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Dynamic Slot Allocation Protocol for Multi-Channel LoRa Communication 20

Dynamic Slot Allocation Protocol for Multi-Channel LoRa Comm...

20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Chen, Chanokchon Lion, Suchaisri Jansang, Aphirak Jaikaeo, Chaiporn Phonphoem, Anan Tangtrongpairoj, Withawat Kasetsart University Intelligent Wireless Network Group Department of Computer Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

LoRa is a promising solution for long-range transmission with low power consumption, which is suitable for small vessel tracking systems. However, the LoRa is not designed for a scalable tracking system resulting in the most deficient performance. This paper proposes a dynamic time slot allocation protocol based on multi-channel LoRa communication. The protocol combines the idea of CSMA/CA Time Division Multiplexing and Frequency Division Multiplexing. This protocol has an adaption channel rate that chooses the appropriate channel for each device and a dynamic max slot mechanism that adjusts the number of max slots for each round. The experimental results on NS3 show that this protocol has better network performance in terms of average time to send all packets compared to general Static and Dynamic TDMA. This paper designs for small vessel tracking system based on multi-channel LoRa communication. © 2023 IEEE.

关键词： Time division multiple access

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Weakly supervised temporal action localization with proxy metric modeling

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Frontiers of computer Science 2023年第2期17卷 63-72页

作者： Hongsheng XU Zihan CHEN Yu ZHANG Xin GENG Siya MI Zhihong YANG NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China School of Computer Science and Engineering and the Key Lab of Computer Network and Information Integration(Ministry of Education)Southeast UniversityNanjing 211189China School of Cyber Science and Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China

Temporal localization is crucial for action video *** the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but *** this paper,we propose a weakly-supervised temporal action localization approach in untrimmed *** settle this issue,we train the model based on the proxies of each action *** proxies are used to measure the distances between action segments and different original action *** use a proxy-based metric to cluster the same actions together and separate actions from *** with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.

关键词： temporal action localization weakly supervised videos proxy metric

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LTE network Resource Management for Live Video Streaming in Dense Area 6

LTE Network Resource Management for Live Video Streaming in ...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Moonphala, Borirak Jansang, Aphirak Tangtrongpairoj, Withawat Jaikaeo, Chaiporn Phonphoem, Anan Kasetsart University Faculty of Engineering Intelligent Wireless Network Group Department of Computer Engineering Bangkok Thailand

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

For live video streaming in areas with unexpected events, such as accidents or protests, the network traffic can dramatically increase beyond the network capacity. All the live streams in the same area become broken, or video quality becomes jittering. Therefore, this paper proposed the LTE radio resource management for live video streaming in dense area. The system will allocate the reserved resource block for priority UE using the concept of prioritization and the Chanel Quality Indicator (CQI) value. The simulation experiment investigated the appropriate reserved block size of priority UE considering the effect on the non priority UE. The results show that priority UE's throughput and inter packet arrival time meet the required criteria with less impact on the non priority UE. © 2022 IEEE.

关键词： Resource allocation

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Secure Rotation Invariant Face Detection System for Authentication

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computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic *** rotation is a variable component and makes face detection a complex and challenging task with varied angles and *** problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been *** objective of the paper is to implement a robust method for face detection taken at various *** to achieve better results than known algorithms for face *** RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid *** MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation *** this manner,RIFDS can detect human faces at different rotations and with different facial *** RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena *** results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations face detection frontal faces facial expressions emotions

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Probabilistic Analysis and Optimization of Packet Losses in Dense LoRa networks

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SN computer Science 2022年第1期3卷 25页

作者： Sa-Ingthong, Jirapon Phonphoem, Anan Jansang, Aphirak Jaikaeo, Chaiporn Intelligent Wireless Network Group Department of Computer Engineering Faculty of Engineering Kasetsart University 50 Ngamwongwan Rd. Ladyao Jatujak Bangkok Thailand

LoRa is a low-power wide area network technology capable of delivering data over very long distances. Due to its wider range, a LoRa network may be subject to a higher probability of packet collisions when deployed with a large number of end devices. To help LoRa network planners analyze the performance in terms of packet losses, and adjust communication parameters to improve reliability, a proper mathematical model is needed. In this paper, we develop a packet loss model that takes into account individual end devices’ packet arrival rates, transmission powers, and distances from a gateway. The derived model is applied to an optimization problem to find appropriate transmission powers in form of a linear program. Experiments show that packet loss probabilities predicted by the model mostly conform with the results from simulations and the model accurately predicts the aggregated interference, along with the expected loss rate, under moderate to heavy data traﬃc. In addition, the proposed optimization technique allows lowering the overall transmission powers while maintaining comparable signal-to-interference ratio and packet loss rate. © The Author(s).

