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Research on Key Generation Performance of Wireless Channel Based on LTE-V2X

Research on Key Generation Performance of Wireless Channel B...

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Wenhao Yin Linning Peng Hua Fu Aiqun Hu School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China

Due to the openness of wireless networks, the direct C-V2X communication faces many security issues. Wireless key generation technology can extract channel features to generate a consistent key, enhancing communication security. This paper designs a key generation system for vehicle communication based on the LTE-V2X protocol and the Universal Software Radio Peripheral (USRP) software radio platform. We observe the relationship between key generation efficiency and the transmission interval in the mobile state. The transmission interval required to ensure a low Key Disagreement Rate (KDR) is verified in practical environments. Experimental results show that with the increase of the transmission delay, the channel state information (CSI) correlation gradually decreases, and the KDR gradually increases. When the transmission delay is within 0.4ms, the CSI correlation is about 0.93 and the KDR is about 0.14. When the transmission delay exceeds 1.1ms, CSI decreases correlation to about 0.75 and the KDR increases to about 0.23.

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

引用

ieee visual communications and image processing (VCIP)

作者： Shiyu Huang Ziyuan Luo Jiahua Xu Wei Zhou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Measurement Codecs Databases visual communication image processing Buildings Bit rate

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ESP32-based Multi-User Secret Key Generation System

ESP32-based Multi-User Secret Key Generation System

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Dongyang Li Linning Peng Hua Fu Fengsheng Wang Ye Tian Shen He School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Department of Security Technology Research Institute of China Mobile Communications Corporation Beijing China

This paper proposes a multi-user secret key generation system using off-the-shelf ESP32 modules. We first propose a new differential quantization algorithm and contrast the features of differential quantization with uniform quantization and existing equal-probability quantization. Then, we propose a differential compensation algorithm, which can effectively improve the randomness of differential quantization. The improved randomness is higher than that of equal-probability quantization, and the key disagreement rate (KDR) is lower than that of equal-probability quantization. After that, we create an ESP32-based multi-user secret key generation system. We compare the differences of KDR in three movement modes, stable, people-move and trolly-move, and in two scenarios, line-of-sight (LOS) and non-line-of-sight (NLOS). The results show that the KDR with third-order quantization is less than 0.1 for the LOS scene and less than 0.15 for the NLOS scene.

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Research on Retinal Screening and Recognition Algorithm Based on Deep Convolutional Network

Research on Retinal Screening and Recognition Algorithm Base...

引用

ieee International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Lin Wei Department of Rehabilitation and Healthcare Fujian Vocational College of BioEngineering Fuzhou Fujian Province China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

The rising incidence and diversity of retinal diseases pose a significant threat to visual health. Traditional screening methods, which involve manual analysis of fundus images by professional doctors, are not only time-consuming but also prone to errors. To address this, our study introduces an optimized deep convolutional network architecture tailored for retinal images. This architecture minimizes computing resource consumption while maintaining high precision, thereby enhancing screening efficiency. We propose an algorithm based on a deep convolutional integrated network for screening and recognizing retinal diseases, which automatically learns and extracts features from numerous fundus images to ensure accurate and efficient identification of various retinal conditions. To bolster the model's generalization capabilities, data enhancement techniques were employed, enabling it to maintain robust recognition performance across different image qualities and angles. Additionally, practical considerations such as processing speed and user-friendliness were factored into the design for clinical applications. To further refine recognition accuracy, a model integration strategy was adopted, combining predictions from multiple models to derive the final diagnosis. Experimental results show that our algorithm has high accuracy in screening tasks and high in recognition tasks, proving its high accuracy and strong practical value. This method can serve as an effective diagnostic tool for doctors managing retinal diseases.

关键词： Accuracy image recognition Data enhancement Medical services Predictive models Retina Feature extraction Data models Convolutional neural networks Diseases

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DenseNet201 based Feature Extraction for Remote Sensing image Classification in Land Cover Monitoring

DenseNet201 based Feature Extraction for Remote Sensing Imag...

