Digital signal processing (DSP) is one of the most prominent areas in engineering, including subfields such as speech and image processing, statistical data processing, spectral estimation, biomedical applications, and many others. As the name suggests, the goal is to perform various signal processing tasks (e.g., filtering, amplification, and more) in the digital domain where design, verification, and implementation are considerably simplified compared with analog signal processing. DSP is the basis of many areas of technology, and is one of the most powerful technologies that have shaped science and engineering in the past century.

In order to represent and process analog signals on a computer the signals must be sampled with an analog-to-digital converter (ADC) which converts the signal to a sequence of numbers. After processing, the samples are converted back to the analog domain via a digital-to-analog converter (DAC). Consequently, the theory and practice of sampling are at the heart of DSP. Evidently, any technology advances in ADCs and DACs have a huge impact on a vast array of applications.

The goal of this book is to provide a comprehensive treatment of the theory and practice of sampling from an engineering perspective. Although there are many excellent mathematical textbooks on signal expansions and harmonic analysis, our aim is to present an up-to-date engineering textbook on the topic by combining the fundamental mathematical underpinnings of sampling with practical engineering applications and principles.