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MULTI-partitioning LINEAR-ESTIMATION algorithms - CONTINUOUS SYSTEMS

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1979年第6期24卷 937-944页

作者： LAINIOTIS, DG ANDRISANI, D CALSPAN ADV TECHNOL CTR BUFFALONY 14225

The partitioned estimation algorithms of Lainiotis for the linear continuous-time state estimation problem have been generalized in this paper in two important ways. First, the initial condition of the estimation problem can, using the results of this paper, be partitioned into the sum of an arbitrary number of jointly Gaussian random variables; and second, these jointly Gaussian random variables may be statistically dependent. The form of the resulting algorithm consists of an imbedded Kalman filter with partial initial conditions and one correction term for each other partition or subdivision of the initial state vector. Emphasis in this paper is on ways in which this approach, called multipartitioning, can be used to provide added insight into the estimation problem. One significant application is in the parameter identification problem where identification algorithms can be formulated in which the inversion of the information matrix of the parameters is replaced by simple division by scalars. A second use of multipartitioning is to show the specific effects on the filtered state estimate of off-diagonal terms in the initial-state covariance matrix.

关键词： Continuous time systems State estimation partitioning algorithms Covariance matrix Noise measurement Random variables Nonlinear filters Smoothing methods Vectors Density measurement

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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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Product Recognition for Inventory Management Using Spatial partitioning in a Multi-Camera System

Product Recognition for Inventory Management Using Spatial P...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Viduranga Munasinghe Tae-Ho Lee Tae Sung Kim Jin-Sung Kim Hyuk-Jae Lee Department of Electrical and Computer Engineering Seoul National University Seoul South Korea Department of Electronic Engineering SunMoon University Asan South Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Recent advancements in deep learning have significantly improved object detection capabilities. However, accurately recognizing objects in environments with complex occlusions remains a challenge. For instance, detecting densely packed items on shelves in unmanned stores presents difficulties even for the most cutting-edge deep-learning object detectors. To address this issue, we propose a deep learning-based inventory management system tailored for multi-camera unmanned stores. The proposed method utilizes a Voronoi diagram-based ground plane localization technique along with regions of interest (ROI) obtained by using image difference. A weighted voting algorithm is employed to ensure an accurate inventory check. This approach outperforms conventional ROI detection methods by 10.45% in terms of recognition accuracy.

关键词： Deep learning Location awareness Accuracy Event detection Object detection Inventory management partitioning algorithms Reliability Object recognition Optimization

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Equalizer Optimization Method Based on Local Multi-Constraint Modeling-Bayesian Optimization With Region partitioning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Li, Xiang-Ru Zhang, Peng Wang, Da-Wei Liu, Jun Sun, Lingling Zhao, Wen-Sheng Hangzhou Dianzi Univ Sch Elect & Informat Sch IC Sci & Engn Innovat Ctr Elect Design Automat Technol Hangzhou 310018 Peoples R China

As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing tim

关键词： Optimization Data models partitioning algorithms Integrated circuit modeling Genetic algorithms Accuracy Predictive models Analytical models Software algorithms Linear programming 3-D integration equalizer signal integrity eye diagram Bayesian optimization region partitioning multi-constraint modeling

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Comparison of heuristic algorithms for variable partitioning in circuit implementation 16

Comparison of heuristic algorithms for variable partitioning...

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16th International Conference on VLSI Design/2nd International Conference on Embedded Systems Design

作者： Muthukumar, V Selvaraj, H Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA

ISBN: (纸本)0769518680

Functional decomposition is a process of splitting a complex circuit into smaller sub-circuits. This paper deals with the problem of determining the set of best free and bound variables (variable partitioning problem) for disjoint (disjoint serial) decomposition, such that the decomposed circuits are smaller in size and its truth table representation have maximal don't cares. A novel pruned breadth first search (PBFS/IPBFS) approach is proposed to determine the set of good variable partitions with minimal time and computational complexity. The heuristics proposed minimize the size of the sub-functions. The proposed approach has been successfully implemented and test with MCNC and Espresso benchmarks.

关键词： Benchmark testing Circuit synthesis Circuit testing Computational complexity Cost function Field programmable gate arrays Heuristic algorithms Input variables partitioning algorithms Search methods

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The column-oriented database partitioning optimization based on the natural computing algorithms

The column-oriented database partitioning optimization based...

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Nowosielski, Artur Kowalski, Piotr A. Kulczycki, Piotr Polish Acad Sci Syst Res Inst PhD Studies Ul Newelska 6 PL-01447 Warsaw Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland AGH Univ Sci & Technol Fac Phys & Appl Comp Sci Div Informat Technol & Biometr PL-30059 Krakow Poland

ISBN: (纸本)9788360810668

This paper describes the basic components of a research project aimed at the application of natural computing metaheuristics to optimize the horizontal scaling of databases. Column oriented databases were selected for the project because of their unique properties. A mathematical model has been created in order to align the problem of horizontal scalability to the general optimization methods, such as natural computing algorithms.

