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TransShard: A Dynamic Transaction-Aware Sharding Scheme for Account-Based Blockchain

IEEE ACCESS 2024年 12卷 179797-179812页

作者： Wang, Qian Guan, Yepeng Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China Shanghai Univ Key Lab Silicate Cultural Rel Conservat Minist Educ Shanghai 200444 Peoples R China

The poor scalability of blockchain technology restricts its application in large-scale networks. Sharding technology is viewed as the most promising on-chain solution to improving blockchain scalability. However, the high proportion of cross-shard transactions (TXs) and imbalanced transaction load on different shards resulting from the previous sharding schemes prevent further performance improvements. To address this problem, a dynamic transaction-aware sharding scheme (TransShard) is proposed for account-based blockchain. Firstly, a token-based cryptographic sortition approach is introduced to form shards. Nodes are weighted based on the tokens they own and assigned to different shards randomly in a private and non-interactive way. The design of this approach is the basis of transaction allocation and ensures the security of the whole blockchain. Secondly, an on-the-fly transaction placement algorithm is designed to optimize initial transaction allocation. The blockchain transactions are modeled as a dynamic graph, and a new scoring heuristic is introduced to find the optimal shards for incoming transactions and determine the placement of new accounts. Lastly, an incremental account reallocation algorithm is designed to maintain the quality of account allocation as the transaction graph updates. When the allocation quality is suboptimal, some of the hot accounts are examined to determine how to migrate their ledger states. Simulation results show that the proposed scheme has some excellent performances compared to some other state-of-the-art sharded blockchains in system throughput, transaction confirmation latency, and workload balance.

关键词： Blockchains Resource management Sharding Heuristic algorithms partitioning algorithms Protocols Dynamic scheduling Security Scalability Reviews Blockchain sharding scalability dynamic graph analysis

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Efficient Network partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Efficient Network Partitioning: Application for Decentralize...

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Innovative Smart Grid Technologies (ISGT)

作者： Biswajeet Rout Govind Saraswat Balasubramaniam Natarajan Kansas State University Manhattan KS USA National Renewable Energy Laboratory (NREL) Golden CO USA

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.

关键词： Simulation Clustering algorithms Power distribution Distribution networks Real-time systems Sensors partitioning algorithms

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Performance-Driven Simultaneous partitioning and Routing for Multi-FPGA Systems 58

Performance-Driven Simultaneous Partitioning and Routing for...

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58th ACM/IEEE Design Automation Conference (DAC)

作者： Chen, Ming-Hung Chang, Yao-Wen Wang, Jun-Jie Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan

ISBN: (纸本)9781665432740

A multi-FPGA system consists of multiple FPGAs connected by physical wires, and a circuit is partitioned to fit each FPGA and routed on the system by such physical wires. Due to the limited numbers of input/output (I/O) pins in an FPGA, however, not all signals can be transmitted between FPGAs directly. Moreover, the routing resource may not be sufficient to accommodate many cross-FPGA signals from circuit partitioning. As a result, input/output time-division multiplexing (TDM) is introduced to send a group of cross-FPGA signals in a routing channel with a timing penalty. To optimize the performance of such a system, we shall develop a simultaneous partitioning and routing algorithm considering the timing penalty caused by I/O TDM. Considering the TDM delay penalty, we propose a simultaneous partitioning and routing algorithm to remedy the insufficiency of the two-stage flow of partitioning followed by routing. Our algorithm consists of two major steps: (1) a novel routing-aware partitioning framework to obtain an initial solution considering irregular, asymmetric connections, and (2) a partition-aware routing scheme to optimize routing in each partitioning pass. Experimental results show that our proposed algorithm can achieve better timing than the classical flow.

