BEng (Hons) or MEng — 2026 entry Electronic Engineering with Artificial Intelligence

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Expected prior learning:  Learning equivalent to Year 1, and Year 2 Semester 1, of EE Programmes. Module purpose: This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds up EEE1035 Programming in C. Mathematical methods used to represent cameras, scene geometry and lighting in both computer vision and graphics are covered. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games, and scientific visualisation. Practical implementation of computer graphics will be introduced using the OpenGL libraries which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032 Computer Vision and Pattern Recognition.    

Expected prior learning:  A good working knowledge of procedural programming, preferably in the C programming language.  [Surrey EEE students should have achieved this in their Year 1 studies. Module purpose:  Object orientated programming (OOP) is a popular programming methodology for large application programming. C++ is a powerful programming language which, being backwards compatible with C, provides efficient access to low level components of a system. This makes it important for Electronic Engineering yet it is also a fully functioning industrially recognized language for large scale application programming. The module will provide students with the fundamentals of Object Orientated Design and Programming, with specific emphasis on its implementation in the C++ language.

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

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)

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

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:  Module EEE2033 – Electronics III: Circuits, Control and Communications or equivalent learning. Module purpose:  Control Engineering covers classical control theory as well as more modern methods. Students have the opportunity in this module to evaluate and apply various control techniques. This module builds on basic concepts previously introduced in Electronics III: Circuits, Control and Communications EEE2033, or equivalent learning and builds the fundamentals for a subsequent career as a control engineer or systems engineer.

Expected prior learning: Modules EEE2033 – Circuits, Control and Communications, or equivalent learning. Knowledge of linear systems and of the basics of control engineering is particularly helpful. Module purpose: This module aims to develop a better understanding of transistor amplifiers, power semiconductor switching devices and various power converters. A detailed analysis of power converters like AC to DC phase controlled rectifiers, AC to AC, DC to DC converter & Pulse Width Modulated (PWM) inverters will be provided. In order to develop a broader understanding of this subject, a few domestic & industrial applications will be taught in this module.

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

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Expected prior learning:  Learning equivalent to Year 1, and Year 2 Semester 1, of EE Programmes. Module purpose: This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds up EEE1035 Programming in C. Mathematical methods used to represent cameras, scene geometry and lighting in both computer vision and graphics are covered. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games, and scientific visualisation. Practical implementation of computer graphics will be introduced using the OpenGL libraries which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032 Computer Vision and Pattern Recognition.    

Expected prior learning:  A good working knowledge of procedural programming, preferably in the C programming language.  [Surrey EEE students should have achieved this in their Year 1 studies. Module purpose:  Object orientated programming (OOP) is a popular programming methodology for large application programming. C++ is a powerful programming language which, being backwards compatible with C, provides efficient access to low level components of a system. This makes it important for Electronic Engineering yet it is also a fully functioning industrially recognized language for large scale application programming. The module will provide students with the fundamentals of Object Orientated Design and Programming, with specific emphasis on its implementation in the C++ language.

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

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)

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

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:  Module EEE2033 – Electronics III: Circuits, Control and Communications or equivalent learning. Module purpose:  Control Engineering covers classical control theory as well as more modern methods. Students have the opportunity in this module to evaluate and apply various control techniques. This module builds on basic concepts previously introduced in Electronics III: Circuits, Control and Communications EEE2033, or equivalent learning and builds the fundamentals for a subsequent career as a control engineer or systems engineer.

Expected prior learning: Modules EEE2033 – Circuits, Control and Communications, or equivalent learning. Knowledge of linear systems and of the basics of control engineering is particularly helpful. Module purpose: This module aims to develop a better understanding of transistor amplifiers, power semiconductor switching devices and various power converters. A detailed analysis of power converters like AC to DC phase controlled rectifiers, AC to AC, DC to DC converter & Pulse Width Modulated (PWM) inverters will be provided. In order to develop a broader understanding of this subject, a few domestic & industrial applications will be taught in this module.

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

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Expected prior learning:  Learning equivalent to Year 1, and Year 2 Semester 1, of EE Programmes. Module purpose: This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds up EEE1035 Programming in C. Mathematical methods used to represent cameras, scene geometry and lighting in both computer vision and graphics are covered. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games, and scientific visualisation. Practical implementation of computer graphics will be introduced using the OpenGL libraries which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032 Computer Vision and Pattern Recognition.    

