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Optimization of Depth Map Intra Coding algorithms Based on Machine Learning

IEEE ACCESS 2025年 13卷 5846-5857页

作者： Song, Wenjun Yang, Jiaying Zhang, Qiuwen Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

As a cutting-edge field of global research, 3D video technology faces the dual challenges of large data volumes and high processing complexity. Although the most recent video coding standard VVC, surpasses HEVC in coding efficiency, dedicated research on 3D video coding remains relatively scarce. Building on existing research, this study aims to develop a 3D video coding algorithm based on VVC that lowers the complexity of the encoding procedure. We focus specifically on the depth maps in 3D video content and introduce an extreme forest model from machine learning to optimize intra-frame coding. This paper proposes a novel CU partitioning strategy implemented through a two-stage extreme forest model. First, the initial model predicts the CU partitioning type, including no partition, QuadTree partitioning, Multi-type tree horizontal partitioning, and Multi-type tree vertical partitioning. For the latter two cases, a second model further refines the partitioning into binary or ternary trees. Through this two-stage prediction mechanism, we effectively bypass CU partitioning types with low probability, significantly reducing the coding complexity. The experimental results demonstrate that the proposed algorithm saves 47.46% in encoding time while maintaining coding quality, with only a 0.26% increase in Bjontegaard Delta Bitrate. This achievement provides an effective low-complexity solution for the 3D video coding field.

关键词： Encoding Prediction algorithms Complexity theory Video coding partitioning algorithms Three-dimensional displays Forestry Predictive models Adaptation models Classification algorithms Depth map intra-frame coding extreme forest

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Network-Wide Data Collection Based on In-Band Network Telemetry for Digital Twin Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第1期24卷 86-101页

作者： Wang, Zhihao Jiang, Dingde Mumtaz, Shahid Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Sch Informat & Commun Engn Chengdu 611731 Peoples R China Lab Electromagnet Space Cognit & Intelligent Contr Beijing 100091 Peoples R China Sichuan Conservatory Mus Key Lab Sichuan Prov Digital Media Art Chengdu 610041 Peoples R China Nottingham Trent Univ Dept Comp Sci Nottingham NG1 4FQ England Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea

The Digital Twin Network (DTN) establishes a real-time virtual mirror of physical networks. Data collection plays an essential role in DTN, which collects the status data of physical network for building highly consistent digital twins. In this paper, we present a network-wide data collection scheme based on In-band Network Telemetry (INT). To build a lifelike mirror of the physical network, the probing path set is required to cover all links so that network topology, traffic load, and port-level device information is captured. We present a Latency-aware High-degree Replicated First (LHRF) vertex-cut graph partitioning algorithm to partition the network into several balanced subgraphs while trying to replicate the high-degree vertexes among partitions first. LHRF aims to balance the length and accumulated latency of the probing paths. With shorter and stabler probing latencies, the information received by digital twin can reflect the latest and consistent network-wide status. To prevent the packets from being fragmented due to overlong paths, a deep limited search (DLS) based path planning algorithm is employed to generate non-overlapped probing paths covering all edges in the separated subgraphs. Simulation results demonstrate that the proposed scheme generates more balanced INT paths with constrained path length and shorter, stabler probing delay.

关键词： Data collection Telemetry Path planning Accuracy Digital twins partitioning algorithms Artificial intelligence Network telemetry digital twin network graph partition data collection probing path

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Accelerating Distributed Repartition Joins on Skewed Datasets via Patch-Based Shuffling

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IEEE ACCESS 2025年 13卷 41068-41096页

作者： Kassela, Evdokia Konstantinou, Ioannis Koziris, Nectarios Natl & Tech Univ Athens Sch Elect & Comp Engn Athens 11527 Greece Univ Thessaly Dept Informat & Telecommun Lamia 35100 Greece