关键词： Design automation Wires Routing Time division multiplexing partitioning algorithms Delays Pins

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Clustering-Based Reconstruction Algorithm for Electrical Impedance Tomography

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhu, Shiyuan Li, Kun Yue, Shihong Liu, Liping Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Electrical impedance tomography (EIT) is an advanced visualization technique characterized by its cost-effectiveness, rapid response, non-ionizing radiation, and non-intrusiveness compared with other tomography modalities. However, the quality of EIT imaging is compromised by the inherent ill-posed solution and the soft-field effect, which hinder the effective reconstruction of target objects. Various methods have been employed to address these issues, but they can make limited progress in high-quality reconstruction. In this article, efforts are made by applying the clustering technique to the EIT reconstruction. The key features hidden in all EIT measurements are extracted, and whereby each pixel can be represented by a feature vector. After clustering these vectors over all pixels, we proposed a novel EIT reconstruction algorithm. Experimental results show that the novel algorithm can reconstruct the EIT images more accurately and effectively than the existing algorithms. Consequently, the implementation of a clustering algorithm instead of the existing algorithm offers a practical and effective approach to enhance the EIT imaging quality.

关键词： Clustering algorithms Electrical impedance tomography partitioning algorithms Image reconstruction Vectors Feature extraction Imaging Clustering methods electrical impedance tomography (EIT) feature extraction image quality improvement image reconstruction

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A Streaming Data Clustering Method Based on Dual Strategies Improved DENCLUE

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IEEE ACCESS 2024年 12卷 153709-153726页

作者： Cai, Ting Lv, Jiazhi Ye, Zhiwei Li, Xiang Zhou, Wen Kochan, Orest Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Lviv Polytech Natl Univ Dept Measuring Informat Technol UK-79013 Lvov Ukraine

Streaming data arrives continually and is characterized by fast, massive, dynamic evolution and instability. Different from traditional static data clustering, streaming data clustering algorithms need to consider concept drift, outlier handling, identification and updating of dynamic clustering patterns, etc. DENCLUE is one of the most classical algorithms, which adopts nonparametric estimation and utilizes a finite number of samples to make inferences, to get the distribution of the overall data. However, the basic DENCLUE algorithm suffers from the problem that the Kernel Density Estimation (KDE) window width and density threshold parameter are difficult to choose, which cannot be directly applied to streaming data clustering. Therefore, in this paper, we propose a dual strategies improved DENCLUE streaming data clustering method based on KDE optimization and two-stage clustering, which takes into account the concept drift problem in streaming data. Firstly, a density threshold parameter optimization method based on KDE is proposed to address the challenges associated with selecting the KDE window width and density threshold in the traditional DENCLUE algorithm. Secondly, a two-stage clustering and merging method is designed to improve the performance of traditional DENCLUE clustering. The experimental results show that our algorithm outperforms the traditional Clustream and Denstream algorithms on datasets with arbitrary shapes and sizes, and has good performance on streaming data clustering.

关键词： Clustering algorithms Streams Concept drift Clustering methods partitioning algorithms Kernel Heuristic algorithms Estimation Inference algorithms DENCLUE stream data clustering kernel density estimation two-stage clustering

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An Extension of High-Capacity Reversible Data Hiding in Encrypted Images with Bit-Plane Partition

An Extension of High-Capacity Reversible Data Hiding in Encr...

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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Arai, Eichi Imaizumi, Shoko Chiba Univ Grad Sch Sci & Engn Chiba Japan Chiba Univ Grad Sch Engn Chiba Japan

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

In this paper, we propose an efficient extension of a previous reversible data hiding method in encrypted images that uses bit-plane partition. A main feature of our method is an extended self-embedding process. We introduce a more precise algorithm for prediction and a more effective embedding procedure for the self embedding. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and marked image quality.

关键词： Image quality Image coding Focusing Prediction algorithms Encryption partitioning algorithms Image restoration

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A fast geometric prediction merge mode decision algorithm based on CU gradient for VVC

A fast geometric prediction merge mode decision algorithm ba...

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Data Compression Conference (DCC)

作者： Zhang, Mengmeng Deng, Siqi Liu, Zhi North China Univ Technol Beijing Beijing Peoples R China

ISBN: (纸本)9781665478939

To address the problem of large computational redundancy in the geometric prediction merge mode with motion vector refinement (GPM with MMVD), a new decision algorithm based on CU gradient is proposed. By comparing the mean value of the gradient in four directions to determine whether GPM can be terminated early. The advance decision of GPM partition mode can be determined by the calculated gradient direction of CU. Meanwhile, the proposed algorithm saves coding time by reducing the number of candidate modes during the mode selection process.

