This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework. Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the context of additive Gaussian processes. It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.

This book presents new concepts, techniques and promising programming models for designing software for chips with "many" (hundreds to thousands) processor cores. Given the scale of parallelism inherent to these chips, software designers face new challenges in terms of operating systems, middleware and applications. This will serve as an invaluable, single-source reference to the state-of-the-art in programming many-core chips. Coverage includes many-core architectures, operating systems, middleware, and programming models.

This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Parallel Processing and Applied Mathematics, PPAM 2007, held in Gdansk, Poland, in September 2007. The 63 revised full papers of the main conference presented together with 85 revised workshop papers were carefully reviewed and selected from over 250 initial submissions. The papers are organized in topical sections on parallel/distributed architectures and mobile computing, numerical algorithms and parallel numerics, parallel and distributed non-numerical algorithms, environments and tools for as well as applications of parallel/distributed/grid computing, evolutionary computing, meta-heuristics and neural networks. The volume proceeds with the outcome of 11 workshops and minisymposia dealing with novel data formats and algorithms for dense linear algebra computations, combinatorial tools for parallel sparse matrix computations, grid applications and middleware, large scale computations on grids, models, algorithms and methodologies for grid-enabled computing environments, scheduling for parallel computing, language-based parallel programming models, performance evaluation of parallel applications on large-scale systems, parallel computational biology, high performance computing for engineering applications, and the minisymposium on interval analysis.

This book comprises select peer-reviewed papers presented at the International Conference on Biomedical Engineering Science and Technology: Roadway from Laboratory to Market (ICBEST 2018) organized by Department of Biomedical Engineering, National Institute of Technology Raipur, Chhattisgarh, India. The book covers latest research in a wide range of biomedical technologies ranging from biomechanics, biomaterials, biomedical instrumentation to tele-medicine, internet of things, bioinformatics, medical signal and image processing. The contents aim to bridge the gap between laboratory research and feasible market products by identifying potential technologies to enhance functionalities of diagnostic and therapeutic devices. The book will be of use to researchers, biomedical engineers, as well as medical practitioners.

The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel gemometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.

The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel gemometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.

Describes practical programming approaches for scientific applications on exascale computer systems Presents strategies to make applications performance portable Provides specific solutions employed in current application porting and development Illustrates domain science software development strategies based on projected trends in supercomputing technology and architectures Includes contributions from leading experts involved in the development and porting of scientific codes for current and future high performance computing resources

The official reference for developing and deploying parallel, scalable OpenGL applications based on the Equalizer parallel rendering framework.

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.