireless communication systems, such as 5G, while also exploring emerging concepts that are likely to shape future technologies, including 6G. By completing this module, students will gain the expertise needed to work with wireless communications technologies in industry or to pursue further academic research in this field. The module bridges foundational knowledge in wireless communications with cutting-edge techniques and promising innovations that can enhance future communication systems, fostering cognitive and conceptual connections across these domains.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.

Expected prior learning: It is helpful, but not essential, to have taken module EEE2040 – Communications Networks (EEE2040), or to have equivalent learning. Module purpose:  This module deals with the three important processing stages of modern digital communication systems which are source coding for signal compression, channel error control coding for robust transmission and modulation for efficient digital interface with the available channel. The module is designed to provide basic-to-intermediate scale introduction of the subject at the UG level and the learning developed in this module can be enhanced further at relevant MEng / MSc level modules (EEEM017, EEEM030, EEEM031)

Radio frequency (RF) and microwave engineers require proficiency in a specific set of skills to in electronic circuitry that does not exist for other typical applications. Therefore, a good grounding in the electronics associated with RF and microwave devices and important underlying essential fundamentals are delivered for any form of RF or microwave engineering. The module will be important for other RF related modules such as EEEM044 RF System and Circuit Design and EEEM064 Microwave Design Techniques

Expected prior learning:  Students should have an interest in materials and devices Module purpose:  Nanotechnology promises new strong and light materials, faster electronic devices which consume less energy and have enhanced functionality. Nanotechnology and nanomaterials are everywhere; it is on the nanometer scale that many of the well-known descriptions and properties of materials breakdown and where an understanding of quantum effects becomes vital.  New materials such as graphene and carbon nanotubes with outstanding physical and electronic properties have emerged. This module will introduce quantum engineering and new nanomaterials as well as showing how developments have led to unprecedented ability to see and manipulate atoms and materials on the nanoscale. The module builds upon prior study of electronic materials delivered in Year 2 of the undergraduate programme such as EEE2040 Electronic and Photonics Devices, EEE2045 Electrical Science II. This module facilitates future advanced master level leaning in advanced and modern electronic materials and devices such as in module EEEM022 Nanoelectronics and Devices.

Expected prior/parallel learning:   BEng-level understanding of digital telecommunications systems. MEng students might have partly acquired this through study of EEE3006 – Digital Communications. Module purpose: Satellite communications are an important component of modern telecommunication systems. This module provides the student with an overall understanding of satellite communication systems, technologies and techniques and equips him/her with the design tools to enter employment in the sector.   The main goal of this module is to design a satellite communication link to fix or mobile users given a specified quality of service (QoS). To do so, one needs to take into account both telecommunication and satellite related parameters. In this module, you will learn about the satellite payload and how it operates, what is in the earth station and what are the characteristics of the satellite to earth link. In particular, we clearly state the differences in design compared to the terrestrial communication systems We also study in some details the modern satellite networks and constellations. [EEE3006]: This module uses several techniques addressed in digital communications module such as coding and modulation. [EEEM032]: This module is a prerequisite for the advanced satellite communication techniques. [EEEM009],[EEEM059]: Even though other satellite applications such as earth observation or satellites for navigation and positioning are not addressed in this module, however, all such application will eventually need to stablish a communication line with earth. In this sense, this module is closely related to modules on space avionics and navigation, guidance and control.

This module provides knowledge about advanced digital circuit design and the hardware description language VHDL. The practical part of the course is concerned with FPGA implementation, using modern CAD tools and FPGA prototyping boards. This module builds on from many Electronic Engineering modules at undergraduate level in the topics of Circuit Design and Processor design such EEE2045 Electrical Science II and will lead on to EEEM059 Space Avionics

Expected prior learning:  None. Module purpose:  To inform students as to the importance of renewable energy in the energy mix required for generation within nations. This is now becoming law in developed countries following the Kyoto agreement and green energy obligations. Students will learn as to the various energy generation options available from power scavengers for handheld calculators to energy generation to power the world’s energy need. Furthermore, an appreciation for the need for energy storage at all power levels will be discussed, with special emphasis on the materials requirements. Students will be able to examine next generation materials being proposed for meeting world's demand based on green energy.  

