Introduction

The field of digital signal processing (DSP) has experienced a considerable growth in the past two decades, primarily due to the availability and advancements in digital signal processors (also called DSPs). Nowadays, DSP systems such as cell phones and high-speed modems have become an integral part of our lives.

In general, sensors generate analog signals in response to various physical phenomena that occur in an analog manner (i.e., in continuous-time and amplitude). Processing of signals can be done either in analog or digital domain. To perform the processing of an analog signal in digital domain, it is required that a digital signal is formed by sampling and quantizing (digitizing) the analog signal. Hence, in contrast to an analog signal, a digital signal is discrete in both time and amplitude. The digitization process is achieved via an analog-to-digital (A/D) converter. The field of DSP involves the manipulation of digital signals in order to modify their characteristics or to extract useful information from them.

There are many reasons why one wishes to process an analog signal in a digital fashion by converting it into a digital signal. The main reason is that digital processing offers programmability, which means the same processor hardware can be used for many different applications by simply changing the code residing in memory. Another reason is that digital circuits provide a more stable and tolerant output than analog circuits—for instance, when subjected to temperature changes. In addition, the advantage of operating in digital domain may be intrinsic. For example, a linear phase filter or a steep-cutoff notch filter can be easily realized by using digital signal processing techniques, and many adaptive systems are achievable in a practical product only via digital manipulation of signals. In essence, digital representation (0’s and 1’s) allows voice, audio, image, and video data to be treated the same for error-tolerant digital transmission and storage purposes.

1.1 Digital Signal Processing Hands-On Lab Courses

Nearly all electrical engineering curricula include DSP courses. DSP lab or design courses are also being offered at many universities concurrently or as follow-ups to DSP theory courses. These hands-on lab courses have played a major role in better understanding of DSP concepts. A number of textbooks, e.g. [1–5], have been written to provide the teaching materials for DSP lab courses. The programming language used in these textbooks consists of either C, MATLAB®, or Assembly, which are all text-based languages. In addition to these text-based languages, it is becoming important for students to gain experience in block-based or graphical (G) programming or environment for the purpose of designing DSP systems in a relatively short amount of time. Graphical programming offers an interactive and a more intuitive approach toward building DSP systems. Thus, the main objective of this book is to provide a block-based or system-level programming approach in DSP lab courses. The system-level programming environment chosen is LabVIEW.

Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) is a graphical programming environment developed by National Instruments (NI) which allows performing high-level or system-level designs. It uses a graphical programming language to create so-called Virtual Instrument (VI) blocks in an intuitive flowchart-like manner. A design is achieved by integrating different blocks, components, or subsystems within a graphical framework. LabVIEW provides data acquisition, analysis, and visualization features well suited for DSP system design. It is also an open environment accommodating MATLAB and C Dynamic Link Libraries (DLLs).

This book is written primarily for those who are already familiar with signal processing concepts and are interested in designing signal processing systems without needing them to be proficient C or MATLAB programmers. After familiarizing the reader with LabVIEW, the book covers a LabVIEW-based approach to generic experiments encountered in a typical DSP lab course. It brings together in one place the information scattered in several NI LabVIEW manuals to provide the necessary tools and know-how for designing signal processing systems within a one-semester lab course. This book can also be used as a self-study LabVIEW guide toward designing and analyzing signal processing systems.

In addition, for those interested in DSP hardware implementation, two chapters in the book are dedicated to executing selected portions of a LabVIEW designed system on an actual DSP processor. The DSP processor chosen is TMS320C6000. This processor has been manufactured by Texas Instruments (TI) for computationally intensive signal processing applications. The DSP hardware utilized to interface with LabVIEW is the widely adopted TI’s C6416 or C6713 DSP Starter Kit (DSK) board. It should be mentioned that since the DSP hardware implementation aspect of the labs (which includes C programs) is independent of the LabVIEW implementation, those who are not interested in the DSP hardware implementation may skip these two chapters.

