StudyQA — Master: Advanced Instrumentation and Smart Systems

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Description Contents Requirements

The world has now entered a new era with many great challenges and opportunities. Increasingly the satisfactory solutions to many of the problems the world and the societies are facing require knowledge-based smart solutions built on the networked sensors and instrumentations. Smart systems have a broad technological portfolio and wide applications, such as ICT, Internet of Things (including RFIDs), medical technologies, security, automotive and aeronautics. In the Internet of Things (IOT) alone, it is predicted that there will be 25 billion devices connected to the Internet by 2015 and 50 billion devices connected by 2020. Smart systems are also closely related to the IBMs initiative on a Smarter Planet which is defined as instrumented, intelligent and interconnected. Smart systems have much wider applications, including Smarter Industries (Banking, Communications, Education, Electronics, Energy, Food, Government, Healthcare, Insurance, Media, Oil and Gas, Retail, Transportation Systems) and Smarter Cities (Buildings, Cities, Energy, Public Safety, Rail, Traffic and Water).

The strategic importance of this emerging technology has also been increasingly recognised by various major economies. Smart systems are closely related to, for example, the Wireless Initiative in the US, the Smart Growth (particularly its digital society initiative) in Europes 10 year growth strategy, one of Seven Strategic Emerging Industries in Chinas 12th Five-year Plan (2011-2015) and several of the Eight Great Technologies identified by the UK government (eg the Big Data Revolution and Energy-Efficient Computing; Satellites and Commercial Applications of Space; Robotics and Autonomous Systems, and Nano-technology).

The future smart industries and smart societies will undoubtedly need a large number of scientists, researchers and engineers. This programme has been carefully designed to exploit and explore these opportunities, covering the core and full spectrum of smart system technologies and also appropriately addressing their wide applications through lectures, seminars, workshops and projects.

Aims

This programme aims to provide a unique learning opportunity for the students to develop in-depth knowledge and advanced skills in the theory and practice of the advanced instrumentation and smart systems and to produce the leading engineers/scientists required for future smart industries and smart societies.

Advanced Sensors and Instrumentation (15 credits)

This module aims to provide detailed knowledge of the principles and technologies of sensors, actuators and instrumentation systems; and develop the practical skills of analysis, design and evaluation of advanced sensor and instrumentation systems. The topics covered include measurement system characteristics (static and dynamic) and their models and classifications; non-optical transducers (resistive, inductive, capacitive, piezoelectric, ultrasonic, etc); optical transducers (optical encoders, etc); MEMS sensors and actuators; signal conditioning, noise reduction and signal recovering techniques; smart sensors and smart instrumentation; and virtual instrumentation, etc.

Advanced Manufacturing Measurement (15 credits)

This module aims to provide in-depth knowledge of the theories and principles of advanced measurement systems (including micro/nano metrologies) and develop the practical skills of uncertainty evaluation and error analysis/reduction of measurement systems. The topics covered include basics of measurement; measurement error theory and uncertainty evaluation; coordinate measuring machines; industrial non-contact dimensional metrology; surface metrology (stylus and optical instruments; profile and areal characterisation; calibration); scanning probe microscopy (SPM, including STM, AFM, EFM, etc.) and their applications; and low force and mass measurement.

This module will be mainly taught by the leading international experts from the National Physical Laboratory (NPL) and the students completing this module will receive an additional NPL certificate.

Data Analysis and Computing (15 credits)

This module aims to provide knowledge of linear / nonlinear models and big data analytics and to develop practical skills in implementing and using numerical methods for experimental data analysis. The topics covered include: linear models (polynomials, splines, general basis functions); statistical models associated with measured data and maximum likelihood estimation; least-squares estimation with associated uncertainty evaluation; numerical methods for linear models (matrix factorisation techniques); nonlinear models and nonlinear least-squares estimation with associated uncertainty evaluation; numerical methods for nonlinear models; Bayesian approaches; model validation. This module also covers big data, big data analytics and technologies.

This module will be mainly taught by the leading international experts from the NPL and the students completing this module will receive an additional NPL certificate.

Embedded Systems Engineering (15 credits)

This module aims to provide detailed knowledge and practical skills for the design of advanced embedded and control computer systems. The module will promote the understanding of the various engineering, scientific and economic tradeoffs necessary in the design of advanced embedded systems. It will cover the structure, organisation, and implementation of advanced embedded systems for solving real engineering problems. Various advanced design tools and practical embedded system design labs based on both FPGAs and microcontroller are two important components of the module.

Advanced Wireless Networks (15 credits)

The aim of this module is to introduce students to the design and operation of wireless networks through concepts, terminologies, performance analysis and industrial standards (WiFi,3G, LTE-Advanced and Ad-Hoc Networks). It will cover architectures, standards, technologies (eg wireless transmission and access), devices and performance analysis of wireless network; selection of appropriate solutions for wireless LAN and wireless telecommunication networks. This module will also cover wireless sensor networks, M2M and Internet of Things.

Knowledge-based Systems and Intelligent Control (15 credits)

This module aims to provide in-depth knowledge of theories, principles, methods and tools of knowledge-based systems and intelligent control; and develop the practical skills of applying various theories and techniques of knowledge-based systems and developing intelligent control applications. The topics covered include general theory of information and knowledge; knowledge representation; XML; expert systems; semantic web (RDF, OWL); Bayesian control (Kalman filters and particle filters); fuzzy logic control and intelligent agents.

Quality Engineering and Condition Monitoring (15 credits)

This module aims to provide in-depth knowledge of the strategies, principles and practices of modern quality engineering and develop the practical skills of analysing real quality problems and applying online/offline techniques to improve product/process quality. The topics covered include history and nature of quality management; views of the gurus and ISO 9000; TQM; problem solving tools; SPC; FMEA; QFD; significance testing; Taguchi methods; lean six sigma; TPM; online quality monitoring; condition monitoring and diagnostics, including: acoustic emission and vibration; signal processing; quantised- and non-quantised-based diagnostics; classification algorithms (linear classifiers, support vector machines, neural networks and Bayesian networks); and case studies.

Smart Systems and Applications (15 credits)

This module aims to introduce the principle, architecture, system characteristics and real applications of smart systems. This module will also provide training in research methodology (typical research process, research methods and critical literature review) and project management (critical path analysis, project scheduling, project monitoring and control). Through group projects, this module will require the students to apply the full range of their knowledge and skills to develop smart solutions to some real problems. The students will have opportunity to work in a team, learn and practise research methodology and critical literature reviews. This will prepare the students for their major dissertation projects.

Dissertation (60 credits)

The dissertation provides an excellent opportunity for the students to integrate and apply the knowledge and skills learnt in the taught part of the programme to solve some interesting and challenging problems in an area relevant to sensors, instrumentation and smart systems, based on the student choice. The projects may be based at Brunel University, the National Physical Laboratory, or other industrial companies, although industrial based projects are generally encouraged. Industrial projects often lead to career opportunities offered by the collaborative companies.

A UK first or second class Honours degree or equivalent internationally recognised qualification in an engineering; appropriate science or technology discipline.Applicants with other qualifications and/or experiences will be considered on an individual basis. English Language Requirements IELTS band: 6.5 IMPORTANT NOTE: Since April 2014 the ETS tests (including TOEFL and TOEIC) are no longer accepted for Tier 4 visa applications to the United Kingdom. The university might still accept these tests to admit you to the university, but if you require a Tier 4 visa to enter the UK and begin your degree programme, these tests will not be sufficient to obtain your Visa. The IELTS test is most widely accepted by universities and is also accepted for Tier 4 visas to the UK- learn more.