In this module, students  will learn about the different key concepts and elements comprising a modern wireless communication system, e.g. signal, transmitter, receiver, wireless channel, as well as understand the primary objectives of communication systems, i.e. reliability and sprectrum efficiency. They will do so by engaging with the various types of teaching materials presented to them as well as participating in our MATLAB lab sessions where they will be given the opportunity to analyse and simulate radio propagation behaviours/coverage as well as to model and simulate key aspects of a simple wireless communication system.

This module offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on its applications in modern communication systems. By taking this module, the student builds a strong foundation of AI/ML starting from the mathematical basics (linear algebra, probability, optimization) and the main algorithms and models (e.g. Decision Trees, Support Vector Machines, Deep Neural Networks, etc.) to the modern generative models, and reinforcement learning. Through the coursework, the student will gain hands-on experience with key AI/ML models applied to modern communication systems. By the end of the module, the student will not only understand the theory of machine learning but also develop the practical skills needed to implement and evaluate models for real-world applications in communications.

Module purpose: Advances related to energy efficiency issues and cost reductions have resulted in the rapid growth and deployment of networked devices and sensing/actuation systems that connect the physical world with the cyber world. This evolving framework, now recognized as the Artificial Intelligence of Things (AIoT), integrates IoT technologies with artificial intelligence (AI) to enhance automation, intelligence, and decision-making. AIoT incorporates several cutting-edge technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, distributed systems, and machine learning-driven analytics. Module Overview: The advanced AIoT module is designed to provide a comprehensive understanding of how machine communications , coupled with AI, contribute to creating smart artificial intelligence-driven environments, focusing on networking and communication systems. The module provides an overview of the key concepts and enabling technologies for AIoT. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for AIoT across physical, media access, and network layers. This includes security considerations, satellite AIoT, positioning and tracking for industrial applications, AIoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Also, the module explores AI-driven methodologies such as ensemble learning, multi-armed bandits for sequential decision-making, and lightweight machine learning Mmodels for AIoT applications. These techniques enhance real-time data analysis, predictive maintenance, anomaly detection, and adaptive control while ensuring computational efficiency.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: EEE3033 – RF and Microwave Fundamentals, or equivalent learning. Module purpose: Advanced communications systems and radar operate at RF and microwave frequencies. The design principles and circuit operation, underlying these systems, are quite different from those of electronics used in signal processing at baseband frequencies. This module will cover the key elements of RF and microwave system design as well as analysis concepts for a range of commonly used active circuits, including: oscillators, frequency synthesisers, amplifiers and mixers. The module will also cover the circuit design and operation of non-linear devices used in active circuits together with deployment considerations. This module will include and build on many of the concepts studied in EEE3033 RF and Microwave Fundamentals and address further advanced features of non linear RF devices and system optimisation. It is complementary to EEEM064 Microwave Design Techniques and also EEEM006 Antennas and Propagation.

