Singapore University of Social Sciences

Adaptive Signal Processing

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Science & Technology

Schemes: To be confirmed

Funding: To be confirmed


Adaptive Signal Processing (ASP) is an area of science and engineering that has developed rapidly over the past 20 years, especially with the significant advances in research and development into high performance & high speed Digital Signal Processor as well as FPGA technologies. This course aims to equip participants with the fundamental skill set, basic analysis methodology and design techniques for adaptive digital processing of signals. In general, the course provides the fundamental platform for student to pursue and build the strong foundation for module like “Digital Communication”, and other Communication related modules. Besides being theoretical on the analytical skill set & methodology, participants will also be given the opportunity to work on Industrial well-known Signal Processing Tools, such as MATLAB (and/or with real Target Digital Signal Processor). With such practical Industrial software packages, participants will be equipped with the practical aspect of the ASP or ADSP solutions design & implementation/realization The beginning of the module get directly into multi-rate signal processing, where discussion on varies aspect of sampling domain and principle of sampling frequency, where build on the fundamental of Signals & Systems theory. Immediately following that would be linear prediction and linear combiner solution, where discussion on stationary random process provides a fundamental understand of statistical prosperities of signal. Discussion on Autoregressive process and Moving Average process will then be focused, and finally get into Autoregressive Moving Average process. Power spectrum estimation and energy density spectrum of random signal will also be studied in the following chapter. Finally, analysis of Lease Means Square (LMS) algorithm for linear combiner, and convergent rate, steady state error, etc will also be focused.

Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY


  • Sampling and Reconstruction of Signals
  • Multi-rate Sampling
  • Filter Design and Implementation for Sampling-Rate conversion. Application of Multi-rate Signal Processing.
  • Adaptive Filter Design
  • Power Spectral Estimation
  • Parametric Methods for Power Spectrum Estimation

Learning Outcome

  • Illustrate the sampling and reconstruction of signals.
  • Use the following operations in signal processing: decimation, interpolation, filtering and sampling rate conversion.
  • Solve sampling-rate conversion problems.
  • Examine the properties of linear predictors/filters and their relationship with AR and ARMA implementations.
  • Formulate the power spectrum expression using parametric and minimum variance estimation methods.
  • Construct adaptive filters using least mean squares and recursive least squares techniques.
  • Design forward and backward linear predictors and optimum linear filters.
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