关键词： Linear programming LoRa LPWAN Overlapped collision Packet loss model Reliability

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Decision Conflict Resolution Model for Equipment Manufacturing Enterprises Based on Human-machine Coordination

Decision Conflict Resolution Model for Equipment Manufacturi...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： An, Jian Zhu, Hongyi Wang, Feifei Wu, Siyuan Gui, Xiaolin Xi'an Jiaotong University Shaanxi Province Key Laboratory of Computer Network School of Computer Science and Technology Xi'an China Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Henan Jiaoyuan Engineering Technology Group Co. Ltd China

ISBN: (纸本)9798350312270

The equipment manufacturing industry plays a pivotal role in determining a country's overall competitive standing within the manufacturing sector. Nevertheless, conventional equipment manufacturing enterprises frequently grapple with challenges related to untimely information sharing and a lack of synergy among various departments during their operational processes. These issues often lead to analytical complexities and decision-making conflicts. Acknowledging the inherent subjectivity in interdepartmental decision-making, we introduce reinforcement learning as a framework to scrutinize and derive decisions that best align with the enterprise's requirements. Furthermore, owing to the volatile nature of both internal and external enterprise environments, relying solely on machine-generated decisions typically falls short of meeting the evolving developmental needs of the enterprise. To address this, we advocate for a human-machine collaborative approach to adapt effectively to environmental changes. Consequently, this paper proposes a decision conflict resolution framework tailored specifically for equipment manufacturing enterprises, grounded in the principles of human-machine coordination. Empirical validation through experiments conducted on an openly accessible dataset, sourced from the Allstate Purchase Prediction Challenge, underscores the superiority of our model when it comes to executing decision conflict resolution as compared to existing methodologies. © 2023 IEEE.

关键词： Decision making

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A Federated Learning Scheme Based on Reputation Evaluation and Blockchain 29

A Federated Learning Scheme Based on Reputation Evaluation a...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tang, Siyu An, Jian Sun, Xiangyan Hu, Xiancheng Wang, Feifei Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Shaanxi Province Key Laboratory of Computer Network Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350330717

Federated learning is an emerging distributed machine learning paradigm, where multiple data sources collaborate to train a model without sharing the original data, safeguarding user data privacy. Nevertheless, users, whether intentionally or unintentionally, may contribute low-quality data that can impact model quality or infer user information from the model parameters, potentially leading to privacy breaches. Selecting appropriate participants for training is still a challenge needing resolution. This paper introduces a federated learning scheme called FREB, which leverages a reputation evaluation mechanism and blockchain technology. The foundation of this scheme is blockchain, and it incorporates reputation as an indicator of participant reliability. Reputation is computed using a multi-weight subjective logic model. Before the training process begins, participants are chosen based on their reputation. Furthermore, the paper presents a novel model aggregation approach. By injecting noise into the uploaded model parameters, the scheme ensures that potential attackers cannot glean sensitive data information from the parameters. To validate the efficacy of FREB, the authors conducted simulation experiments using real datasets. The results of these experiments demonstrate that FREB is capable of thwarting malicious node attacks and promoting high-quality collaborative learning. It achieves this while preserving data privacy and maintaining model accuracy. © 2023 IEEE.

关键词： Blockchain Differential privacy Federated learning Reputation evaluation

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Reward-Based Spectrum Sensing and Allocation Mechanism Defending Against SSDF Attacks 14th

Reward-Based Spectrum Sensing and Allocation Mechanism Defe...

引用

14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Huang, Liubi Wang, Wei Lu, Xiaozhen Sun, Jinge Zhou, Bo Wang, Mingjie College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Academy for Network and Communications of China Electronics Technology Group Corporation Shijiazhuang050081 China

ISBN: (纸本)9783031862021

The acquisition of reliable spectrum data is a prerequisite for spectrum sharing. However, current spectrum sensing faces serious Spectrum Sensing Data Falsification (SSDF) attacks. To mitigate this challenging issue, we propose a reward-based joint spectrum sensing and channel allocation scheme, where secondary users are required to submit estimated revenue values along with their sensing results to the fusion center, the center then allocates channels based on the revenue results. The channel allocation problem is formulated as an optimization problem to maximize revenue, which is then solved using the Hungarian algorithm. Simulation results show that the obtained revenue of the secondary user with normal sensing is significantly higher than that of when launching SSDF attacks, demonstrating the effectiveness of the proposed scheme in mitigating SSDF attacks. Moreover, the proposed scheme outperforms existing spectrum sensing and channel allocation schemes when the number of trusted users is small. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Credible Spectrum Sensing Spectrum Access SSDF Attacks

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Dynamic Slot Allocation Protocol for Multi-Channel LoRa Communication

Dynamic Slot Allocation Protocol for Multi-Channel LoRa Comm...

引用

International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Chanokchon Chen Suchaisri Lion Aphirak Jansang Chaiporn Jaikaeo Anan Phonphoem Withawat Tangtrongpairoj Department of Computer Engineering Faculty of Engineering Intelligent Wireless Network Group Kasetsart University Bangkok Thailand

关键词：

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Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv

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arXiv 2025年

作者： Wang, Zhenglin Wu, Jialong Li, Pengfei Jiang, Yong Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Tongyi Lab Alibaba Group China

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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