School of Engineering, Computing and Mathematics Semester Abroad Scheme

The School of Engineering, Computing and Mathematics (SECaM) invite BTech and BE students in their eighth semester who are taking courses in the areas of electronics; robotics; computer science or electrical engineering to join the second semester in Plymouth in January/February 2020 to complete a project alongside our own BEng students.

This year, nominated students will be able to apply for one of our proposed projects listed below. 

The application process is competitive and applicants will be judged via the following criteria: 

• previous academic performance (academic transcripts must be supplied)

• your personal statement (you must explain why you want to apply for one of our projects and how it aligns with your own academic background, interests and career prospects)

• quality of application.

We will not accept applications for projects that are not listed on this page.

How to apply

To apply please complete the application form along with the following supporting documents:

• personal statement – acceptance of your application will be based mainly on the quality of your application. This should express why you have applied for your selected project and how your current studies, academic background and career prospects match with your selected project

• your third, fourth, fifth and sixth (seventh if possible) semester results

• copy of the photo page of your passport

• evidence of English Language (IELTS or Indian Senior School Certificate Examination).

Project 1

Biomemristors Network

The Interdisciplinary Centre for Computer Music Research in developing new kinds of computers using electronic components grown out of biological material. The team has developed an unprecedented biological memristor and an approach to using such components to build interactive generative music systems. The memristor is a relatively less well-known electronic component regarded as a resistor with memory. This project is aimed at developing a neural network of biomemristors, develop an algorithm for machine learning and test it with music data.

Skills required to develop this project are knowledge of Python, knowledge of microcontroller boards such as Arduino and/or Rasperry Pi, some understanding of music technology, neural networks and electronics.

Supervisors: Professor Eduardo R. Miranda and Dr Edward Braund

Project 2

Hybrid Memristive Circuits

The Interdisciplinary Centre for Computer Music Research developed an unprecedented biological memristor and an approach to using them to build interactive generative music systems. Biomemristors present more non-linear behaviour than silicon-based ones. This project is to develop experiments to understand of (a) how to control its non-linearity and (b) how to combine bio and silicon memristors in the same circuit. This technology is aimed at the development of hybrid wetware-hardware processors for the computers of the future. The domain application of the experiments will be interactive music systems.

Skills required to develop this project are knowledge of Python, knowledge of microcontroller boards such as Arduino and/or Rasperry Pi, some understanding of music technology, benchmarking methods, and electronics.

Supervisor:  Professor Eduardo R. Miranda

Project 3

Bio-logic Gates

The Interdisciplinary Centre for Computer Music Research has developed an unprecedented biological memristor, which is capable of functioning as a resistor with memory and as a transistor. This project is aimed at developing ways to enable the biomemristor to perform digital logic gates. The overall aim of the project is to develop ways to harness the analogue nature of the biomemristors in order to perform sequences of low-level programming instructions. The experiments will be developed in the context of computer music.

Skills required to develop this project are good grasp of Boolean logics, knowledge of Python, knowledge of microcontroller boards such as Arduino and/or Rasperry Pi. Familiarity with low-level computer programming (e.g., assembler) is desirable, but not essential.

Supervisors:  Professor Eduardo R. Miranda and Dr Edward Braund

Project 4

Brain-Computer Interfacing for Robotic Control

The Interdisciplinary Centre for Computer Music Research is a world-leader in the field of Brain-Computer Music Interfacing (BCMI). A BCMI systems uses electrical activity of the brain, referred to as EEG (short for electroencephalogram) to control musical systems, such as generative music software and digital sound synthesisers. The EEG is detected by means of electrodes placed on the scalp of a person. The main aim of our research is to enable people with severe motor impairment (e.g. paralysis of the limbs) to make music. This project is to develop a BCMI system to control robotic devices, such as an arm or a prosthetic hand. The end goal of this research is to develop robotic technology to play musical instruments; e.g., play drums with the robotic arm.

Skills required to develop this project are knowledge of Matlab and Python. Knowledge of signal processing is required to analyse EEG signals.

