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Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 5035-5044页

作者： Srivastava, Vishal Singh, Shashank Sheshar Jain, Ankush Motilal Nehru Natl Inst Technol MNNIT Dept Comp Sci & Engn Prayagraj 211004 India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 India Netaji Subhas Univ Technol Dept Comp Sci & Engn New Delhi 110078 India

Network partitioning has been studied extensively on undirected and weighted networks that need to partition the graph into small clusters. Graph-cutting is a widely known approach that removes the inter-cluster edges to find the local network clusters. Cutting a network into small clusters is pivotal in a mixed integer optimization problem. Proper selection of cut sequences discards the possibility of trivial partitions and reduces the computation load to improve cluster quality. Proper cut-sequence selection relies on multiple heuristics that restrict this problem from being generalized. Cut-sequence selection is an NP-hard problem that turns out to be challenging for weighted networks. This paper presents a swarm-heuristics-based framework to solve the cut-sequence selection problem in weighted networks. First, we generate an affinity network from a given data set. A cost-based objective function is formalized that takes cut sequences as input and returns the weighted intra-cluster connected components. Subsequently, heuristics-based cut sequences are initialized, and krill-herd optimization is used to solve the objective function. The framework is empirically tested on simulated and real-world networks. Network-based indices are used to measure the quality of partitions. The comparative analysis, computation time, and convergence analysis are performed with state-of-the-art methods to report the competitive behavior of the framework. The framework is highly effective and has paved new ways for future research to solve the cut-sequence selection problem without prior knowledge.

关键词： Optimization Clustering algorithms partitioning algorithms Image edge detection Social networking (online) Linear programming Topology Network partitioning K Cut-sequences meta-heuristics krill herd optimization

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Achievable Rates for Low-Complexity Posterior Matching Over the Binary Symmetric Channel

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第8期70卷 5471-5497页

作者： Antonini, Amaael Gimelshein, Rita Wesel, Richard D. Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA

Horstein, Burnashev, Shayevitz and Feder, Naghshvar et al. and others have studied sequential transmission of a k-bit message over the binary symmetric channel (BSC) with full, noiseless feedback using posterior matching. Yang et al. provide an improved lower bound on the achievable rate using martingale analysis that relies on the small-enough difference (SED) partitioning introduced by Naghshvar et al. SED requires a relatively complex encoder and decoder. To reduce complexity, this paper replaces SED with relaxed constraints that admit the small enough absolute difference (SEAD) partitioning rule. The main analytical results show that achievable-rate bounds higher than those found by Yang et al. (2021) are possible even under the new constraints, which are less restrictive than SED. The new analysis does not use martingale theory for the confirmation phase and applies a surrogate channel technique to tighten the results. An initial systematic transmission further increases the achievable rate bound. The simplified encoder associated with SEAD has a complexity below order O(K-2) and allows simulations for message sizes of at least 1000 bits. For example, simulations achieve 99% of of the channel's 0.50-bit capacity with an average block size of 200 bits for a target codeword error rate of 10 (-3) .

关键词： Symbols Complexity theory Transmitters Systematics Receivers partitioning algorithms Error analysis Posterior matching binary symmetric channel noiseless feedback random coding

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Acceleration of an Optimized Kvazaar All Intra Prediction on Embedded Systems Based on the Directional Texture Complexity

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IEEE EMBEDDED SYSTEMS LETTERS 2025年第1期17卷 38-41页

作者： Majok, James R. Abo-Zahhad, Mohammed Inoue, Koji Sayed, Mohammed S. Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria 21934 Egypt Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan

The high growth of real-time video applications on embedded systems poses challenges for practical encoders aiming to deliver high quality and high speed simultaneously. In real-world video applications, the slower preset in Kvazaar HEVC encoder can achieve impressive quality, with a penalty of extensive computational time. This is ultimately due to rate-distortion optimization that involves a comprehensive analysis of all possible quad-tree partitioning within the coding tree unit (CTU) structure, resulting in unpleasant encoding complexity. This letter proposes a method of accelerating All Intraprediction on Nvidia Jetson TX1 using early termination of CTU partitioning and a method of selecting only eight modes for intraframe search. The proposed technique reduces the running time of an optimized Kvazaar all intraprediction by 48.4% and 40.24% at slower and higher presets, respectively, with an average BD rate lost of 1.5% and 0.682% compared to the optimized Kvazaar running under the same coding configuration.

