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IAENG International Journal of computer science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Blockchain-Based Layered Secure Edge Content Delivery in UAV-Assisted Vehicular Networks

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7914-7927页

作者： Xu, Qichao Jin, Jie Su, Zhou Li, Ruidong Wang, Yuntao Fang, Dongfeng Wu, Yuan Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China Kanazawa University Institute of Science and Engineering Kanazawa Japan California Polytechnic State University Department of Computer Science Obispo San Luis United States University of Macau Department of Computer and Information Science China

Recently, edge content delivery has been promoted for video applications in unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs). UAVs could proactively cache (video) contents and transmit them to nearby vehicular users, thereby significantly mitigating delivery latency. However, since UAVs are typically deployed by untrusted third parties, the contents may be illicitly accessed by some curious and even malicious UAVs, compromising users' privacy. Besides, due to the limited resources, UAVs may be unwilling to deliver contents without adequate compensations. To address these issues, in this paper, we propose a novel secure edge content delivery scheme in UVNs. Specifically, we first devise a blockchain-based layered secure edge content delivery framework. With the scalable video coding (SVC) that encodes each content into a base layer and multiple enhancement layers, only the enhancement layers of the content are delivered by UAVs, ensuring that untrusted UAVs without the base layer cannot recover the content to access explicit information. Meanwhile, a lightweight consortium blockchain is utilized to supervise the enhancement layer delivery services of UAVs in a distributed fashion. The delegated proof of stake (DPoS)-based practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to immutably and traceably record content delivery transactions between UAVs and vehicular users. Then, we formulate the content delivery incentive problem as a Stackelberg game, where UAVs act as game leaders to determine content delivery prices and vehicular users act as game followers to determine the number of required enhancement layers. Afterwards, through game analysis using the backward induction approach, the Stackelberg equilibrium is attained as the solution to the formulated problem, where the optimal strategies of both UAVs and vehicular users are derived by the Q-learning algorithm. Finally, extensive simulations are conducted to demonstrate that

关键词： Scalable video coding

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Optimized ROI Extraction and Pixel Reduction Methods for Kidney Abnormality Detection

Optimized ROI Extraction and Pixel Reduction Methods for Kid...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hossain, Md Junayed Monir, Md Fahad Ahmed, Tarem Department of Computer Science & Engineering Bangladesh Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331504847

A global public health concern, Chronic Kidney Disease causes renal failure, cardiovascular disease, and premature death. Given the frequency of kidney diseases and their significant impact on public health, detection of any abnormalities in the kidney is crucial. Effective patient treatment and medical care planning depend on the early and precise diagnosis of a variety of kidney abnormalities. Early detection enable treatments that enhance patient outcomes, slow the progression of disease, and lessen the strain on healthcare systems. In order to detect anomalies in kidney, we utilized the Region of Interest (ROI) and Pixel Reduction techniques separately in this work. We have applied the Bicubic Interpolation method to reduce the size of individual pixels in an image while preserving a significant data of the original image data. A novel hybrid strategy that aims to preserve diagnostic data while reducing computing complexity uses the original images for ROI extraction. The deep learning method is then independently trained and tested using the pixel-reduced and localized dataset. In order to categorize the images, we use Inception V3 and EfficientNet B7 as Deep Learning models. Using the ROI-extracted data, we were able to achieve approximately 99.75% accuracy with the EfficientNet B7 model. Moreover, the same algorithm and dataset were used to obtain 98.45%, 99.02%, and 98.78% Precision, Recall, and F1-score, respectively. This framework provides medical professionals with a helpful prognostic tool to early detect identify various forms of kidney abnormalities. © 2025 IEEE.

