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Research RSA Accumulator-Based Stateless Blockchain Optimisation for Smart Contracts

Research RSA Accumulator-Based Stateless Blockchain Optimisa...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Zhuang, Jinyu Fan, Hongbo Li, Xiang Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of computer technology application Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9781510666597

Scalability has grown more important as blockchain technology is adopted. This study introduces RSA-accumulator-based stateless blockchain performance optimization. Verifiable computing technology and distributed design compress node state data and prove smart contract validity, while a new smart contract execution logic and transaction verification scheme solve the problem of transaction verification and execution under stateless conditions, increasing system throughput. The technique reduces consensus node storage and improves throughput compared to Ethereum blockchains, according to experiments. This study examines the scheme's concept, implementation, and blockchain scalability potential. © 2023 SPIE.

关键词： Smart contract

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Dangerous Goods Detection in X-ray Security Inspection Images Based on Improved YOLOv7 5

Dangerous Goods Detection in X-ray Security Inspection Image...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Zhang, Enyan Yang, Yunhong Feng, Yanxiang Hu, Zeyu Wang, Aili Iwahori, Yuji Heilongjiang Province Key Laboratory Of Laser Spectroscopy Technology And Application Harbin University Of Science And Technology Harbin150080 China Department Of Computer Science Chubu University Aichi487- 8501 Japan

ISBN: (纸本)9798400709272

According to multi-scale targets and overlapping targets in X-ray security inspection images, this paper proposes a dangerous goods detection algorithm called as YOLOv7-MPCN(MaxPooling with CA and BiFPN). As the convolutional network layers overlap, the resolution of the feature map decreases, resulting in the loss of some position information. Firstly, the coordinate attention mechanism is combined with the MPConv structure in the downsampling stage to enrich semantic information while preserving position perception information, improving the ability to extract dangerous goods feature information in complex overlapping backgrounds. Secondly, using the BiFPN(Bidirectional Feature Pyramid Network) feature fusion module with smaller parameter values as the feature pyramid structure of the model effectively reduces the computational complexity of the model;Finally, referring to the residual network structure in RepVGG, adding skip connections in the lower branch of MPConv enables the network to reach deeper depths without the gradient vanishing. The average detection accuracy of YOLOv7-MPCN on the public dataset SIXray reached 92.5%, which is 0.9% higher than YOLOV7. The experimental results show that the proposed algorithm in this paper has an improvement in detection accuracy compared to the original YOLOv7, proving the effectiveness of YOLOv7-MPCN. © 2023 ACM.

关键词： Network layers

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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Deep Learning Assisted Enhance Vertical Field Confinement of Spot Size Converter

Deep Learning Assisted Enhance Vertical Field Confinement of...

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2023 Cross Strait Radio Science and Wireless technology Conference, CSRSWTC 2023

作者： Tan, Qinghao Huang, Jiandao Chen, Zan Hui School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin541004 China

We propose a spot size converter using pixelated nano-dots waveguide, to enhance the vertical field confinement. Theoretical analysis shows a converter efficiency of -2.1 dB for the TE mode at λ0=1.55 μm by using 30... 详细信息

ISBN: (纸本)9798350358971

关键词： deep learning Integrated optics spot size converter

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NOFTrack: Efficient Multi-Object Tracking with a Speed Advantage

NOFTrack: Efficient Multi-Object Tracking with a Speed Advan...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Jiang, Ming Ke, Jiehao Guo, Biao Metaverse Application Engineering Center Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In the field of multi-object tracking (MOT), accurately tracking the position and characteristics of targets is crucial. However, traditional methods often overlook detection results with low confidence, which can lead to the omission of targets and the fragmentation of trajectories. To overcome these challenges, we have introduced a novel NOF algorithm. This algorithm efficiently handles detection boxes through a tiered strategy, initially using an adaptive Kalman filter for preliminary matching, followed by a secondary screening of detection boxes with lower confidence, and a final stage for processing dubious detection results. This approach not only enhances the speed and accuracy of tracking but also performs exceptionally well when dealing with complex scenes. Moreover, it is capable of achieving high efficiency, with a rate of over 300 frames per second (FPS), ensuring the efficiency and precision of the tracking process. © 2024 IEEE.