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Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Srinivas Aluvala Ali Ashoor Issa K. Saranya Zainab abed Almoussawi Aboothar Mahmood Shakir School of Computer Science & Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq Department of computer science and engineering Bannari Amman institute of technology Sathyamangalam India College of Islamic Science Ahl Al Bayt University karbala Iraq The Islamic university Najaf Iraq

The expedition of the classification of the remote sensing images based on their freights have a wide range of applications in the number of fields. The manual yield interpretation for the land cover monitoring is the challenging task, as well as it has consumed much time to classify the Land Cover (LC) image. To address these challenges, this research proposed a pre-trained DenseNet201 architecture of the Deep Learning (DL) technique-based feature extraction for the land cover monitoring. This research utilized the UCM dataset which contains 2100 aerial images clipped from aerial ortho imagery is used for evaluated the performance of the proposed DenseNet201 framework. In pre-processing stage, the noise removal technique is used for removing the unwanted noise in the collected dataset. After pre-processing, the DenseNet201is used for the feature extraction and Convolutional Neural Network (CNN) is used for the classification of the LC aerial images. The proposed DenseNet201 framework achieves better results and attains the values of accuracy of 93.67%, precision of 96.28%, recall of 94.32% as well as F1-score of 94.88% when compared to the existing methods like Prototype based Memory Network (PM-Net), Pass Over (POEP) and Recurrent Neural Network-Random Forest (RNN-RF).

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Machine Vision Aided Adaptive Beamforming Decision for IRS-Assisted Wireless Networks

Machine Vision Aided Adaptive Beamforming Decision for IRS-A...

引用

European conference on Networks and communications (EuCNC)

作者： M. Munawar M. Guenach I. Moerman IMEC Leuven IDLab Ghent University

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

This study leverages machine vision to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless networks. Instead of depending on traditional schemes such as alternating optimization or semidefinite relaxation to maximize signal strength in an IRS-assisted network, which are computationally expensive and impractical, we use visual data to make low-complexity beamforming decisions for users. Our approach involves employing a ceiling camera with a fish-eye view, covering a wide communication area. The user within the network is initially detected using the YOLOv2 object detection method. Subsequently, we propose closed-form analytical expressions to determine the distances between the access point (AP), user, and IRS. Accounting for the non-uniform nature of the fish-eye image, we introduce a novel method to determine non-uniform pixel weightages using trigonometric techniques. Based on the calculated distances, we make beamforming decisions depending on the user’s proximity to the AP or IRS. The proposed method significantly reduces computational complexity, making it nearly independent of the number of reflecting elements at the IRS. Simulation results indicate that the proposed approach exhibits extremely lower computational costs compared not only to conventional schemes such as alternating optimization and semidefinite relaxation-based convex solvers but also to low-complexity heuristic schemes.

关键词： Degradation visualization Array signal processing Wireless networks Machine vision Simulation Object detection

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Coarse-fine Surrogate Model Driven Preference-based Multi-objective Evolutionary Fuzzy Clustering Algorithm for Color image Segmentation 3

Coarse-fine Surrogate Model Driven Preference-based Multi-ob...

引用

3rd International conference on Natural Language processing, ICNLP 2021

作者： Zhao, Feng Liu, Feifan Xi'An University of Posts and Telecommunications School of Communications and Information Engineering School of Artificial Intelligence Xi'an China

ISBN: (纸本)9781665414111

When applied to image segmentation, most existing multi-objective evolutionary clustering algorithms usually consider the information at the pixel level. Sometimes ignoring the region information of the image may lead to unideal segmentation performance. Moreover, these multi-objective evolutionary clustering algorithms require expensive computation for evaluating fitness functions. In order to improve the segmentation performance and time efficiency for image segmentation, a coarse-fine surrogate model driven preference-based multi-objective evolutionary fuzzy clustering (CFS-PMEFC) algorithm is proposed in this paper. First of all, the coarse-fine surrogate model and the preference information derived from the user are introduced to design multi-objective evolutionary clustering framework, which can improve the evolutionary efficiency and quality of clustering centers. Then, Mahalanobis distance-based fitness functions fusing the region information of the image are constructed to further promote the segmentation performance. In addition, a clustering index with the region information of the image is designed to select the optimal solution from the final solution set of CFS-PMEFC. Experiments on Berkeley images show that CFS-PMEFC greatly improves the accuracy of image segmentation and meanwhile reduces the computational cost. © 2021 ieee.