1.2 Organization

The book includes 12 chapters and 12 labs. After this introduction, the LabVIEW programming environment is presented in Chapter 2. Lab 1 and Lab 2 in Chapter 2 provide a tutorial on getting familiar with the LabVIEW programming environment. Lab 1 provides a general introduction to LabVIEW, and Lab 2 covers building signal processing systems graphically. Lab 2 also shows how to incorporate M-file nodes or blocks within LabVIEW. The topic of analog-to-digital signal conversion is presented in Chapter 3 followed by Lab 3 covering signal sampling experiments. Chapter 4 involves digital filtering. Lab 4 in Chapter 4 shows how to use LabVIEW to design FIR and IIR digital filters. In Chapter 5, fixed-point versus floating-point implementation issues are discussed, followed by Lab 5 covering data type and fixed-point effect experiments. In Chapter 6, the topic of adaptive filtering is discussed. Lab 6 in Chapter 6 covers two adaptive filtering systems consisting of system identification and noise cancellation. Chapter 7 presents frequency domain processing, followed by Lab 7 covering the three widely used transforms in signal processing: fast Fourier transform (FFT), short-time Fourier transform (STFT), and discrete wavelet transform (DWT). Chapter 8 discusses the implementation of a LabVIEW-designed system on the TMS320C6000 DSP processor. First, an overview of the TMS320C6000 architecture is provided. Then, in Lab 8, a tutorial is presented to show how to use the Code Composer Studio (CCStudio) software development tool to achieve the DSP hardware implementation. As a continuation of Chapter 8, Chapter 9 and Lab 9 discuss the issues related to the interfacing of LabVIEW and the DSP processor. Chapters 10 through 12 Chapter 11 Chapter 12 and Labs 10 through 12, respectively, discuss the following three DSP systems or project examples that are designed in a hybrid mode or a combination of graphical and textual modes: (i) dual tone multi-frequency (DTMF) signaling, (ii) software-defined radio, and (iii) cochlear implant simulator.

1.3 Software Installation

LabVIEW 8.5, which is the latest version at the time of this writing, can be installed by running setup.exe on the LabVIEW Core DVD. Some lab portions use the LabVIEW toolkits “Digital Filter Design,” “Advanced Signal Processing,” and “DSP Test Integration for TI DSP.” The toolkit “Digital Filter Design” appears under the LabVIEW Core DVD and can be included while installing LabVIEW 8.5. The toolkits “Advanced Signal Processing” and “DSP Test Integration for TI DSP” appear on the Signal Processing and Communications DVD and can be installed by running setup.exe on this DVD. To generate C DLLs, it is required to have Microsoft Visual Studio® or a similar C development environment installed. To use the MATLAB script node feature of LabVIEW, it is required to have MATLAB Version 6.0 or later installed.

If one desires to run parts of a LabVIEW-designed system on a DSP processor, then it is required to install the Code Composer Studio (CCStudio) software tool by running setup.exe on the CCStudio CD. In the DSK related labs, CCStudio v3.0 is used.

The accompanying CD includes all the files necessary for running the labs covered throughout the book.

1.4 Updates

Considering that any programming environment goes through enhancements and updates, it is expected that there will be updates of LabVIEW and its toolkits. To accommodate for such updates and to make sure that the labs provided in the book can still be used in DSP lab courses, any new version of the labs will be posted at the website http://www.utdallas.edu/~kehtar/LabVIEW for easy access. It is recommended that this website is periodically checked to download any necessary updates.

1.5 Bibliography

1. Kehtarnavaz N. Real‐Time Digital Signal Processing Based on the TMS320C6000 Elsevier 2005.

2. Kuo S, Gan W‐S. Digital Signal Processors: Architectures, Implementations, and Applications Prentice‐Hall 2005.

3. Chassaing R. DSP Applications Using C and the TMS320C6x DSK Wiley Inter‐Science 2002.

4. Welch T, Wright C, Morrow M. Real‐Time Digital Signal Processing from MATLAB to C with the TMS320C6x DSK CRC Press 2006.

5. Tan...