关键词： Encoding Complexity theory partitioning algorithms Embedded systems Visualization Streaming media Standards All intra coding tree unit (CTU) partition Kvazaar HEVC encoder rough mode decision (RMD) texture-based complexity

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Adaptive Binning Coincidence Test for Uniformity Testing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2421-2435页

作者： Salgia, Sudeep Wang, Xinyi Zhao, Qing Tong, Lang Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA

We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions whose l(1) distances from the uniform distribution are bounded by epsilon from below. We propose a sequential test that adapts to the unknown distribution under the alternative hypothesis. Referred to as the Adaptive Binning Coincidence (ABC) test, the proposed strategy adapts in two ways. First, it partitions the set of alternative distributions into layers based on their distances to the uniform distribution. It then sequentially eliminates the alternative distributions layer by layer in decreasing distance to the uniform, allowing it to take advantage of favorable situations of a distant alternative by terminating early. Second, it adapts, across layers of the alternative distributions, the resolution level of the discretization for computing the coincidence statistic. The farther away the layer is from the uniform, the coarser the discretization necessary for eliminating this layer or terminating altogether. It thus terminates the test both early (via the layered partition of the alternative set) and quickly (via adaptive discretization) to take advantage of favorable alternative distributions. The ABC test builds on an adaptive sequential test for discrete distributions, which is of independent interest.

关键词： Testing Complexity theory Signal processing algorithms Symbols Random variables partitioning algorithms Anomaly detection Uniformity testing adaptivity coincidence test

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Attrition-Aware Adaptation for Multi-Agent Patrolling

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第8期9卷 7230-7237页

作者： Goeckner, Anthony Li, Xinliang Wei, Ermin Zhu, Qi Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing, which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works do not provide performance guarantees and either do not consider agent attrition or impose significant communication requirements to enable adaptation. In this work, we present the Adaptive Heuristic-based Patrolling Algorithm, which is capable of adaptation to agent loss using minimal communication by taking advantage of Voronoi partitioning, and which meets guaranteed performance bounds. Additionally, we provide new centralized and distributed mathematical programming formulations of the patrolling problem, analyze the properties of Voronoi partitioning, and finally, show the value of our adaptive heuristic algorithm by comparison with various benchmark algorithms using physical robots and simulation based on the Robot Operating System (ROS) 2.

关键词： Task analysis partitioning algorithms Resource management Heuristic algorithms Electric breakdown Vehicle routing Vectors Multi-robot systems robotics in hazardous fields path planning for multiple mobile robots or agents

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Community-Based Task Assignment Method in Mobile Crowd Sensing

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IEEE ACCESS 2024年 12卷 84387-84400页

作者： Long, Hao Hao, Jiawei Zhang, Shukui Zhang, Yang Zhang, Li Xuzhou Coll Ind Technol Sch Informat Engn Xuzhou 221140 Jiangsu Peoples R China Global Inst Software Technol Suzhou 215163 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China

With the rapid development of mobile networks and widespread use of mobile devices, there is an increasing focus on assigning location-based tasks to mobile users in the context of Mobile Crowd Sensing (MCS). One of the primary challenges in MCS is task assignment, i.e., distributing tasks to suitable users for completion. However, existing work often assumes static offline scenarios where the spatiotemporal information of all users and tasks is pre-determined and known. Neglecting the dynamic spatiotemporal distribution of users and tasks can lead to suboptimal assignment results. In this study, we investigate a novel task assignment problem called Community Task Assignment (CTA). The objective is to enhance the effectiveness and precision of task distribution by considering the sociality of current users and distributing location-based tasks through communities. Initially, we partition users into different communities by abstracting and identifying behavior patterns through the computation of minimum spanning trees, connectivity parameters, and community cohesion. Subsequently, we calculate the match between perception tasks and community behavior pattern features, and task distribution is carried out by the central nodes of the communities based on this match. Experimental validation first confirms the effectiveness of the community partitioning algorithm. Compared to existing algorithms, the proposed method more accurately detects community structures with similar behavioral features in the network. Furthermore, a comparison with existing task assignment algorithms verifies the superiority of the proposed method in terms of average task completion time, task matching rate, and overall utility of task assignments.

关键词： Task analysis Sensors Resource management Robot sensing systems partitioning algorithms Clustering algorithms Heuristic algorithms Crowdsourcing Behavioral sciences Mobile crowd sensing task assignment community sociality behavioral features

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A Commonality Modeling Framework for Enhanced Video Coding Leveraging on the Cuboidal partitioning Based Representation of Frames

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 4446-4457页

作者： Ahmmed, Ashek Murshed, Manzur Paul, Manoranjan Taubman, David Charles Sturt Univ Sch Comp & Math Bathurst NSW 2795 Australia Federat Univ Sch Sci Engn & Informat Technol Mt Helen Vic 3350 Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. Modern video coding systems are block-based wherein commonality modeling is carried out only from the perspective of the block that need be coded next. In this work, we argue for a commonality modeling approach that can provide a seamless blending between global and local homogeneity information. For this purpose, at first the frame that need be coded, is recursively partitioned into rectangular regions based on the homogeneity information of the entire frame. After that each obtained rectangular region's feature descriptor is taken to be the average value of all the pixels' intensities encompassing the region. In this way, the proposed approach generates a coarse representation of the current frame by minimizing both global and local commonality. This coarse frame is computationally simple and has a compact representation. It attempts to preserve important structural properties of the current frame which can be viewed subjectively as well as from improved rate-distortion performance of a reference scalable HEVC coder that employs the coarse frame as a reference frame for encoding the current frame.