关键词： image segmentation

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Pop-up World: Synthesis of 2.5D Models Based on Monocular images

Pop-up World: Synthesis of 2.5D Models Based on Monocular Im...

引用

Virtual Reality (ICVR), ieee International conference on

作者： Jingyao Cai Boyuan Cheng Yuqian He Zhidong Xiao Jian Jun Zhang Xiaosong Yang National Center for Computer Animation Bournemouth University Bournemouth United Kingdom Industrial Light & Magic (UK) Ltd London United Kingdom

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

For current VR technology, stereoscopic scenes are crucial for an immersive experience. However, the representation of vast 3D scenes in VR poses a significant challenge due to the extensive memory usage and consumption associated with large 3D models. To address this, we propose the use of 2.5D models as a substitute for traditional 3D models in the construction of distant scenes. The method leverages unsupervised clustering algorithm to segment the depth map of the input image. Users can decide the number of segmented layers according to the complexity of the input images and their demands. Each layer is refined using inpainting techniques, especially pre-trained mask-aware transformer models, to ensure a seamless and realistic visual experience. This approach not only reduces the size of the scenes but also maintains the fidelity of the VR experience, striking a balance between technical efficiency and user immersion. The system can effectively handle complex scenes and can be integrated into tools such as Maya for model customization. The source code is available at https://***/XChengCode/Synthesis-of-2.5D-Models-Based-on-Monocular-images.

关键词： Solid modeling image segmentation visualization Three-dimensional displays Stereo image processing Source coding Streaming media Transformers Software Real-time systems

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Perceptual Quality Assessment for Digital Human Heads

Perceptual Quality Assessment for Digital Human Heads

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zicheng Zhang Yingjie Zhou Wei Sun Xiongkuo Min Yuzhe Wu Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Peng Cheng Laboratory China College of Information Science & Technology Donghua University China

Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, a simple yet effective full-reference (FR) projection-based method is proposed to evaluate the visual quality of DHHs. The pretrained Swin Transformer tiny is employed for hierarchical feature extraction and the multi-head attention module is utilized for feature fusion. The experimental results reveal that the proposed method exhibits state-of-the-art performance among the mainstream FR metrics. The database is released at https://***/zzc-1998/DHHQA.

关键词： visualization Acoustic distortion Three-dimensional displays Databases Feature extraction Transformers Rendering (computer graphics)

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Urban Planter: A Web App for Automatic Classification of Urban Plants

Urban Planter: A Web App for Automatic Classification of Urb...

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ieee visual communications and image processing (VCIP)

作者： Sarit Divekar Irina Rabaev Marina Litvak Shamoon College of Engineering Israel

ISBN: (纸本)9781728173221

Plant classification requires an expert because subtle differences in leaves or petal forms might differentiate between different species. On the contrary, some species are characterized by high variability in appearance. This paper introduces a web app for assisting people in identifying plants for discovering the best growing methods. The uploaded picture is submitted to the back-end server, and a pre-trained neural network classifies it to one of the predefined classes. The classification label and confidence are displayed to the end user on the front-end page. The application focuses on the house and garden plant species that can be grown mainly in a desert climate and are not covered by existing datasets. For training a model, we collected the Urban Planter dataset. The installation code of the alpha version and the demo video of the app can be found on https://***/UrbanPlanter/urbanplanterapp.

关键词： Training Codes visual communication image processing Neural networks Servers Meteorology

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