In distributed workloads involving joins and aggregations, skewed attribute values often cause load balancing issues, leading to stragglers and increased execution times. Existing solutions often rely on cost-based models, require extensive parameter tuning, or necessitate modifications to distributed execution engines, limiting their usability and generality. To address this challenge, we present a novel patch-based repartitioning algorithm that eliminates load imbalances while minimizing network overhead. Our approach extends the subset-replicate technique by leveraging data distribution and location statistics to optimize data locality and reduce unnecessary data movement. Unlike traditional hash-based methods, our technique is skew-insensitive, requires no parameter tuning, and integrates seamlessly into existing distributed execution engines as a drop-in replacement for the shuffle mechanism. The proposed method operates in three distinct stages: (1) statistics collection and load threshold computation, (2) patch-based subgroup assignment to ensure optimal load balancing with minimal replication, and (3) informed data shuffling and join execution. This structured process ensures even workload distribution across workers while reducing network I/O. Theoretical analysis proves the scalability, skew robustness, and load-balancing guarantees of our approach, establishing bounds on maximum worker load and network data movement. Experimental evaluations demonstrate that our method achieves perfectly balanced workloads and reduces execution time by up to 81% compared to conventional hash-based joins under moderate to high skew, while introducing negligible overhead at low skew levels. These improvements are attributed to reduced data movement, optimized worker utilization, and the algorithm's robust theoretical foundation. Our research provides a versatile and practical solution for skewed data processing, significantly advancing the efficiency of distributed data man

关键词： Engines partitioning algorithms Load management Distributed databases Costs Telecommunication traffic Optimization Logic Load modeling Histograms Load balancing repartition join shuffling subset-replicate

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Accelerated Reeds-Shepp and Underspecified Reeds-Shepp algorithms for Mobile Robot Path Planning

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2691-2708页

作者： Ibrahim, Ibrahim Decre, Wilm Swevers, Jan Katholieke Univ Leuven Dept Mech Engn MECO Res Team B-3000 Leuven Belgium Katholieke Univ Leuven Flanders Make B-3000 Leuven Belgium

In this study, we present a simple and intuitive method for accelerating optimal Reeds-Shepp path computation. Our approach uses geometrical reasoning to analyze the behavior of optimal paths, resulting in a new partitioning of the state space and a further reduction in the minimal set of viable paths. We revisit and reimplement classic methodologies from literature, which lack contemporary open-source implementations, to serve as benchmarks for evaluating our method. In addition, we address the underspecified Reeds-Shepp planning problem where the final orientation is unspecified. We perform exhaustive experiments to validate our solutions. Compared to the modern C++ implementation of the original Reeds-Shepp solution in the Open Motion Planning Library, our method demonstrates a 15x speedup, while classic methods achieve a 5.79x speedup. Both approaches exhibit machine-precision differences in path lengths compared to the original solution. We release our proposed C++ implementations for both the accelerated and underspecified Reeds-Shepp problems as open-source code.

关键词： Robots Planning Path planning Optimal control Motion segmentation partitioning algorithms Turning Large Hadron Collider Kinematics Autonomous vehicles Computational geometry constrained motion planning motion and path planning nonholonomic motion planning

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Group Synchronization of Heterogeneous Multiagent System With Hierarchical Structure and Beyond Pairwise Interactions

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 1578-1590页

作者： Chen, Lin Xia, Yuanqing Zhang, Yuan Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Henan Peoples R China

Systems of different types of agents coupled with high-order interactions can emerge complex synchronization patterns. This article presents an analysis framework to study the group synchronization of heterogeneous multiagent systems with hierarchical structure and directed interactions of arbitrary order. First, directed hypergraph is introduced to describe the arbitrary-order interactions among the agents. Then, to explore the possible group synchronization patterns, the agent partition methods commonly used in dyadic graphs are extended to the directed hypergraph. Meanwhile, to verify the admissibility of the group synchronization patterns, two equitable partition types of agent partition are defined from the perspective of hyperedge partition. Next, using the simultaneous block diagonalization method, the stability problem of the group synchronous solution is decoupled into several independent subblocks. These subblocks are associated with the perturbations along and transverse to the synchronous manifold, with transverse subblocks related to loss of synchronization. Therefore, the stability of the group synchronous solution can be investigated by evaluating the stability of each transverse subblock via the master stability function method, thus significantly reducing the stability calculation complexity. Finally, the effectiveness of the analysis framework is illustrated via the simulation.

