Applications of Digital Siganal Processing

Digital Signal Processing is concerned with the representation, transformation and manipulation of signals on a computer. After half a century advances, DSP has become an important field, and has penetrated a wide range of application systems, such as consumer electronics, digital communications, medical imaging to name a few. Applications of signal processing include some of the hottest current technology trends: internet of things (IoT), cloud computing, software-defined radios, robotics, autonomous vehicles, etc. We are also starting to see higher levels of performance and reduced computational requirements by combining DSP and machine learning techniques.

Objectives

This course will introduce the basic concepts and techniques for processing discrete-time signal on a computer. By the end of this course, the students should be able to understand the most important principles in DSP. The course emphasizes understanding and implementations of theoretical concepts, methods and algorithms.

Prerequisites

Fundamentals of linear algebra, calculus, probability theory, system theory and digital signal processing are highly recommended. Familiarity with MATLAB and scientific computation are recommended.

Syllabus

Introduction to digital signal processing in real time. Introduction to processor family TMS320C6x, basic architectural elements. Performances fixed and floating point, advantages and disadvantages in implementing applications of digital signal processing in computing environments based on digital signal processors. Analog input / output module. Unit Direct Memory Access, holiday mechanism. Main characteristics of the repertoire of symbolic language commands, programming in C. Environment Code Composer Studio. The code optimization tool. The development tool TMS320C6713 DSK. Implementation of FIR filters and basic experiments in voice signals. Implementation of adaptive FIR filter based on LMS and application in spectral line improvement problem and the communication channel without problem equalizer and presentation additive Gauss noise. Implementation of spectrum analyzer based on the periodogram. Signaling system Dual Tone Multiple Frequency using FIR and IIR Filter Banks. Goertzel Algorithm. Video Signal processing in real time. Implementation in the computing environment MATLAB, Source coding DPCM and ADPCM and their use in coding speech signals and images. Implementation in the computing environment MATLAB, compression techniques based on wavelet transform.