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检索条件"主题词=Partitioning Algorithms"

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Joint Sparse Recovery Algorithm for Channel Estimation in XL-MIMO Systems

Joint Sparse Recovery Algorithm for Channel Estimation in XL...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Qing Liu He Gao Kaiyue Bai Xuehan Zheng Wenjing Zheng Jun Li School of Information and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Key Laboratory of Intelligent Buildings and Building energy-saving Technology School of Information and Electrical Engineering Shandong Jianzhu University Shandong Zhengchen Technology Co. LTD Jinan China Shandong Key Laboratory of Intelligent Buildings and Building energy-saving Technology School of Information and Electrical Engineering Shandong Jianzhu University Jinan China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

In Ultra-large-scale multiple-input multiple-output (XL-MIMO) systems, high-precision estimation of far-field channel state information (CSI) remains a core challenge for 6G high-speed transmission. Addressing the accuracy limitations of traditional orthogonal matching pursuit (OMP) algorithms caused by fixed grid partitioning, as well as the deficiencies of compressed sampling matching pursuit (CoSaMP) algorithms in capturing the common sparse structure of multi-antenna systems and their high computational complexity, this paper proposes a joint method combining improved CoSaMP with grid-free optimization. Specifically, we enhance sparse structure capture capability through a multi-signal residual correlation accumulation (MSRCA) mechanism, improve noise robustness via dynamic support set updates integrated with adaptive threshold strategies, and adopt continuous-domain grid-free optimization to directly estimate channel parameters, thereby avoiding grid mismatch. The simulation results show that the proposed scheme significantly outperforms existing methods in terms of channel estimation accuracy and pilot efficiency in far-field scenarios, while maintaining strong robustness under near-field interference, providing a highly adaptable solution for XL-MIMO systems.

关键词： Seminars Accuracy Simulation Matching pursuit algorithms Channel estimation Estimation Robustness partitioning algorithms Noise robustness Optimization

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

Semantic Neuroanatomical Segmentation of Brain MRI Scans Usi...

引用

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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Automatic Partition for Hybrid Stochastic-Binary-Based Circuits

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

引用

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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Partitioned Successive-Cancellation List Flip Decoding of Polar Codes

Partitioned Successive-Cancellation List Flip Decoding of Po...

引用

IEEE Workshop on Signal Processing Systems (SIPS)

作者： Charles Pillet Ilshat Sagitov Grégoire Domer Pascal Giard Department of Electrical Engineering École de technologie supérieure Montréal Québec Canada Department of Electrical Engineering Enseirb-Matmeca Bordeaux INP Bordeaux France

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

The recently proposed Successive-Cancellation List Flip (SCLF) decoding algorithm for polar codes improves the error-correcting performance of state-of-the-art SC List (SCL) decoding. However, it comes at the cost of a higher complexity. In this paper, we propose the Partitioned SCLF (PSCLF) decoding algorithm, an algorithm that divides a word in partitions and applies SCLF decoding to each partition separately. Compared to SCLF, PSCLF allows early termination but is more susceptible to cyclic-redundancy check (CRC) collisions. In order to maximize the coding gain, a new partition design tailored to PSCLF is proposed as well as the possibility to support different CRC lengths. Numerical results show that the proposed PSCLF algorithm has an error-correction performance gain of up to 0.15 dB with respect to SCLF. Moreover, the proposed CRC structure permits to mitigate the error-correction loss at low frame-error rate (FER) due to CRC collisions, showing a gain of 0.2 dB at a FER of 10– 4 with respect to the regular CRC structure. The average execution time of PSCLF is shown to be 1.5 times lower than that of SCLF, and matches the latency of SCL at FER = 4.10 −3 and lower.

关键词： Measurement Codes Signal processing algorithms Signal processing Performance gain Encoding Decoding partitioning algorithms Polar codes Gain

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Research on Basketball Player Tracking Method Based on Multi-Feature Fusion

Research on Basketball Player Tracking Method Based on Multi...

