Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics-including familiarity with Fourier series, matrices, probab
This book introduces the basic theory of digital signal processing, with emphasis on real-world applications.
Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression ...
This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental...
This book describes the essential tools and techniques of statistical signal processing.
Addressing a fundamental problem in complex signal processing systems, this book offers not only theoretical development, but practical recommendations for raising noise immunity in a wide range of applications.
Historically, the topic of different sampling rates in signal processing was first treated in detail in R. E. Crochiere and L. R. Rabiner's, Multirate Digital Signal Processing (Prentice-Hall, 1983). With the advent of filter banks and ...
In addition to its thorough coverage of DSP design and programming techniques, Smith also covers the operation and usage of DSP chips.
The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering.
Digital Signal Processing
This book covers all the major topics in digital signal processing (DSP) design and analysis, supported by MatLab examples and other modelling techniques.