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Adaptively Weighted Balanced Feature Pyramid for Object Detection 6

Adaptively Weighted Balanced Feature Pyramid for Object Dete...

引用

6th ieee Information Technology, Networking, Electronic and automation Control conference, ITNEC 2023

作者： Jiao, Jian Qin, Hongwu Northwest Normal University College of Computer Science and Engineering Lanzhou China

ISBN: (纸本)9781665460033

Object detection is a common computer vision *** task of object detection is to find all instances of a given category in an image, classify and locate *** of the current models will densely sample the input image when detecting. Therefore, the quality requirements for feature maps are very high. In many object detection models, the performance is not good due to insufficient feature extraction. Especially for datasets with variable object *** at the problem of imbalanced information contained in different levels of the feature pyramid, we propose an adaptive balanced feature pyramid that fuses global *** pyramid will adaptively weight and fuse the features of each level, and then rebuild the feature pyramid so that each level has the same *** proposed adaptively weighted balanced feature pyramid achieves 36.8%in mAP on FCOS with ResNeXt-64x4d-101. © 2023 ieee.

关键词： Object detection

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Privacy-Preserving Data Aggregation in IoT Networks Using Homomorphic Encryption 2

Privacy-Preserving Data Aggregation in IoT Networks Using Ho...

引用

2nd ieee international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Shnain, Ammar H. Sruthi, P. Subburam, S. Agarwal, Rohit Kalyani, M. Naga Kavya Jeelani, Mohammad The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Department of AIML Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India CMR College of Engineering & Technology Department of CSE India SIMATS Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9798350368109

The work being undertaken in this study focuses on the privacy-preserving data aggregation in Internet of Things (IoT) networks via homomorphic encryption. Since data is being collected and transmitted by IoT devices, protection of data is essential particularly at aggregation stage. Most conventional aggregation procedures involve decryption of data which in turn leaves it vulnerable to hacking. Homomorphic encryption is presented as a solution to compute on the encrypted data, so that the details remain private and there is no necessity to decrypt them. Therefore, we discuss about the existing homomorphic encryption like Paillier, BGV, and CKKS to identify the potentiality of those schemes in IoT networks. All the schemes differ in terms of the rate of encryption and decryption, computational complexity, and accuracy. Our approach is to encrypt the IoT sensor data with these schemes, apply the aggregation operations on the encrypted data and see the decryption to confirm the precision. The fact is that according to the performance analysis, Paillier encryption can be considered as a secure enough approach as for the computational load it is lower compared to the BGV and CKKS methods. Even though BGV and CKKS increase the security levels of computing, they require higher levels of computation. Analysis of accuracy results indicate that HE schemes have a minimal level of errors in the aggregated data to attain privacy. © 2024 ieee.

关键词： Differential privacy

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A Novel Framework Combining VSL and Vehicle Platooning for Freeway Bottleneck

A Novel Framework Combining VSL and Vehicle Platooning for F...

引用

2024 ieee international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Tong Qi, Liang Luan, Wenjing Liu, Kun Guo, Xiwang Ann Talukder, Qurra Tul College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Freeway bottlenecks caused by traffic incidents contribute significantly to large-scale traffic congestion. Traditional strategies, including variable speed limit (VSL) and ramp metering, are commonly used for freeway traffic congestion management. Recently, vehicle platooning has become a promising way to alleviate traffic bottlenecks. This work proposes a novel framework that combines VSL and vehicle platooning for freeway bottleneck, referred to as VSL-VP, in mixed traffic of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). First, the upstream road of a bottleneck is divided into two segments, called the former and the latter. VSL limits vehicle speed at the former segment, thereby reducing inflow traffic to the latter one. Then, deep reinforcement learning is employed for CAV platooning at the latter segment, where low traffic flow density and large car-following distance create conditions for smooth lane change and platoon formulation of CAVs. Simulation results demonstrate that VSL-VP significantly enhances the bottleneck throughput and reduces traffic congestion at elevated levels of CAV penetration rates. © 2024 ieee.

关键词： Traffic congestion

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Enhanced QoS Optimization of Opportunistic Networks using Decision Tree

Enhanced QoS Optimization of Opportunistic Networks using De...

引用

2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mathur, Mansi Sawanni, Ritu Kumar, Anuj Jain, Ayushi Pal, Atul Kumar Manwal, Manika Department Of Information Technology New Delhi India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun India Department Of Computer Science And Engineering Lucknow Kannauj India Graphic Era Deemed To Be University Department Of Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Opportunistic networks are a type of dynamic wireless network that lack a fixed infrastructure and rely on intermittent connections between nodes to facilitate communication. Quality of Service (QoS) is an essential factor for the efficient operation of opportunistic networks, as it ensures that data is delivered in a timely and reliable manner. This paper proposes an enhanced QoS optimization approach for opportunistic networks using a Decision Tree algorithm. The algorithm considers various network parameters such as node mobility, data transmission rate, and available storage capacity to predict the optimal routing path for data transmission. The Decision Tree algorithm uses a classification technique to build a model based on historical data to identify the most suitable routing path for each data transmission. This approach allows for dynamic adaptation to changing network conditions, improving the overall performance and QoS of the opportunistic network. By incorporating the Decision Tree algorithm into the routing decisions of the opportunistic network, we can achieve better QoS optimization than traditional routing protocols. Our experimental results show that the proposed approach outperforms existing routing protocols regarding data delivery ratio, end-to-end delay, and energy consumption, making it a promising solution for enhancing QoS in opportunistic networks. © 2024 ieee.