引用

Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Meiheng Zhang College of General Education Harbin Huade University Harbin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Multi-object tracking is a hot topic in the field of computer vision, which is widely used in intelligent transportation, intelligent surveillance, autonomous driving, and other fields. With the rapid development of the sports industry, the problem of athlete tracking in sports has gradually attracted attention. Unlike pedestrian tracking in ordinary surveillance scenarios, athlete tracking in sports scenarios faces great challenges due to frequent occlusion, large pose changes, and the similar appearance of athletes. This article takes basketball player tracking as the research object, focusing on the methods of athlete detection and tracking. To address the problem of low accuracy due to the similarity of athletes’ appearance, this paper proposes a basketball player tracking algorithm based on multi-feature fusion. First, a multi-branch re-identification model for athletes is designed. The global branch learns the global semantic information of the athletes; the local branch horizontally partitions the body parts of the athletes to learn fine-grained features; the feature deletion branch deletes the most activated part of the features, forcing the network to learn the remaining significant features. Then, the features of the three branches are merged to improve the discriminability of the athletes. Finally, experiments are conducted on the dataset, and the results show that the proposed method achieves good performance.

关键词： Accuracy Pedestrians Surveillance Semantics Transportation Feature extraction Trajectory partitioning algorithms Time factors Sports

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Novel L1-Based Neural Gas Clustering algorithms

Novel L1-Based Neural Gas Clustering Algorithms

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Nico L. Cavalcanti Francisco de A. T. de Carvalho Centro de Informática - CIn Universidade Federal de Pernambuco - UFPE Recife PE Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Clustering algorithms of the Neural Gas (NG) type take into consideration the dissimilarities between prototypes in the original input space. It has been successfully applied in vector quantization, topology creation as well as clustering. NG algorithms conventionally are based on the squared Euclidean distance or L2 distance, which has several known setbacks (not robust to noise and outliers). Our goal is to introduce new NG clustering algorithms (online and batch) based on the L1 distance (more robust to noise and outliers). We propose three Neural Gas algorithms based on the L1 distance using two different algorithms to find the optimal prototypes and compare them with another well-known clustering algorithm. Given the experiments performed, the proposed methods showed a competitive performance. Preliminary results indicate that research on Neural Gas algorithms based on L1 distance is promising.

关键词： Machine learning algorithms Shape Vector quantization Noise Clustering algorithms Prototypes Euclidean distance Machine learning partitioning algorithms Topology

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Rule-based Spectrum Allocation algorithms for Heterogeneous Cognitive Radio Wireless Networks

Rule-based Spectrum Allocation Algorithms for Heterogeneous ...

引用

IEEE Asia Pacific Conference on Wireless and Mobile

作者： Moshe T. Masonta Next Generation Enterprises and Institutions Council for Scientific and Industrial Research (CSIR) Pretoria South Africa

ISBN: (数字)9798331521240

ISBN: (纸本)9798331521257

The growing number of heterogeneous cognitive radio wireless network (CRWN) deployments sharing the same radio frequency spectrum band will lead to a number of new coexistence challenges when it comes to channel allocation. In this paper we propose rule-based spectrum allocation algorithms for CRWN. In the channel partitioning (CP) rule, white space (WS) channels with the biggest bandwidth are partitioned to the required bandwidth sizes before being allocated to a CRWN that requires smaller bandwidths. In the channel bonding (CB) rule, a number of WS channels are bonded together to meet the needs of the CRWNs requiring the widest bandwidth. Performance eval-uation results show that the total number of WS channels allocated under rule-based scheme was equivalent to those allocated under an interference aware WS allocation scheme. However, in order to satisfy quality of service of secondary users, the rule-based scheme searches for the most suitable WS channels among the list of discovered channels through nartitioning rules.

关键词： Performance evaluation Natural resources Regulators Wireless networks Bandwidth Quality of service White spaces partitioning algorithms Resource management Cognitive radio

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Optimizing Privacy and Efficiency in Brain Tumor Classification through Advanced Non-IID Federated Deep Learning

Optimizing Privacy and Efficiency in Brain Tumor Classificat...

