Описание: This book gathers selected papers presented at the conference "Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology," one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas.
Описание: Taking the Qinghai–Tibet Railway as an example, this book introduces intelligent processing for Global Positioning Data (GPS) data. Combining theory with practical applications, it provides essential insights into the Chinese Qinghai–Tibet Railway and novel methods of data processing for GPS satellite positioning, making it a valuable resource for all those working with train control systems, train positioning systems, satellite positioning, and intelligent data processing. As satellite positioning guarantees the safe and efficient operation of train control systems, it focuses on how to best process the GPS data collected, including methods for error detection, reduction and information fusion.
Описание: This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems.
Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry Название: Advances in Heuristic Signal Processing and Applications ISBN: 364237879X ISBN-13(EAN): 9783642378799 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chap. 1: Nonconvex Optimization via Joint Norm Relaxed SQP and Filled Function Method with Application to Minimax Two-Channel Linear Phase FIR QMF Bank Design.- Chap. 2: Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters.- Chap. 3: Designing OFDM Radar Waveform for Target Detection Using Multiobjective Optimization.- Chap. 4: Multiobject Tracking using Particle Swarm Optimization on Target Interactions.- Chap. 5: A Comparative Study of Modified BBO Variants and Other Metaheuristics for Optimal Power Allocation in Wireless Sensor Networks.- Chap. 6: Joint Optimization of Detection and Tracking in Adaptive Radar Systems.- Chap. 7: Iterative Design of FIR Filters.- Chap. 8: A Metaheuristic Approach to Two-Dimensional Recursive Digital Filter Design.- Chap. 9: A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization.- Chap. 10: Swarm Intelligence Techniques Applied to Nonlinear Systems State Estimation.- Chap. 11: Heuristic Optimal Design of Multiplier-less Digital Filter.- Chap. 12: Hybrid Correlation-Neural Network Synergy for Gait Signal Classification.- Chap. 13: Image Denoising Using Wavelets: Application in Medical Imaging.- Chap. 14: Signal Separation with A Priori Knowledge Using Sparse Representation.- Chap. 15: Definition of a Discrete Color Monogenic Wavelet Transform.- Chap. 16: On Image Matching and Feature Tracking for Embedded Systems: State of the Art.
Описание: Presents current research relating to multimedia technologies including video and image restoration and enhancement as well as algorithms used for image and video compression, indexing and retrieval processes, and security concerns. It features insight from researchers from around the world.
Описание: Mathematical morphology is a powerful methodology for the processing and analysis of geometric structure in signals and images. This book contains the proceedings of the fifth International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing, held June 26-28, 2000, at Xerox PARC, Palo Alto, California. It provides a broad sampling of the most recent theoretical and practical developments of mathematical morphology and its applications to image and signal processing.
Areas covered include: decomposition of structuring functions and morphological operators, morphological discretization, filtering, connectivity and connected operators, morphological shape analysis and interpolation, texture analysis, morphological segmentation, morphological multiresolution techniques and scale-spaces, and morphological algorithms and applications. Audience: The subject matter of this volume will be of interest to electrical engineers, computer scientists, and mathematicians whose research work is focused on the theoretical and practical aspects of nonlinear signal and image processing. It will also be of interest to those working in computer vision, applied mathematics, and computer graphics.
Описание: This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.
Описание: This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals.
Описание: This book presents a collection of the most recent hybrid methods for image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence.
Автор: Bogdan Dumitrescu; Paul Irofti Название: Dictionary Learning Algorithms and Applications ISBN: 3319786733 ISBN-13(EAN): 9783319786735 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures.Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation;Covers all dictionary structures that are meaningful in applications;Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
Автор: Mutingi Michael, Mbohwa Charles Название: Grouping Genetic Algorithms: Advances and Applications ISBN: 3319830481 ISBN-13(EAN): 9783319830483 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment.
Автор: Erik Cuevas; Daniel Zald?var; Marco P?rez-Cisneros Название: Advances in Metaheuristics Algorithms: Methods and Applications ISBN: 3030077365 ISBN-13(EAN): 9783030077365 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Нет в наличии.
Описание:
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
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