Module Catalogue 2024/25

NUS8308 : Project Dissertation – II

NUS8308 : Project Dissertation – II

  • Offered for Year: 2024/25
  • Module Leader(s): Dr Pavan Kumar Naraharisetti
  • Lecturer: Dr Khalid Abidi, Dr Zi Jie Choong, Dr Naayagi Ramasamy, Dr Noori Kim, Dr Arun Dev, Dr Mohammed Abdul Hannan
  • Owning School: NUIS
  • Teaching Location: Newcastle City Campus

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 20
Semester 3 Credit Value: 20
ECTS Credits: 20.0
European Credit Transfer System

Modules you must have done previously to study this module

Pre Requisite Comment



Modules you need to take at the same time

Co Requisite Comment



To enable students to apply the proposal from Project Dissertation-I. Students will build prototype software tools and/or analyse a system of significant importance considering topics in industrial automation and machine learning and where applicable connect the prototype with physical systems

Outline Of Syllabus

The individual dissertation should have some aspects from the Core modules, while much of the dissertation will be an independent work by the student. The following elements are expected as part of the dissertation.
•       Presentation.
•       Literature review.
•       Independent research and analysis.
•       Building a prototype software system/tool that has elements of industrial
automation and machine learning.
•       Where applicable, the software system should integrate well with those systems
developed by other students if a group of students are working on sub-modules of a very large project.

Where possible, part-time students will analysis systems/processes within their organizations in their dissertation and deliver results that are practically and industrially relevant. Students are required to obtain necessary approvals from their employers should they decide to choose this path. In such a scenario, feedback will be taken from the co-supervisor (employer) on how the student performed.

Learning Outcomes

Intended Knowledge Outcomes

At the end of the module, students should:
•       Be able to bring together concepts from different domains, including but not
limited to the core modules, in the area of industrial automation and machine learning.
•       Be able to explain how complex systems are built.
•       Be able to avoid design and implementation issues by utilising background knowledge
and coming up with an implementable plan.
•       Be able to explain how their project is relevant in an ever-changing digital world
and also explain what they will do to stay on top of things.
•       Be able to select the right methodology for the selected problem and be able to select
an alternative best available methodology so that comparisons can be made if required.
•       Know how to incorporate best practices in GUI design, Data exchange and Storage, and Data Analysis.
•       Be able to extract insights through Data Analysis and Machine Learning.

Intended Skill Outcomes

At the end of the module, students should:
•       Be able to implement best practices in GUI design, Data exchange and Storage,
Data Analysis, and extract insights through Data Analysis and Machine Learning.
•       Be able to troubleshoot problems to design and develop a software program that tries
to solve specific industrially relevant problems.
•       Develop analytical and evaluating skills on functionalities of build-it open-source
software tools and develop solutions that can make a significant contribution to
industrial automation and machine learning.
•       Ability to criticise the work of others, in addition to the student’s work to find
better solutions.
•       Ability to identify and develop solutions to important inter-disciplinary topics and
make meaningful contributions in the digital world.
•       Develop excellent communication skills, report writing, paper writing and presentation
•       Be able to summarise information and draw conclusions from that information

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion11:001:00Presentation
Guided Independent StudyAssessment preparation and completion19:009:00Preparation for presentation
Scheduled Learning And Teaching ActivitiesLecture43:3014:00Ethics, Research and Methodologies, Dissertation preparation
Guided Independent StudyDirected research and reading16:006:00Supervision meetings
Guided Independent StudyProject work190:0090:00Working on a prototype Phase-I
Guided Independent StudyProject work190:0090:00Working on a prototype Phase-III
Guided Independent StudyProject work190:0090:00Working on a prototype Phase-II
Guided Independent StudyIndependent study180:0080:00Dissertation writing
Guided Independent StudyIndependent study120:0020:00Concluding the project
Teaching Rationale And Relationship

This module (Project Dissertation - II) enables students to study the topics at a deeper level and come up with a prototype software system/tool/program that addresses the needs of the industry. Students are required to attend lectures on ethics and research methodologies and meet the supervisor regularly so that the submitted final report acceptable. Students will pick up project management skills via guided independent learning.

Due to the emerging Covid-19 situation, it is likely that some or all of the meetings/seminars are conducted online.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Oral Presentation603A20One 60-minute oral presentation (including Q&A)
Other Assessment
Description Semester When Set Percentage Comment
Dissertation3M80Maximum of 75 pages of A4 including references but excluding any appendixes
Assessment Rationale And Relationship

The report enables students to comprehensively present what they have done as part of the proposal. In addition to the report, presentation allows the students to present and discuss what they have done in the context of the ever-changing digital world. Weightage is given separately to presentation and dissertation report.


Past Exam Papers

General Notes


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The information contained within the Module Catalogue relates to the 2024 academic year.

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Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.