Digital Signal Processing MSc

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Comments about Digital Signal Processing MSc - At the institution - Uxbridge - Greater London

  • Objectives
    The programme aims to produce graduates with advanced knowledge and transferable skills in the design, modelling, implementation and evaluation of Digital Signal Processing systems, enabling them to contribute effectively to the increasingly complex and rapidly evolving technologies that are prevalent in industry or research. Upon completion, graduates will have the independent learning ability required for continuing professional development, further research, and for acquiring new skills at the highest level.
  • Entry requirements
    * Students with appropriate degree qualifications and related experiences (see entry requirements) who wish to enhance their employability in a broad range of technological industries; * Those seeking in-depth knowledge and skills relevant to the theory and best practise of modern high-tech DSP; * Industrialists with some experience of working in signal processing, who wish to obtain a formal qualification or need to expand their knowledge and skills in the varied fields of DSP applications.
  • Academic title
    Digital Signal Processing MSc
  • Course description
    Digital Signal Processing (DSP) is one of the most powerful modern technologies that enable information to be processed digitally. It has helped revolutionise science and engineering in the 21st century. DSP technology is manifested in a broad range of high-tech applications. The widespread use and deployment of DSP in complex systems like communications, multimedia, broadcasting, transportation, aerospace etc., have witnessed an unprecedented expansion in the last few years.

    Consequently, high-tech industries are actively recruiting skilled DSP engineers to support their increasing development of these complex technologies and applications. As these industries offer very few graduate training schemes, employment in this sector requires engineers with high level, advanced digital engineering skills. Therefore, engineers proficient in digital signal processing and relevant development tools are in ever-growing demand.

    The School of Engineering and Design, Brunel University, recognising the industry need for skilled DSP engineers developed this brand new, unique MSc programme.

    This new MSc in Digital Signal Processing involves extensive study of DSP mathematics, algorithms design, modelling and implementation at the core of its programme. The course also offers the opportunity for students to exploit DSP techniques in three major areas, namely, communication, multimedia and intelligent systems through a set of carefully designed application-based modules.

    Programme Outline

    The School has a very successful team of lecturers enjoying an international reputation in research related to DSP technologies, ie signal processing, communication, multimedia and intelligent systems, and this expertise has been brought together to develop and deliver the new programme. On-going staff research means that the programme is enhanced by new academic research findings and update-to-date technology which is significant to academically and industrially competent professionals. The programme (requiring 180 credits in total for the MSc award) consists of eight taught modules each worth 15 credits, totalling 120 credits, and a dissertation project worth 60 credits. The programme modules are as follows:

    Mathematics of DSP
    Reviews the theory and the mathematics of continuous-time/space and discrete-time/space DSP and develops in-depth understanding of the theory and application of signal and system, analysis and synthesis. Main topics include: signal and systems - theory and applications; transforms; digital filters; adaptive filters; multirate signal processing.

    Statistical Signal Processing
    Introduces the principles of statistical signal processing theories, and provides the up-to-date knowledge of the analysis and design underlying various signal processing applications. Main topics include: introduction to statistics and random process; signal detection and parameter estimation; modern spectrum analysis and estimation.

    Real-time DSP Systems
    Develops an in-depth knowledge and understanding of real-time signal processing and DSP system architecture and provides the hands-on experience in software implementation of DSP algorithms that works in real-time. Main topics include: introduction to real-time dsp; dsp hardware, software and programming; implementation of dsp algorithms; dsp applications – multimedia and communications.

    Advanced Digital Communications
    Includes advanced topics in digital communication systems and up-to-date knowledge of the techniques used in digital communication systems. Main topics include: wireless channel modelling; digital transmission through wireless channels; multicarrier digital transmission and multiple access techniques;
    applications of digital communication techniques in commercial wireless/mobile networks.

    Advanced Multimedia Processing
    Develops an in-depth understanding of the state-of-the-art DSP mathematics and algorithms used in multimedia processing including signal representation of audio and visual media, data compression, and media retrieval and encryption. Main topics include: audio/speech processing; visual/image processing; multimedia data processing.

    Intelligent Signal Processing
    Raises critical awareness of the issues affecting the performance of intelligent system and develops the skills required to develop intelligent software DSP applications. Main topics include: overview of intelligent systems techniques; intelligent computation techniques; intelligent data processing techniques; applications: bioinformatics, medical imaging and visualisation, pattern recognition and biometrics, computer vision, future trends.

    Reconfigurable Computing for DSP
    Provides in-depth understanding of the principles and the role of Reconfigurable Computing (RC) for DSP implementation. Main topics include: introduction; advanced computer arithmetic for dsp implementation; reconfigurable computing and fpgas; hardware compilation; low power dsp architectures and implementation.

    DSP Workshop
    Practical experience in the principles of DSP and its applications. Main topics include: mathematics of DSP; statisticalsSignal processing; multimedia processing; digital communications; intelligent signal processing; group project.


    The programme fully prepares students for work in the technological industries that require DSP knowledge and skills. There are no limits where DSP can be applied; however, companies that develop high-tech products often offer jobs in the following areas:

        * Design, modelling and development of DSP algorithms and systems;
        * Matlab / Simulink prototyping and system level design tools;
        * DSP hardware/software and device derivers development;
        * Real-time embedded system design and mobile handset development;
        * Speech recognition and synthesis;
        * Image processing and recognition;
        * Audio and video processing systems;
        * Digital measuring equipments and instrumentations;
        * Wireless telecommunication and broadcast: WCDMA (Wideband Code Division Multiple Access), 802.11, Bluetooth, GSM, WiMAX, DVB applications;
        * And various field such as Satellite Communications, RF Engineering, Real-Time Control Systems.

    The programme also prepares students to carry out related academic research. A number of students in various disciplines within the School have stayed on to carry out a PhD at Brunel University – on average, one student of high academic achievement per year is offered a PhD position.

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