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Verification of quantum networks using the GHZ paradox

Communications in Theoretical Physics 2024年第10期76卷 55-61页

作者： Huan Ye Xue Yang Ming-Xing Luo School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China

The Greenberger–Horne–Zeilinger(GHZ)paradox shows that it is possible to create a multipartite state involving three or more particles in which the measurement outcomes of the particles are correlated in a way that cannot be explained by classical *** extend it to witness quantum *** first extend the GHZ paradox to simultaneously verify the GHZ state and Einstein–Podolsky–Rosen states on triangle *** then extend the GHZ paradox to witness the entanglement of chain networks consisting of multiple GHZ *** the present results are robust against the noise.

关键词： quantum entanglement GHZ paradox quantum network

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A Support Data-Based Core-Set Selection Method for Signal Recognition

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China Communications 2024年第4期21卷 151-162页

作者： Yang Ying Zhu Lidong Cao Changjie National Key Laboratory of Wireless Communications(University of Electronic Science and Technology of China) Chengdu 611731China College of Computer Science and Cyber Security(Chengdu University of Technology) Chengdu 610059China College of Mathmatics and Physics(Chengdu University of Technology) Chengdu 610059China

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic ***,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high *** paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal ***,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data *** support data is crucial for model training and can be found using a border sample *** results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 *** SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

关键词： core-set selection deep learning model training signal recognition support data

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Energy Efficiency Maximization for Cooperative NOMA with Hardware Impairments

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China Communications 2024年第12期21卷 80-91页

作者： Wang Zhengqiang Chang Ruifei Wan Xiaoyu Fan Zifu Duo Bin School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China

The massive connectivity and limited energy pose significant challenges to deploy the enormous devices in energy-efficient and environmentally friendly in the Internet of Things(IoT).Motivated by these challenges,this paper investigates the energy efficiency(EE)maximization problem for downlink cooperative non-orthogonal multiple access(C-NOMA)systems with hardware impairments(HIs).The base station(BS)communicates with several users via a half-duplex(HD)amplified-and-forward(AF)***,we formulate the EE maximization problem of the system under HIs by jointly optimizing transmit power and power allocated coefficient(PAC)at BS,and transmit power at the *** original EE maximization problem is a non-convex problem,which is challenging to give the optimal solution ***,we use fractional programming to convert the EE maximization problem as a series of subtraction form ***,variable substitution and block coordinate descent(BCD)method are used to handle the ***,a resource allocation algorithm is proposed to maximize the EE of the ***,simulation results show that the proposed algorithm outperforms the downlink cooperative orthogonal multiple access(C-OMA)scheme.

关键词： block coordinate descent cooperative non-orthogonal multiple access energy efficiency hardware impairments resource allocation

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A Multi-Platform Compatible Remote Control based on Gesture Recognition Algorithm 3

A Multi-Platform Compatible Remote Control based on Gesture ...

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3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Chen, Yan Wang, Xingchen Yang, Jingyuan Wu, Zhipeng Chen, Yangyang Peng, Yunyue College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350380989

With the rapid advancement of information technology, one pressing concern lies in how to make electronic devices more user-friendly across diverse scenarios. This article proposes a novel gesture-based remote control system built on the Android platform, utilizing deep learning, the SSD algorithm, HOG features, and an SVM classifier. The system aims to foster the advancement of smart classrooms, medical command systems, smart homes, and other fields, while also enhancing accessibility for special groups by improving their interaction with mobile phones and other electronic devices. Through accurate gesture recognition and rapid response, it will provide users with a more convenient and efficient operating experience, thus promoting the wide application of artificial intelligence technology across various domains. © 2024 IEEE.

关键词： Android platform Deep Learning Gesture Recognition

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Employing YOLOv7-tiny for Identifying Road Vehicles in Drone-Based Aerial Photography 9

Employing YOLOv7-tiny for Identifying Road Vehicles in Drone...

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9th International Conference on Information science, computer technology and Transportation, ISCTT 2024

作者： Guo, Xia College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350388435

Unmanned Aerial Vehicles (UAVs) integrated with imaging technology offer aerial advantages and intelligent computer vision analysis capabilities, making significant contributions in various domains including geoinformatics, agriculture, environmental conservation, and infrastructure surveillance. This integration facilitates efficient and precise data assistance crucial for numerous sectors. However, challenges persist in balancing the scale and accuracy of target recognition, discrepancies in object recognition versus their actual sizes and issues like occlusions. To enhance the efficacy of UAV-based real-time vehicle monitoring, this study integrates an innovative Advanced Vehicle Detection Network (AVDN) into the fundamental network architecture of the YOLOv7-tiny model. This integration aims to enable swift, cost-effective, and accurate vehicle monitoring on roadways. Our findings indicate that the enhanced model achieves a mean Average Precision (mAP) of 56.14%, significantly surpassing the baseline YOLOv7-tiny model. The affordability and efficiency of this model underscore its practical application potential. ©2024 IEEE.

关键词： Aerial photography

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Detection of Weakly Labeled Remote Sensing Images Based on Adaptive Weighted Fusion

Detection of Weakly Labeled Remote Sensing Images Based on A...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Wang, Ziyu Gong, Haijun Li, Xue College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9781510680012

Object detection is an important computer vision task that deals with detecting instances of visual objects of a certain class in digital images. It is also the basis for many other computer vision tasks, such as instance segmentation, image caption, and object tracking. Object detection has now been widely used in many real-world applications, such as autonomous driving, robot vision, and remote sensing image detection. Especially With the development of remote sensing technology, the requirement of remote sensing image object detection technology is further improved. It is of great significance for the development of remote sensing image detection to recognize the target of remote sensing image quickly and accurately. However, Supervised training of traditional object detectors requires well-labeled large-scale datasets, and for the problem of low detection accuracy on remote sensing images with dense targets and poor labeling quality, a object detection method for weakly labeled remote sensing images based on adaptive weighted fusion algorithm is proposed. Object detection experiments are done on the RSOD data set and part of the DOTA data set under different degrees of localization annotation offset, and the experimental results show that the optimization method achieves a mAP value of 73.2%, which is 10.6 percentage points higher than the original algorithm, and it significantly improves the detection accuracy of inaccurately localized and annotated targets under the maximum degree of offset of the RSOD data set. © 2024 SPIE.