Supervisor:  Professor Eduardo R. Miranda

Project 5

Quantum Computing Library for Musical Applications

The Interdisciplinary Centre for Computer Music Research is pioneering research into using quantum computing for creativity, in particular musical composition. As part of a collaboration with a company that is developing Quantum Computers, we have access to quantum computing hardware and powerful simulators to develop our research. This project is aimed at the development of a library with tools for implementing algorithms for musical composition with quantum computers, using classical quantum algorithms (e.g., Shor’s algorithms) and also develop new ones specific for music.

Skills required to develop this project are solid knowledge of Python programming, matrix operations, and a good understanding of quantum physics principles. Familiarity with the field of computer music, in particular algorithmic music composition methods, is desirable.

Supervisor: Professor Eduardo R. Miranda

Project 6

Generative Systems with Quantum Annealing

Generally speaking, there are two kinds of quantum computing technology being developed today: gate-based quantum computers and quantum annealers. Quantum annealers are aimed at finding optimal solutions to problems by quickly searching over a large space of possibilities. Musical composition can be considered as a task involving search for solutions among a set of combinatorial possibilities.  The aim of this project is to develop a system to generate musical tunes using quantum annealing. The skills gained from developing this project would be transferable other creative problems that involve searching in very large space of possible solutions, such as games and natural language processing.

Skills required to develop this project are solid knowledge of Python programming, familiarity with methods to address optimisation problems. Strong mathematical background is required. Familiarity with the field of computer music, in particular algorithmic music composition methods, is desirable.

Project 7

Analysis of Twitter Trending Topics

Social media companies, such as Twitter, deal with petabytes of information on a daily basis. Regrettably, people often find it difficult to cope with the overwhelming stream of data published by millions of users worldwide. Scientists have already worked out ways to identify Twitter trending topics, as a means to index information and make sense of it. However, we know little about the impact on trending topics of various intrinsic factors associated with the Twitter ecosystem. For example, anecdotal evidence suggests that trending topics are characterised by highly polarised tweets, or that large audiences typically host the emergence of trending topics. However, no study has yet addressed these issues formally. To remedy this situation, we will investigate the nature of trending topics in this project. We will uncover any correlation between sentiment polarity and trending topics (and confirm whether the strength of the polarity drops as the trending topics fade away). Similarly, we will run experiments to determine the relationship between the size of a Twitter audience and the rise of trending topics.

Skills required: Good programming skills are required. Knowledge of a high level programming language (such as Java, C# or Python) is indispensable. Knowledge of databases (either relational or non-relational) is desirable, but not indispensable. Ability to understand and convey complex information is expected. Help will be provided throughout the project, but students will be required to develop their own software, based on algorithm descriptions.

Supervisor: Dr Marco Palomino

More information (PDF)

Project 8

SDN-Assisted Delivery of Adaptive Video Streaming

Over the recent years, real-time Internet applications and services (e.g. VOIP, Video streaming, and online gaming) have been increasing at a tremendous rate. The increasing consumption of multimedia services and the demand of high quality services from customers pose challenges to service providers and mobile operators in terms of Quality of Service/Quality of Experience (QoS/QoE) control and management. Recently, Software-defined networking (SDN) has emerged as a network model that decouples forwarding elements (e.g. switches, routers, etc.) from control features and provides an efficient way for resources monitoring and utilization, centralized network management, and network programmability. Acquiring a guaranteed QoS and QoE for network traffic and end user by using SDN is drawing the attention of industrial and academic sectors. Therefore, there is a need for QoS/QoE control and management solutions pertaining to programmable networks, cost reduction of IT systems, high speed and reliable transfer of heterogeneous data over software defined and virtualized infrastructures. The main objective of this project is to develop an SDN-based frameworks for improving the received quality at the end-users. The developed framework should be able to provide automated functionality and adjust dynamically to changing network conditions (e.g., during congestion state) and allow for proactive network management regardless of any situation within the network or the end user’s side.

Skills required: SDN, Python, JavaScript, Websocket, RESTful programming, Node.js & WebSocket, HTML5. Also, it would be useful if the applicants have the following skills: SDN Ryu/Opendayligh controller, DASH.js, WebRtc, Video encoding, TCPdump, Wireshark

Supervisor: Dr Bogdan Ghita

Project 9 

User IP traffic profiling

In order to analyse network traffic and describe the user activities, raw traffic must be processed and converted into a set of statistical parameters suitable for analysis. This project will use tcpdump or Netflow as input to produce a set of IP traffic parameters for each user in a local network. The project can be implemented in python and must be run in the Linux environment; the implemented script/software must take the input (Netflow or tcpdump), parse it, calculate a list of parameters for each IP address (e.g. number of remote IP addresses or ports contacted) and produce a text-based output (csv); the software must also be able to include DNS queries/information (which will be collected separately) into the output (e.g. number of remote domains contacted). 