Expected prior learning:  A good working knowledge of procedural programming, preferably in the C programming language.  [Surrey EEE students should have achieved this in their Year 1 studies. Module purpose:  Object orientated programming (OOP) is a popular programming methodology for large application programming. C++ is a powerful programming language which, being backwards compatible with C, provides efficient access to low level components of a system. This makes it important for Electronic Engineering yet it is also a fully functioning industrially recognized language for large scale application programming. The module will provide students with the fundamentals of Object Orientated Design and Programming, with specific emphasis on its implementation in the C++ language.

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

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)

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

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:  Module EEE2033 – Electronics III: Circuits, Control and Communications or equivalent learning. Module purpose:  Control Engineering covers classical control theory as well as more modern methods. Students have the opportunity in this module to evaluate and apply various control techniques. This module builds on basic concepts previously introduced in Electronics III: Circuits, Control and Communications EEE2033, or equivalent learning and builds the fundamentals for a subsequent career as a control engineer or systems engineer.

Expected prior learning: Modules EEE2033 – Circuits, Control and Communications, or equivalent learning. Knowledge of linear systems and of the basics of control engineering is particularly helpful. Module purpose: This module aims to develop a better understanding of transistor amplifiers, power semiconductor switching devices and various power converters. A detailed analysis of power converters like AC to DC phase controlled rectifiers, AC to AC, DC to DC converter & Pulse Width Modulated (PWM) inverters will be provided. In order to develop a broader understanding of this subject, a few domestic & industrial applications will be taught in this module.

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

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

Expected prior/parallel learning: It is helpful, but not essential, to take module EEE3006 – Digital Communications, or to have equivalent learning.  Module purpose:  This module equips students with fundamental knowledge and skills of mobile/personal communications systems design and forms the basis for the students to conduct further learning of advanced mobile technologies in EEEM018 – Advanced Mobile Communication Systems and EEEM061 – Advanced 5G Wireless Technologies.

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/parallel learning: Basic knowledge of hardware systems and module EEE2047 (Object-Oriented Programming and C++) or equivalent knowledge of C++ or Java programming. 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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The advanced IoT module is designed to provide a comprehensive understanding of how machine communications 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 the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Â Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.  

Module purpose: This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

The module introduces general information and network security principles, challenges and goals and then focuses on main security mechanisms and protocols for protecting network communication across different layers of the Internet protocol stack. This will include discussion on various attacks on the networks, penetration testing tools and possible countermeasures to ensure protection of authentication, confidentiality and end-to-end security of communications. In labs students will be able to practice experience with various network security protocols and tools.

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:     N/A Module purpose:  Mobile communications systems have been among the fastest growing sectors of the global economy in the past decade and are expected to drive tremendous developments in the upcoming decade. Modern mobile communication systems employ a range of advanced wireless communication techniques and networking technologies/protocols to deliver high rate and high-quality services for a variety of mobile applications. This module aims to cover a number of key advanced concepts that are either used in the modern mobile communication systems or expected to be deployed in the future. Module EEEM018 benefits from Module EEE3007 Data and Internet Networking in relation to Internet protocols.  

Computer networks are an essential part of almost all corporate computing facilities and even most domestic ones.  Interoperability is the key – all components must conform to the same hardware and packet specifications in order that they can be interconnected successfully.  This module introduces essential concepts about all the computer networking layering levels with some emphasis on the routing algorithms and implementation of network sensing. 

Expected prior learning:  Learning equivalent to Year 1, and Year 2 Semester 1, of EE Programmes. Module purpose: This module provides an introduction to the process of digital image formation in real and computer-generated imagery and builds up EEE1035 Programming in C. Mathematical methods used to represent cameras, scene geometry and lighting in both computer vision and graphics are covered. The course introduces both the theoretical concepts and practical implementation of three-dimensional computer graphics used in visual effects, games, and scientific visualisation. Practical implementation of computer graphics will be introduced using the OpenGL libraries which are widely used in industry. Some of the concepts developed in this module will be useful in other computer vision modules such as EEE3032 Computer Vision and Pattern Recognition.    

Expected prior learning:  A good working knowledge of procedural programming, preferably in the C programming language.  [Surrey EEE students should have achieved this in their Year 1 studies. Module purpose:  Object orientated programming (OOP) is a popular programming methodology for large application programming. C++ is a powerful programming language which, being backwards compatible with C, provides efficient access to low level components of a system. This makes it important for Electronic Engineering yet it is also a fully functioning industrially recognized language for large scale application programming. The module will provide students with the fundamentals of Object Orientated Design and Programming, with specific emphasis on its implementation in the C++ language.

This module introduces general concepts of privacy enhancing technologies and aligns with key concepts recommended by the CyBoK. It will motivate the need for privacy in the modern world and touch on legal considerations, introduce concepts of transparency, control and confidentiality for privacy, and look at privacy preserving and democratic values. This module will also explore how these are realised in a range of applications.