关键词： Object detection

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Research on Satellite Image Object Detection Based on Neural Network Algorithm 24

Research on Satellite Image Object Detection Based on Neural...

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Xiao, Jiefu College of Computer Science and Cyber Security Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798400707254

Currently, in the field of object detection and recognition, data-driven deep learning technologies have achieved groundbreaking progress, significantly outperforming traditional methods in detecting and recognizing near-field images. To address the deficiencies of the Faster R-CNN algorithm, which has a low detection rate for small and weak targets and a high false alarm rate, this paper proposes a SE (Squeeze and Excitation) Faster R-CNN. By integrating feature maps of different scales, it generates a multi-scale joint feature map, thereby enhancing the detection capability for small and weak targets. By constructing a Squeeze and Excitation module, we learn a serialized weight vector for each region of interest (RoI) corresponding to the extracted feature submap during the discrimination process. This vector is used to recalibrate the weights of each channel in the feature submap, suppressing redundant dimensions in the multi-scale joint feature submap and consequently enhancing the algorithm’s performance. Furthermore, by incorporating a sorting operation, we propose a SER (Squeeze and Excitation Rank) Faster R-CNN, which completely suppresses redundant feature dimensions in the multi-scale joint feature submap, further improving the overall detection performance. Experimental results based on the Sentinel-1 satellite dataset demonstrate that the improvements made to the algorithm can effectively enhance the performance of ship target detection in SAR images. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Improving Road Defect Detection Precision and Efficiency with Structural Pruning Techniques 21

Improving Road Defect Detection Precision and Efficiency wit...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Ouyang, Hang Li, Tailin Wang, Chongru Gu, Yuanting Yang, Fan Deng, Kaixin College of Computer Science and Cyber Security Chengdu University of Technology Sichuan Chengdu China College of Energy Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798331519254

Timely detection and treatment of road defects are essential to ensure the road safety, comfort, and the longevity. However, due to high usage frequency and complex, variable environmental conditions, road defects are inevitable. Therefore, establishing an efficient system for road defect detection is particularly critical. This paper investigates the current state of object detection technologies, analyzes various existing detection methods, and proposes a lightweight road defect detection model, based on YOLOv11n. In the experimental evaluation, the improved model was compared against multiple versions of YOLO lightweight models to assess its performance. By employing the DepGraph structural pruning method, the model size was reduced, leading to improved operational efficiency and reduced computational resource consumption while maintaining detection accuracy. Experimental results demonstrate that, compared to YOLOv11n, the improved model achieved a 2.2% increase in precision, a 2.8% increase in recall, a 2.3% improvement in mAP50, a 72.2% reduction in parameters, and a 50.8% decrease in computational load. This research aims to contribute to road safety and extend the lifespan of road infrastructure. © 2024 IEEE.

关键词： Lightweight model Performance evaluation Road defect detection Structural pruning YOLOv11n

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An Energy-Efficient UAVS Ad Hoc Network MAC Protocol 11

An Energy-Efficient UAVS Ad Hoc Network MAC Protocol

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Zhou, Yanhong Ning, Jin Ding, Zhiheng Duo, Bin Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China

ISBN: (纸本)9798350314014

Unmanned Aerial Vehicle Swarm (UAVS) Ad Hoc networks often face the challenge of short life cycle due to the limitation of on-board battery capacity. To extend the network's life cycle and reduce energy consumption, an energy-efficient media access control (EE-MAC) protocol is proposed. This protocol establishes three modes: sleep mode, energy-saving mode, and high-performance mode. It can dynamically switch between these modes based on real-time network conditions and the requirements of network services. Additionally, in the energy-saving mode, a very low power hybrid MAC protocol with varying transmission power for different time slots is utilized to ensure that the network can operate successfully at very low transmission power. Finally, an adaptive backoff algorithm that considers both data priority and residual energy is proposed. Simulation results demonstrate that the proposed EE-MAC protocol reduces the energy consumption, improves the network life cycle and network throughput compared to other three MAC protocols. © 2023 IEEE.

关键词： Energy utilization

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A Medical Image Fusion Method Based on Unique Features Guidance and Deep Convolutional Networks 6

A Medical Image Fusion Method Based on Unique Features Guida...

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6th International Conference on Electronics technology, ICET 2023

作者： Li, Zhongyin Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China

ISBN: (纸本)9798350337693

Multi-modal medical image fusion technique plays an increasingly important role in the field of image fusion. In this paper, we propose a novel medical image fusion model based on unique features guidance and deep convolutional networks. In our approaches, firstly, feature information from source images is fully extracted by the convolutional neural network. Secondly, under the unique feature guidance, the unique feature information of fused images is enhanced. At the same time, the joint gradient maps are utilized to preserve more texture details in the fused image when performing image reconstruction. Finally, the fusion image is outputted by the feature reconstruction module of the convolutional neural network. The experimental results demonstrate that the proposed method outperforms the other five classic methods in terms of both visual quality and objective assessment. © 2023 IEEE.

关键词： Convolutional neural networks

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