Peer-to-Peer (P2P) applications are used to communicate among peers or groups. P2P computing draws growing interest as a new distributedcomputing paradigm for its potential to use user's computing resources effic...
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With a camera mobile phone, which has become a, "must-have" device, 2D-barcode works as an interface to bridge the physical and digital world. As the notion of ubiquitous computing has permeated, developing ...
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ISBN:
(纸本)9780769534718
With a camera mobile phone, which has become a, "must-have" device, 2D-barcode works as an interface to bridge the physical and digital world. As the notion of ubiquitous computing has permeated, developing a new 2D-barcode and its applications has been a growing trend worldwide. A 2D-barcode symbol consists of two broad areas: data area and guide area. The components of the latter is collectively called "finder pattern" and used in locating the 2D-barcode symbol. The failure of finding the target symbol prevents a barcode reader from successfully decoding the barcode. Hence, designing a functional finder pattern is Me of the key for improving the robustness of barcode reading, and thus, the entire 2D-barcode system. We have designed a novel finder pattern integrated with a color 2D-barcode for camera mobile phone applications. Through the development and evaluation of the finder pattern for effective color 2D-barcode detection, this paper discusses keys to improve the functionality and reliability of finder patterns, which should be kept in mind when designing a finder pattern for any 2D-barcode symbol.
Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this pe...
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ISBN:
(纸本)9781538643686
Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential requires software to effectively partition the hardware resource to maximize the overlap between host-device communication and accelerator computation, and to match the granularity of task parallelism to the resource partition. However, determining the right resource partition and task parallelism on a per program, per dataset basis is challenging. This is because the number of possible solutions is huge, and the benefit of choosing the right solution may be large, but mistakes can seriously hurt the performance. In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given streamed application, targeting the Intel XeonPhi architecture. Instead of hand-crafting the heuristic for which the process will have to repeat for each hardware generation, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs;we then use the learned model to predict the resource partition and task granularity for any unseen programs at runtime. We apply our approach to 23 representative parallel applications and evaluate it on a CPU-XeonPhi mixed heterogenous many-core platform. Our approach achieves, on average, a 1.6x (upto 5.6x) speedup, which translates to 94.5% of the performance delivered by a theoretically perfect predictor.
Multi-cluster Grids have emerged as the most popular type of Grid environments. On multi-cluster Grid, resources are distributed across different networks. Hence, scheduling and dispatching jobs are difficult and inef...
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The k-means clustering algorithm is a widely used scheme to solve the clustering problem which classifies a given set of n data points in m-dimensional space into k clusters, whose centers are obtained by the centroid...
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The prediction of the time-series data stream in AIOps is an important research field of data mining. However, due to the non-stationary and non-linear characteristics of time series data, many existing methods cannot...
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ISBN:
(纸本)9781665414852
The prediction of the time-series data stream in AIOps is an important research field of data mining. However, due to the non-stationary and non-linear characteristics of time series data, many existing methods cannot comprehensively solve the accuracy and reduce time consumption. To solve this problem, we propose a new MWNN (Memory Wavelet Neural Network) algorithm. It can effectively overcome the contradiction between accuracy and time consumption. In MWNN, we designed a new hidden layer structure. By adding a new memory storage unit to the hidden layer, it can be ensured that the hidden layer can make the best use of historical data and greatly improve the prediction accuracy. Moreover, the model does not require any prior information or data distribution assumptions. This paper selects real Ops data for verification. The final experimental results show that, compared with the commonly used prediction models, this model has the highest prediction accuracy and lower time consumption. The data set used in the experiment has been uploaded to https://***/Yang-Yun726/MWNN/tree/master/DATA.
Summary form only given. Evolutionary algorithms have become an important problem solving methodology among many researchers working in the area of computational intelligence. The population based collective learning ...
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ISBN:
(纸本)9780769527406
Summary form only given. Evolutionary algorithms have become an important problem solving methodology among many researchers working in the area of computational intelligence. The population based collective learning process; self adaptation and robustness are some of the key features of evolutionary algorithm when compared to other global optimization techniques. Due to its simplicity, evolutionary algorithms have been widely accepted for solving several important practical applications in engineering, business, commerce etc. However, experimental evidence had indicated cases where evolutionary algorithms are inefficient at fine tuning solutions, but better at finding global basins of attraction. The efficiency of evolutionary training can be improved significantly by hybridization of some search procedures or incorporating some heuristics into the evolution process. In this talk, we will review how particle swarm optimization algorithm and bacterial foraging algorithm could be used to optimize the performance of evolutionary algorithms. The performance of the hybridized algorithms will be illustrated using some benchmark problems
To gain high performance computing or store large amount of data using inexpensive devices, grid system is one of the well-known solutions. In most cases, the grid can be categorized into two types: computational grid...
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ISBN:
(纸本)9783540747413
To gain high performance computing or store large amount of data using inexpensive devices, grid system is one of the well-known solutions. In most cases, the grid can be categorized into two types: computational grid and data grid. Data grid is used for data intensive applications. In data grids, replication is used to reduce access latency and bandwidth consumption. Furthermore, it can also improve data availability, load balancing and fault tolerance. If there are many replicas, they may have coherence problems while being updated. In this paper, based on the aggressive-copy method, we propose a novel Greedy Pipeline-based Aggressive Copy (GPAC) protocol. The performance of pipelining dataset blocks and greedy sequencing in the GPAC can accelerate data replication speed in compared with previous works. Both analytical and experimental results show promising performance enhancements.
ISGC 2010, The internationalsymposium on Grid computing was held at Academia Sinica, Taipei, Taiwan, March, 2010. The 2010 symposium brought together prestigious scientists and engineers worldwide to exchange ideas, ...
ISBN:
(纸本)9781489982476
ISGC 2010, The internationalsymposium on Grid computing was held at Academia Sinica, Taipei, Taiwan, March, 2010. The 2010 symposium brought together prestigious scientists and engineers worldwide to exchange ideas, present challenges/solutions and to discuss new topics in the field of Grid computing. Data Driven e-science: Use Cases and Successful applications of distributedcomputing Infrastructures (ISGC 2010), an edited volume, introduces the latest achievements in grid technology for Biomedicine Life sciences, Middleware, Security, Networking, Digital Library, Cloud computing and more. This book provides Grid developers and end users with invaluable information for developing grid technology and applications. The last section of this book presents future development in the field of Grid computing. This book is designed for a professional audience composed of grid users, developers and researchers working in the field of grid computing. Advanced-level students focused on computer science and engineering will also find this book valuable as a reference or secondary text book.
distributedcomputing based on the Master-Worker and PULL interaction model is applicable to a number of applications in high energy physics, medical physics and bioinformatics. We demonstrate a realistic medical phys...
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