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IET Blockchain 2024年第S1期4卷 570-585页

作者： Lin, Licheng Pan, Senshan Jing, Pujie Song, Xiangmei School of Computer Science and Telecommunication Engineering Jiangsu University Jiangsu Zhenjiang China

Logistics supply chain (LSC), a chain structure that integrates and coordinates all logistics transactions, has become an essential component of the modern logistics industry. By using blockchain, trusted logistics services enable participants to effectively record and track transactions during the logistics process. Current blockchain-based LSC features distributed structure and data privacy requirements, hindering the supervision of logistics transactions. Leveraging emerging dual-blockchain architecture to separate logistics transactions from supervision is a promising direction. However, the dual-blockchain collaboration restricts supervision due to its cross-chain privacy and efficiency. To address these issues, a logistics supply chain supervision scheme based on dual-blockchain collaboration (DBC) is proposed. First, an independent supervision blockchain is constructed to balance the contradiction between distributed structure and supervision requirements. Second, two mechanisms are designed to enhance the privacy and performance of collaborative supervision. The hybrid access control mechanism enables fine-grained supervision for different participants, and the aggregated transaction verification method supports efficient collaboration for logistics transactions. Security analysis and performance evaluation demonstrate the feasibility of DBC in enhancing the security and supervision of logistics data on the dual-blockchain architecture. Experimental results show that the cross-chain supervision overhead of DBC is reduced to (Formula presented.) of the baseline schemes. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Cryptography

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SPRA: Scalable policy-based regulatory architecture for blockchain transactions

IET Blockchain

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IET Blockchain 2023年第4期3卷 265-282页

作者： Yu, Chuntang Zhan, Yongzhao Jing, Pujie Song, Xiangmei School of Computer Science and Telecommunication Engineering Jiangsu University Jiangsu Zhenjiang China

Implementing effective regulation of blockchain transactions has become a research hotspot in recent years. However, most of the current regulatory schemes are customized for specific blockchain applications and lack versatility and scalability. Meanwhile, these schemes cannot guarantee fairness due to the non-disclosure of regulatory policies and regulatory processes. To address these issues, a scalable policy-based regulatory architecture (SPRA) is proposed for blockchain transactions that separates regulation and application to provide sufficient scalability. SPRA is a four-layer model (permission layer, regulation layer, bridge layer, and business layer). A regulatory policy description language (XRPL) is designed to define the regulatory rules and specifications for interoperability between the layers. A decentralized jury mechanism (JuryBC) based on the Shamir threshold secret sharing algorithm and Pedersen commitment is proposed at the regulation layer to avoid regulatory arbitrariness and unfairness. We also construct a secure and efficient regulatory data sharing scheme (RDShare) at the business layer using an attribute-based encryption algorithm. The key parameters in both JuryBC and RDShare can be specified in the regulatory policy to suit different application scenarios. Finally, the security of the architecture is analyzed and the feasibility and scalability of the architecture through simulation experiments are demonstrated. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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PERFORMANCE ENHANCEMENT OF CIRCULARLY POLARIZED MICROSTRIP ANTENNA USING SINGLE-LAYER FOAM SUBSTRATE FOR 5.8 GHZ ISM BAND APPLICATIONS

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telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 43-65页

作者： Hake, Shahadev Jaiswal, Rupesh Moon, Sunil Nikam, Pritam Kumar, Jayendra Department of Electronics and Telecommunication Engineering SCTR’s Pune Institute of Computer Technology Pune India Department of Electronics and Telecommunication Engineering KIT’s College of Engineering Kolhapur India School of Electronics Engineering VIT-AP University Andhra Pradesh India

Since last decade, a microstrip patch antenna has played a very important role in industrial, scientific, and medical (ISM) band applications, but single-layer antennae suffer from low gain and low radiation efficiency. For an ISM band, there is always a trade-off between these parameters, so maintaining it on a single-layer microstrip antenna is a big challenge. A circularly polarized (CP) microstrip antenna using a single-layer foam substrate has been designed for 5.8 GHz ISM band which offers high gain, improved radiation efficiency, and a lightweight design. It is constructed as a low-profile, single coaxial feed microstrip antenna on a foam substrate, achieving circular polarization by integrating two slots into the radiating patch antenna has dimensions of 0.98 λ0 × 0.98 λ0 × 0.096 λ0, impedance bandwidth of 600 MHz (5.56–6.16) GHz, which underscores its adaptability across a wide range of frequencies within the specified band. Furthermore, it elaborates the axial ratio bandwidth which extends over 130 MHz (5.77–5.90) GHz, showing the antenna’s ability to maintain circular polarization characteristics over a significant frequency range with a gain of 8.87 dB. The experimental and simulation results confirmed a remarkable radiation efficiency of 95.62%, showing its superior effectiveness compared to any existing single-layer microstrip antenna. © 2025 by Begell House, Inc.