关键词： Synchronization Multi-agent systems Stability criteria Circuit stability partitioning algorithms Neurons Nonhomogeneous media Tensors Social networking (online) Mathematical models Directed hypergraph group synchronization heterogeneous multiagent systems high-order interactions master stability function (MSF)

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Fuzzy Min-Cut With Soft Balancing Effects

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 767-778页

作者： Chen, Huimin Zhang, Runxin Wang, Rong Nie, Feiping Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China

The clustering algorithm has always been a hot spot in machine learning, which has made great progress and been widely used in different scenarios. Due to the characteristics and requirements of some application scenarios, the branch of the balanced clustering algorithm has been developed. The ideal of these algorithms is to obtain clusters containing approximately the same number of samples. However, when there are data points distributed at the boundary of different clusters, resulting in different probabilities of their belonging, hard-partitioned balanced clustering may not be able to handle these boundary data well, thus limiting their performance. Motivated by this, we propose a Fuzzy Min-Cut with Soft Balancing Effects (FCBE) method in this article. Specifically, the FCBE model utilizes fuzzy constraints to simultaneously enhance the ability of the balanced algorithm to capture boundary data members and the advantage of directly obtaining the partitioning results of graph-cut problem without postprocessing. In addition, a sparse regularization is introduced to avoid trivial solutions and maintain the separability of the relationship matrix. Furthermore, the proposed FCBE method can be viewed as a flexibly adjustable generalization pattern that not only has clear interpretability but also can become special cases with clear physical meanings under different parameter values. The feasibility of FCBE has been verified on real datasets.

关键词： Clustering algorithms partitioning algorithms Fuzzy systems Vectors Sparse matrices Switches Data models Clustering methods Wireless sensor networks Uncertainty Fuzzy constraints soft-balanced sparse regularization spectral clustering (SC)

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An Energy Effective RIS-Assisted Multi-UAV Coverage Scheme for Fairness-Aware Ground Terminals

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2025年第1期9卷 164-176页

作者： Lin, Na Wu, Tianxiong Zhao, Liang Hawbani, Ammar Wan, Shaohua Guizani, Mohsen Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

Unmanned aerial vehicle (UAV)-assisted communications are critical in regional wireless networks. Using reconfigurable intelligent surfaces (RISs) can significantly improve UAVs' throughput and energy efficiency. Due to limited communications resources, the data transfer rate of ground terminals (GTs) could be slower, and the throughput may be low. Using RIS-assisted UAVs can effectively address these limitations. This paper focuses on optimizing the three-dimensional (3D) trajectory of the UAV and the scheduling order of the GTs and designing the phase shift of the RIS to maximize energy efficiency while meeting the limited energy and fair service constraints in the case of fair service GTs. To address the non-convexity of this problem, we propose a triple deep q-network (TDQN) algorithm, which better avoids the overestimation problem during the optimization process. We propose an improved k-density-based spatial clustering of applications with noise (K-DBSCAN) clustering algorithm, which is characterized by the ability to output the initial movement range of the UAV and prune the deep reinforcement learning (DRL) state space by the initial movement range to speed up DRL training based on the completion of the partitioning deployment work. A fair screening mechanism is proposed to satisfy the fairness constraint. The results show that the TDQN algorithm is 2.9% more energy efficient than the baseline. The K-DBSCAN algorithm speeds up the training of the TDQN algorithm by 59.4%. The fair screening mechanism reduces the throughput variance from an average of 114099.9 to an average of 46.9.

