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Matching-Theory-Based Multi-User Cooperative Computing Framework

IEEE COMMUNICATIONS LETTERS 2022年第2期26卷 414-418页

作者： Zhou, Ya Zhang, Guopeng Wang, Kezhi Yang, Kun China Univ Min & Technol Minist Educ Engn Res Ctr Mine Digitalizat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

In this letter, we propose a matching theory based multi-user cooperative computing (MUCC) scheme to minimize the overall energy consumption of a group of user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU). We first determine the role of each UE by leveraging the roommate matching method. Then, we propose the college admission based algorithm to divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability. Finally, we present an effective task offloading algorithm to minimize the energy consumption of all the cooperation groups. The simulation results verify the effectiveness of the proposed scheme.

关键词： Task analysis Energy consumption NOMA Solid modeling partitioning algorithms Information science Computational modeling Multi-user cooperative computing matching theory computing task offloading

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Performance Analysis of an IRS-Aided Wireless Communication System With Spatially Correlated Channels

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第3期11卷 563-567页

作者： Ajayan, Arathi P. Dash, Soumya P. Ramkumar, Barathram Indian Inst Technol Bhubaneshwar Sch Elect Sci Bhubaneswar 752050 Odisha India

An intelligent reflecting surface (IRS) assisted wireless communication system, with statistically correlated channels between the transmitter and the IRS pair and between the IRS and the receiver pair, is studied in the absence of channel state information (CSI). Assuming the phases added by IRS elements to be deterministic, the closed-form analytical expression for the symbol error probability (SEP) and the upper bound on the channel capacity are derived, with the transmitter employing binary phase-shift keying (BPSK) modulation for data transmission. The asymptotic SEP expression is derived and the achievable diversity order of the system is shown to be independent of the number of IRS elements. A search based algorithm is proposed to obtain the optimal phase at IRS minimizing the average SEP, as a equal phase configuration. The optimal equal phase is also found to maximize the average channel capacity. Numerical results are presented for the case of channels following a practical IRS correlation model.

关键词： Receivers Transmitters Correlation partitioning algorithms Channel capacity Numerical models Measurement Binary phase-shift keying (BPSK) channel correlation channel capacity diversity order intelligent reflecting surfaces (IRS) symbol error probability (SEP)

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An Accelerated SBR Method for RCS Prediction of Electrically Large Target

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2022年第10期21卷 1930-1934页

作者： Meng, Wei Li, Juan Guo, Li-Xin Yang, Qi-Jun Xidian Univ Sch Phys Xian 710071 Peoples R China

This letter proposes an improved shooting and bouncing ray (SBR) method to efficiently predict the radar cross (RCS) of electrically large targets. First, the scan line algorithm is used to convert three-dimensional (3-D) processing to the 2-D graphic display, which expedites the elimination of shadows. Then, the ray tracing process is accelerated by the ray-marching technique which greatly decreases the intersection test. The validity of the proposed method is verified by comparing the simulation results with those of the multilevel fast multipole method. The runtime performance contrast of the traditional Octree-SBR method and ours indicates that the accelerated algorithm is more computationally efficient. Therefore, the presented algorithm has an excellent performance in the RCS prediction of electrically large targets.

关键词： Prediction algorithms Ray tracing Octrees partitioning algorithms Two dimensional displays Surface waves Radar cross-sections Accelerated shooting and bouncing ray (SBR) method electrically large target ray-marching technique scan line algorithm

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Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines

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APPLIED ENERGY 2023年第1期339卷

作者： Cai, Wei Hu, Yang Fang, Fang Yao, Lujin Liu, Jizhen North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

Due to the wake effect, there is a high degree of coupling between wind turbines in a wind farm. In order to optimize the power and load performance of wind farms, it is crucial to propose an optimization strategy to attenuate the wake interference between wind turbines by wake redirection. This study proposes a wake interference model that can consider wake deflection based on the yawed wake model. Accordingly, the interference relationship between turbines is represented as the topology of the graph. By solving for the connected components of the graph, the wind farm is divided into almost uncoupled partitions. In each partition, separate optimization problems with the objectives of power enhancement and load reduction are established. An improved Non-Dominated Sorting Genetic Algorithm with multiple crossover operators is used for solving the multi-objective optimization problem. Simulation results show that the proposed strategy can effectively lead to power enhancement and load reduction for the regular layout wind farm. For the curvilinear layout wind farm with an optimized layout considering wake effects, proposed strategy can further reduce wake disturbances to improve the performance of wind farms.

关键词： Wind farm Wake effect partitioning algorithms Wake redirection Multi-objective optimization

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Object Detection Using Efficient partitioning and Frame Reduction

Object Detection Using Efficient Partitioning and Frame Redu...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Omar Imran Sreeraman Rajan Shikharesh Majumdar Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Rapid object detection is crucial for safety-critical applications, such as post-event analysis of surveillance videos in crime investigations and the operation of autonomous vehicles. The objective of this paper is to improve the speed of object detection through efficient partitioning and frame reduction using parallel processing. Techniques to improve Spark's default partitioning by using entropy-based algorithms to create more evenly distributed partitions based on the estimated workload of the frames are considered. Redundant frames are removed to efficiently reduce the workload. algorithms for removing redundant frames are introduced and evaluated for their effectiveness in comparison to random and fixed frame removals. The results from this paper demonstrate that partitioning algorithms that estimate workload using entropy provide faster processing time when compared to those that use random partitioning. The results also indicate that frame removal algorithms that use frame-specific information, like entropy difference between frames, further improve performance without notably reducing detection accuracy when compared to those that do not utilize frame-specific information.

关键词： Accuracy System performance Surveillance Object detection Parallel processing Transformers Entropy partitioning algorithms Sparks Videos

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Clustering-Based Hierarchical Framework for Multiclass Classification of Leaf Images

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第3期58卷 4076-4085页

作者： Goyal, Neha Gupta, Kapil Kumar, Nitin Natl Inst Technol Kurukshetra Kurukshetra 136119 Haryana India Natl Inst Technol Uttarakhand Srinagar 246174 India

This article introduces a multiclass classification approach accustoming the benefits of partitioning-based strategies and hierarchical techniques. The proposed hierarchical framework creates a hierarchy with the notion of grouping classes with similar traits as one group. It overcomes the deficiency of the existing multiclass extension approaches, viz., nonlinearity, imbalanced class classification, and increasing classification cost with increasing number of classes. The hierarchical framework presents the idea of decomposing several classes hierarchically, where every cluster contains a set of classes having similar traits. The approach aims to maximize the intercluster distance and minimize the intracluster distribution. The effectiveness of the proposed method is evaluated on real-world and complex problems of plant recognition. Three leaf image datasets are considered for performance evaluation using a support vector machine. The results signify that the proposed approach for multiclass classification is an efficient approach with significantly improved recognition accuracy. It is a robust and effective approach with the least computational cost. The speedup factor of the proposed approach in the binary structure is 16, 6.5, and 5.5 as compared to a one-versus-one traditional support vector machine for Flavia, Swedish, and self-collected leaf datasets, respectively.

关键词： Support vector machines Feature extraction Training Indexes Task analysis partitioning algorithms Clustering algorithms Dunn index (DI) hierarchical approach multiclass classification separability matrix silhouette value

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Collaborative Inference Acceleration with Non-Penetrative Tensor partitioning

Collaborative Inference Acceleration with Non-Penetrative Te...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibang Liu Chaonong Xu Zhenjie Lv Zhizhuo Liu Suyu Zhao Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The inference of large-sized images on Internet of Things (IoT) devices is commonly hindered by limited resources, while there are often stringent latency requirements for Deep Neural Network (DNN) inference. Currently, this problem is generally addressed by collaborative inference, where the large-sized image is partitioned into multiple tiles, and each tile is assigned to an IoT device for processing. However, since significant latency will be incurred due to the communication overhead caused by tile sharing, the existing collaborative inference strategy is inefficient for convolutional computation, which is indispensable for any DNN. To reduce it, we propose Non-Penetrative Tensor partitioning (NPTP), a fine-grained tensor partitioning method that reduces the communication latency by minimizing the communication load of tiles shared, thereby reducing inference latency. We evaluate NPTP with four widely-adopted DNN models. Experimental results demonstrate that NPTP achieves a 1.44-1.68× inference speedup relative to CoEdge, a state-of-the-art (SOTA) collaborative inference algorithm.