关键词： Slot antennas

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Robust Stereo Matching Using Noise Feature Extraction Network with Dynamic Convolution 7

Robust Stereo Matching Using Noise Feature Extraction Networ...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Yuan, Kang Xu, Zhenxing Akoudad, Youssef Gao, Wei School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang212013 China

ISBN: (纸本)9798350350890

Stereo matching in noisy scenes presents significant challenges, as interference noise blurs the image, leading to ambiguities in matching. Most existing methods attempt to denoise the images prior to performing stereo matching. However, these approaches are not ideal for stereo matching, as denoising algorithms may not only eliminate noise but also valuable texture information, consequently causing matching errors. Furthermore, previous methods struggle to utilize depth cues from the noise effectively, leading to inefficient cost aggregation during stereo matching. To address these issues, we introduce a noise feature extraction network with dynamic convolution (NFENDC) that can extract depth cues from noisy images and improve stereo matching using these cues. In addition, we devise a noise volume representation to systematically capture these depth cues from the noise and fuse it with the cost volume for improved cost aggregation, rectifying the ambiguous matching caused by noise. Experimental results on KITTI benchmarks substantiate that our proposed method significantly enhances the matching performance of state-of-the-art algorithms in noisy scenes. © 2024 IEEE.

关键词： Stereo image processing

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Automatic numeration and localization of I-125 seeds in the post-implant prostate images based on CT and radiography image fusion

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Research on Biomedical engineering 2023年第2期39卷 415-425页

作者： Gholamian, Marsa Yazdi, Mehran Faghihi, Reza School of Mechanical Engineering Department of Nuclear Engineering Shiraz University Shiraz Iran School of Electrical and Computer Engineering Department of Electronics and Telecommunication Engineering Shiraz University Shiraz Iran

Purpose: Permanent prostate seed implant brachytherapy is widely adopted as a successful treatment method for prostate cancer. While radiographic planar fluoroscopy images are a well-established method to determine the location of seeds implanted in the prostate, many have investigated computed tomography (CT) to determine it. In this study, our purpose is to develop an automatic algorithm to accurately identify the seed positions, direction, and at the same time, to estimate the number of I-125 seeds implanted during the treatment procedure, as implanted seeds location is helpful information in intraoperative dosimetry in low-dose-rate (LDR) permanent prostate brachytherapy. Methods: A prostate phantom with a total of 40 I-125 implanted dummy seeds and 22 patients with a total of 1091 implanted seeds were scanned using CT imaging device with 2.5 mm slice thickness and radiography imaging. Phase congruency (PC) was performed to enhance edge detection and seed visualization. Seed detection was performed using the histogram analysis of the processed image, and the calculation of the probability of the number of separate and overlapped seeds was performed using registration process between CT and Fluoroscopy images. Results: This algorithm could successfully recognize 98.28% of the seeds without losing any image information, and therefore could correctly estimate the number of seeds, which is an essential step to maintain the high-quality treatment of prostate brachytherapy. Conclusion: While the simulation results demonstrate the capability of the proposed algorithm, it is sensitive to the imaging process due to its dependency upon the CT and radiography image fusion procedure. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering.

关键词： Diseases

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Distributed Energy Bank Optimization Towards Outage Aware Sustainable Cellular Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 503-514页

作者： Balakrishnan, Ashutosh De, Swades Wang, Li-Chun Delhi Department of Electrical Engineering New Delhi India IIT Delhi Department of Electrical Engineering Bharti School of Telecommunication New Delhi India Department of Electrical and Computer Engineering Taiwan

Grid connected and solar powered base stations (BSs) acting as distributed energy sources are increasingly becoming a popular solution to mobile operators. These networks experience double stochasticity due to the space-time variations in energy harvest and BS traffic. Hence, accurate and efficient green energy outage estimation in such networks is a challenging task. In this work, we propose a complutationally efficient cooperative energy transfer based distributed energy bank strategy to alleviate green energy outage and design energy sustainable networks. We first develop low-complexity Markovian frameworks to estimate green energy outage in a standalone BS without energy cooperation (WEC) and a multi-BS energy-cooperative (EC) setting, respectively. For the WEC system, we present a computationally efficient three-state discrete time Markovian statistical model, while the multi-BS EC framework is characterized by a two-state Markov model. The energy outage is studied as a function of capital expenditure (CAPEX), manifesting engineering insights from a service provider's perspective. Subsequently for the EC framework, we formulate a CAPEX optimization problem by jointly optimizing the BS cluster size and solar provisioning on individual BSs. Our results demonstrate that the proposed EC framework alleviates the green energy outage significantly, providing computational efficiency gains and CAPEX savings over the state-of-art approaches. © 2016 IEEE.

