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 2022 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).
The application deadline for 2022 is Wednesday 1 December 2021.

Successful students will be notified within two weeks of the application deadline passing (Friday 3 December 2021).  

Applications must be emailed to: ScienceandEngineeringInternational@plymouth.ac.uk

Important dates

Application deadline – Wednesday 1 December 2021
Induction session – Friday 28 January 2022
Project start date – Monday 31 January 2022
Project end date – Friday 27 May 2022


You must arrive in Plymouth in time to attend your induction session. For further information on preparing for your travel to the UK and Plymouth, please visit our International Student Advice (ISA) webpage.

Exchange students from partner universities in India

Exchange students from partner universities in India

Project supervisors

Projects are listed below:

Project 1

Electrospun Nanofibre Membranes 

This project uses Electrospinning to produce nanofibre membranes for water filtration. Polymers doped with graphene/CNT/graphene oxide to be fabricated and characterised to assess their potential to remove lead (Pb) from water. Ramam / FTIR / SEM will be used to evaluate them.

Supervisors: Dr David Jenkins

Project 2

Electrospun Nanofibre Membranes 2 

This project uses will develop a drum collection system to Electrospinn aligned nanofibre membranes for pressure sensing. The membranes will be 'tri-layer' to form a capacitive piezoelectric membrane, and characterised to assess their potential as sensors. Ramam / FTIR / SEM will be used to evaluate them.

Supervisors: Dr David Jenkins

Project 3

Detection of Nanoplastics in Water 

Nanoplastics are a health risk as they can cause disruption on the cellular level. The project will focus on developing the existing optical system (Surface Plasmon Resonance (SPR)) and the LabVIEW code used to control it

Supervisors: Dr David Jenkins

Project 4

A Brain Computer (Music) Interface BC(M)I system for neurodegeneratively affected human control 

This project is jointly with Professor Eduardo Miranda to design better electrodes and portable wireless amplifiers for the current BCI sytem. If we put everything is a single board we could eliminate laptop/computer and make it communicate direct with a robot to assist affected persons. 

http://neuromusic.soc.plymouth.ac.uk/

Supervisors: Dr David Jenkins and Professor Eduardo R. Miranda

Project 5

Surprise and other emotions 

This project experiments with a quantitative approach to identify surprise and other emotions in avant-garde poetry. Students need to be able to pre-process text and identify the emotions expressed on it. Python, or C#, or Java are the preferred programming languages (one of them should be enough, but more than one would be advantageous). Advice on the use of off-the-shelf tools to identify sentiment will be provided. However, the opportunity to develop your own heuristics or machine learning approaches is available.

Supervisor: Dr Marco Palomino

Project 6

Data Analysis (Assistive Robots) 

Socially assistive robots have the potential to prevent isolation, especially in the case of elderly people who cannot leave home often. We have organised meetings to explore this idea and, in particular, the acceptability of humanoid robots at home. This project involves analysing the conversations and other data derived from these meetings. You will need to determine the sentiment and emotion expressed in the conversations.

Supervisor: Dr Marco Palomino

Project 7

The Emotion of Trends

It appears sensible to assume that highly polarised Twitter comments are more likely to become trends than neutral observations that hardly spark any turmoil. However, specific experiments intended to measure the correlation between the emergence of a Twitter trend and the overall emotion expressed on it have been few and limited. Thus, this project investigates the nature of the relationship between Twitter trends, strength of sentiment and type of emotion.


Supervisor: Dr Marco Palomino

Project 8

Maritime Cyber Security 

There a number of projects that could go under this theme, but they would be on examining cyber-security in the maritime sector (ports, ships). Looking at other aspects of transportation like rail and road are also possibilities, but we are mostly interested in maritime. Would be interested to understand the state of cybersecurity more in India, and working with students who are also interested in this.


Supervisor: Dr Kimberly Tam

Project 9 

Smartphone / IoT security 

Interested intrusion detection and malware/virus detection in smartphones and IoT.

Supervisor: Dr Kimberly Tam

Project 10

Federated Learning (FL) for Cyber Security 

This project is the extension of my recent work supported by National Research Foundation, South Korea.

The key to Internet of Things (IoT) data analysis by applying Machine Learning (ML) algorithms is whether there is accurate and sufficient data to support training. While it is also important to prevent the sensitive data collected by the IoT devices from being stolen by attackers or untrusted third parties. The aim of this project is to design and simulate an advanced federated learning-based privacy-preserving mechanism suitable for IoT. And employ anomaly detection as a use case to achieve a trade-off between privacy and accuracy of ML. 

