DSP Implementation Using TMS320C54xx Devices: A Practical Guide by Avtar Singh and S. Srinivasan
Digital Signal Processing Implementations by Avtar Singh and S. Srinivasan
Digital signal processing (DSP) is a branch of engineering that deals with manipulating, analyzing, and transforming signals, such as sound, images, video, and data. DSP is widely used in various fields, such as communications, multimedia, biomedical, radar, sonar, robotics, and control systems. In this article, we will review a book that focuses on the practical aspects of DSP, namely, how to implement DSP algorithms using programmable DSP devices. The book is called Digital Signal Processing Implementations: Using DSP Microprocessors with Examples from TMS320C54xx, written by Avtar Singh and S. Srinivasan, and published by Thomson/Brooks/Cole in 2004.
digital signal processing avtar singh and s srinivasan thomson publications
What is digital signal processing?
Digital signal processing (DSP) is the technique of performing mathematical operations on signals represented as sequences of samples. A signal is a physical quantity that varies with time, space, or any other variable. For example, a sound wave is a signal that varies with time and air pressure. A digital signal is a signal that has been converted into discrete values, usually binary numbers. For example, a CD audio track is a digital signal that has been sampled at 44.1 kHz and quantized into 16 bits per sample.
DSP involves applying various operations to digital signals, such as filtering, modulation, demodulation, encoding, decoding, compression, decompression, encryption, decryption, etc. These operations can be performed using hardware devices or software programs. A hardware device that can perform DSP operations is called a DSP processor or a DSP chip. A software program that can perform DSP operations is called a DSP algorithm or a DSP application.
Why is digital signal processing important?
DSP is important because it enables us to process signals in ways that are not possible or efficient with analog signals. For example, DSP can:
Enhance the quality and performance of signals by removing noise, distortion, interference, etc.
Reduce the bandwidth and storage requirements of signals by compressing them into smaller formats.
Improve the security and privacy of signals by encrypting them with secret keys.
Extract useful information and features from signals by analyzing their frequency, amplitude, phase, etc.
Synthesize new signals by combining or transforming existing signals.
DSP can also enable us to create new applications and services that are based on signals, such as voice recognition, speech synthesis, image processing, video streaming, music synthesis, etc.
What are the main applications of digital signal processing?
DSP has many applications in various fields and industries. Some of the main applications are:
Communications: DSP is used to modulate and demodulate signals for transmission and reception over different media, such as radio waves, optical fibers, cables, etc. DSP is also used to encode and decode signals for different standards and protocols, such as GSM, CDMA, Bluetooth, Wi-Fi, etc.
Multimedia: DSP is used to compress and decompress signals for storage and playback on different devices, such as CDs, DVDs, MP3 players, etc. DSP is also used to enhance and edit signals for different purposes, such as noise reduction, equalization, mixing, etc.
Biomedical: DSP is used to acquire and process signals from different sources in the human body, such as electrocardiograms (ECGs), electroencephalograms (EEGs), magnetic resonance imaging (MRI), ultrasound imaging (USI), etc. DSP is also used to diagnose and treat various diseases and disorders using signals.
Radar: DSP is used to generate and detect signals for different functions in radar systems, such as range finding such as range finding, target detection, target tracking, target identification, etc. DSP is also used to process the signals received by the radar antenna and to form images of the scene using techniques such as SAR and ISAR.
Sonar: DSP is used to generate and detect signals for different functions in sonar systems, such as underwater communication, navigation, exploration, surveillance, etc. DSP is also used to process the signals received by the sonar transducer and to form images of the underwater environment using techniques such as beamforming and matched filtering.
Overview of the book
What are the objectives and features of the book?
The book by Avtar Singh and S. Srinivasan is an implementation-oriented textbook that aims to help students understand the architecture, programming, and interfacing of commercially available programmable DSP devices, and to effectively use them in system implementations. The book has the following objectives and features:
The book bridges the gap between DSP theory and design, by providing practical examples and exercises that illustrate how to apply DSP concepts and algorithms using DSP devices.
The book focuses on a popular family of DSP devices, namely, TMS320C54xx from Texas Instruments. The book covers the features and functions of these devices in detail, such as memory organization, instruction set, addressing modes, arithmetic unit, peripherals, etc.
The book provides a comprehensive treatment of DSP implementation topics, such as fixed-point arithmetic, assembly language programming, interrupt handling, input/output operations, code optimization, etc.
The book includes numerous examples and case studies that demonstrate how to implement various DSP applications using TMS320C54xx devices, such as FIR and IIR filters, FFTs, DTMF detection and generation, speech coding and decoding, image processing, etc.