Expected prior learning:  Module EEE3008–Fundamentals of Digital Signal Processing  or equivalent learning about signal processing. Module purpose:  The module discusses basic concepts, signal processing methods and human computer interaction applications of speech processing and recognition including auditory perception and psychoacoustics. You will be taught how to extract salient features from speech signals, how to design a model of spoken language, how to perform recognition and training, and given an insight into current research on spontaneous speech recognition, such as speaker adaptation and solutions for robustness to noise. Demonstrations, interesting illustrations and working examples will be given. Successful students can either proceed to do PhDs or get jobs in the R & D departments of industry, i.e. jobs that are at a higher level than mere software package operators. The presented techniques have many other applications beyond speech, including expert systems and financial modelling. Module EEEM030 contributes to the development of student’s knowledge in audio and speech processing and recognition, which may be useful for their taking of other modules such as: EEEM071 Advanced Topics in Computer Vision and Deep Learning EEEM004 60 Credit Standard Project EEEM005 AI and AI Programming EEEM066 Fundamentals of Machine Learning EEEM067 AR, VR and Metaverse EEEM068 Applied Machine Learning Module EEEM030 contributes to student knowledge in audio and speech processing and thus useful for students taking 60 credit project (EEEM004) related to audio and speech processing and recognition. EEEM030 is related to EEEM005, EEEM066 and EEEM068 due to the fact that machine learning/AI techniques are used for speech and speaker recognition, therefore, EEEM030 contributes to the development of student knowledge in machine learning/AI, which is beneficial for their taking of machine learning and AI related modules such as EEEM005, EEEM066 and EEEM068. One of the applications of audio and speech processing is to apply it to AR, VR and Metaverse for spatial sound production and reproduction which is a key enabling technology for AR, VR, and Metaverse, for virtual sound reproduction. Therefore, knowledge gained from EEEM030 would be useful for the taking of the module EEEM067. Module EEE030 also benefits from knowledge gained from other modules such as: EEE3008 Digital Signal Processing EEE1033 Computer and Digital Logic EEE1035 Programming  in C EEE3042 Audio and Video Processing EEE3032 Computer Vision and Pattern Recognition The modules EEE1033 and EEE1035 provide students with some useful skills in programming, which will be beneficial for them to complete the computer programming based coursework components, by turning the signal processing theories and methods into working program codes. The module EEE3008 provides students with knowledge and skills on fundamental digital signal processing skills which are essential in understanding the application of these skills to audio and speech data. EEE3042 covers both audio and video processing and coding, and the audio related materials are highly relevant and thus useful for the EEEM030 module. The pattern recognition skills gained from EEE3032 would be useful for understanding the use of pattern recognition algorithms to speech data for achieving speech and speaker recognition.  

Expected prior learning:  None specifically advised. Module purpose:  Earth and planetary observation with remote sensing data is playing a key role in the present understanding of natural phenomena, prevention of disasters, resources monitoring, comprehension of origins of life. Through a series of lectures, seminars, open discussions and “thinking breaks” in class, the module aims to give an introduction to the scientific principles of remote sensing – both passive and active – as carried out by spacecraft. Remote sensing is discussed in terms of instrumentation, missions, products and applications. IMPORTANT: The Second assessment pattern (Written Exam) is only applicable to the MSc Short Course Students.

Expected prior learning: Module EEE2040 – Communications Networks or equivalent learning. Module purpose: The Internet is an important worldwide communications system; the module provides an in-depth treatment of current and evolving Internet protocols and standards, and the algorithms that underlie them. The module also permits further study on networking in modules such as EEEM018 Advanced Mobile Communication Systems, EEEM023 Network Service management and Control, EEEM032 Advanced Satellite Communication Techniques

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

Expected prior/parallel learning:  Students should have an interest in materials and devices; it would be of benefit to have studied EEE3037 Nanoscience and Nanotechnology. Module purpose:  Nanoelectronics represent the ultimate in advanced electronic device design and operation. At the nanoscale the quantum nature of matter is evident in the operation of faster, energy efficient devices with greater functionality. New materials, such as graphene and new molecular electronic materials, offer unique electronic properties which continually emerge in parallel to advances in device architecture and performance aligned to the International Roadmap for Devices and Systems. This module will introduce some of the key concepts in low dimensional mesoscopic science and engineering, and molecular electronics and associated devices. The module builds on materials seen earlier in the undergraduate programme such as in EEE2042 Electronic and Photonic Devices, EEE2045 Electrical Science II, EEE3037 Nanoscience and Nanotechnology and EEE3041 Semiconductor Devices and Optoelectronics.

