DSP Systems Engineering

Embedded software can be found in many electronic devices today. Increase your understanding of the essential embedded language features required for embedded systems programming. Embedded software developers benefit from this hands-on course by expanding their knowledge of using pointers and arrays, bit manipulation, using key words such as "volatile" and "register," and learning more about source code solutions to common embedded software problems.

This course presents engineering and scientific problem solving techniques used for mathematical computations, algorithm development, modeling and simulation, data analysis, scientific and engineering graphics, and application development. MATLAB utilizes various toolboxes such as symbolic math, signal processing, control systems, MATLAB C math library, and MATLAB compiler to facilitate the solving of many technical computing problems. Topics include: Introduction to MATLAB; engineering and scientific computations, numerical techniques for linear equations, overview of the Symbolic math toolbox, and application development.

Increase your knowledge of DSP technology by learning the mathematical modeling of signals and mathematical techniques to solve complex tasks in contrast to conventional analog techniques. The digital signal processing system is reviewed detailing analog-to-digital conversion, signal processing, and digital-to-analog conversions. Participants learn how to perform fundamental digital filtering along with classical filter design methods, realizations of finite impulse response (FIR) filters, and infinite impulse response (IIR) filters. Software tools are used to simulate and analyze digital signals.

C programming is a preferred high-level programming language for digital signal processing (DSP) applications. Increase your knowledge on efficient DSP programming techniques and how to write DSP code in C. Participants are guided through the DSP software design flow, common DSP hardware architectures, and fixed-point and floating-point considerations and limitations. An emphasis is placed on C programming techniques for DSP such as FIR and IIR filters, noise reduction algorithms, sample rate conversion, parametric and non-parametric power spectrum estimation techniques, speech and music processing, and adaptive filter applications.

Increase your level of expertise of embedded digital signal processing as well as DSP programming techniques. Participants learn about several important algorithms, such as Digital FIR, IIR filters, FFTs and advanced digital signal processing algorithms for embedded applications. An overview of fixed and floating point DSP processors is presented including memory use and management. DSP assembly-language programming is emphasized in conjunction with development tools that allow DSP code written in higher level languages, such as C and MATLAB.

An overview of DSP architectures is presented along with discussions of the overall DSP development process addressing; development tools, hardware interface issues and system design issues. Increase your knowledge on the use of Field-Programmable Gate Arrays (FPGAs) technology, devices, and tools for designing state-of-the-art DSP Systems. Learn how to develop and apply VHDL and Verilog coded DSP algorithms for FIR and IIR filters, and multirate DSP systems. Gain further knowledge on developing DFT and FFT algorithms, and advanced algorithms.

Grasp new insight into the use of digital image and video compression algorithms embedded in various digital imaging products and learn how to “engineer” a compression system. Participants will study the technical details of MPEG algorithms and compare them with other approaches to video compression. MPEG video compression standards include: MPEG-1-2-4 Part 2 and Part 10 (AVC/H.264). MPEG-2 is the most widely used standard and Advanced Video Coding Standard (AVC) is the latest and most complex standard. In addition, learn about the Chinese Audio Video Standard (AVS) and SMPTE VC-1.