-Uniquely covers both theoretical development and detailed flight control application examples
-Of interest to both control and aeronautical engineers as well as being of use to doctoral students and flight control researchers in universities

This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed;Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Review:
Once again author Jean Meeus demonstrates his expertise in astronomical positioning and applied mathematics with Astronomical Algorithms. He brings us into the 21st century with the new J2000.0 epoch and FK5 system. His chapters on the primary corrections of precession, nutation, parallax, and aberration are thorough and presented in both the equatorial and ecliptic coordinate systems. Planetary positioning, including the Sun, is provided by the principal elements of Bretagnon and Francou's VSP087 theory. Chapront's ELP-2000/82 theory provides Lunar positioning. The Galilean satellites of Jupiter are located with Lieske's E2x3 theory. The author systematically steps us through each method with real examples.

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject.

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles.

Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms.

From the reviews of the previous editions ".... The book is a first class textbook and seems to be indispensable for everybody who has to teach combinatorial optimization. It is very helpful for students, teachers, and researchers in this area. The author finds a striking synthesis of nice and interesting mathematical results and practical applications. ... the author pays much attention to the inclusion of well-chosen exercises.

The most comprehensive, practical overview of algorithms in computational molecular biology
Computational molecular biology has emerged from the Human Genome Project as an important discipline for academic research and industrial application. Due to the exponential growth?in the size of biological databases and the increasing intricacy of biological problems, high-performance algorithms are now necessary to deal with errors in biological sequences.?Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology is the first in-depth examination of these important algorithms, which are based on the newest and most improved approaches and techniques in the field.

Practical Genetic Algorithms /by Randy L. Haupt, Sue Ellen Haupt.
* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science

Digital Video and HD: Algorithms and Interfaces provides a one-stop shop for the theory and engineering of digital video systems. Equally accessible to video engineers and those working in computer graphics, Charles Poynton's revision to his classic text covers emergent compression systems, including H.264 and VP8/WebM, and augments detailed information on JPEG, DVC, and MPEG-2 systems. This edition also introduces the technical aspects of file-based workflows and outlines the emerging domain of metadata, placing it in the context of digital video processing.

Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program.