Skills required: Working experience of Linux, network analysis tools (tcpdump and netflow), and programming (python or other linux-based scripting/programming languages). 

Supervisor: Dr Bogdan Ghita

Project 10

Visually Representing Insight into the Future

The importance of anticipating future events on the basis of diverse sources has long been recognised. Presently, future-oriented analysis plays a significant role in policy and decision making, and the use of search engines to gain insight into the future is gaining momentum. In this project, we will approach the design and implementation of an interface to browse future-related information derived from search engine results. Our main contribution will consist of engineering a solution to the problem of retrieving future information that combines existing technologies into a working system. While parts of our solution can be found separately in other systems, we combine them all in a suitable manner. 

Skills required:  Good programming skills are expected for this project. Knowledge of a high level programming language—such as Java, C# or Python—is indispensable. Knowledge of databases—either relational or non-relational—is desirable, but not indispensable. Ability to understand and convey complex information in a clear and accurate manner is expected. Help will be provided throughout the project, but students will be required to develop their own software derived from algorithm descriptions. 

Supervisor: Dr Marco Palomino

More information (PDF)

Project 11

Graphene Biomedical Sensors

This project will explore fabrication and testing of graphene sensors for a range of biomedical applications (Alzheimers's, Cancer and heart related conditions). The successful applicant will work as part of a team of students and research staff with access to state-of-art instruments in a Class-100 Clean Room, such as Parameter analysers, atomic force microscope, Raman spectroscopy etc. 

Skills required: This is a challenging project and requires the applicant to be familiar with accurate electrical current, voltage, resistance measurements and software tools such as MatLab, Origin, Excel and preferably LabView also. 

Supervisor: Dr Shakil Awan

Project 12

LabView based measurements of electronic components

This project will interface MyDAQ with LabView to collect current-voltage data from electronic components. The data collected will contribute to our research on graphene biosensors. 

Supervisor: Dr Shakil Awan

Project 13

LabVIEW Controller for a Nanoparticle Measurement System

The Nanomaterials laboratory has a highly sensitive optical system – Surface Plasmon Resonance – for detecting very low concentrations of nanoparticles and micro-plastics. A new control system using LabVIEW is required to control the motors which synchronously move the laser (probe) and the detector with high accuracy and precision. The position signals are determined from a 14 bit optical encoder. The detection of NPs is based upon the measurement of the detected signal vs angle of a small angular range. Detection capability can be further enhanced by measuring the detected signal using a lock-in amplifier.

In this project the opto-mechanical system is fully functional. The LabVIEW controller software will enable some exciting measurements to be made, with many possibilities to become a co-author in high impact factor publication. This is excellent for your CV and will give added benefit to those wishing to do research after graduating.

Skills required: Knowledge of LabVIEW. This project falls into the arena of nanotechnology and nanodevices and is particularly suitable for electrical and electronic engineering students.

Supervisor: Dr David Jenkins

Project 14

Gelf’and Pinsker coding of relatively low noise narrowband ultrasound channels

Ultrasound transducers have very narrow band width resulting in low data rates for binary transmission. However the SNR is in some cases high, indicating the possibility of increasing the data rate through multilevel coding, equalisation or, more recently and more generally through side information (Gelf’and-Pinsker) known interference cancellation techniques. The project will practically measure a low noise ultrasound channel and propose novel and adequate interference cancellation techniques based on side information coded modulation. It is anticipated that the signal conditioning techniques proposed will be implemented and tested on an ultrasound transmitter/receiver based on embedded programming on an ARM processor board (STM). Ultrasound communications are rapidly gaining interest due to their biomedical and underwater applications. An immediate application of the proposed techniques would also be the communications lab for the BEng/BSc EEE/Robotics Stage 2.

Skills required: Embedded C programming and signal processing.

Supervisor: Dr Adrian Ambroze