关键词： Redundancy Data compression Prediction algorithms Encoding partitioning algorithms

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Efficient Backbone Network Construction in Wireless Artificial Intelligent Computing Systems

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TSINGHUA SCIENCE AND TECHNOLOGY 2025年第5期30卷 2300-2319页

作者： Sun, Ming Wu, Xinyu Zhou, Yi Hao, Jin-Kao Fu, Zhang-Hua Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Angers LERIA F-49045 Angers France Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

In wireless artificial intelligent computing systems, the construction of backbone network, which determines the optimum network for a set of given terminal nodes like users, switches, and concentrators, can be naturally formed as the Steiner tree problem. The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph. As a well-known graph problem, many algorithms have been developed for solving this computationally challenging problem in the past decades. However, existing algorithms typically encounter difficulties for solving large instances, i.e., graphs with a high number of vertices and terminals. In this paper, we present a novel partition-and-merge algorithm for effectively handle large-scale graphs. The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner. In the merging procedure, partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization. When the merging procedure ends, the algorithm terminates and reports the final solution for the input graph. We evaluated the algorithm on a wide range of benchmark instances, showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances.

关键词： Steiner trees Wireless communication Heuristic algorithms Merging Benchmark testing Cyber-physical systems Search problems Graph theory partitioning algorithms Optimization Wireless Artificial Intelligent Computing Systems (WAICS) backbone network Steiner Tree Problem (STP) partition-and-merge

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An Unsupervised Classification of PolSAR Image Based on Polarimetric Scattering Similarity and Complex Wishart Classifier

An Unsupervised Classification of PolSAR Image Based on Pola...

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Photonics and Electromagnetics Research Symposium (PIERS)

作者： Li, Jiatong Li, Dong Zhang, Yunhua Yang, Jiefang Liang, Liting Chinese Acad Sci Natl Space Sci Ctr CAS Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665460231

Terrain and land-use classification based on polarimetric synthetic aperture radar is one of the most important research hotspots in the field of geoscience and remote sensing. In recent years, polarimetric scattering similarity (PSS) as an effective parameter representing the target scattering characteristics has been widely applied to terrain classification. Among the existing classification algorithms based on PSS, the adaptive model-based classification (AMC) proposed by Li and Zhang shows superior classification result and calculation speed. As this algorithm requires no eigen-vector decomposition, the calculation speed has been greatly improved compared with the widely-used H/(alpha) over bar classification. However, the AMC has the hard partitioning that may cause the misclassification of the pixels at the classification boundary. In this paper, a new terrain classification is proposed, which combines AMC and the complex Wishart classifier with a fast implementation of Wishart distance. This approach improves the misclassification problem caused by the hard threshold in AMC and enriches the image details of the complex Wishart classifier. The effectiveness of the proposed algorithm is demonstrated on the L-band AIRSAR data of San Francisco.

关键词： Adaptation models Scattering Geoscience and remote sensing Polarimetric synthetic aperture radar Classification algorithms partitioning algorithms Electromagnetics

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On the Relationship Between the Minimum of the Bethe Free Energy Function of a Factor Graph and Sum-Product Algorithm Fixed Points

On the Relationship Between the Minimum of the Bethe Free En...

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IEEE Information Theory Workshop (ITW)

作者： Huang, Yuwen Vontobel, Pascal O. Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (数字)9781665483414

ISBN: (纸本)9781665483414

The sum-product algorithm (SPA) is a popular algorithm for efficiently approximating the marginals and the partition function of a factor graph. Some key results for this algorithm were established by Yedidia et al., who proved that, roughly speaking, fixed points of the SPA correspond to stationary points of the Bethe free energy function. However, some of their results were only for factor graphs where the local functions take on strictly positive values. They also conjectured that similar results hold for factor graphs where the local functions take on non-negative values. In this paper we make progress toward resolving this conjecture. In particular, we present examples where the results of Yedidia et al. generalize and examples where their results do not generalize. Finally, we present a general framework for analyzing fixed-points of the SPA based on a suitable dualization of the Bethe free energy function.

关键词： Conferences Energy resolution Approximation algorithms partitioning algorithms Sum product algorithm Information theory

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