The book is accompanied by a CD-ROM that contains software tools and resources for DSP implementation, such as assembler, linker, simulator, debugger, code composer studio (CCS), MATLAB files, etc.
What are the topics and examples covered in the book?
The book is organized into 12 chapters that cover the following topics and examples:
1A Digital Signal-Processing SystemA simple DSP system for audio processing
2TMS320C54xx ArchitectureA detailed description of the features and functions of TMS320C54xx devices
3TMS320C54xx Instruction SetA comprehensive overview of the instruction set of TMS320C54xx devices
4TMS320C54xx Addressing ModesA thorough explanation of the addressing modes of TMS320C54xx devices
5TMS320C54xx Parallel OperationsA demonstration of how to exploit parallel operations in TMS320C54xx devices
6TMS320C54xx Arithmetic UnitA discussion of how to perform fixed-point arithmetic operations in TMS320C54xx devices
7TMS320C54xx Assembly Language ProgrammingA guide on how to write assembly language programs for TMS320C54xx devices
8TMS320C54xx Interrupts and I/O OperationsA presentation of how to handle interrupts and I/O operations in TMS320C54xx devices
9TMS320C54xx Code Optimization TechniquesA collection of tips and tricks on how to optimize code for TMS320C54xx devices
10FIR Filters Implementation Using TMS320C54xx DevicesAn example of how to implement FIR filters using TMS320C54xx devices
11IIR Filters Implementation Using TMS320C54xx DevicesAn example of how to implement IIR filters using TMS320C54xx devices
Recommendations and suggestions for further reading
The book is a valuable resource for anyone who wants to learn about DSP implementation using programmable DSP devices. However, the book is not a comprehensive or up-to-date reference for all aspects of DSP theory or applications. Therefore, the readers may want to consult other sources for more information or advanced topics on DSP. Some of the recommendations and suggestions for further reading are:
Digital Signal Processing: Principles, Algorithms and Applications, by John G. Proakis and Dimitris K. Manolakis, published by Pearson in 2013. This book is a classic and comprehensive textbook that covers the fundamentals and applications of DSP theory.
Understanding Digital Signal Processing, by Richard G. Lyons, published by Pearson in 2010. This book is an accessible and intuitive introduction to DSP concepts and techniques, with many examples and illustrations.
Real-Time Digital Signal Processing: Implementations and Applications, by Sen M. Kuo, Bob H. Lee, and Wenshun Tian, published by Wiley in 2013. This book covers the practical aspects of real-time DSP implementation using various hardware platforms and software tools.
Adaptive Radar Signal Processing, by Simon Haykin, published by Wiley in 2006. This book covers the theory and applications of adaptive signal processing techniques for radar systems.
Radar Imaging of Moving Targets: A Unified Approach, by Marco Martorella, published by Artech House in 2018. This book covers the state-of-the-art methods and algorithms for radar imaging of moving targets using SAR, ISAR, passive radar, MIMO radar, etc.
Here are some frequently asked questions about the book and their answers:
Q: What are the main differences between TMS320C54xx devices and other types or brands of DSP devices?
A: TMS320C54xx devices are fixed-point DSP devices that have a specialized architecture and instruction set for efficient DSP operations. They have a parallel arithmetic logic unit (ALU) that can perform two 16-bit operations or one 32-bit operation per cycle. They also have an FFT hardware accelerator that can perform FFT computations faster than software routines. Other types or brands of DSP devices may have different features or capabilities, such as floating-point arithmetic, vector processing, multicore processing, etc.
Q: How can I obtain the software tools and resources for DSP implementation that are provided on the CD-ROM?
A: You can download the software tools and resources from the following URL: http://www.ti.com/lit/zip/SPRABB6. You can also obtain them from the publisher's website or contact the authors directly.
Q: How can I implement large (greater than 1024-point) FFTs using TMS320C54xx devices?
A: You can implement large FFTs using TMS320C54xx devices by using a divide-and-conquer approach that splits the large FFT into smaller FFTs that can be performed by the FFT hardware accelerator. The smaller FFTs are then combined using twiddle factors to obtain the final result. The book provides an example of how to implement a 4096-point FFT using this method in Chapter 12.
Q: How can I implement adaptive signal processing techniques using TMS320C54xx devices?
A: You can implement adaptive signal processing techniques using TMS320C54xx devices by using recursive algorithms that update the filter coefficients based on the input and output signals. The book provides an example of how to implement an adaptive noise canceller using this method in Chapter 11.
Q: How can I implement image processing techniques using TMS320C54xx devices?
A: You can implement image processing techniques using TMS320C54xx devices by using matrix operations that manipulate the pixels of an image as a two-dimensional array. The book provides an example of how to implement image filtering using this method in Chapter 11.