BEng (Hons) or MEng — 2026 entry Computer and Internet Engineering

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.

The understanding of security issues is arguably more important than ever before. This module covers the basic principles behind computer security.

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: Knowledge of basic properties of signals and related methods, including Fourier series, Fourier transforms, Laplace transforms, and convolution. BEng/MEng students might have acquired this through study of module EEE2035 Engineering Mathematics III and/or module EEE2033 Circuits, Control and Communications. MSc students might have acquired this through study of an undergraduate module on “Signals and Systems” or through independent study. Module purpose: This introductory course in Digital Signal Processing explores mathematical tools used to represent, analyse and design basic DSP systems. This module underpins many key areas of digital systems, including audio-visual technology, digital communications, control systems, and computer vision. This topic is therefore of paramount importance to any electronic engineer. This module EEE3008 provides the expected prior learning for the following module EEEM030 Speech & Audio Processing and Recognition Taking module EEE3008 may also contribute to a deeper understanding of the following modules EEE3032 Computer Vision and Pattern Recognition, EEE3042 Audio and Video Processing and EEEM071 Advanced Topics in Computer Vision and Deep Learning

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.  

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.

The understanding of security issues is arguably more important than ever before. This module covers the basic principles behind computer security.

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: Knowledge of basic properties of signals and related methods, including Fourier series, Fourier transforms, Laplace transforms, and convolution. BEng/MEng students might have acquired this through study of module EEE2035 Engineering Mathematics III and/or module EEE2033 Circuits, Control and Communications. MSc students might have acquired this through study of an undergraduate module on “Signals and Systems” or through independent study. Module purpose: This introductory course in Digital Signal Processing explores mathematical tools used to represent, analyse and design basic DSP systems. This module underpins many key areas of digital systems, including audio-visual technology, digital communications, control systems, and computer vision. This topic is therefore of paramount importance to any electronic engineer. This module EEE3008 provides the expected prior learning for the following module EEEM030 Speech & Audio Processing and Recognition Taking module EEE3008 may also contribute to a deeper understanding of the following modules EEE3032 Computer Vision and Pattern Recognition, EEE3042 Audio and Video Processing and EEEM071 Advanced Topics in Computer Vision and Deep Learning

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.  

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.

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.    

The understanding of security issues is arguably more important than ever before. This module covers the basic principles behind computer security.

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: Knowledge of basic properties of signals and related methods, including Fourier series, Fourier transforms, Laplace transforms, and convolution. BEng/MEng students might have acquired this through study of module EEE2035 Engineering Mathematics III and/or module EEE2033 Circuits, Control and Communications. MSc students might have acquired this through study of an undergraduate module on “Signals and Systems” or through independent study. Module purpose: This introductory course in Digital Signal Processing explores mathematical tools used to represent, analyse and design basic DSP systems. This module underpins many key areas of digital systems, including audio-visual technology, digital communications, control systems, and computer vision. This topic is therefore of paramount importance to any electronic engineer. This module EEE3008 provides the expected prior learning for the following module EEEM030 Speech & Audio Processing and Recognition Taking module EEE3008 may also contribute to a deeper understanding of the following modules EEE3032 Computer Vision and Pattern Recognition, EEE3042 Audio and Video Processing and EEEM071 Advanced Topics in Computer Vision and Deep Learning

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.  

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.  

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.

Students in this module will learn about the different key concepts and elements comprising a wireless communication system, e.g. signal, transmitter, receiver, channel, as well as understand the primary objectives of communication systems, i.e. reliability and 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 via a state-of-the-art software platform as well as to model and simulate a simple wireless communication system.

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.

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.

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.

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.

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.

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.

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.

The understanding of security issues is arguably more important than ever before. This module covers the basic principles behind computer security.

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: Knowledge of basic properties of signals and related methods, including Fourier series, Fourier transforms, Laplace transforms, and convolution. BEng/MEng students might have acquired this through study of module EEE2035 Engineering Mathematics III and/or module EEE2033 Circuits, Control and Communications. MSc students might have acquired this through study of an undergraduate module on “Signals and Systems” or through independent study. Module purpose: This introductory course in Digital Signal Processing explores mathematical tools used to represent, analyse and design basic DSP systems. This module underpins many key areas of digital systems, including audio-visual technology, digital communications, control systems, and computer vision. This topic is therefore of paramount importance to any electronic engineer. This module EEE3008 provides the expected prior learning for the following module EEEM030 Speech & Audio Processing and Recognition Taking module EEE3008 may also contribute to a deeper understanding of the following modules EEE3032 Computer Vision and Pattern Recognition, EEE3042 Audio and Video Processing and EEEM071 Advanced Topics in Computer Vision and Deep Learning

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.  

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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.

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

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

This module introduces students to the techniques and tools to discover known vulnerabilities in systems and applications, and use appropriate techniques to carry out attacks. A practical project-based assessment allows students to demonstrate their ability to successfully exploit vulnerable systems to exfiltrate data and circumvent access controls. The module will introduce students to the legal and ethical considerations of ethical hacking, as well as the processes by which vulnerabilities are reported, classified, documented, and mitigated by security practitioners.

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.  

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.

Students in this module will learn about the different key concepts and elements comprising a wireless communication system, e.g. signal, transmitter, receiver, channel, as well as understand the primary objectives of communication systems, i.e. reliability and 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 via a state-of-the-art software platform as well as to model and simulate a simple wireless communication system.

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.

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.

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.

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.

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.

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.