Expected prior/parallel learning: This module covers advanced topics on satellite communications and networks, following the Satellite Communication fundamentals (EEEM031). An alternate module containing suitable prior learning is, Space System Design (EEE3040). Module purpose: This module covers advanced topics on satellite communications and networks. These networks are an important part of global information infrastructure providing broadcasting, mobile and broadband services to millions of homes and offices as well as disaster relieves and emergency communications services.

Developing high-performance energy storage devices such as lithium-ion batteries could greatly promote the development of portable electronics, vehicle electrification and smart grid, alleviate environmental pollution and reduce our dependence on fossil fuels, addressing the “grand challenges” in the sustainability and resilience of environments. This module aims to introduce fundamental scientific, technological or engineering principles and technology applications of batteries used in different electrical systems. Students will learn as to the battery types, battery parts and how to test/monitor a battery. Battery performance requirement by electric vehicles, smart grids, next-generation electronics and electrical systems will also be covered in this module. The discussion of new materials goes beyond that found in EEE3037 Nanoscience and Nanotechnology.

Wireless networks (wide area cellular / satellite, local area residential / business, and ad-hoc / mesh) are an essential part of modern day communication / computing facilities. The network deployment, planning and management are the key issues ¿ all the network components must conform to the given hardware and software specifications in order that they can be interconnected and work successfully for the intended Quality of Service (QoS). This module introduces essential concepts about the network planning, network deployment and network management for a successful running of the network with a focus on software defined networking and virtualisation.

Expected prior learning:  Module EEE2042 – Electronic and Photonic Devices, or equivalent learning, is advisory but not required. Module purpose:  Semiconductor devices and optoelectronics play a major technology enabling amongst other things the internet. The course is given via a series of lectures and aims to give a background to the interaction of light with key photonic materials and devices. This module will introduce students to modern energy efficient electronic and photonic devices concentrating on the fundamental science of operation, device structure and characteristics.

IMPORTANT: The second assessment pattern is only applicable to the MSc Short Course Students Expected prior learning: None. Module purpose: Antennas and the propagation of radio on the physical layer (PHY) are a fundamental aspect of communications, space and radar as well as any other device that will radiate electromagnetic waves over an air interface. In order for wireless devices to operate and comply with suitable standards, it is important that they use a suitable antenna design, while also modelling the propagation environment the device may encounter is important for purposes of testing the radio transceiver, thus knowledge of propagation modelling is required to achieve this.  

Expected prior learning: EEE3033–RF and Microwave Fundamentals, or equivalent learning. Module purpose: At Gigahertz frequencies the operating wavelength is small. Devices operating in the Gigahertz frequency range are therefore electrically large compared to the operating wavelength. In such cases, it is no longer appropriate to use traditional lumped element circuit components. The passive devices used at Gigahertz frequencies thus have to be made either using printed transmission lines (e.g. microstrip, coplanar waveguide) or waveguide. This module will present microwave design and analysis concepts for a range of commonly used passive circuits using both microstrip lines and waveguides, including: transmission lines, coupling networks, antennas and filters. This module will revisit and build on the use of the Smith chart also introduced in EEE3033 RF and Microwave Fundamentals and use it for further design applications of microstrip circuits. It is complementary to module EEEM044 RF Systems and Circuit Design and EEEM006 Antennas and Propagation.

The purpose of this module is to equip students with advanced knowledge of key concepts in modern mobile and wireless communication systems, such as 5G, while also exploring emerging concepts that are likely to shape future technologies, including 6G. By completing this module, students will gain the expertise needed to work with wireless communications technologies in industry or to pursue further academic research in this field. The module bridges foundational knowledge in wireless communications with cutting-edge techniques and promising innovations that can enhance future communication systems, fostering cognitive and conceptual connections across these domains.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.  

Module purpose: This course offers an introduction to image processing and computer vision for those interested in the science and technology of machine vision. It provides background and the theory for building artificial systems that manipulate videos and images and alter or analyse their information content. This is done by various computer algorithms that are discussed, implemented and demonstrated.