Learning outcomes

A foundation in Digital Signal Processing theory including filtering concepts and techniques, convolutions, and transforms. Concepts that are introduced will be reinforced by several hands-on exercises.

Who is the course for?

Schedule

The lectures of the course take place for approximately 5 hours, followed by exercises to be completed by students on their own time after the end of each session or before the start the next day. Day 2 and 3 start with a review of the exercises from the previous day. The instructor will be available by email for questions related to the lectures and exercises after the session ends.

Day 1
• Back to Basics – Introduces basic concepts and origins of signal processing, quantization, sampling theory and methods, fixed point and floating point numbers.
• Linear Systems – Discusses the requirements and special properties of linear systems. Comparison of linear systems and non-linear systems.
• Correlation and Convolution – Introduces concepts and properties of correlation and convolution. Introduces the Delta function and impulse response. Discusses common impulse responses and mathematical properties.
• Exercise 1 – MATLAB® refresher, Linear Time Invariant systems and properties.
• Exercise 2 – Correlation, convolution, impulse response, and autocorrelation.
Day 2
• Filter Basics – Introduces basic concepts of filtering, filter classification, properties, and types, including Butterworth, Chebyshev, Elliptic, and Bessel filters.
• Z-Transform – Discusses the z-Transform and applications to filter design. Examines Region of Convergence and stability.
• Digital Filters – Discusses the design specification, advantages, and requirements of digital filters. The FIR filter is introduced and discussed in detail.
• Advanced Digital Filters – Introduces IIR filters and design considerations. Compares FIR and IIR filter techniques. Discusses comb filters, integrators, differentiators, fractional delay, and adaptive filters.
• Exercise 3 – Filters and z-Transforms.
• Exercise 4 – Digital filter design and analysis.
Day 3
• Discrete Fourier Transforms – Discusses periodic signals, properties of the Continuous Time Fourier Transform, concepts and applications of the DFT, and practical considerations.
• Fast Fourier Transforms – Introduces the FFT mechanics and operation. Examines Decimation in Time and Decimation in Frequency structures for implementation. Discusses FFT limitations compared to DFT.
• Continuous Signal Processing – Discusses limitations and challenges of continuous signal processing compared to discrete time signal processing.
• Hardware Design Considerations – Discusses the advantages and disadvantages of various hardware implementation architectures and strategies. Relates hardware architectural features to mathematical theory of operation. Discussion of Filter implementation and Fourier Transform operation. Examines performance vs. precision and resource tradeoffs. Explores memory usage and limitations in practical hardware applications.
• Exercise 5 – Fourier Transform analysis and implementation.
• Exercise 6 – Hardware architecture implementation.

Trainer Profile

Reg Zatrepalek is an FPGA Specialist and Instructor at 2RTP (and previously at Hardent), based in Montreal, Canada.
Since 2009, he has taught courses in DSP and Xilinx FPGAs to thousands of engineers worldwide. During this time, he has also developed multiple DSP courses and algorithms covering a wide range of applications for Xilinx and for leading wireless communications multinational companies.
Reg worked as a Field Applications Engineer and DSP Specialist for Xilinx for 13 years. He also has 10 years design experience involving high performance filters, transforms, and other DSP algorithms for both industrial and military applications.