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作者： Chua, M. Elidrissi, M.R. Eason, K. Zhang, S.H. Qin, Z.L. Chai, K.S. Tan, K.P. Cai, K. Chan, K.S. Goh, W.L. Dong, Y. Victora, R.H. Data Storage Institute (DSI) Agency for Science Technology and Research (A STAR) 117608 Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University 637457 Singapore Singapore Center for Micromagnetics and Information Technologies Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN United States

Bit patterned media recording (BPMR) is a candidate technology proposed to extend the areal density growth capability of magnetic recording systems. In conventional granular magnetic recording (CGMR), bits of information are recorded onto a number of randomly distributed magnetic grains. The difficulty for the granular media approach is that due to the randomness of the grains, a certain minimum number of grains are required in order to maintain the media signal to noise ratio (SNR). This requires smaller grains as the bits are shrunk and we end up with either unstable, or unwriteable grains as per the well-known media-trilemma. In BPMR, instead of random positions, the ''grains'' are ordered into a well-defined lattice of magnetic islands. Because of this ordering, the media SNR is no longer determined by the number of grains per bit as in CGMR, but is defined by the geometrical and magnetic distributions of the fabrication process of the media. Analytical models exist for BPMR, that flip grains in the media based on the switching field distribution (SFD) and the knowledge of the applied head field. On the other side, a statistical model for CGMR has been proposed that measures probabilities of grains flipping based on micromagnetic simulations, named the grain flipping probability (GFP) model. In this work, we adapt the GFP model for BPMR and perform a comparison between densities predicted via the analytical and GFP models, by processing the signals from these models with the appropriate detectors and LDPC (low density parity check) decoders. © 2012 IEEE.

关键词： Signal to noise ratio

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Graph Neural Network Methods and Applications in Scene Understanding 1

Graph Neural Network Methods and Applications in Scene Under...

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1000年

作者： Weibin Liu Huaqing Hao Hui Wang Zhiyuan Zou Weiwei Xing

ISBN: (数字)9789819799336

ISBN: (纸本)9789819799329;9789819799350

The book focuses on graph neural network methods and applications for scene understanding. Graph Neural Network is an important method for graph-structured data processing, which has strong capability of graph data learning and structural feature extraction. Scene understanding is one of the research focuses in computer vision and image processing, which realizes semantic segmentation and object recognition of image or video. In this book, the algorithm, system design and performance evaluation of scene understanding based on graph neural networks have been studied. First, the book elaborates the background and basic concepts of graph neural network and scene understanding, then introduces the operation mechanism and key methodological foundations of graph neural network. The book then comprehensively explores the implementation and architectural design of graph neural networks for scene understanding tasks, including scene parsing, human parsing, and video object segmentation. The aim of this book is to provide timely coverage of the latest advances and developments in graph neural networks and their applications to scene understanding, particularly for readers interested in research and technological innovation in machine learning, graph neural networks and computer vision. Features of the book include self-supervised feature fusion based graph convolutional network is designed for scene parsing, structure-property based graph representation learning is developed for human parsing, dynamic graph convolutional network based on multi-label learning is designed for human parsing, and graph construction and graph neural network with transformer are proposed for video object segmentation.

关键词： Computational Intelligence engineering Mathematics Artificial Intelligence

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Trends and Applications in Knowledge Discovery and data Mining 1

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丛书名： Lecture Notes in computer science

1000年

作者： Jiuyong Li Longbing Cao Can Wang Kay Chen Tan Bo Liu Jian Pei Vincent S. Tseng

ISBN: (数字)9783642403194

ISBN: (纸本)9783642403187

This book constitutes the refereed proceedings at PAKDD Workshops 2013, affiliated with the 17th Pacific-Asia Conference on Knowledge Discovery and data Mining (PAKDD) held in Gold Coast, Australia in April 2013.;The 47 revised full papers presented were carefully reviewed and selected from 92 submissions. The workshops affiliated with PAKDD 2013 include: data Mining Applications in Industry and Government (DMApps), data analytics for Targeted Healthcare (DANTH), Quality Issues, Measures of Interestingness and Evaluation of data Mining Models (QIMIE), Biologically Inspired Techniques for data Mining (BDM), Constraint Discovery and Application (CDA), Cloud Service Discovery (CloudSD).

关键词： data Mining and Knowledge Discovery Artificial Intelligence Health Informatics Information Storage and Retrieval Information Systems Applications (incl. Internet)

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data Management, analytics and Innovation 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Neha Sharma Amol Goje Amlan Chakrabarti Alfred M. Bruckstein

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Machine Vision and Advanced Image Processing in Remote Sensing 1

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1000年

作者： Ioannis Kanellopoulos Graeme G. Wilkinson Theo Moons

ISBN: (数字)9783642601057

ISBN: (纸本)9783540655718;9783642642609

Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this area, DG XII responded to the need to increase the cost-effectiveness of space derived environmental information. At the same time, it has contributed to a better exploitation of this unique technology, which is a key source of data for environmental monitoring from local to global scale. MAVIRIC is part of the investment made in the context of the Environ ment and Climate Programme (1994-1998) to strengthen applied techniques, based on a better understanding of the link between the remote sensing signal and the underlying bio- geo-physical processes. Translation of this scientific know-how into practical algorithms or methods is a priority in order to con vert more quickly, effectively and accurately space signals into geographical information. Now the availability of high spatial resolution satellite data is rapidly evolving and the fusion of data from different sensors including radar sensors is progressing well, the question arises whether existing machine vision approaches could be advantageously used by the remote sensing community. Automatic feature/object extraction from remotely sensed images looks very attractive in terms of processing time, standardisation and implementation of operational processing chains, but it remains highly complex when applied to natural scenes.

