Data Acquisition and Signal Processing

This chapter discusses the fundamental aspects of signal processing, including sampling, quantization, and aliasing. Both analog and digital signal processing techniques are discussed. The main usage of analog signal processing is in preprocessing of sensor signals to deal with the so-called aliasing phenomenon that is common in data acquisition systems and generally to clean up highly noisy signals. This is accomplished using passive components (resistors, capacitors) or a combination of passive and active components (operational amplifiers). The latter approach provides a more robust approach to implementing analog filters and is the recommended approach in practical setting. Postprocessing is typically accomplished via software algorithms that are implemented using a digital computer (or a digital signal processing device). Implementation via Matlab is demonstrated through embedded examples and exercises. These include moving average (alternately, finite impulse response) and autoregressive moving average (alternately, infinite impulse response) filters. We also discuss built-in functions of Matlab such as the Butterworth filter generator, which is among the tools that can be utilized in developing quick and effective digital signal processing algorithms.