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)

Expected prior learning: Module EEE2041 – Computer Vision & Graphics, or equivalent learning about the geometric interpretation of Linear Algebra (e.g. homogeneous coordinates and matrices for point transformation e.g. rotation, translation, scaling). Module purpose: The module delivers a grounding in Computer Vision, suitable for students with a grounding in linear algebra similar to that provided by EEE2041 – Computer Vision & Graphics) and will help with modules such EEEM071 Advanced Topics in Computer Vision and Deep Learning. Content is presented as an application-focused tour of Computer Vision from the low-level (image processing), through to high level model fitting and object recognition.  

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:  Module EEE2033 – Electronics III: Circuits, Control and Communications or equivalent learning. Module purpose:  Control Engineering covers classical control theory as well as more modern methods. Students have the opportunity in this module to evaluate and apply various control techniques. This module builds on basic concepts previously introduced in Electronics III: Circuits, Control and Communications EEE2033, or equivalent learning and builds the fundamentals for a subsequent career as a control engineer or systems engineer.

Expected prior learning: Modules EEE2033 – Circuits, Control and Communications, or equivalent learning. Knowledge of linear systems and of the basics of control engineering is particularly helpful. Module purpose: This module aims to develop a better understanding of transistor amplifiers, power semiconductor switching devices and various power converters. A detailed analysis of power converters like AC to DC phase controlled rectifiers, AC to AC, DC to DC converter & Pulse Width Modulated (PWM) inverters will be provided. In order to develop a broader understanding of this subject, a few domestic & industrial applications will be taught in this module.

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

Module purpose: Modern robotics brings together many aspects of engineering including electronics, hardware, software and AI. This leads to complex asynchronous systems that requires a systems engineering approach. The Robotics Operating System (ROS), is an extensive community built software suite that underpins most leading-edge robotics development. It provides extensive hardware interfacing and high-level functionality which allows complex systems engineering and control while abstracting away much of the complexity inherent to robotics systems design. This module will use ROS to provide a solid foundation in systems engineering based robotics.

Expected prior/parallel learning: It is helpful, but not essential, to take module EEE3006 – Digital Communications, or to have equivalent learning.  Module purpose:  This module equips students with fundamental knowledge and skills of mobile/personal communications systems design and forms the basis for the students to conduct further learning of advanced mobile technologies in EEEM018 – Advanced Mobile Communication Systems and EEEM061 – Advanced 5G Wireless Technologies.

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/parallel learning: Basic knowledge of hardware systems and module EEE2047 (Object-Oriented Programming and C++) or equivalent knowledge of C++ or Java programming. 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. The resulting framework, known as the Internet of Things (IoT), incorporates several technologies, including wireless sensor networks, pervasive systems, ambient intelligence, context awareness, and distributed systems. The advanced IoT module is designed to provide a comprehensive understanding of how machine communications 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 the Internet of Things. It encompasses a cross-layer approach, allowing students to explore the practical aspects of sensors, actuators, and mainly communication systems for IoT across physical, media access, and network layers. This includes security considerations, satellite IoT, positioning and tracking for industrial applications, IoT Platforms (Hardware, Software), protocols and standards (e.g. 6LowPAN, ZigBee, CoAp), semantic technologies, and data and information processing mechanisms. Â Also, the module seamlessly integrates cutting-edge machine learning techniques tailored for IoT applications, ensuring optimal performance and adaptability.  

Module purpose: This course offers an introduction to machine learning for those interested in the science and technology of Artificial Intelligence (AI). It provides background and the theory for building fundamental artificial systems that can process a variety of data and analyse their semantic information of interest. This is implemented by various fundamental learning algorithms that will be discussed and demonstrated in an easy-to-approach manner.

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

The module introduces general information and network security principles, challenges and goals and then focuses on main security mechanisms and protocols for protecting network communication across different layers of the Internet protocol stack. This will include discussion on various attacks on the networks, penetration testing tools and possible countermeasures to ensure protection of authentication, confidentiality and end-to-end security of communications. In labs students will be able to practice experience with various network security protocols and tools.

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:     N/A Module purpose:  Mobile communications systems have been among the fastest growing sectors of the global economy in the past decade and are expected to drive tremendous developments in the upcoming decade. Modern mobile communication systems employ a range of advanced wireless communication techniques and networking technologies/protocols to deliver high rate and high-quality services for a variety of mobile applications. This module aims to cover a number of key advanced concepts that are either used in the modern mobile communication systems or expected to be deployed in the future. Module EEEM018 benefits from Module EEE3007 Data and Internet Networking in relation to Internet protocols.