Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices, Suri Manan
Автор: Manan Suri Название: Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices ISBN: 8132237013 ISBN-13(EAN): 9788132237013 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices.
Автор: Qiang Yu; Huajin Tang; Jun Hu; Kay Tan Chen Название: Neuromorphic Cognitive Systems ISBN: 3319553089 ISBN-13(EAN): 9783319553085 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents neuromorphic cognitive systems from a learning and memory-centered perspective.
Автор: Shih?€“Chii Liu,Tobi Delbruck,Giacomo Indiveri,Adr Название: Event?€“Based Neuromorphic Systems ISBN: 0470018496 ISBN-13(EAN): 9780470018491 Издательство: Wiley Рейтинг: Цена: 89710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain`s efficient data-driven communication design, which is key to its quick responses and remarkable capabilities.
Автор: Jeff Z. Pan; Guido Vetere; Jose Manuel Gomez-Perez Название: Exploiting Linked Data and Knowledge Graphs for Large Organisations ISBN: 3319456520 ISBN-13(EAN): 9783319456522 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and "standard" data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.
Автор: Dong Guozhu Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems ISBN: 1681735024 ISBN-13(EAN): 9781681735023 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 57290.00 T Наличие на складе: Невозможна поставка. Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
Автор: Mohamed Khaled Salah Название: Neuromorphic Computing and Beyond: Parallel, Approximation, Near Memory, and Quantum ISBN: 3030372235 ISBN-13(EAN): 9783030372231 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses and compares several new trends that can be used to overcome Moore`s law limitations, including Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing.
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling.
As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields.
Автор: Changjin Wan Название: Electric-Double-Layer Coupled Oxide-Based Neuromorphic Transistors Studies ISBN: 9811333130 ISBN-13(EAN): 9789811333132 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book focuses on essential synaptic plasticity emulations and neuromorphic computing applications realized with the aid of three-terminal synaptic devices based on ion-coupled oxide-based electric-double-layer (EDL) transistors. To replicate the robust, plastic and fault-tolerant computational power of the human brain, the emulation of essential synaptic plasticity and computation of neurons/synapse by electronic devices are generally considered to be key steps. The book shows that the formation of an EDL at the dielectric/channel interface that slightly lags behind the stimuli can be attributed to the electrostatic coupling between ions and electrons; this mechanism underlies the emulation of short-term synaptic behaviors. Furthermore, it demonstrates that electrochemical doping/dedoping processes in the semiconducting channel by penetrated ions from electrolyte can be utilized for the emulation of long-term synaptic behaviors. Lastly, it applies these synaptic transistors in an artificial visual system to demonstrate the potential for constructing neuromorphic systems. Accordingly, the book offers a unique resource on understanding the brain-machine interface, brain-like chips, artificial cognitive systems, etc.
Автор: Qiang Yu; Huajin Tang; Jun Hu; Kay Tan Chen Название: Neuromorphic Cognitive Systems ISBN: 3319856251 ISBN-13(EAN): 9783319856254 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents neuromorphic cognitive systems from a learning and memory-centered perspective.
Автор: Tor Sverre Lande Название: Neuromorphic Systems Engineering ISBN: 1475782985 ISBN-13(EAN): 9781475782981 Издательство: Springer Рейтинг: Цена: 174150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
large scale analog systems in silicon
neuromorphic silicon
auditory (ear) and vision (eye) systems in silicon
learning and adaptation in silicon
merging biology and technology
micropower analog circuit design
analog memory
analog interchipcommunication on digital buses /LIST Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Автор: Kumar Ashish, Babcock Joseph Название: Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictiv ISBN: 1788992369 ISBN-13(EAN): 9781788992367 Издательство: Неизвестно Рейтинг: Цена: 122600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. This book is your guide to getting ...
Автор: Dong Guozhu Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems ISBN: 1681735040 ISBN-13(EAN): 9781681735047 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 77610.00 T Наличие на складе: Невозможна поставка. Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
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