入学要求：
学术要求：Applicants are expected to hold an appropriate honours degree at 2.1 standard or the equivalent from a university recognised by the University of York. This degree should be either in electronics, computing, physics, mathematics or other engineering courses with a significant mathematics content.
英语要求：IELTS 6.0 (6.5 preferred)
学费 Tuition Fee：2011/2012 International Students: £15,600
课程特征 Course Features：
Digital Signal Processing is the core technology of almost every modern electronic system. Brain scanners, blu-ray players, automotive control systems, iPods, all rely on DSP. Complex computational processes like seismic analysis and environmental modelling use DSP to interpret vast quantities of data.
The one year full-time taught MSc in Digital Signal Processing equips students to contribute to this exciting and expanding field. It provides a clear and comprehensive understanding of DSP, from theoretical foundations to practical design. It develops knowledge and skills in DSP methods and tools. It draws on the expertise of all the research groups within our department to include state-of-the-art techniques and applications.
The MSc is designed for graduates of programmes in mathematics, engineering, physics or computer science. DSP provides a good route from this wide range of backgrounds to a career in communications, instrumentation, bio-engineering, medical sensing (including imaging), multimedia or an allied electronics field.
课程内容 Course Content：
The programme aims to provide a broad introduction to modern electronics dealing with design of signal processing systems and to provide a solid grounding in the theory and techniques suitable for students wishing to pursue a career in such areas as the communications, image processing, speech processing, computing, bio-engineering, acoustics, medical research, multimedia and others.
Core modules (students take all these modules):
1. Introduction to Signal Processing
2. Mathematics for Signal Processing
3. Introduction to Logic Design with VHDL
4. Digital Design Techniques
5. MATLAB for Signal Processing
6. Embedded Systems using FPGA
7. Detection and Estimation Theory
8. Adaptive Signal Processing
9. Project
Optional module choice (students choose 2 out of the following 3 modules):
10. Signal Processing for Communications
11. Speech Processing
12. Biomedical Signal Processing
Optional module choice (students choose 2 out of the following 3 modules):
13. Advanced Multimedia Applications
14. Image Processing
15. Error Control Coding
Optional Non-assessed module:
16. Computer Programming Using C
Please note that the detailed module contents are subject to change. The information provided here describes the current contents of the modules that we intend to include in the MSc.
1. Introduction to Signal Processing
This module is designed to introduce students to the fundamental concepts and techniques required for signal processing systems, starting with the characterisation of linear and time-invariant systems, analogue and digital signals, and sampling techniques. From a consideration of the treatment of sampled signals, the discrete Fourier transform (DFT) is derived, and fast Fourier transform (FFT) methods described. Statistical signal processing concepts and the problems of parameter estimation are introduced, with an introduction given to adaptive signal processing.
The module is taught through lectures and supporting workshop sessions. Assessment is in the form of a closed-book written examination.
2. Mathematics for Signal Processing
Development and design of signal processing systems is based on a solid knowledge in mathematics. The aim of the module is to introduce to the students the fundamentals of mathematics required for signal processing.
On completion of this module students are expected to be able to:
Understand the methods of linear algebra used in signal processing.
Understand the numerical methods used in signal processing.
Understand applications of the mathematical methods to different signal processing problems.
The module is taught through lectures and supporting workshop sessions. Assessment is in the form of a closed-book written examination.
3. Introduction to Logic Design with VHDL
The aim of this module is to provide a revision of digital components and design techniques. The study will be guided throughout by examples using VHDL. An introduction to alternate hardware description languages (e.g. Verilog, SystemC) will provide an overview of their features.
On completion of this module students are expected to be able to:
Understand the design flow, as applied to VHDL designs, of digital systems and compare it to schematic entry.
Understand the limitations and advantages of such design flows.
Design and simulate (using scripts) simple digital circuits (e.g. simple arithmetic circuits) with VHDL.
Appreciate similarities and differences between HDL languages (e.g, Verilog, SystemC).
4. Digital Design Techniques
The aim of this module is to provide knowledge and practical experience of advanced digital design methods and techniques. This will be achieved by studying how computation can be mapped on hardware through custom processing units. Theoretical aspects will be accompanied by a physical implementation in hardware of a small dedicated processor.
On completion of this module students are expected to be able to:
Understand and acquire practical experience of advanced sequential and combinational circuit design using VHDL.
Understand the issues related to the use of VHDL for the synthesis of digital circuits.
Understand data processing architectures and appreciate the issues involved in their design.
Use hierarchical design techniques to implement complex designs (e.g. a simple processor) from gate level using VHDL.
Use advanced simulation techniques (scripts, VHDL test-benches).
5. MATLAB for Signal Processing
MATLAB is important software used for development and evaluation of signal processing systems. The aim of this module is to introduce important MATLAB tools used for modelling signal processing systems.
On completion of this module students are expected to be able to:
Understand how to use primary MATLAB tools for modeling signal processing systems.