关键词： Geographical Information Systems/Cartography Optics, Lasers, Photonics, Optical Devices Image Processing and computer Vision Regional/Spatial science Earth sciences, general

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Advanced data Mining and Applications 1

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丛书名： Lecture Notes in computer science

1000年

作者： Reda Alhajj Hong Gao Jianzhong Li Xue Li Osmar R. Zaïane

ISBN: (数字)9783540738718

ISBN: (纸本)9783540738701

The Third International Conference on Advanced data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi’an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences. For example, the independent source *** ranks ADMA (0.65) higher than PAKDD (0.64) and PKDD (0.62) as of June 2007, which are well established conferences in data mining. While the ranking itself is questionable because the exact procedure is not disclosed, it is nevertheless an encouraging indicator of recognition for a very young conference such as ADMA.

关键词： database Management Artificial Intelligence data Mining and Knowledge Discovery Software engineering computer Appl. in Administrative data Processing Information Systems Applications (incl. Internet)

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Controlled modulation of spin transport through an asymmetric ring with spin-orbit interaction

Controlled modulation of spin transport through an asymmetri...

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作者： Xing, M.-J. Jalil, M.B.A. Tan, S.G. Jiang, Y. State Key Laboratory for Advanced Metals and Materials School of Materials Science and Engineering University of Science and Technology Beijing 100083 China Information Storage Materials Laboratory Electronic and Computer Engineering Department National University of Singapore Singapore 117576 Singapore Computational Nanoelectronics and Nano-Device Laboratory Electronic and Computer Engineering Department National University of Singapore Singapore 117576 Singapore Data Storage Institute National University of Singapore Singapore 117608 Singapore

We present a theoretical study of spin transport through an asymmetry ring in which the Rashba spin-orbit interaction (RSOI) is the dominant spin-splitting mechanism. The left part and the right part of the asymmetry ring have different RSOI coefficients α1 and α2. Using Griffith's boundary conditions, we investigate the influence of RSOI coefficients on the conductance and spin polarization. For typical range of RSOI coefficients, the transmitted conductance is much more strongly dependent on α2 than α1. While the transmitted spin polarization is strongly influenced by the RSOI strengths of both arms, i.e., α1 and α2, this dependence is distinct from that observed in a symmetric ring. By considering both the conductance and spin polarization, we can optimize the RSOI parameters in the ring for different sensor applications. For instance, one can change the spin polarization almost from the full range of 0 to 1, whilst keeping the conductance constant (since α1 has little effect on conductance). On the other hand, by varying α2, one can tune the RSOI strengths such that a large change in both conductance and spin polarization can be achieved. © 2006 IEEE.

关键词： Spin polarization

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An Efficient Framework for Unsupervised Anomaly Detection over Edge-Assisted Internet of Things

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ACM Transactions on Sensor Networks 1000年

作者： Yumeng Liu Hongan Wang Xu Zheng Ling Tian Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science and Engineering University of Electronic Science and Technology of China China

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and analyze potential anomalies and react promptly. Current efforts concentrate on offline manners and fail to fit the situation in smart cities, where efficient and online solutions are expected. In this paper, a novel framework is designed for anomaly detection over edge-assisted Internet-of-Things (IoTs). The framework allows periodical data collection from sensors and continuous anomaly detection at the edge node. A novel efficient and unsupervised deep learning model is designed to balance the resource consumption and accuracy for anomaly detection, based on the combination of a convolutional autoencoder and adversarial training. Meanwhile, the proposed framework also adopts an adaptive strategy for continuous anomaly detection to reduce the overall resource consumption. According to theoretical analysis and evaluation on several real-world datasets, the proposed framework can discover the potential correlation features among multi-dimensional sensing data, and efficiently detect the abnormality of public transportation and facilities in smart cities.

关键词： Anomaly detection Smart city Internet of Thing Unsupervised learning

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PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City

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ACM Transactions on Sensor Networks 1000年

作者： Xianhao He Yikun Hu Qing Liao Hantao Xiong Wangdong Yang Kenli Li College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional *** these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.

关键词： Graph Convolution Networks Multiple GPUs Parallel Computing Smart City Spatio-Temporal data Mining Traffic Forecasting

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data Management, analytics and Innovation 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Neha Sharma Amol C. Goje Amlan Chakrabarti Alfred M. Bruckstein

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