Project 11

Blockchain for Next-Generation IoT Security

This project is the extension of my work supported by Ministry of Science and ICT (MSIT), South Korea

Machine-Type Communication (MTC) is the key to Next-Generation Internet of Things (IoT) due to the need for establishing the communication between IoT devices to support various types of Next-Generation IoT services. However, the vulnerability of MTC communication across the internet may result in new vectors of intrusions that lead to loss of information control, theft of important information, and unauthorized access. The aim of this project is to simulate a decentralize solution for secure communication in IoT using Blockchain.

Supervisor: Dr Shailendra Rathore

Project 12

Machine Learning (ML) for Maritime Internet of Things (MIoT) Security

This project is related to Maritime Cyber Threat Research Group’s £3.2 million Cyber-SHIP Lab.

The emergence of Maritime IoT (MIoT) is revolutionizing the ship-to-shore connectivity in modern autonomous shipping and accelerating digitalization to improve productivity of maritime industry. The MIoT application streamline and optimize different functioning units at the ships, seaports, and across the fleets- from predictive maintenance and vessel tracking to welfare and crew safety. The deployment of MIoT is confronting numerous challenges and issues as far as security is concerned. The underlying security problem of MIoT, including data vulnerability and unprotected MIoT sensors has a pronounced effect on the control operations and complex business processes carried out at the ships and seaports. Unprotected Automatic Identification System (AIS) devices further open the way of unauthorised access that lead to loss or theft of navigation information resulting in AIS spoofing attacks. Recently, Machine Learning (ML) has become an emerging solution to cybersecurity. With ML techniques, MIoT systems can analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help ship teams be more proactive in preventing threats and responding to active attacks in real time. Particularly, the aim of this project is design and modelling of AIS spoofing attacks on the MIoT platform and come up with a ML solution to detect this attack.

Supervisor: Dr Shailendra Rathore

Project 13

Machine Learning (ML) Solution to Algorithmic Attack Detection in Maritime 

This project is related to Maritime Cyber Threat Research Group’s £3.2 million Cyber-SHIP Lab.

An attacker can target algorithms through which the sensor data collected and processed provides useful information for autonomous operation. Autonomous ships employ several control algorithms to carry out Maritime IoT (MIoT) operations. For instance, an unmanned surface vehicle (USV) uses object detection algorithms to classify adjacent objects into ships and non-ships using Machine Learning (ML). The control algorithms perform well with the correct input and give correct responses in the normal condition. However, adversaries can possibly trick the control algorithm into false responses by introducing adversarial examples with some certain malicious intent. Most often, the adversarial examples are deployed against ML algorithms in intent to change ML output. The aim of this project is to design and develop a defense method against such algorithmic attacks on MIoT Platform.

Supervisor: Dr Shailendra Rathore

Project 14

Risk Analysis IoT smart home assistant systems

When a voice command is given to the assistant, the command is sent to cloud services over the Internet, enabling a multitude of functions associated with risks regarding security and privacy. Furthermore, with an always active Internet connection, smart home assistants are a part of the Internet of Things, a type of historically not secure devices. Therefore, it is crucial to understand the security situation and the risks that a smart home assistant brings with it.

The goal is to investigate and compile threats towards smart home assistants in a home environment. Such a compilation could be used as a foundation during the creation of a formal model for securing smart home assistants and other devices with similar properties.

Supervisor: Dr Bogdan Ghita

Project 15

Privacy disclosure in Internet forms

Any access to a service typically requires a number of personal information details to be disclosed to the service owner. A considerable number of such online services have been subject to cyber attacks in recent years, exposing the data of the users.

The aim of this project is to characterise data required by online services forms, rank it in terms of associated privacy/personal disclosure risks and provide the users with information about the risks that they are exposing themselves to by sharing this data.

Supervisor: Dr Bogdan Ghita

Project 16

Improving security for smart home environments 

A typical smart home environment will include a combination of networking equipment (router/wireless access point) traditional computers (either workstations or laptops), mobile computers (smart phones and tablets) and IoT devices. All these devices come with inherrent vulnerabilities and risks, which vary from one category to another.

