This text provides students with an introduction to data structures and algorithms. It emphasizes algorithm analysis and the development of efficient code, and includes important background material

From the inventor of Pascal and Modula-2 comes a new version of Niklaus Wirth's classic work, Algorithms Plus Data Structure Equals Programs (PH, l975).

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.

The book Autonomous Vehicles and the Law: Technology, Algorithms and Ethics represents a concise contribution to the debate on artificial intelligence and law, explaining the underlining technology on an example of self-driving vehicles. The book provides a profound background on the self-driving technology with the purpose to explain it in an understandable manner to lawyers.

The book Digital Signal Processing: Principles, Algorithms and System Design provides an introduction to the principals of digital signal processing along with a balanced analytical and practical treatment of algorithms and applications for digital signal processing.

Introduction to Adaptive Filtering; Fndamentals of Adaptive Filtering; The Least-Square Algorithm; LMS-Based Algorithms; Conventional RLS Adaptive Filter; Data-Selective Adaptive Filtering; Adaptive Lattice-Based RLS Algorithms; Fast Transversal RLS Algorithms; QR-Decomposition-Based RLS Filters; Adaptive IIR Filters; Nonlinear Adaptive Filtering; Subband Adaptive Filters; Blind Adaptive Filtering; Complex Differentiation; Quantization Effects in the LMS Algorithm; Quantization Effects in the RLS Algorithm; Kalman Filters.

The book provides a concise background on adaptive filtering, including the family of LMS, affine projection, RLS, set-membership algorithms and Kalman filters, as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Problems are included at the end of chapters. A MATLAB package is provided so the reader can solve new problems and test algorithms. The book also offers easy access to working algorithms for practicing engineers.