关键词： Autonomous aerial vehicles Energy efficiency Clustering algorithms Optimization Throughput partitioning algorithms Wireless communication Reconfigurable intelligent surface (RIS) unmanned aerial vehicle (UAV) deep reinforcement learning (DRL) triple deep q network (TDQN)

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Improved Parallel Algorithm for Finding Minimum Cuts in Stochastic Flow Networks

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IEEE LATIN AMERICA TRANSACTIONS 2025年第4期23卷 285-293页

作者： Joshan, Mohammad Saito, Jose Pedrino, Emerson Univ Fed Sao Carlos Sao Carlos SP Brazil

This article solves the convergence problem in the Parallel BK algorithm for large-scale flow networks. We introduce a merging method and a pseudo-Boolean representation-based invariance analysis that optimize the algorithm's performance compared to classical approaches such as Ford-Fulkerson, Edmonds-Karp, Push-Relabel, and Kargers Algorithm. Our approach improves energy efficiency, reduces memory usage, and lowers time complexity by leveraging parallelization techniques. We evaluate the performance of these algorithms using Python simulations on various benchmark graphs, ranging from small to large-scale networks. The results show that our method reduces memory consumption by up to 40% and speeds up execution time by 30%, while maintaining high accuracy in finding minimum cuts. This paper demonstrates the algorithm's potential for applications in image segmentation, wireless sensor networks, and network reliability analysis

关键词： Heuristic algorithms Merging Parallel algorithms Convergence Accuracy partitioning algorithms Optimization Graph theory Computational efficiency Reliability Stochastic processes Parallel Algorithm Connectivity Min-Cuts Stochastic-Flow Networks Network Reliability

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Benchmarking Derivative-Free Global Optimization algorithms Under Limited Dimensions and Large Evaluation Budgets

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 187-204页

作者： Stripinis, Linas Kudela, Jakub Paulavicius, Remigijus Vilnius Univ Inst Data Sci & Digital Technol LT-08663 Vilnius Lithuania Brno Univ Technol Inst Automat & Comp Sci Brno 61600 Czech Republic

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.

关键词： Optimization partitioning algorithms Evolutionary computation Benchmark testing Stochastic processes Linear programming Closed box Derivative-free global optimization deterministic algorithms evolutionary computation (EC) algorithms nature-inspired meta-heuristics numerical benchmarking

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Maximizing Influence Query Over Indoor Trajectories

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第3期37卷 1294-1310页

作者： Chen, Jian Gao, Hong Shi, Yuhong Chen, Junle Yang, Donghua Li, Jianzhong Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Maximizing Influence (Max-Inf) query is a fundamental operation in spatial data management. This query returns an optimal site from a candidate set to maximize its influence. Existing work commonly focuses on outdoor spaces. In practice, however, people spend up to 87% of their daily life inside indoor spaces. The outdoor techniques fall short in indoor spaces due to the complicated topology of indoor spaces. In this paper, we formulate two indoor Max-Inf queries: Top-kk Probabilistic Influence Query (TkkPI) and Collective-kk Probabilistic Influence Query (CkkPI) taking probability and mobility factors into consideration. We propose a novel spatial index, IT-tree, which utilizes the properties of indoor venues to facilitate the indoor distance computation, and then applies a trie to further organize the trajectories with similar check-in partitions together, based on their sketch information. This structure is simple but highly effective in pruning the trajectory search space. To process TkPI efficiently, we devise subtree pruning and progressive pruning techniques to delicately filter out unnecessary trajectories based on probability bounds and the monotonicity of influence probability. For CkPI queries, which is a submodular NP-hard problem, three approximation algorithms are provided with different strategies of computing marginal influence value during the search. Through extensive experiments on several real indoor venues, we demonstrate the efficiency and effectiveness of our proposed algorithms.

关键词： Trajectory Approximation algorithms Probabilistic logic Indexing Emergency services Costs Topology Spatial indexes Roads partitioning algorithms Indoor query pruning strategy spatial index

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