关键词： Decision trees

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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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2024 ieee international conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ward, Blake Gonzalez, Daniel Alt, Jake Fang, Dongfeng Xu, Shengjie The Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis ObispoCA93407 United States The Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9798350350548

The increasing adoption of IoT devices in healthcare necessitates the development of lightweight and fast intrusion detection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime. © 2024 ieee.

关键词： Network intrusion

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indepth study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 ieee.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Underwater Polarization Imaging Method Based on Frequency Domain Processing Combined with Global Estimation 4

Underwater Polarization Imaging Method Based on Frequency Do...

引用

4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Zhaolst, Zhi Zhao, Haili Zhao, Pengfei School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

This paper proposes an underwater polarization imaging method that combines frequency domain processing with global estimation to improve scattered light suppression. By preprocessing orthogonal polarization images with histogram stretching and high-pass filtering, the method reduces low-frequency scattered light and accurately estimates the degree of polarization. A low-pass filter and optimal threshold factor in the frequency domain further refine this estimation. Enhanced Metric Evaluation (EME) is used to optimize the de-scattering process, leading to significant improvements in image contrast, sharpness, and clarity compared to traditional methods. Experimental results show that the contrast, sharpness, and EME of the restored images were improved by 80.68%, 30.89%, and 66.22% for one type of target, and by 34.32%, 34.48%, and 104.76% for another type, respectively, compared to Schechner's method. The data indicate that this method effectively suppresses scattered light in different water quality environments, significantly enhancing the contrast and clarity of the images, with a noticeable improvement over traditional methods. © 2024 ieee.

关键词： Frequency domain analysis

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Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leveraging AI and 6G for Enhanced Productivity 17

Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leve...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Patwary, Mohammad N. Khan, Samiya Nawaz, Syed Junaid University of Wolverhampton Faculty of Science and Engineering WolverhamptonWV1 1LY United Kingdom University of Greenwich School of Computing and Mathematical Sciences LondonSE10 9LS United Kingdom Comsats University Islamabad Department of Electrical & Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350351637

The exponential growth in computing devices and the emergence of high-computing-dependent applications have led to a significant increase in energy demand. In response, this paper proposes the development of a novel green computing testbed aimed at addressing energy efficiency and sustainability in computing infrastructure. The testbed integrates highly energy-efficient computing infrastructure, a virtual machine-driven on-demand computing ecosystem, and Green AI model development as a service. The state-of-the-art 5G/6G infrastructure and services, such as ultra-low-latency communications, are investigated and identified as suitable for connecting the zero clients (with minimal capabilities) to remote computing and other facilities. Through a comprehensive measurement campaign, the testbed's energy efficiency and sustainability have been evaluated. This research presents a pioneering effort towards achieving sustainability in computing to support Small and Medium-sized Enterprises (SMEs) to enhance productivity with AI, with reduced cost, energy, and minimizing e-waste. © 2024 ieee.

关键词： Green development

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Study of the Movements of Active Upper Limb Prostheses

Study of the Movements of Active Upper Limb Prostheses

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2025 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Padmasini, V. Palaniappan, Prathiba Shruthi, K. Neela Harish, M. Easwari Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798331529833

The absence or amputation of an arm presents a significant difficulty, greatly reducing an individual's autonomy and hindering daily tasks. Current prosthetic devices fail to address user needs, particularly due to insufficient tactile feedback, which limits their grasping capabilities. This paper conducts a thorough literature review on active upper limb prostheses, analyzing movement dynamics and user requirements. It emphasizes key shortcomings in existing prostheses regarding user satisfaction during daily activities and highlights factors contributing to the high rate of device abandonment. The overarching goal is to drive advancements in prosthetic development by improving user satisfaction and creating a prosthetic hand that more closely mimics natural human function. A collection of criteria for upper limb prosthetics is suggested, informed by movement analysis and tailored to user requirements. The objectives are to (i) offer recommendations to enhance prosthetic performance by considering both hand functionality and technical characteristics;(ii) introduce a prosthetic design that optimizes dexterity and mobility;and (iii) refine methods for evaluating user satisfaction with prosthetic devices, thereby offering a clearer understanding of user experience and device performance. These improvements could lead to prostheses that offer greater utility and user satisfaction. © 2025 ieee.

关键词： Artificial limbs

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Spatial-Temporal Network Model for Action Recognition Based on Keyframe Extraction 4

Spatial-Temporal Network Model for Action Recognition Based ...

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Zhao, Dongjie Zhao, Haili Yao, Kai Meng, Jiahe Liang, Meihui School of Electronic Information Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

This paper proposes a spatiotemporal network model TransTC3D for behavior recognition. The proposed SIC is used to sample video key frames, and then the key frames are sent to the TransTC3D network to complete behavior recognition. Based on the fine-grained feature extraction capability of the C3D model, the TransTC3D network adds a temporal attention mechanism (SVAM) and a channel attention mechanism (SCAM) to the seventh and twelfth layers respectively. The high-dimensional fine-grained feature information is extracted through the improved C3D network to obtain fine-grained spatial and temporal feature information, which is then sent to the Transformer for long-term sequence modeling to finally achieve behavior recognition. It has been verified that SIC sampling combined with the TransTC3D model has achieved an effect of 90.23% on the UCF101 dataset. © 2024 ieee.

关键词： Action Recognition component Keyframes Spatial-temporal model

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