Introduction: Terminology and Motivation

Abstract

This book is concerned with the processing of discrete-time signals or data sequences. Such signals arise in two distinct ways: They may be inherently discrete in time, or they may be sampled versions of signals that are continuous in time. Examples of data sequences that are inherently discrete in time abound in our daily lives: for example, our daily or monthly checking account balances, the daily high/low temperatures and other weather data, monthly or quarterly sales figures and inventory levels, the annual GNP and other economic data, and so forth. Meteorologists, economists, and business people often process such data to determine cyclic patterns, averages, or norms, and long-term trends. In so doing, they usually employ filters to smooth out noisy data or to enhance certain patterns of interest, whether or not they call them by that name.