The Burrows-Wheeler Transform:, Donald Adjeroh; Timothy Bell; Amar Mukherjee
Автор: Daisuke Takahashi Название: Fast Fourier Transform Algorithms for Parallel Computers ISBN: 9811399646 ISBN-13(EAN): 9789811399640 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Following an introduction to the basis of the fast Fourier transform (FFT), this book focuses on the implementation details on FFT for parallel computers. FFT is an efficient implementation of the discrete Fourier transform (DFT), and is widely used for many applications in engineering, science, and mathematics. Presenting many algorithms in pseudo-code and a complexity analysis, this book offers a valuable reference guide for graduate students, engineers, and scientists in the field who wish to apply FFT to large-scale problems.Parallel computation is becoming indispensable in solving the large-scale problems increasingly arising in a wide range of applications. The performance of parallel supercomputers is steadily improving, and it is expected that a massively parallel system with hundreds of thousands of compute nodes equipped with multi-core processors and accelerators will be available in the near future. Accordingly, the book also provides up-to-date computational techniques relevant to the FFT in state-of-the-art parallel computers. Following the introductory chapter, Chapter 2 introduces readers to the DFT and the basic idea of the FFT. Chapter 3 explains mixed-radix FFT algorithms, while Chapter 4 describes split-radix FFT algorithms. Chapter 5 explains multi-dimensional FFT algorithms, Chapter 6 presents high-performance FFT algorithms, and Chapter 7 addresses parallel FFT algorithms for shared-memory parallel computers. In closing, Chapter 8 describes parallel FFT algorithms for distributed-memory parallel computers.
Автор: Haitham Hassanieh Название: The Sparse Fourier Transform ISBN: 1947487078 ISBN-13(EAN): 9781947487079 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12860.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT), which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even sample all the data points, have become necessary.This book addresses the above problem by developing the Sparse Fourier Transform algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits.This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award.
Автор: Shukla KK Название: Efficient Algorithms for Discrete Wavelet Transform ISBN: 1447149408 ISBN-13(EAN): 9781447149408 Издательство: Springer Рейтинг: Цена: 5589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients.
Electric power and the evolution of utilities fueled the growth of industrialization and the incredible innovation of the 20th century by allowing companies to focus on what they where best at rather than the generation and management of power.
That's exactly what's happening today as an almost unimaginable abundance of data is moving into the cloud where it will fuel the revolution in artificial intelligence and machine learning. However, most people are still using an industrial age lens, built on the economics of scarcity, to view the opportunities that the cloud creates for a future of limitless data; it's the equivalent of trying to compete using waterwheels to power a modern factory.
The Bottomless Cloud challenges the scarcity-driven mindset by taking a hard look at how industrial age business models are failing us by regarding data as a commodity and a cost that needs to be constrained, rather than an near infinite resource that can be mined to build entirely new sources of value and insight. From Uber, to Nike, to Netflix, The Bottomless Cloud is defining the tenets of success in the 21st Century by changing the way we view data, from being a byproduct of business to a foundational driver of radically new business models.
On the heels of his Amazon bestselling book Revealing The Invisible, 11 time author Tom Koulopoulos has teamed up with his long time colleague and serial entrepreneur David Friend to provide business leaders with a clear and straightforward understanding of the incredible power of the cloud and data abundance; it's an entirely new way to think about the value and the role of data in building tomorrow's enterprise.
Written in a way that is easy to follow, The Bottomless Cloud provides a compelling vision of a future in which data abundance will alter nearly every aspect of how we live, work, and play.
Автор: Hassanieh Haitham Название: The Sparse Fourier Transform ISBN: 1947487043 ISBN-13(EAN): 9781947487048 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10352.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT), which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even sample all the data points, have become necessary.This book addresses the above problem by developing the Sparse Fourier Transform algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits.This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award.
Автор: Kochenderfer Mykel J., Wheeler Tim A. Название: Algorithms for Optimization ISBN: 0262039427 ISBN-13(EAN): 9780262039420 Издательство: MIT Press Рейтинг: Цена: 14390.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.
This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.
Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
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