作者：
Zadeh, Lotfi A.Computer Science Division
Department of Electrical Engineering and Computer Sciences and the Electronics Research Laboratory University of California Berkeley 94720 CA United States
This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart *** begin by summarizing the existing surveys and describing emergent technologies for the agricultural I...
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This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart *** begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware *** also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural ***,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural ***,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart ***,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.
The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping...
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Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral...
ISBN:
(数字)9781598290011
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which...
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Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.
Recent advancements in unmanned aerial vehicles (UAVs), has allowed their deployment for numerous applications like aerial photography, infrastructure inspection, search and rescue, and surveillance. Despite the poten...
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It is often undesirable or impossible to provide redundant indices for all domains of a file existing on a secondary storage device. The problem considered in this paper is the selection of a limited number of indices...
A multiparadigm approach is developed and demonstrated for exploiting knowledge about structure for the purpose of extracting information from noisy textual data. A motivating example of a potential application would ...
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We study the structure of fault-tolerant multiprocessor systems that allow one or more communication links to fail. Spare links are employed to tolerate link failures;no redundant processing units are required. Such a...
We study the structure of fault-tolerant multiprocessor systems that allow one or more communication links to fail. Spare links are employed to tolerate link failures;no redundant processing units are required. Such a multiprocessor is modeled by a graph G whose nodes and edges correspond to the processing units and the links, respectively. G is a k-edge fault-tolerant design of a basic graph H, denoted k-EFT(H), if every graph obtained by removing any k edges from G embeds H. If G contains the fewest edges among all k-EFT designs of H, then G is said to be optimally k-EFT with respect to H. We introduce the concept of critical edges and graphs and derive some of their properties that are generally useful in designing optimal EFT supergraphs. We investigate the theory of edge fault tolerance for tree-structured networks, in particular, nonhomogeneous trees and stars. For both cases, we obtain optimal k-EFT designs for all k. (C) 1996 John Wiley & Sons, Inc.
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