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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

IEEE Transactions on Circuits and Systems for Video technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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A Reputation System for Distributed Intelligent Blockchain Decision-Making 5

A Reputation System for Distributed Intelligent Blockchain D...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Rawlins, Charles Jagannathan, S. Nadendla, Venkata Sriram Siddhardh Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Computer Science Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350339239

Discerning ground-truth amidst misinformation is a nontrivial task in decentralized networks. While trivial for traditional blockchain networks using Proof of Work (PoW) consensus, it requires immense computational resources at the cost of network scalability and therefore has compatibility challenges with limited devices like Internet of Things (IoT) networks. Moving towards a lightweight alternative-a novel scheme is proposed for provable identification of reputable nodes for making intelligent decisions using distributed machine-learning optimization, called Weight-Based Reputation (WBR). A distributed learning task identifies nodes that are willing to cooperate as a means for securing ground-truth, and is provably resistant to data poisoning in theory and experimentation. A novel method combining WBR with a classifier creates lightweight and robust blockchain decision-making. The results of this two-step approach is a novel method for predicting various attacks that result transaction log, such as fraud or the double-spend. A Bayesian game against adversary manipulation formulation and experimental results show superior performance and robustness to the state-of-the-art. Resource consumption compared against blockchain protocols is also provided. Future work will explore WBR in distributed consensus as a complete alternative to blockchain PoW. © 2023 IEEE.

关键词： Internet of things

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Open-Vocabulary Multi-Object Tracking With Domain Generalized and Temporally Adaptive Features

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IEEE Transactions on Multimedia 2025年 27卷 3009-3022页

作者： Li, Run Zhang, Dawei Wang, Yanchao Jiang, Yunliang Zheng, Zhonglong Jeon, Sang-Woon Wang, Hua Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of Victoria University Institute for Sustainable Industries and Liveable Cities College of Engineering and Science MelbourneVIC8001 Australia

Open-vocabulary multi-object tracking (OVMOT) is a cutting research direction within the multi-object tracking field. It employs large multi-modal models to effectively address the challenge of tracking unseen objects within dynamic visual scenes. While models require robust domain generalization and temporal adaptability, OVTrack, the only existing open-vocabulary multi-object tracker, relies solely on static appearance information and lacks these crucial adaptive capabilities. In this paper, we propose OVSORT, a new framework designed to improve domain generalization and temporal information processing. Specifically, we first propose the Adaptive Contextual Normalization (ACN) technique in OVSORT, which dynamically adjusts the feature maps based on the dataset's statistical properties, thereby fine-tuning our model's to improve domain generalization. Then, we introduce motion cues for the first time. Using our Joint Motion and Appearance Tracking (JMAT) strategy, we obtain a joint similarity measure and subsequently apply the Hungarian algorithm for data association. Finally, our Hierarchical Adaptive Feature Update (HAFU) strategy adaptively adjusts feature updates according to the current state of each trajectory, which greatly improves the utilization of temporal information. Extensive experiments on the TAO validation set and test set confirm the superiority of OVSORT, which significantly improves the handling of novel and base classes. It surpasses existing methods in terms of accuracy and generalization, setting a new state-of-the-art for OVMOT. © 1999-2012 IEEE.

关键词： Spatio-temporal data

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Platoon-Based Resource Allocation in NOMA-Integrated V2X Networks 23

Platoon-Based Resource Allocation in NOMA-Integrated V2X Net...

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Bao, Haizhou Huo, Yiming School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China University of Victoria Department of Electrical and Computer Engineering Victoria Canada

ISBN: (纸本)9798350325959

This paper discusses the application of Cellular Vehicle-to-Everything (C-V2X) communications, specifically focusing on the integration of non-orthogonal multiple access (NOMA) in platoon technology within the V2X network. These systems have the potential to significantly enhance a variety of critical, content-oriented applications that extend beyond 5G and Internet of Things (IoT) infrastructures. Crucially, we argue that optimized communication at the edge of the network can effectively mitigate network congestion, augment delivery efficiency, and optimize energy use. Nonetheless, constraints inherent to vehicular networks, such as limited backhaul bandwidth and ever-changing topology, can compromise the reliability and latency of multimedia services in C-V2X networks, thereby affecting overall user experience. To address the mobility-related challenges of the topology, we introduce an intelligent driver model (IDM)-based platoon paradigm, and subsequently propose a NOMA-based platoon transmission model designed specifically for C-V2X networks. This NOMA-based transmission approach is intended to enhance vehicle-to-vehicle communications within the platoon, with the objective of optimizing throughput. Our simulation outcomes affirm the effectiveness of our proposed model in improving throughput while reducing response latency. © 2023 IEEE.

关键词： 5G Cellular Vehicular-to-Everything (C-V2X) Internet-of-Things (IoT) Non-Orthogonal Multiple Access (NOMA) Platoon

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 IEEE.