关键词： Batteries Green Energy Computational Modeling Power Demand Green Products Computational Efficiency Sustainable Development Green Computing Estimation Energy Exchange Cooperative Energy Transfer Computation Efficiency Energy Sustainability Solar Powered Base Station Traffic Energy Imbalance Network Of Cells Energy Source Optimization Problem Computational Efficiency Renewable Energy Energy Transfer Base Station Energy Harvesting Solar Power Capital Expenditure Three State Model Energy Cooperation Skewness Intermediate State Transition Probabilities Power Grid Average Energy Energy Deficit Probabilistic Analysis Sustainability Performance Net Energy Outage Probability Battery Level Photovoltaic Panels Consecutive Hours Discrete Time Markov Chain Solar Harvesting Outage Performance Depth Of Discharge Upcoming Sections

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Disaster Prediction Using Appropriate Machine Learning Techniques 7

Disaster Prediction Using Appropriate Machine Learning Techn...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Bangar, Aishwarya Ansari, Shubhan Ali, Sumer Shahed Baderao, Visharad Alhat, Bhagyashree Mit Academy of Engineering Alandi School of Electronics and Telecommunication Pune India Mit Academy of Engineering Alandi School of Computer Engineering Pune India

ISBN: (纸本)9798331527822

This paper provides the insights of disaster forecasting methods, focusing on the strengths, limitations, and applications of other models and ideas to predicting natural disasters. Disasters and pandemics are most unlikely events which concerns many nations. Till date multiple ways are used to detect or predict natural disasters. Various machine learning techniques are used in detecting, preventing or mitigating disasters. We focus on predicting natural disasters beforehand to reduce its effects using XGBoost algorithm. The study shows the challenges of accurate forecasting and early warning *** paper also highlights about user friendly and interactive integrated website for disaster prediction. The paper gives the overview of the successful development and implementation of various models to predict earthquake, tsunami, flood and landslide. © 2024 IEEE.

关键词： Prediction models

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Congestion Control Technique for VANETs Using Seagull Optimization Algorithm 15

Congestion Control Technique for VANETs Using Seagull Optimi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Behura, Aradhana Nayak, Sankalp Patra, Radhashyam University School of Computer Engineering Odisha Bhubaneswar India Vssut Department of Electronics and Telecommunication Engineering Odisha Burla India

ISBN: (纸本)9798350370249

Vehicular Ad Hoc Networks (VANETs) face significant challenges including packet transmission delays, high power consumption, and complex existing algorithms. These issues are exacerbated by the limited battery life of sensor nodes, which leads to energy wastage due to packet collisions and the need for retransmissions. Additionally, node failures occur when nodes with limited energy resources are used to transmit large packets. To address these challenges, this research introduces a multi-fitness function designed to monitor the arrival pattern of information from each vehicular sensor to the cluster head(CH) based master node, thereby optimizing data flow. The Seagull optimization technique (SOT) is utilized to manage congestion within the vehicular sensor network. Key parameters such as throughput, packet loss, queue size, message sending rate, delay, and congestion level are incorporated into the fitness function to enhance network performance. The proposed algorithm aims to minimize energy consumption and prevent node failures by efficiently managing the flow of data. By monitoring these critical parameters, the algorithm dynamically adjusts to ensure optimal performance and longevity of the network. The effectiveness of the designed algorithm is calculated using MATLAB 2022 and match up with existing algorithms including Adaptive Cuckoo Search (ACS), Particle Swarm algorithm - Gravitational Search optimization approach (PSOGSA), and Cuckoo Search (CS) optimized technique. In conclusion, the multi-fitness function combined with the Seagull Optimization Algorithm offers a robust solution to the challenges faced by VANETs, providing a more efficient and sustainable network operation. This research highlights the potential of advanced optimization techniques in enhancing the performance and reliability of vehicular networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

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Journal of Electronic Science and Technology 2024年第3期22卷 57-68页

作者： Yan Li Tai-Kang Tian Meng-Yu Zhuang Yu-Ting Sun School of Economics and Management University of Electronic Science and Technology of ChinaChengdu611731China School of Economics and Management Beijing University of Posts and TelecommunicationBeijing100876China School of Electrical Engineering and Computer Science The University of QueenslandBrisbane4072Australia

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various ***,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still *** address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification *** the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing *** on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student *** approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student *** conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

关键词： De-biasing Deep learning Knowledge distillation Model compression

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