关键词： Surgery

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Massive Parallel Simulation for Tensor Network Contraction Ordering Solving 4

Massive Parallel Simulation for Tensor Network Contraction O...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Xu, Wei Tong, Weiqin Liu, Xiaoyang Shanghai University School of Computer Engineering and Science Shanghai China Rensselaer Polytechnic Institute Department of Computer Science New York United States

ISBN: (纸本)9798350394375

Tensor network contraction is a powerful mathematical tool for dealing with complex systems and high-dimensional data, which is widely used in classical simulations of molecules and quantum circuits. To improve the performance of simulations, existing methods are implemented by searching the optimal contraction orderings for tensor networks. In particular, some reinforcement learning (RL) methods show a greater potential for solving. However, with the increasing scale of simulation, the number of tensors in the corresponding tensor network is huge, the existing RL methods suffer from sampling bottlenecks and slow solving speed, which make it difficult to efficiently solve for high-quality contraction orderings. In this paper, we design a massive parallel simulation environment for tensor network contraction ordering solving on a single GPU. We experimented with multiple numbers of parallel environments on the tensor-train network, and achieved the highest speedup when the number of environments was 26. We also have experimented on large-scale tensor-train networks and tree tensor networks, and compared to Kahypar and RL-Baseline, our method achieves average speedup of 8.96× and 7.48× on tensor-train networks, and 10.03× and 7.48× on tree tensor networks. © 2024 IEEE.

关键词： Tensors

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Comparative Performance of Object Detection Algorithms Under Low Illumination Conditions

Comparative Performance of Object Detection Algorithms Under...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hossain, Md Junayed Rahman, Shoptorshi Monir, Md Fahad Ahmed, Tarem Department of Computer Science & Engineering Bangladesh Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331504847

Real-time object detection is pivotal in various computer vision and robotics applications, such as security surveillance, autonomous navigation, and interactive systems. This study evaluates several object detection algorithms under low illumination conditions, comparing traditional methods including SIFT, SURF, ORB, BRISK, KAZE, and AKAZE with the deep learning model YOLOv5. To simulate these conditions, we utilized the COCO dataset and implemented a sequence of preprocessing procedures, such as adjusting the gamma correction throughout the dataset, introducing Gaussian noise, and equalizing the histogram to improve image contrast. Here, YOLOv5 exhibited superior performance, achieving a detection accuracy of 92.47% and a processing time of 45 milliseconds per image. Traditional algorithms like SURF and KAZE, although slower, demonstrated precision rates of 78.54% and 76.86%, respectively. YOLOv5 also identified the highest number of key points and matches. Our findings indicate that while YOLOv5 excels in real-time detection under low-light conditions, traditional methods are effective for specific tasks in resource-constrained environments. This study benchmarks these algorithms and proposes a hybrid approach to enhance real-time object detection systems under low illumination conditions. © 2025 IEEE.

关键词： Deep learning

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Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems

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computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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Research on MapReduce Heuristic Multi Table Join Algorithm Based on Binary Optimization and Pancake Parallel Strategy

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Recent Patents on engineering 2023年第6期17卷 49-62页

作者： Jia, Songhao Yang, Jizheng Yang, Cai Zhang, Haiyu School of Computer Science and Technology Nanyang Normal University Nanyang China Faculty of Electronic Information Engineering Henan Polytechnic Institute Nanyang China

Background: With the development of technology, the data amount has increased signif-icantly. In data processing, the multi table query is the most frequent operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper pro-poses a heuristic multi table join optimization method. By sharing information, the unconnected tu-ples are eliminated so as to reduce the amount of data transmitting. This shortens response time and improves execution performance. Methods: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying. © 2023 Bentham science Publishers.

关键词： Optimization

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Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

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Digital Communications and Networks 2024年第1期10卷 38-44页

作者： Mianjie Li Senfeng Lai Jiao Wang Zhihong Tian Nadra Guizani Xiaojiang Du Chun Shan School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhou510665China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Schoolof Electrical Engineering and Computer Science Washington State UniversityPullmanWA99164USA Department of Computer and Information Science Temple UniversityPhiladelphiaPA19122USA

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication *** that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of *** multiwatermarking process employs spread transform dither *** the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading ***,multiple watermarks can be simultaneously embedded into the same position of a multimedia ***,the multiple watermarks can be extracted without affecting one another during the extraction *** analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

关键词： B5G Multimedia information security Spread transform dither modulation Spreading vector measurement Unit maximum capacity

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Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion 100

Enhancing Track Management Systems with Vehicle-To-Vehicle E...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Billington, Thomas Gwash, Ansh Kothari, Aadi Izquierdo, Lucas Talty, Timothy Virginia Polytechnic Institute and State University Department of Computer Science BlacksburgVA United States Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798331517786

In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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