关键词： Target tracking

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SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image

SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Jiatai Han, Guoqiang Zhou, Jingxuan Shi, Zhenwei Liu, Zaiyi Han, Chu South China University of Technology Guangzhou China Shenzhen China South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China

ISBN: (纸本)9798350386226

Semantic segmentation for histopathology images is one of the fundamental tasks in computational pathology. Due to the high cost of pixel-level annotation acquisition, the weakly supervised semantic segmentation (WSSS) attempts to achieve information-intensive segmentation task for histopathology images to reduce the labeling effort of pathologists by leveraging image-level labels. However, traditional WSSS requires a large-scale training set with image-level labels, which still imposes considerable labeling costs on pathologists. To this end, this work proposes a Small-Scale Weakly Supervised Semantic Segmentation (SS-WSSS) approach to achieve the comparable performance only with small-scale weakly-labeled data to further reduce pathologist's labeling effort. Since histopathology images can easily generate massive unlabeled data, our SS-WSSS aims to learn with the unlabeled data to bridge the information gap. First, we propose a Single-to-Multi Prototype Similarity (S2M-PS) method to generate reliable pseudo-labels for unlabeled data by measuring the similarity between single-label prototypes and multi-label feature maps. Then, we introduce a Cross-Task CoTraining (CT2) method for pseudo-supervision of models with pseudo-labels self-refinement to avoid overfitting to noisy labels. We conduct the experiment on two public datasets to demonstrate the effectiveness of our SS-WSSS. In the experiment, our method achieves comparable performance with SOTA methods only using 30% labeled data. © 2024 IEEE.

关键词： Semantic Segmentation

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Design of P Band Filter Antenna with High Out-of-Band Suppression

Design of P Band Filter Antenna with High Out-of-Band Suppre...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Yuan, Yinsu Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

P-band antennas offer tremendous potential in various fields, including remote sensing, communication, and radar applications. However, It is crucial to enhance their effectiveness in these domains that the current challenges in the P-band, particularly wide bandwidth and exceptional out-of-band suppression be achieved. This paper aims to introduce an efficient design scheme for a high-efficiency, high out-of-band suppression P-band filter antenna. The antenna structure comprises a Y-shaped feeding network, upper and lower parallel dielectric plates, a vertical dielectric plate, a central metal plate, and a pair of microstrip patches. The bottom feeding network employs a Y-shaped feeding network. The coupling of the antenna was effectively controlled and the good bandwidth and impedance matching have been achieved. In order to achieve a wider bandwidth and lower return loss for the antenna, slotting technology was employed on the semi-circular radiation patch. The introduction of a vertical metal plate enhances filtering performance of antenna. Additionally, there are radiation zeros at 0.68 GHz and 1.43 GHz, high-frequency selectivity is imparted and signal reflection losses are effectively reduced. key performance indicators include a filter antenna center frequency of 0.75 GHz, a -10 dB impedance bandwidth of 32.9% (0.89 GHz-1.24 GHz). In the upper stopband (0.5 GHz-0.8 GHz), the suppression level exceeds 20 dB, while in the lower stopband (1.1 GHz-1.8 GHz), suppression level exceeds 15 dB. Significant suppression characteristics are observed in the 0.5 GHz-0.8 GHz and 1.1 GHz-1.8 GHz frequency ranges. Design elements such as the semi-circular radiating patch, slotting technology, and rectangular slots on the substrate have been employed in the antenna. The suppression level, frequency selectivity had been enhance of antenna. Simulation results indicate a gain of 7.06 dB, overall efficiency beyond 95%, co-pol superior to cross-pol by more than 35 dB, and antenna

关键词： Slotting

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Intelligent Food Identification System Using Improved YOLOv8 13

Intelligent Food Identification System Using Improved YOLOv8

引用

13th International Symposium on computer Science and Educational technology, ISCSET 2024

作者： Nitiavintokana, Francia Rouf, Md Abdur Yu, Xiaoyu Wu, Haibin Wang, Aili Iwahori, Yuji Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin China College of Electron and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Chubu University Department of Computer Science Aichi Japan

