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Anonymous Authentication Technology Review in Vehicle Networking Environment

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Anonymous Authentication Technology Review in Vehicle Networ...

作者： Haoyan Zhang Guangjun Liang Jiacheng He Mingtao Ji Department of Computer Information and Cyber Security Jiangsu Police Institute Jiangsu Electronic Data Forensics Analysis Engineering Research Center Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department

In the era of big data, the development of the Internet of things is in the ascendant, and the technology of car networking is becoming more and more mature. This paper will first introduce the anonymous authentication technology widely used in the Internet of things, and then take the Internet of Vehicles as an example to analyze the symbolic achievements of anonymous authentication technology in the development process, and point out the existing technical problems. Finally, the future application prospect and research direction of anonymous authentication technology are prospected. It is hoped that this paper can provide scholars with new ideas and certain reference value, and make people have a systematic understanding of anonymous authentication technology, improving people's security awareness of protecting their own privacy information.

关键词： Internet of Things Car networking Anonymous authentication Block chain PKI authentication

Cyber Exploitation through Cybercrimes & Challenges 3

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Cyber Exploitation through Cybercrimes & Challenges

3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Meena, Yogesh Sankhla, Mahipal Singh Sonone, Swaroop S. Parashar, Aastha Parihar, Kapil Saini, Kavita Galgotias University Computer Science and Engineering U.P Greater Noida India Department of Forensic Science Vivekananda Global University Rajasthan Jaipur India Department of Digital & Cyber Forensics Government Institute of Forensic Science Maharashtra Aurangabad India Advanced Practice Teesside University United Kingdom State Forensic Science Laboratory Rajasthan Jaipur India Galgotias University Delhi NCR India

ISBN: (纸本)9781665438117

The growth of the internet's web is increasing day by day. Everyone is trapped in this web of the internet. Every coin has its two aspects like that internet has two aspects good and bad. Cybercrime is one aspect that falls under the bad aspect of the internet. The most influenced population by the cybercrime is children and the adults. Nowadays exploitation through the cyber is easy for the attackers and it is the most common way to exploit someone because now all are using the internet. Attackers use many tricks for exploitation they not only use social media but they can exploit you by even your web searches or watch video content on the internet. Children are an easy target for the attackers to commit any crime. They exploit the victim by cybercrime like cyberbullying, cyberstalking, pornography, child abuse, identity theft and fraud, live streaming on the web, and many more different ways. We have made an attempt in this article to bring the Cyber Exploitation into the limelight which are caused due to Cybercrimes their Challenges and Prevention methods. © 2021 IEEE.

关键词： Crime

MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

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SSRN

SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

A joint energy and latency framework for transfer learning over 5G industrial edge networks

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arXiv 2021年

作者： Yang, Bo Fagbohungbe, Omobayode Cao, Xuelin Yuen, Chau Qian, Lijun Niyato, Dusit Zhang, Yan Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Credit Center Prairie View A and M University Texas A and M University System Prairie ViewTX77446 United States School of Computer Science and Engineering Nanyang Technological University Singapore Singapore University of Oslo Norway Simula Metropolitan Center for Digital Engineering Norway

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

Random resistive memory-based deep extreme point learning machine for unified visual processing

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arXiv 2023年

作者： Wang, Shaocong Gao, Yizhao Li, Yi Zhang, Woyu Yu, Yifei Wang, Bo Lin, Ning Chen, Hegan Zhang, Yue Jiang, Yang Wang, Dingchen Chen, Jia Dai, Peng Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Xu, Xiaoxin So, Hayden Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. In this study, we propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. Data-wise, the multi-sensory data are unified as point sets and can be processed universally. Software-wise, most weights of deep extreme point learning machines are exempted from training, which significantly reduce training complexity. Hardware-wise, nanoscale resistive memory not only enables collocation of memory and processing, mitigating the von Neumann bottleneck and the slowdown of Moore's law, but also leverages the inherent programming stochasticity for generating the random and sparse weights of the DEPLM, which also lessens the impact of read noise. We demonstrate the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves 5.90×, 21.04×, and 15.79× energy efficiency improv

关键词： Cost reduction

Pre-Chirper Free Nonlinear Fiber Amplifier Generating Tunable Picosecond Pulses for Coherent Anti-Stokes Raman Imaging

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Pre-Chirper Free Nonlinear Fiber Amplifier Generating Tunabl...

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jiaying Li Jiamei Wu Kangwen Yang Qiang Hao Minbiao Ji Ming Yan Kun Huang Heping Zeng Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and System Ministry of Education School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China State Key Laboratory of Surface Physics and Department of Physics Human Phenome Institute Multiscale Research Institute of Complex Systems Academy for Engineering and Technology Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) Fudan University Shanghai China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai China Chongqing Institute of East China Normal University Chongqing China Jinan Institute of Quantum Technology Jinan China

A pre-chirper free, core-pumped nonlinear fiber amplifier was demonstrated to generate parabolic pulse with spectral bandwidth of 53 nm, these pulses were filtered and passively synchronized for conducting coherent an...

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Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer

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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

Modeling Method for DFIG-Based Wind Farm in High-Efficiency Real-Time Electromagnetic Transient (EMT) Simulations

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Yifan Xu, Jianzhong Zhu, Yiyang Tian, Zhaoxuan Zhao, Chengyong Li, Gen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources China Energy Technology and Computer Science Section Department of Engineering Technology and Didactics Ballerup2750 Denmark

With the increasing integration of renewable energy into power systems, electromagnetic transient (EMT) simulation has become indispensable for accurate system analysis. However, the complexity of wind turbine (WT) modeling, characterized by a large number of electrical nodes, poses significant challenges and necessitates substantial real-time simulation hardware. Existing methods for reducing circuit complexity improve simulation efficiency but are each associated with inherent limitations. Aggregation methods sacrifice considerable internal station information, while existing decoupling techniques are constrained by specific requirements. This paper proposes a real-time simulation model for a Doubly Fed Induction Generator (DFIG)-based wind farm (WF) using latency decoupling and a multi-level nested fast and simultaneous solution (M-NFSS) approach, effectively reducing node count while preserving internal details. A WF test model is implemented in RTDS for validation. Results demonstrate highly accurate impedance characteristics and time-domain waveforms of the proposed model, utilizing only 33.3% of the hardware resources compared to the traditional detailed model. © 1986-2012 IEEE.

关键词： Electromagnetic transients

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting