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检索条件"主题词=Partitioning Algorithms"

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Quantum Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites

Quantum Annealing-Based Algorithm for Efficient Coalition Fo...

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Supreeth Mysore Venkatesh Antonio Macaluso Marlon Nuske Matthias Klusch Andreas Dengel University of Kaiserslautern-Landau (RPTU) Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Saarbruecken Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the number of communication links to maintain also rises, making the management of this vast network increasingly challenging and highlighting the need for clustering satellites into efficient groups as a promising solution. This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem and leverages quantum annealing to solve it. We represent the satellite network as a graph and obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q. The algorithm follows a top-down approach by iteratively splitting the graph at each step using a quadratic unconstrained binary optimization (QUBO) formulation. To evaluate our approach, we utilize real-world three-line element set (TLE/3LE) data for Starlink satellites from Celestrak. Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime while maintaining the solution quality. The performance achieved with quantum annealers surpasses the capabilities of classical computers, highlighting the transformative potential of quantum computing in optimizing the management of large-scale satellite networks.

关键词： Satellites Annealing Runtime Low earth orbit satellites Clustering algorithms Quantum annealing partitioning algorithms Manufacturing Low latency communication Optimization

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Driving Behavior Extraction Based on Eyes Movement Patterns

Driving Behavior Extraction Based on Eyes Movement Patterns

引用

International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Alexandru M. Pescaru Mihai V. Micea Department of Computer and Information Technology Politehnica University Timisoara Timisoara Romania

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Interest in autonomous driving has increased in the last decade. This generates a growing demand in developing algorithms suitable for such vehicles. In this context, modeling and understanding human driving behavior became increasingly important. The method presented in this paper addresses the problem of identifying driving behavior patterns that can be related to specific vehicle maneuvers. This method uses as input the estimation of driver gaze direction. The extracted patterns are grouped using clustering and linked with a specific driver maneuver. The validation of gaze estimation and pattern clustering was performed against two public datasets, DrivFace and DR(eye)VE.

关键词： Time-frequency analysis Estimation Pattern clustering Feature extraction Vectors partitioning algorithms Elbow

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Efficient Zone Routing through K-Nearest Neighbours Optimization in Wireless Sensor Networks

Efficient Zone Routing through K-Nearest Neighbours Optimiza...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Rahul Mishra Sudhanshu Kumar Jha Department of Electronics and Communications University of Allahabad Prayagraj India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks faces significant challenges due to limited energy and computational resources, requires efficient routing protocols to optimize network lifetime and performance. Traditional Zone Routing Protocol (ZRP) employs mixed routing approach to minimize overhead by partitioning the network in various zones, and utilises the proactive routing approach within zones and follow the reactive routing among zones. However, inefficient zone formation increases routing overhead and reduce scalability, limiting the protocol's effectiveness in dynamic environments. This paper addresses these limitations by integrating the K-Nearest Neighbours (KNN) algorithm into ZRP, optimizing zone creation based on node proximity to enhance the routing efficiency and network organization. The proposed KNN-ZRP approach aims to create more cohesive and strategically formed zones, thus reduces the number of zones needed and improves the communication efficiency. Through extensive simulations, the paper presents a comparison among the performance of traditional ZRP with the KNN-enhanced ZRP across metrics such as zone count, routing overhead, and average distance to the nearest neighbour. The result shows that the KNN-ZRP approach achieves a significant reduction in routing overhead and prolongs network lifetime, with results showing up to a 30% reduction in zone numbers and improved resource conservation. This approach demonstrates substantial improvements in network scalability and efficiency, making it highly suitable for deployment in resource constrained WSN environments.

关键词： Wireless communication Measurement Wireless sensor networks Scalability Organizations Nearest neighbor methods Routing Routing protocols partitioning algorithms Optimization

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Automatic Partition for Hybrid Stochastic-Binary-Based Circuits

Automatic Partition for Hybrid Stochastic-Binary-Based Circu...

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China Semiconductor Technology International Conference (CSTIC)

作者： Zexi Li Ruogu Ding Haojia Sun Donghao Chen Weikang Qian University of Michigan-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China MoE Key Lab of AI Shanghai Jiao Tong University China

ISBN: (数字)9798331513351

ISBN: (纸本)9798331513368

Stochastic computing (SC) is an emerging computing paradigm with a smaller hardware cost than conventional binary computing (BC). In this paper, we study the effects of circuit partitions that determine which parts are implemented in the SC or BC domain on several applications. We further propose an automatic partition algorithm to find the best partition meeting the accuracy and area requirements. Experimental results demonstrate the trade-off among accuracy, area, and energy using different partitions and an average of 46.53% in energy reduction of the circuits generated by the proposed partition algorithm.

关键词： Integrated circuits Accuracy Costs Iterative algorithms Hardware partitioning algorithms

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Semantic Neuroanatomical Segmentation of Brain MRI Scans Using Pretrained SynthSeg Neural Network

Semantic Neuroanatomical Segmentation of Brain MRI Scans Usi...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Kaiya Baker-Adell Nian Zhang Max Denis Department of Mechanical Engineering University of the District of Columbia Washington D.C. USA Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Deep 3D convolutional neural networks (CNNs) with trained algorithms have proven the possibility to distinctively partition brain regions to reduce manual division and expert labeling of neuroanatomical structures. Although many machine learning techniques implement this strategy, most can be time-consuming and quite difficult to interpret as contrast modeling with varying resolutions can be obscure upon differentiating across regions. SynthSeg, a 3D U-Net algorithm, contains the ability to segment scans in datasets of multiple modalities and a variety of target domains without the need to retrain as it is trained with preconstructed data derived from a general 3D U-Net model for volumetric segmentation, thus facilitating the analysis of large quantities of bioimaging data in short periods of time. This is made possible due to the randomization technique where the contrast and resolution of the preconstructed training data are selected randomly to increase deep learning performance and accuracy. Due to its ability to learn from fluctuating contrast and resolution types via the randomization technique, its segmentation robustness is maintained across datasets. SynthSeg can learn from pretrained designated labels for characterization of diverse populations to improve detection and diagnoses of a vast array of diseases. Using SynthSeg, a subset of the Child and Adolescent NeuroDevelopment Initiative (CANDI) dataset containing brain MRI volume for a single patient was used with its corresponding ground truth image to analyze and exhibit highly accurate predictive modeling within each scan. The average Dice index across all labels for the MRI volume is 0.80793, which indicates an almost perfect segmentation accuracy.

关键词： Solid modeling Accuracy Three-dimensional displays Magnetic resonance imaging Semantics Predictive models Brain modeling Robustness partitioning algorithms Convolutional neural networks

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Research on Configuration Data Generation Algorithm for HyperParser

Research on Configuration Data Generation Algorithm for Hype...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peijun Ma Gongzhi Zhao Jiangyi Shi Cong Yao Weitao Pan Xidian University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The evolution of technologies such as Big Data, Cloud Computing, and Artificial Intelligence is inextricably linked to the transmission of network data. Programmable network devices that facilitate high-speed processing are becoming increasingly crucial. The significance of programmable parsers, as a constituent element of programmable network devices, is readily apparent. HyperParser has the advantages of low latency, high throughput, small area, low power consumption, and full programmability, but its configuration data generation algorithm is inefficient and has limited optimality. In this paper, the inefficiency in running speed is first optimized by subtree partitioning, and the preference for removing certain columns more conducive to approaching the global optimal solution is then analyzed from a probabilistic point of view. Using the big-union, edge network and enterprise network three kinds of parse graph test, the original algorithm in processing big-union in 10min failed to end the run, processing the rest of the two parse graphs consumed 0.1545s, 0.1513s, extracted the number of key bits 7 and 18; the algorithm proposed in this paper processing the three kinds of parse graphs consumed 3.679s, 0.019s and 0.019s, 3.679s, 0.019s and 0.027s, and the number of key bits extracted are 160, 7 and 17 respectively.

关键词： Cloud computing Power demand Computer applications Big Data Throughput Probabilistic logic Power electronics partitioning algorithms Low latency communication Optimization

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Design and Implementation of a Low-Power TCAM Controller Based on Bloom Filter

Design and Implementation of a Low-Power TCAM Controller Bas...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Tingfeng Sun Jianxin Cui Yifan Zhang Wuhan Research Institute of Posts and Telecommunications Wuhan China Air Force Early Warning Academy Wuhan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Ternary Content Addressable Memory (TCAM) has a wide range of applications in network packet filtering, route lookup, and other scenarios due to its extremely fast lookup speed. However, the high storage cost and power consumption of TCAM limit its application in large-scale deployment. To address this challenge, this paper proposes a Bloom Filter-based low-power TCAM controller (BF-LPTCAM) design scheme, which aims to reduce the storage cost and power consumption of TCAM while maintaining its efficient data matching capability. In this paper, a pipelined structure is used to filter the looked-up TCAM cells using logical table partitioning method and Bloom filtering technique to support the storage and look-up of 80/160/320 bits of data. In this paper, RTL implementation, functional simulation and logic synthesis are carried out for the algorithm, which can save at least more than 83.2 % power consumption and storage utilisation can be better without matching entries in TCAM. Under the TSMC 7nm process, the system can support a 1GHz frequency clock and a single lookup operation takes only 40ns.

关键词： Matched filters Power demand Costs Systematics Filtering Scalability Pipelines Timing partitioning algorithms Clocks

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Difficulties of Cluster Analysis on Mixed-Type Variables

Difficulties of Cluster Analysis on Mixed-Type Variables

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Tina Rakic Lyudmila Gadasina Saint Petersburg State Uiversity Saint Petersburg Russia Center for econometrics and business analytics Saint Petersburg State Uiversity Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Working with real data often involves clustering of mixed-type variable datasets, which is followed by certain difficulties resulting from lack of guidelines on the usage of clustering methods for mixed-type data. Instead of choosing the optimal method for mixed data clustering, this study aims to determine on which conditions do the specialized cluster analysis methods - hierarchical clustering using Gower's distance and ${k}$ -prototypes algorithm, produce similar partitioning results. Resulting clusterings were after compared with clusters attained by the usage of one-hot encoding technique for transforming categorical data into numerical values. To determine the degree of similarity between methods special metrics were used. Data used in this study is real data obtained from real estate sales advertisements in Saint Petersburg.

关键词： Measurement Industries Hands Clustering methods Prototypes Clustering algorithms Encoding partitioning algorithms Guidelines Business

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Allocation Strategies for Disaggregated Memory in HPC Systems

Allocation Strategies for Disaggregated Memory in HPC System...

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International Conference on High Performance Computing

作者： Robin Boëzennec Danilo Carastan-Santos Fanny Dufossé Guillaume Pallez University of Rennes Inria CNRS IRISA Rennes France Univ. Grenoble Alpes Grenoble INP Inria CNRS LIG Grenoble France INRIA Rennes France

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

In this work we consider scheduling strategies to deal with disaggregated memory for HPC systems. Disaggregated memory is an implementation of storage management that provides flexibility by giving the option to allocate storage based on system-defined parameters. In this case, we consider a memory hierarchy that allows to partition the memory resources arbitrarily amongst several nodes depending on the need. This memory can be dynamically reconfigured at a cost. We provide algorithms that pre-allocate or reconfigure dynamically the disaggregated memory based on estimated needs. We provide theoretical performance results for these algorithms. An important contribution of our work is that it shows that the system can design allocation algorithms even if user memory estimates are not accurate, and for dynamic memory patterns. These algorithms rely on statistical behavior of applications. We observe the impact on the performance of parameters of interest such as the reconfiguration cost.

关键词： Costs Runtime Heuristic algorithms High performance computing Storage management Memory management Stochastic processes Dynamic scheduling partitioning algorithms Resource management

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Refining Transportation Automation with Convolutional Neural Network-Based Vehicle Detection via UAVs

Refining Transportation Automation with Convolutional Neural...

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International Russian Automation Conference (RusAutoCon)

作者： Murat Bakirci Irem Bayraktar Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

In response to the surge in private vehicle usage exacerbated by the pandemic, transportation infrastructure faces heightened demands for efficiency and safety. This trend, fueled by concerns over social distancing and changes in work patterns, has intensified traffic congestion in urban centers and tourist destinations. Consequently, local governments are exploring strategies such as smart traffic management systems and alternative transportation modes to alleviate congestion and promote sustainability. However, the escalating vehicular influx has led to a surge in traffic accidents and air pollution, posing significant threats to public health and ecological balance. Continual monitoring of road traffic is crucial for preempting traffic bottlenecks, averting accidents, and promoting sustainable transportation practices. Advanced technologies, including unmanned aerial vehicles (UAVs), have emerged as powerful tools for real-time traffic monitoring. Equipped with high-resolution cameras and sensor systems, UAVs facilitate efficient data acquisition from desired regions, offering advantages over traditional aerial vehicles. While UAVs present promising solutions, real-time utilization poses challenges such as complex background scenes and detection misses. Additionally, advancements in object detection algorithms, particularly the You Only Look Once (YOLO) algorithm, have revolutionized vehicle detection. This study focuses on evaluating vehicle detection using YOLOv9, the latest iteration, and compares it with YOLOv7, its predecessor, leveraging aerial monitoring via UAVs. By elucidating the innovations introduced by YOLOv9, this study contributes to enhancing traffic monitoring capabilities and promoting effective transportation management strategies.

关键词： YOLO Automation Vehicle detection Transportation Lighting Real-time systems partitioning algorithms Surges Monitoring Accidents

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