关键词： Video coding Encoding Video sequences partitioning algorithms Standards Rate-distortion Optimization Video coding commonality HEVC

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Traffic Origin-Destination Flow-Inspired Dynamic Urban Arterial Partition for Coordinated Signal Control Using Automatic License Plate Recognition Data

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2024年第1期16卷 132-147页

作者： Ke, Siping Wang, Yinpu An, Chengchuan Lu, Zhenbo Xia, Jingxin Southeast Univ Intelligent Transportat Syst Res Ctr Nanjing 211189 Peoples R China

The partition of a long urban arterial is necessary for efficient and reliable traffic signal coordination. Previous studies have utilized fixed detector data or aggregated vehicle trajectory data to measure the correlation between adjacent intersections for an arterial partition. However, the appropriate partition of a long urban arterial remains a challenge because of the difficulty in quantifying the correlation between any pair of intersections instead of two adjacent intersections. The availability of spatiotemporal continuous individual vehicle trajectory data provides opportunities for a more accurate dynamic urban arterial partition by constructing a correlation matrix between intersections. In this article, a novel three-step framework is proposed for a dynamic urban arterial partition using the data collected by automatic license plate recognition devices, which are widely installed in China. First, the arterial traffic origin-destination (OD) flow between any pair of intersections along an arterial is extracted, and a similarity model is proposed to identify the spatiotemporal pattern of the arterial traffic OD flow matrixes. Second, a community aggregation-based algorithm is developed to produce candidate arterial partition solutions using the distributed characteristics of the arterial traffic OD flow matrixes. Third, two new metrics are designed to determine the optimal arterial partitions considering the splits of vehicle trajectories and their coordination utilization in partitioned intersection groups. The proposed framework is implemented on an actual arterial in Kunshan, China. The comparison to conventional adjacent correlation methods indicates that the OD flow-based partition method can generate better signal coordination control performance, in terms of average delay, average speed, and so on.

关键词： Correlation Measurement Vehicle dynamics Manganese License plate recognition partitioning algorithms Trajectory

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Topological Signal Processing Over Generalized Cell Complexes

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 687-700页

作者： Sardellitti, Stefania Barbarossa, Sergio Sapienza Univ Rome Dept Informat Engn Elect & Telecommun I-00184 Rome Italy

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes are required to satisfy the so-called inclusion property, according to which, if a set belongs to the complex, then all its subsets must also belong to the complex. In some applications, this is a severe limitation. To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells. We show that, even if the algebraic formulation does not change significantly, the extension to the generalized cell complexes considerably broadens the number of applications. Most important, the new representation provides a much better trade-off between the complexity of the representation and its accuracy. In addition, we propose a method to infer the structure of the cell complex from data and we propose distributed filtering strategies, including a method to retrieve the sparsest representation of the harmonic component. We quantify the advantages of using cell complexes instead of simplicial complexes, in terms of the complexity/accuracy trade-off, for different applications such image segmentation and recovering of real flows measured on data traffic and transportation networks.

关键词： Signal processing Data mining Topology Finite impulse response filters Artificial neural networks Transportation partitioning algorithms Topological signal processing algebraic topology cell complexes simplicial complexes FIR filters topology inference

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Continuous Object Tracking via Joint Global-Local Binary Tree Topological Transformation in Underwater Acoustic Sensor Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 11091-11104页

作者： Liu, Li Zhao, Tengfei Chan, Sammy Wu, Changmao Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Hohai Univ Coll Informat Sci & Engn Changzhou 213022 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

Frequent activities in marine energy exploration and transportation have led to the ongoing presence of continuous objects, such as oil spills and radioactive waste, in the ocean. This article focuses on enhancing the understanding of these objects' boundaries for accurate assessment of their shape, coverage, and evolution. We introduce a continuous object tracking algorithm named JGL-COT, based on joint global-local binary tree topological transformations and specifically designed for underwater acoustic sensor networks. The contribution of JGL-COT lies in its ability to leverage the correlation between the morphologies of a continuous object's boundaries over time, alternating between global and local binary tree topological transformations. When the present boundary features a strong resemblance to its previous form, JGL-COT switches to a local transformation by establishing a semi-infinite region. Otherwise, it transitions to a global transformation. Following this, JGL-COT chooses a group of binary tree-structured cells for boundary mapping, creating virtual boundary nodes as sampling points for boundary fitting. Building upon graph theory, we derive the lower bound on the effectiveness and time complexity of the proposed joint global and local binary tree topological transformations when applied to object boundary tracking. Experiments in both realistic and simulated settings confirm that JGL-COT provides highly accurate tracking and significantly reduces network energy consumption.

关键词： Binary trees partitioning algorithms Object tracking Target tracking Protocols Oils Underwater acoustics Continuous objects boundary tracking joint topological transformation sensor networks

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