Applied DSP - DFT & FFT Algorithms and Realizations (DS107-43-0)

SynopsisDigital Signal Processing (DSP) is concerned with the digital representation of signals and the use of digital systems to analyze, modify, store, or extract information from these signals. In recent years, DSP is widely used for frequency analysis such as spectral analysis, fast convolution and power spectrum density estimation. This course allows practicing engineers and development researchers to master the concepts of frequency analysis and the real-time DSP implementation techniques.

This course starts with essential fundamentals of DSP. The participants are then introduced to the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms and typical examples of frequency analysis. In addition, Scilab/MATLAB programming is introduced and used to perform various real life applications.

Course Highlight
The fundamental DSP principles, Discrete Fourier Transform (DFT), and Fast Fourier Transform (FFT) algorithms are first introduced in classroom setting with the aid of video and software tools. The concepts are then re-enforced through programming exercises and examples on how the FFT algorithms are designed in real-life. Demonstration and practical hands-on using Siclab/MATLAB programming tools will be carried out to illustrate various practical applications.

What You Will Learn

• Essential concepts and practical applications for those designing and developing DSP-based systems
• Typical frequency analysis examples, which include spectral analysis, power spectrum density estimation, fast convolution, and more
• DSP programming fundamentals and tips

Who Should AttendEngineers and technicians who are involved in DSP-based design and algorithm development, and wish to apply this technology to achieve optimum performance with low design and manufacturing costs.

PrerequisiteTechnical background in digital signal processing with good understanding of DSP fundamentals for example Z-Transform, Fourier Transform, convolution etc. Working knowledge of Scilab/MATLAB and C programming would be helpful.

Course MethodologyThis course starts with essential fundamentals of DSP. The participants are then introduced to the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms. Once equipped with the basics and knowledge of DSP, the participants are then introduced to the DSP programming and typical DSP applications, particularly focusing on frequency analysis.

The following software tools are used in this training:

• Scilab 4.0 or its latest version OR
• MATLAB

Course Duration3 days, 9am - 5pm

Course StructureDay 1
Introduction to DSP

• Overview of DSP, basic concepts, elements of a typical DSP system

Fourier Transforms

• Fourier Series and Fourier Transform (FT), Discrete-Time FT, Discrete FT, and more

Fast Fourier Transform

• FFT algorithms, decimation in time/frequency, bit-reversal sorting

Experiements and Hands-on (1)

• Introduction to Scilab, bit-reversal algorithms

Day 2
Fast Fourier Transform (cont.)

• Butterfly network, computational issues, finite precision effects

FFT Windowing

• Choosing a suitable window, rectangular, triangular, cosine windows, flattop windows, etc

Experiments and Hands-on (2)

• Butterfly network, FFT variants and windowing

Day 3
Practical Applications

• Spectral analysis, spectra leakage and resolution, power spectrum density, fast convolution

Experiments and Hands-on (3)

• Real-time FFT, fast convolution, psd estimation, periodogram, digital signal proceementation of FFT, and more