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Information and Communication Technology (ICICT), International Conference on

作者： Md Samsuzzaman Rakibul Hasan Sezan Arjon Golder Md. Abdul Masud Golam Md. Muradul Bashir Faculty of Computer Sc. and Engr. Patuakhali Sc. and Tech. University Patuakhali Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Recent advancements in Artificial Intelligence (AI) have significantly transformed various fields, especially medical diagnostics. However, centralized deep learning models face significant challenges due to the increasing data volumes from edge devices, including issues related to data privacy and heterogeneity. In this study, a federated deep learning model for brain tumor classification using MRI data is presented. The model enhances the MobileNetV3 architecture with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks. A novel non-IID (non-Independent and Identically Distributed) data partitioning strategy, proposed in this study, ensures robust data distribution across clients. Utilizing the Flower framework and the FedOpt algorithm, the model achieved a notable accuracy of 97.40% in eight training rounds, operating efficiently on 15GB of RAM and 10GB of GPU memory. This research underscores the potential of Federated Learning (FL) to address data privacy and resource constraints, demonstrating its efficacy in accurate brain tumor classification. These findings pave the way for future scalable and secure medical AI applications.

关键词： Deep learning Data privacy Accuracy Random access memory Brain tumors Brain modeling Data models partitioning algorithms Classification algorithms Artificial intelligence

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Analysis of the Intra-Picture Coding Tools of Versatile Video Coding (VVC) Standard for 8K Video Resolution

Analysis of the Intra-Picture Coding Tools of Versatile Vide...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Altuğ Şimşek Günhan Dündar Electrical & Electronics Engineering Dept. Boğaziçi University Istanbul Türkiye

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Versatile Video Coding (VVC), standardized in 2022 as ITU-T Recommendation H.266, ISO/IEC 23090–3 and MPEG-I Part3, is the latest block-based hybrid video coding standard which claims to fulfill all the requirements of todays and tomorrow's video applications. VVC defines more tools to increase compression efficiency while maintaining the same level of quality. The tradeoff is the computational complexity. H.266/VVC, especially the intra-coding loop, is computationally very complex due to its nature of iteratively trying all possible QTMT partitioning tree alternatives with all options of compression technologies. In this paper, we focus on the intra-coding loop of VVC for 8K video and study the effects of technologies like MTS, LFNST, MIP etc. on the compression efficiency (bits), quality (Y-PSNR), and computational complexity (runtime). By switching off all those optional technologies, we observe 46.07% runtime gain with only 5.00% increase in bitrate, and 0.0423 dB decrease in Y-PSNR on average. Since this is a remarkable gain in complexity with a limited effect on efficiency and quality, this tradeoff can be taken into consideration for many 8K applications utilizing VVC.

关键词： Video coding Runtime ISO Standards Bit rate Transform coding Switches partitioning algorithms Computational complexity Standards Gain

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Resource Optimized Task Offloading in Delay Sensitive Edge Networks

Resource Optimized Task Offloading in Delay Sensitive Edge N...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Nasif Fahmid Prangon Abdalaziz Sawwan Jie Wu Center for Networked Computing Temple University USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Task offloading is a popular approach in distributed systems to optimize resource usage by splitting the workload among devices in the network. This technique is effective in reducing the workload on individual devices, and helping achieve more efficient task execution by reducing execution time and conserving energy among devices. In this paper, we propose a novel method to evaluate task partitioning using a unique metric, effective processing rate (EPR) for edge networks with heterogenous devices. This unique approach focuses on simplifying task offloading based on the communication delay, task processing rate, and the energy available in each device. This approach also covers the impact of extending offloading beyond one-hop neighbors to two-hop neighbors and single-source versus multi-source offloading. We also discuss the willingness to help factor among one-hop and two-hop neighbors and its significance in task offloading performance. An effective strategy to meet these challenges is introduced and validated through theoretical analysis and extensive simulations.

关键词： Performance evaluation Analytical models Multi-access edge computing Computational modeling Collaboration Energy efficiency Delays partitioning algorithms Resource management Optimization

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