"Digital Signal Processing: A Computer-Based Approach" is intended for a two-semester course on digital signal processing for seniors or first-year graduate students. Based on user feedback, a number of new topics have been added to the second edition, while some excess topics from the first edition have been removed. The author has...

Linear systems have all the necessary elements (modeling, identification, analysis and control), from an educational point of view, to help us understand the discipline of automation and apply it efficiently. This book is progressive and organized in such a way that different levels of readership are possible. It is addressed both to...

• Learn the fundamentals behind commonly used scanning techniques
• Deploy powerful scanning tools that are integrated into the Kali...

This book delivers theoretical and practical knowledge on extension of Genetic Programming (GP) for practical applications. It provides a methodology for  integrating Genetic Programming and machine-learning techniques. The developmentof such tools contributes to the establishment of a more robust evolutionary framework when addressing...

This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in...

Covering a wide array of topics from bacteria to human cells, this book summarizes recent developments in DNA damage repair and recognition while providing timely reviews on the molecular...

With the character of the doctor as her subject, Tabitha Sparks follows the decline of the marriage plot in the Victorian novel. As Victorians came to terms with the scientific revolution in medicine of the mid-to-late nineteenth century, the novel's progressive distance from the conventions of the marriage plot can be indexed through a rising...

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets...