Getting Started
 
Signal processing is widely used in engineering, and scientific research and development for representing, transforming, and manipulating signals and the information they contain. This rapidly advancing technology has applications in many areas including speech processing, data communications, acoustics, radar, sonar, seismology, remote sensing, scientific and medical instrumentation, consumer electronics, time-series analysis, and finance.
The PV‑WAVE Signal Processing Toolkit is a collection of digital signal processing (DSP) functions that work in conjunction with PV‑WAVE Advantage. This chapter discusses the following main categories of functions found in the Signal Processing Toolkit:
*Signals and Systems (Models and Analysis)
*Filter Approximation
*Filter Realization
*Transforms and Spectrum Analysis
*Statistical Signal Processing
*Polynomial Manipulation
*Optimization
*Plotting and Signal Generation
The Signal Processing Toolkit functions are designed for easy use, while providing many options for solving difficult problems.
An important part of the PV‑WAVE Signal Processing Toolkit is the PV‑WAVE Advantage platform. Several fundamental signal processing functions already exist in PV‑WAVE Advantage: including fast Fourier transform functions, numerical optimization functions, matrix manipulation functions, and functions for finding polynomial roots. The PV‑WAVE Signal Processing Toolkit greatly extends the signal processing capabilities of PV‑WAVE Advantage through a combination of additional signal processing routines, and the ability to extend the functionality by customizing the source code in the Signal Processing Toolkit to meet your needs.
Purpose of this Section
The purpose of this section is to establish terminology and provide a brief overview of the functionality of the PV‑WAVE Signal Processing Toolkit. Examples in this chapter demonstrate how the Signal Processing Toolkit functions can be used together to solve signal processing problems. It is assumed that you have a basic working knowledge of signals and systems, including linear systems, transform analysis of linear systems (Fourier and z-transforms) and filtering.
Where appropriate, outside sources are cited, and full bibliographic entries are listed in Appendix A: Bibliography. In addition, the section "Background Reading" is included in this chapter for those wishing to explore in greater detail the signal processing topics discussed in this manual.
Notation and Conventions Used in this User’s Guide
The standard notation in signal processing texts uses lower-case letters for time-domain signals and upper-case for frequency-domain signals.
Use of Upper and Lower-Case Letters in This Manual
This manual follows the standard signal processing notation in the function discussions and descriptions; however, in all code examples, the PV-WAVE convention for capitalization is followed.
To illustrate the notation used in this manual, let’s look at the calling sequence and discussion for the FIRFILT function. The FIRFILT calling sequence containing the filter structure H(z) and the input array to be filtered, x, is as shown.
result = FIRFILT(h, x)
While the calling sequence uses h (lower-case) to represent the filter structure, the discussion uses the standard notation for signal processing, H (upper-case).
Frequency Normalization in the Signal Processing Toolkit
All Signal Processing Toolkit functions use normalized frequencies for ease in manipulation. The frequency normalization used results in a normalized Nyquist frequency equal to one.
 
note
Sometimes it is preferable to show actual frequency values on an output plot. This can be easily accomplished by multiplying the normalized axis by the actual Nyquist frequency when setting the plot parameters.