ISBN: (纸本)9798350390803

The field of computer vision has witnessed significant advances in object detection with real-world applications ranging from autonomous driving to surveillance systems. Due to its importance in a number of areas, such as dietary evaluation, food industry automation, and health monitoring, food identification has attracted a lot of interest in this context. The suggested approach makes use of the capabilities of two cutting-edge object recognition models, YOLOv8. Because of its speed and accuracy, YOLOv8 is a great option for real-Time food detection in a variety of settings. Our most recent YOLOv8 model is called BiTrains-YOLOv8. Supervision techniques are applied to improve the combined model's performance even more. This increases overall detection accuracy and helps the algorithm to more effectively generalize to novel food items. Based on the detection results, 96.7% of an average of detection accuracy, 96.4% for precision, 95.7% for recall, and 96.04 % for F1-Score were obtained. The results show that the combination of YOLOv8 and BiTrains-YOLOv8 improved model techniques, significantly improved the performance of food detection, outperforming the previous methods in terms of accuracy, speed and robustness. Furthermore, the paper analyzes potential real-world applications of the system, such as calorie counting, dietary assessment and smart kitchen automation. Implications of advanced intelligent food detection in healthcare, the culinary industry and personalized nutrition are also discussed. In conclusion, the results provide valuable insights for pushing the boundaries of food recognition technology, with promising applications in various fields that promote healthy lifestyles and improve efficiency in food-related processes. © 2024 IEEE.

关键词： Food industry

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application of AmsGradP optimizer in fault diagnosis of bearing with few samples 6

Application of AmsGradP optimizer in fault diagnosis of bear...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Fang, Jinfa Liang, Qiong Dong, Xiangjun Wang, Qi Xiong, Jianbin Guangdong Polytechnic Normal University School of Automation Guangzhou China The Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources China Guangdong Polytechnic Normal University School of Computer Guangzhou China

ISBN: (纸本)9798350386974

Bearing fault diagnosis methods based on deep learning usually require a large number of training samples. However, obtaining large amounts of faulty bearing data can be challenging. How to diagnose faults with limited or few samples becomes a research problem. In order to solve the problem of bearing fault diagnosis with few samples, a rolling bearing fault diagnosis method based on improved Adam (Adaptive Moment Estimation) is proposed. First, it combines the advantages of both Adam and AmsGrad optimizers. Second, similar to AdamP, AmsGradP (Adaptive Gradient Optimizer with Penalty Parameters) introduces the penalty term of the parameter norm to mitigate the problem of premature decay of the learning rate. Finally, by penalizing the norm of the parameter during optimization, AmsGradP can prevent excessive parameter growth, thus further stabilizing the learning rate. The experimental results show that when only 1.58% of the data is used as the training dataset, the average accuracy of 98.26% can be achieved by using the AmsGradP optimizer, which is 6.68% higher than that by using the Adam optimizer. © 2024 IEEE.

关键词： Roller bearings

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A Multi-objective Antenna Optimization Method Based on Preferred Parameter Spaces

A Multi-objective Antenna Optimization Method Based on Prefe...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Dou, Jiangling Lin, Siyu Song, Jian Wang, Qingwang Shen, Tao Kunming University of Science and Technology School of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Graduate School Kunming650500 China

ISBN: (纸本)9798350375909

Machine learning has been successfully used in the field of multi-objective antenna design, enabling the enhancement of antenna performance and optimizing efficiency. However, the prediction within an exceptionally vast parameter space imposes significant demands on machine learning algorithms. Moreover, minor adjustments in dimensions can bring about significant alterations in antenna performance, which renders simultaneous optimization for all objectives exceedingly challenging. To solve the multi-objective antenna optimization problem, a multi-objective antenna optimization method based on a preferred parameter space is proposed. Specifically, this method utilizes the KNN algorithm to preliminarily optimize the antenna and acquire a high-quality dataset. Subsequently, the sensitivity of ten selected parameters based on the electromagnetic (EM) simulation results is calculated by the XGBoost algorithm. The value of the parameter sensitivity is acquired by averaging ten calculations. The top four ranked parameters are designated as variables while the rest are fixed as constants. This approach reduces the number of parameters for subsequent optimization, thus greatly improving the subsequent prediction accuracy while ensuring the antenna performance. Finally, the variables derived from XGBoost are input into the fruit-fly optimization algorithm (FOA) to find the near-optimal solution. Hence, the task of parameter optimization under the multi-objectives of the impedance bandwidth and circular polarization characteristics can be effectively accomplished. To further validate its performance, the proposed algorithm is applied to optimize the S-band miniaturized broadband circularly polarized antenna. Compared to the results obtained by electromagnetic simulation only, the optimized results exhibit a 17.95% improvement of impedance bandwidth and a 42.86% enhancement of axial ratio bandwidth. Numerical outcomes validate that the proposed method achieves multi-objective o

关键词： Circular polarization

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