关键词： Tensors Convolution Collaboration Signal processing algorithms Artificial neural networks Inference algorithms partitioning algorithms Internet of Things Speech processing Load modeling

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Machine Learning-Based Pruning Algorithm of partitioning Techniques for Circuit Simulation

Machine Learning-Based Pruning Algorithm of Partitioning Tec...

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IEEE International Symposium on Quality Electronic Design

作者： Qian Chen Xiaofeng Yang Shengli Lu Department of National ASIC System Engineering Research Center Southeast University China

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Large-scale integrated circuit simulation faces challenges in simulation speed and memory consumption. Based on the divide-and-conquer idea, partitioning techniques can well mitigate these issues. However, as the number of partitioned subcircuits increases, the larger and denser Schur complement matrix destroys the scalability. Although previous studies introduce iterative methods to reduce the order and density of Schur matrices, the convergence and convergence speed of iterative methods are sensitive to circuit structures, limiting the simulation scenarios. This paper proposes a machine learning-based pruning algorithm that reduces the density of Schur matrices while precisely controlling simulation accuracy. In addition, a partitioning-based circuit simulation tool is developed and a constrained random scanning method is introduced to generate reliable samples for training a high-precision model. Combined with direct methods, such as LU factorization, the proposed approach exhibits robust performance. Experiments on real circuit netlists and matrices demonstrate that our model achieves an accuracy of 94.23%. Compared to the traditional method, this work reduces the density by an average of 15.54 × and accelerates the simulation by an average of 1.71 ×, while restricting the simulation error to within 0.533%o.

关键词： Training Machine learning algorithms Accuracy Circuit simulation Scalability partitioning algorithms Iterative methods Integrated circuit modeling Integrated circuit reliability Convergence

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Plug-and-Play Network Reconfiguration algorithms to Maintain Regularity and Low Network Reconfiguration Needs

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 3451-3456页

作者： Studli, Sonja Yan, Yamin Seron, Maria M. Middleton, Richard H. Univ Newcastle Sch Engn Callaghan NSW 2308 Australia Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

In this letter we propose algorithms to connect and disconnect nodes to/from an existing graph, which are able to maintain the regularity of a graph while requiring minimal reconfiguration of the network. Further, simulations suggest that the algorithms maintain a minimum bound on the algebraic connectivity of the graph. These properties of the algorithms suit their use in plug-and-play networks.

关键词： Graph theory Thermal stability Stability analysis Multi-agent systems partitioning algorithms Laplace equations Information exchange Control of networks networked control systems

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A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks

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IEEE TRANSACTIONS ON BIG DATA 2022年第2期8卷 302-317页

作者： Srinivasan, Sriram Pollard, Samuel D. Norris, Boyana Das, Sajal K. Bhowmick, Sanjukta Univ Nebraska Omaha Dept Comp Sci Omaha NE 68182 USA Univ Oregon Dept Comp & Informat Sci Eugene OR 97403 USA Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

This paper presents a network-based template for analyzing large-scale dynamic data. Specifically, we propose a novel shared-memory parallel algorithm for updating tree-based structures or properties, such as connected components (CC) and minimum spanning trees (MST), on dynamic networks. The underlying idea is to update the information in a rooted tree data structure that stores the edges of the network that are most relevant to the analysis. Extensive experiments on real-world and synthetic networks demonstrate that, with the exception of the inherently sequential component for creating the rooted tree, our proposed updatiing algorithm is scalable and, in most cases, also requires significantly less memory, energy, and time than recomputing-from-scratch algorithm. To the best of our knowledge, this is the first parallel algorithm for updating MST on weighted dynamic networks. The rooted-tree based framework that we propose in this paper can be extended for updating other weighted and unweighted tree-based properties such as single source shortest path and betweenness and closeness centrality.

关键词： Heuristic algorithms Data structures Parallel algorithms Image edge detection Big Data partitioning algorithms Maintenance engineering Dynamic networks minimum spanning tree connected components shared memory algorithms

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