关键词： Efficiency

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A Comprehensive Survey on Machine and Deep Learning for Optical Communications

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IEEE Access 2025年 13卷 88794-88846页

作者： Amirabadi, Mohammad Ali Nezamalhosseini, S. Alireza Kahaei, Mohammad Hossein Chen, Lawrence R. Ferdowsi University of Mashhad Department of Electrical Engineering Mashhad*** Iran Iran University of Science and Technology School of Electrical Engineering Tehran*** Iran McGill University Department of Electrical and Computer Engineering MontrealQCH3A 0G4 Canada

The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential soluti

关键词： Fiber to the x

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Exploring Dynamics and Hardware Implementation of an Enhanced 5D Hyperchaotic Memristive System Inspired by Sprott-C System

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Complex System Modeling and Simulation 2025年第1期5卷 34-45页

作者： Abdulmajeed Abdullah Mohammed Mokbel Fei Yu Yumba Musoya Gracia Bohong Tan Hairong Lin Herbert Ho-Ching Iu School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China School of Electronic Information Central South UniversityChangsha 410083China School of Electrical Electronic and Computer EngineeringUniversity of Western AustraliaPerth 6009Australia

This paper proposes a novel 5D hyperchaotic memristive system based on the Sprott-C system configuration,which greatly improves the complexity of the system to be used for secure communication and signal processing.A critical aspect of this research work is the introduction of a flux-controlled memristor that exhibits chaotic behavior and dynamic responses of the *** this respect,detailed mathematical modeling and numerical simulations about the stability of the system’s equilibria,bifurcations,and hyperchaotic dynamics were conducted and showed a very wide variety of behaviors of great potential in cryptographic applications and secure data ***,the flexibility and efficiency of the real-time operating environment were demonstrated,and the system was actually implemented on a field-programmable gate array(FPGA)hardware platform.A prototype that confirms the theoretical framework was presented,providing new insights for chaotic systems with practical ***,we conducted National Institute of Standards and technology(NIST)testing on the proposed 5D hyperchaotic memristive system,and the results showed that the system has good randomness.

关键词： Sprott-C system field-programmable gate array(FPGA)implementation hyperchaotic system memristor numerical simulation

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Using Markov Chains for Time Series Analysis of Wireless Sensor Networks 2

Using Markov Chains for Time Series Analysis of Wireless Sen...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Saraswat, Birendra Kumar Vikram, Devendra Kumar, Mukesh Dixit, Krishna Kant Sharma, Ram Kumar Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Abes Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

Wireless Sensor Networks (WSNs) have enabled new and revolutionary technological packages by using imparting self-reliant sensing, control, and verbal exchange capabilities. Time collection analysis is an important approach used to become aware of significant styles and correlations within WSNs. especially, Markov chains were used to investigate time series responses of WSNs. A Markov chain is a stochastic version which captures deterministic and stochastic behavior by way of representing the possibility of a destiny kingdom primarily based upon the contemporary state. Inside the context of WSNs, a Markov chain is used to version the reputation of a sensor node given the statuses of its neighboring nodes. Via the usage of Markov chain analysis, important information may be determined which may be used to optimize the performance of a WSN, together with knowledge energy intake quotes and figuring out correlations among nodes. As a result, making use of Markov chains in WSNs can provide higher predictive and analytical capabilities for improving a WSN's operations. © 2024 IEEE.

关键词： chains contemporary especially operations wireless

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Optimization of Cost-based Dynamic Economic Dispatch Problem using Mathematical Programming

Optimization of Cost-based Dynamic Economic Dispatch Problem...

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2024 International Conference on Innovations in science, engineering and technology, ICISET 2024

作者： Mahmood, Mou Chowdhury, Nahid-Ur-Rahman Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This paper introduces a robust and effective methodology for addressing the dynamic economic dispatch (DED) optimization problem, which is pivotal in power system operations and planning. DED is basically an enhanced version of static economic dispatch (SED) problem that involves scheduling a set of generation units over a typical operational period, often spanning 24 hours, to cover the predicted load demand with the on/off status of each generation unit predetermined. The literature distinguishes between two main types of DED: cost-based and price-based. This paper focuses on the cost-based approach, utilizing quadratic constrained programming (QCP) to model the problem effectively. QCP is particularly suited for this task as it optimally manages the ramp rate constraints and capacities of the available generation units maintaining the minimum operating cost. To demonstrate the effectiveness of the proposed methodology, simulations were conducted using two distinct scenarios involving 5-unit and 10-unit power dispatch strategies. The results, obtained through the general algebraic modeling system (GAMS), clearly presents the superiority of GAMS in achieving optimal solutions when compared to other existing metaheuristic techniques. © 2024 IEEE.

关键词： Quadratic programming

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