Design and write MATLAB scripts for simple signal processing problems.
Understand the limitations of MATLAB for modeling signal processing systems.
6. Embedded Systems using FPGAs
The aim of this module is to provide a practical understanding of embedded micro-controller based systems through implementation of differing embedded systems using FPGA platforms. Different design complexities will be covered, with reference to the varying options and constraints imposed by different embedded system designs.
On completion of this module students are expected to be able to:
Program microcontrollers and DSP microprocessors using both high- and low-level languages and understand the issues associated with such programming (e.g., deadlocks, interrupts, I/Os).
Install and run a Linux kernel on an embedded processor.
Map and implement DSP and other algorithms as hardware accelerators in FPGA.
Design custom peripherals for embedded microprocessors and connect off-the-shelf peripheral components to an FPGA-based system.
Design and implement a complete system on an FPGA board, including hardware components (memories, I/Os), connections, IP modules (MicroBlaze), and custom circuits.
7. Detection and Estimation Theory
Detection and estimation theory is essential in development of any modern signal processing system such as communication systems, radar, sonar, sensor networks, tomography, and others. The aim of this module is to give understanding of detection and estimation theory and the main algorithms used in modern signal processing systems.
On completion of this module students are expected to be able to:
Evaluate the most appropriate criterion for use in detection problems.
Devise detection algorithms for a variety of signal processing problems.
Understand how to select the most appropriate criterion for estimation.
Develop estimators for a variety of signal processing problems.
Understand the performance limits of detection and estimation algorithms.
The module is taught through lectures and supporting workshop sessions. Assessment is in the form of a closed-book written examination.
8. Adaptive Signal Processing
The aim of this module is to introduce the students to the fundamental concepts of adaptive signal processing, including adaptive filters and adaptive sensor arrays, and demonstrate different applications of adaptive techniques.
On completion of this module students are expected to be able to:
Understand the theoretical foundation of adaptive signal processing.
Understand applications of adaptive filters for identification of linear systems, equalisation, echo cancellation, and in other areas.
Understand practical issues related to implementation of adaptive filters.
The module is taught through lectures and supporting workshop sessions. Assessment is in the form of a closed-book written examination.
9. Project
Running throughout the one-year programme is a project and transferable skills module.
In the first term, this consists of a series of lectures on transferable skills: presentations, finance and accounting, management, research skills and searching for information, time management and report writing.
The project starts in the second term, and is intended to immerse students in a life-like team project over the remainder of the one-year programme. The project is intended to develop valuable teamwork skills, as well as an appreciation of the interpersonal skills within a company. It involves the investigation of a specified problem in signal processing and/or development of a solution. Project groups, and the project itself, will be decided over the Christmas vacation.
During the second term, the group is required to establish its operating procedures. They should then write a tender proposal, documenting their intended approach to solving the problem, indicating the roles each team member will take, and providing a detailed time-table and costing for the remainder of the project.
The project is assessed by a report written at the end of the project, a viva examination, and observation of performance throughout the project.
10. Signal Processing for Communications
The Signal Processing for Communications module is designed to introduce students to the most relevant techniques of digital signal processing to modern communications systems. It teaches students how to apply these techniques to design problems in the real world, exploring the trade-offs between software and hardware implementations of the methods discussed.
A discussion of random processes and their properties leads to estimation theory, and the development of practical versions of the minimum mean square error (MMSE) and maximum likelihood receivers. The Wiener-Hopf equation is derived as the optimum receive filter, illustrated with applications including adaptive filtering. Synchronisation issues are discussed, and an introduction to advanced techniques including multi-user detection (MUD) given.
The module is taught through lectures and supporting workshop sessions. Assessment is in the form of a closed-book written examination.
11. Speech Processing
Speech processing techniques are used in modern telephony, speech coders, speech and speaker recognition systems, Voice-over-Internet, medicine, entertainment, etc. The aim of this module is to provide a practical introduction to speech physiology and phonetics, to introduce in depth the acoustics of speech production, and to describe speech analysis, coding and synthesis methods.
On completion of this module students are expected to be able to:
Understand the physiology and acoustics of speech production.
Understand time and frequency domain analyses of speech signals.
Understand speech coding methods.
Understand speech synthesis methods.
12. Biomedical Signal Processing
The aim of this module is to provide theoretical and practical understanding of the application of signal processing to biomedical signals. This will be supported through direct reference to research work in biomedical engineering and medicine, in conjunction with software exercises applying signal processing to relevant data sets.
On completion of this module students are expected to be able to:
Appreciate the types of electrical signals that can be generated and recorded from the human body, and the problems associated with analysis of these signals.
Demonstrate an understanding of how different time series analysis techniques can be applied to analysis of these signals.
Describe and compare different approaches that can be applied to analysis of biomedical signals.
Evaluate case studies of signal processing in biomedical engineering and medical research.
The project is assessed via Lab assignments and presentation of a course project.