Below you can find a list of all fourth year modules. Some modules in this year are optional.
Robotics II will fuse elements of embedded programming, control, and mechanical fabrication to provide an overview of the latest research in the field as well as a hands-on approach. The course will draw from the foundational skills established earlier in the curriculum (in particular Computing I, Robotics I, and Gizmo) to bring critical skills together in a project oriented course where students will design mechanical, electrical, and software subsystems of an overall functioning robot. It will offer a practical point of view into how to design systems that close the perception-action loop in both simulation and real mobile robots, as well as critical insights into the design of integrated electro-mechanical systems. Faculty members involved in the design and delivery of the course have been at the forefront of the fabrication of mobile micro-robotic platforms; this experience will be leveraged to design student projects based on the current trends in microelectronics, simple embedded systems, and rapid fabrication (e.g. 3D printing).
Aim: To develop critical insight into, as well as practical skills in, the creation of intelligent robotic systems. This involves understanding and critically analysing the behaviour of natural and artificial systems, and using the resulting insights to build smart technologies.
This unit will address innovation, entrepreneurship and enterprise skills from the viewpoint of setting up a technology-based entrepreneurial venture. It will cover key aspects of creativity and design required to develop a successful idea in a particular market. Fundamental concepts for planning and running a venture will also be addressed, including market analysis and marketing, competitor analysis, pricing, profitability forecasting, risk management, legal issues and intellectual property. The characteristics and skills required by entrepreneurs in various types of organisation will also be explored together with multidisciplinary team working in an entrepreneurial context. Students will be challenged to create a suitable technology-based product, process or system in response to a chosen market.
The broadest view of entrepreneurship and entrepreneurial ventures is taken in the unit. For example, students may consider:
1. a small commercial start-up company which is growth/profit orientated
2. an initiative to promote innovation or change in a large, established organisation (intrapreneurship)
3. a "spin out" from a large technology based organisation
4. an initiative concerned with social entrepreneurship or
5. a not-for-profit organisation. Students work in groups, mentored by young professionals drawn from local organisations.
Aims: To give students the appropriate understanding, abilities and skills necessary for them to set up a successful entrepreneurial venture. This will be achieved by placing students in a semi-realistic entrepreneurial situation, where they work in teams, with mentor advice, to create a business plan to address a defined market need and present this to potential investors.
1. Introduction to Entrepreneurship
2. The Lean Entrepreneurship Process
3. Creativity and Innovation
4. Protecting your idea (IP, Copyright issues)
5. Understanding the Market
6. Business Models, Risk and Growth
7. Creating the MVP – Minimum Viable Product
8. Enterprise Skills (inc. Legal Issues)
9. Valuation and Exit Strategies
10. Gaining Funding
11. Presentation formats and dry-runs
This is a major design engineering individual project in which students will be expected to work with minimal supervision to design a product, service, system or experience of their choice, subject to the satisfaction of the module leader and their supervisor. Students will be expected to select and implement projects 'at the edge' of design know how, perhaps using new or emergent technology, or exploiting recently developed scientific knowledge.
The purpose of the unit is to apply, through advanced project work, the concept of mathematical optimization as applied to system design. Design, in this context, is viewed in the broad sense capturing problems ranging from engineering (technical) design, to plant operations, to complex processes such as civil planning and infrastructure, to financial modelling. The course extends on the concepts and tools introduced in Optimization I and II to complete a rational integration of design methodologies with concepts and techniques of modern optimization theory and practice.
The goal is to enable modelling of constraints and processes in system design project such that single and multivariate optimization may be performed to focus on critical measures of
performance. It will stress rigorous, quantitative multidisciplinary design methodology that works with the non-quantitative and creative side of the design process. The objective of the course is to implement the tools and methodologies for performing system optimization in a multidisciplinary design context. Focus will be on all: (i) the multidisciplinary character of engineering systems, (ii) the creation of reasonable models of such systems, and (iii) tools for optimization, with emphasis on software and numerical analysis. Students will complete the course conversant with techniques in practice to create appropriate mathematical optimization models and to use analytical and computational techniques to solve them.
Industry Placement B
This Industry Placement module aims to provide further practical industry experience on a sustained project hosted within a company which draws on design engineering skills, building on the experience gained through the Industry Placement A module
The placement associated with this module will run mid-June to September, over the summer between the third and fourth year of the MEng programme only. All assessments connected to the Industry Placement A module will be completed before this placement commences.
Care will be taken to ensure that appropriate companies and industrial supervisors are selected, which are prepared to provide suitably challenging and well-defined project objectives to students which cover the requirements of both this module and the Industry Placement A module. Companies will be generally expected to pay the students at a level appropriate for a new graduate.
The students will have two Dyson School of Design Engineering supervisors and one industrial supervisor. The module will be assessed by the School’s supervisors.
Engineering Design Analysis (optional)
This module explores a range of advanced engineering design approaches to enable the quantification of parameters in the design process. Use of computer aided engineering tools. Assessment of uncertainty in parameters including advanced assessment of tolerance stack up on parameters. Modelling of ergonomic function. Six sigma engineering design. People flows. Interface design modelling and assessment. Extreme user modelling. System design principles and modelling.
Enterprise Roll Out
Development of a value proposition for a practical enterprise. Exploration of the linkages between innovation and selling. Selection of an enterprise scheme for launch. Preparation of the enterprise elements for launch. Preparation and production of marketing materials. Production of prototype products or limited production run of products. Deployment of marketing materials. Business launch. The value proposition selected is likely to be based on a prior project.
Optimisation 1 – Computer Aided Engineering
Introduction to System Design: Introduction, review of design, architecting, systems thinking, system life cycle models, functional allocation.
Systems Design and Optimisation I — Concepts: Optimisation formulations. Optimisation and QFD, Pareto optimality. Computer software introduction.
Basic Numerical Analysis: System modelling. State space models. Classification. Linear systems. Numerical integration. Numerical algorithms. Linear programming. Constrained and unconstrained problems. MatLab and Excel tools.
Problem Formulation: Optimality concepts. Convexity. Constrained problems. Examples: power systems planning, robotics, control systems, systems biology, signal processing.
Differential Theory and Bounded Optima: Local approximation, convergence, gradient methods, Newton optimisation, Lagrange multipliers.
Numerical Solutions: Implementation of algorithms. Convergence. Programming. Cost function.
System Design Optimisation II — Implementation: Practical modelling and implementation examples. Design optimisation and product