Course Duration

3 Days

Overview

Recent advances in the computational capabilities of DSP hardware have allowed complex DSP techniques such as equalisation, smart antennas, noise cancellation and MIMO systems to be implemented cost effectively. Adaptive signal processing lies at the core of these DSP techniques.

The aim of this course is to educate participants in the theory and applications of digital adaptive filtering algorithms and architectures. The course considers the use of established linear algebra techniques for applications in audio, wireless and mobile communications such as fast equalisation, noise cancellation, beamforming and MIMO systems. A comprehensive description of adaptive filtering algorithms, architectures and applications is provided. This is complemented with case studies in the areas of audio and digital communication.

The course will include:

Course Aim

The aim of this course is to educate participants in the theory and applications of digital adaptive filtering algorithms and architectures. The course considers the use of established linear algebra techniques for applications in audio, wireless and mobile communications such as fast equalisation, noise cancellation, beamforming and MIMO systems. A comprehensive description of adaptive filtering algorithms, architectures and applications is provided. This is complemented with case studies in the areas of audio and digital communication.

Course Syllabus

Public Course Schedule†

On-Site Delivery

This course is available for On-Site delivery and can be fully tailored to meet the requirements of participants.

Audience

The course is suitable for all engineering, technical marketing and technical management staff with previous knowledge of basic DSP concepts.

Achievable Skills

On successful completion of the course, attendees will be able to:

• Understand basic linear algebra concepts
• Be aware of the most common matrix inversion methods available
• Apply linear algebra techniques to adaptive filtering
• Understand existing adaptive filtering architectures
• Gain a good understanding of applications suitable for adaptive filtering
• Understand the difference between Least Mean Squares (LMS) and Least Squares (LS) algorithms
• Define adaptive algorithm parameters for different applications
• Understand implementation limitations and advantages of common adaptive algorithms

Pre-requisites

Prior knowledge of DSP fundamentals (sampling, quantisation, frequency domain analysis, filtering) and bachelor level mathematics is advisable.

Course Presentation

The course format is:

• 60% Lectures
• 30% Hands-on Labs (simulation based)
• 10% Demonstrations

Laboratory Sessions

Professional DSP design software will be used for the laboratory sessions. This advanced software provides a comprehensive and state of the art DSP toolbox for modern signal processing. Steepest Ascent's adaptive filtering and equalisation simulation libraries will also be used.

Course Materials

All attendees will receive electronic and printed versions of the teaching materials. A DVD containing all the simulation models used during the course will also be distributed.

The notes provided form a superset of the materials presented on the course and will allow further in depth study after the course.

Further Information

To find out further information related to this or any other Steepest Ascent course please submit an on-line enquiry here.

†Steepest Ascent Ltd. reserves the right to cancel or modify courses in this schedule at short notice and will not accept liability for any costs or losses incurred by participants or their organisations as a result of these changes or cancelations. Prices quoted here do not include VAT or any applicable taxes. Steepest Ascent Ltd. reserve the right to change prices without any prior notice.