关键词： database management systems optimisation column-oriented database partitioning optimization general optimization methods horizontal scaling mathematical model natural computing metaheuristics research project Optimization partitioning algorithms Relational databases Scalability Sociology Statistics partitioning algorithms Relational databases research project database management systems Sociology Scalability horizontal circle Mathematical Model Database Natural algorithms

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algorithms for a Set partitioning Problem Arising in the Design of Multipurpose Units

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IEEE Transactions on Computers 1977年第4期C-26卷 321-328页

作者： Garey, Michael R. Hwang, Frank K. Johnson, David S. Bell Laboratories Murray Hill NJ 07974 United States

We consider an abstract partitioning problem which has applications to the design of standard libraries of multipurpose units (such as multi-purpose circuit cards) and to storage allocation in the presence of users with conflicting demands. Although the general problem of finding minimum cost partitions appears to be very difficult, we describe a dynamic programming approach which can be quite efficient if the parameters for the items to be partitioned take on only a limited number of distinct values. Copyright © 1977 by The Institute of Electrical and Electronics Engineers, Inc.

关键词： Circuit card libraries cost minimization design automation dynamic programming partitioning algorithms

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Contrastive Ensemble Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Chen, Man-Sheng Lin, Jia-Qi Wang, Chang-Dong Huang, Dong Lai, Jian-Huang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Peoples R China Guangxi Zhuang Autonomous Reg Informat Ctr Guangxi Key Lab Digital Infrastructure Nanning 530201 Peoples R China Sun Yat Sen Univ Sch Math Zhuhai Guangzhou 510275 Peoples R China South China Univ Dept Comp Sci Guangzhou 510640 Peoples R China Sun Yat sen Univ Sch Comp Sci & Engn Guangdong Key Lab Informat Secur Technol Minist Educ Guangzhou 510275 Peoples R China Sun Yat sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510275 Peoples R China

Ensemble clustering aims to combine different base clusterings into a better clustering than that of the individual one. In general, a co-association matrix depicting the pairwise affinity between different data samples is constructed by average fusion or weighted fusion of the connective matrices from multiple base clusterings. Despite the significant success, the existing works fail to capture the global structure information from multiple noisy connective matrices. Meanwhile, the locality property of the resulting representation matrix could not be explicitly preserved. In this article, we propose a novel contrastive ensemble clustering (CEC) method. Specifically, a consensus mapping model is designed for the discovery of the latent representation from the noisy observations with distinct confidences. Furthermore, a contrastive regularizer is dexterously formulated to refine the latent representation while preserving its locality property. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed CEC method. To the best of our knowledge, it is the first time to explore the potential of latent representation learning and contrastive components for the ensemble clustering task.

关键词： Clustering methods Clustering algorithms Noise measurement Contrastive learning Representation learning partitioning algorithms Noise Mutual information Loss measurement Data structures Adaptive fusion contrastive components ensemble clustering latent representation

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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： Ming Sun Xinyu Wu Yi Zhou Jin-Kao Hao Zhang-Hua Fu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China LERIA Université d'Angers Angers France Shenzhen Institute of Artificial Intelligence and Robotics for Society the Chinese University of Hong Kong Shenzhen 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

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Cluster Sort: A Novel Hybrid Approach to Efficient In-Place Sorting Using Data Clustering

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IEEE ACCESS 2025年 13卷 74359-74374页

作者： Subramaniam, Mohit Tripathi, Tanya Chandraumakantham, Omkumar Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

This research work introduces a clustering-based in-place sorting algorithm, cluster sort. It is designed in such a way that it improves sorting efficiency by using data locality. It works in two phases: first, data elements are clustered based on the similarity in the values and then each of these clusters is sorted independently using comb or shell sort. This is a hybrid approach, and it helps minimize multiple comparisons within the clusters, which in turn improves performance, especially in large datasets with specific distributions. The traditional algorithms were selected due to their well-known efficiency and widespread use in sorting large datasets. This experiment of ours includes ordered, reverse-ordered, Gaussian, repeated values, same values, and uniform distributions. The results acquired from this experiment show that Cluster Sort (comb) outperforms Cluster Sort (shell) by 88% in ordered datasets and is 92.32% faster than Merge Sort. For reverse-ordered datasets, Cluster Sort (Comb) is 70.79% faster than Bucket Sort and 90.69% faster than Cluster Sort (Shell). In Gaussian distributions, Cluster Sort (Shell) improves by 25.88% over Bucket Sort and 74.41% over Merge Sort. In repeated-value datasets, although Quick Sort is faster, Cluster Sort (Shell) surpasses Bucket Sort by 13.64%. For uniform distributions, Cluster Sort (Shell) is 30.28% faster than Bucket Sort and 75% faster than Merge Sort. The reduction in unwanted comparisons through clustering is essentially what made Cluster Sort significantly outperform traditional algorithms in these datasets with inherent patterns, making Cluster Sort a coherent choice for practical applications.

关键词： Clustering algorithms Sorting Heuristic algorithms partitioning algorithms Memory management Merging Complexity theory Random access memory Machine learning algorithms Time complexity Merge sort sorting algorithms shell sort bucket sort heap sort comb sort clustering

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