The aim of this project is to investigate smart home environments and isolate devices with various vulnerabilities/risk levels in such a way that a) attack on a device will have a minimal impact on the rest of the network and b) functionality of the device is preserved under normal conditions and minimally affected when a vulnerability or an attack are identified"

Supervisor: Dr Bogdan Ghita

Project 17

3D animated presentations 

Powerpoint is a well-known standard tool for presentations. It allows to create content pages of various kinds of media (text, figures, video) and connect them using transitions caused by keyboard clicks or a pointer device. Transitions can be animated (although most commonly used are immediate page changes only). Powerpoint creates flat, 2-dimensional presentations. Goal of the present project is to build a system that allows to create presentations in 3 dimensions with 3D-animated transitions. This would make use of formal descriptions of content elements (like Title, Table, Bullet List, Figure etc) and possible transitions (whatever works in 3D). A presentation would be written as a plain text file with additional annotations / tags / syntactic elements that describe the form of presentation and transitions (similar to simple html or LaTeX; a WYSIWYG editor is not required and too complex). The file would be translated into scripts (Python or other) to control the Unity (or other) game engine. It should be possible to control the animation with a pointer device or keyboard. This project requires knowledge in Unity and a scripting language for it. Theoretical and/or practical knowledge in domain specific languages and parser generation would be helpful (e.g. tools like ANTLR).

Supervisor: Dr Thomas Wennekers

Project 18

Monocular depth estimation and stereo visualisation

This project aims at building a system that creates a 3D-image from a monocular one. This would proceed in two steps: 1) estimating depth from a monocular image and 2) creating a pair of stereo images for the left and right eye form the original image and estimated depth information. Technically this would make use of deep-learning based on publicly available stereo image data sets. The first step is known to work (see e.g. Hua et al. 2020, Holopix50: A large-scale in-the-wild stereo image dataset). The second step would probably use an auto-encoder deep neural network and is somewhat experimental (if it doesn't work it would not fail the project if there are enough other achievements, e.g. a successful step 1). The system may be implemented on a work-station, hand-held device or VR headset. The project requires good knowledge and a deep understanding of deep learning.

Supervisor: Dr Thomas Wennekers

Project 19

Living Room 

This project aims at a system that observes a room using a camera (or two) and creates sounds depending on some sort of analysis of the camera input. The output sounds could be ambient music that reacts to ongoing activity in the room, or sound-icons played when certain events happen. The possibilities are very flexible and students are invited to come up with their own ideas. The image analysis could be simply based on some image statistics, but could also be advanced including elements of stereo vision / depth estimation / localisation, object or behaviour recognition, deep learning, etc. On the audio side use of the MIDI protocol to control a soft- or hardware synthesizer seems most appropriate although other options are possible. The simplest possible systems would not require any particular knowledge other than a programming language to access the camera, process some data and generate MIDI events through a MIDI API. Some background in image processing (e.g. OpenCV) or deep learning (Keras, Tensorflow) could obviously be useful. The system may run on a smart-phone, but more complex applications probably require more computing power than those provide.

Supervisor: Dr Thomas Wennekers

Project 20

Speeding up Real World Software Applications on modern Multi-Core Processors and/or Nvidia GPUs (multiple projects) 

The aim of these last year projects is to improve the performance (execution time) of real world software applications on modern multi-core processors and/or GPUs. Technologies: You will implement high performance C/C++ parallel code using technologies such as OpenMP or CUDA. Software Applications: Students can choose any software application or algorithm they like. Processors: Students can choose any processor they want. They can use their PCs or even the school’s Nvidia GPUs (remotely). Outcomes: At the end of this project you will have gained first, important programming skills which will make you desirable in industry (e.g., Arm and Amazon need engineers with such expertise) and second, in depth understanding of performance issues, performance trade-offs and how to write efficient parallel software.

Supervisor: Dr Vasilios Kelefouras

Project 21

Social-Service Robot  

The aim of this project is to design a new robot with the functionality of the both social and service robots to bridge the gap between these two domains of robotics. A social robot is an autonomous robot that interacts and communicates with humans by following social behaviours and rules attached to its role while service robots assist human beings, typically by performing a job that is dirty, dull, distant, dangerous or repetitive.


Supervisor: Dr Hooman samani

 

Tuition fees

This year’s Semester Abroad Scheme will cost £2,000 in tuition fees.

The fee will be refunded to any students who enroll on one of our master programmes within our School of Engineering, Computing and Mathematics starting in September 2022.