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Split Lohmann computer holography: fast generation of 3D hologram in single-step diffraction calculation

Advanced Photonics Nexus 2024年第3期3卷 1-13页

作者： Chenliang Chang Xian Ding Di Wang Zhizhou Ren Bo Dai Qi Wang Songlin Zhuang Dawei Zhang University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringEngineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optics SystemShanghaiChina Beihang University School of Instrumentation and Optoelectronic EngineeringBeijingChina

Holographic display stands as a prominent approach for achieving lifelike three-dimensional(3D)reproductions with continuous depth ***,the generation of a computer-generated hologram(CGH)always relies on the repetitive computation of diffraction propagation from point-cloud or multiple depthsliced planar images,which inevitably leads to an increase in computational complexity,making real-time CGH generation ***,we report a new CGH generation algorithm capable of rapidly synthesizing a 3D hologram in only one-step backward propagation calculation in a novel split Lohmann lens-based diffraction *** introducing an extra predesigned virtual digital phase modulation of multifocal split Lohmann lens in such a diffraction model,the generated CGH appears to reconstruct 3D scenes with accurate accommodation abilities across the display *** with the conventional layer-based method,the computation speed of the proposed method is independent of the quantized layer numbers,and therefore can achieve real-time computation speed with a very dense of depth *** simulation and experimental results validate the proposed method.

关键词： computer holography holographic display diffraction calculation.

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Digital-Based Inverse Design for Ultra-Compact Power Splitter on LNOI

Digital-Based Inverse Design for Ultra-Compact Power Splitte...

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

作者： Zhou, Lingjun Ma, Hansi Nie, Xiaomin Li, Yunchen He, Zhixue Wang, Lei Li, Ke Zhang, Fan Peng Cheng Laboratory Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen China Center of Material Science National University of Defense Technology Changsha China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China

ISBN: (纸本)9798350312614

We propose and experimentally demonstrate an ultra-compact and efficient power splitter on LNOI. The device is realized through the inverse design of a digital meta-structure and occupies a small footprint of only 4.8×4.8μm2. The measured results show that the insertion losses are below 2.5dB across the wavelength band of 1500-1560 nm, and the power imbalance between the two output ports is within 0.3 dB. © 2023 IEEE.

关键词： inverse design LNOI power splitter

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Reconfigurable Intelligent Surfaces and DF-relay Improved Spectral Efficiency in Cognitive Radio Networks 97

Reconfigurable Intelligent Surfaces and DF-relay Improved Sp...

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97th IEEE Vehicular Technology Conference, VTC 2023-Spring

作者： El Mettiti, Abderahmane Saber, Mohamed Chehri, Abdellah Chaibi, Hasna Badaoui, Abdel Saadane, Rachid Faculty of Sciences University Mohamed v Ehtp LRIT-GSCM Morocco SIRC/LAGeS Ehtp Laboratory Engineering System Casablanca Morocco Royal Military College Dep. Math. and Computer Science Kingston Canada Supmti Genuis Laboratory Rabat Morocco Mohammed v University Laboratory Lastimi Casablanca Morocco

ISBN: (纸本)9798350311143

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage. © 2023 IEEE.

关键词： Cognitive radio

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Security Issues and Defensive Approaches in Deep Learning Frameworks

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Tsinghua science and Technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Fed-AugMix: Balancing Privacy and Utility via Data Augmentation

arXiv

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arXiv 2024年

作者： Li, Haoyang Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate this issue, they often lead to notable performance degradation. Existing methods struggle to preserve model performance while ensuring privacy. To address this challenge, we propose a novel data augmentation-based framework designed to achieve a favorable privacy-utility trade-off, with the potential to enhance model performance in certain cases. Our framework incorporates the AugMix algorithm at the client level, enabling data augmentation with controllable severity. By integrating the Jensen-Shannon divergence into the loss function, we embed the distortion introduced by AugMix into the model gradients, effectively safeguarding privacy against deep leakage attacks. Moreover, the JS divergence promotes model consistency across different augmentations of the same image, enhancing both robustness and performance. Extensive experiments on benchmark datasets demonstrate the effectiveness and stability of our method in protecting privacy. Furthermore, our approach maintains, and in some cases improves, model performance, showcasing its ability to achieve a robust privacy-utility trade-off. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Machine learning and theoretical analysis release the non-linear relationship among ozone,secondary organic aerosol and volatile organic compounds

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Journal of Environmental sciences 2022年第4期34卷 75-84页

作者： Feng Wang Zhongcheng Zhang Gen Wang Zhenyu Wang Mei Li Weiqing Liang Jie Gao Wei Wang Da Chen Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin 300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin 300350China State Key Laboratory on Odor Pollution Control Tianjin Academy of Environmental SciencesTianjin 300191China Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution Jinan University Institute of Mass Spectrometry and Atmospheric EnvironmentGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response Civil Aviation University of ChinaTianjin 300300China

Fine particulate matter(PM_(2.5))and ozone(O_(3))pollutions are prevalent air quality issues in *** organic compounds(VOCs)have significant impact on the formation of O_(3)and secondary organic aerosols(SOA)contributing PM_(2.5).Herein,we investigated 54 VOCs,O_(3)and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O_(3),SOA and *** monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September,but the observed O_(3)was exactly the *** learning methods resolved the importance of individual VOCs on O_(3)and SOA that alkenes(mainly ethylene,propylene,and isoprene)have the highest importance to O_(3)formation;alkanes(C_(n),n≥6)and aromatics were the main source of SOA *** learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O_(3)and SOA *** formation potential(OFP)and secondary organic aerosol formation potential(SOAFP)calculated by consumed VOCs quantitatively indicated that more than 80%of the consumed VOCs were alkenes which dominated the O_(3)formation,and the importance of consumed aromatics and alkenes to SOAFP were 40.84%and 56.65%,***,isoprene contributed the most to OFP at 41.45%regardless of the season,while aromatics(58.27%)contributed the most to SOAFP in ***,our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O_(3).

关键词： VOCs Machine learning Photochemical consumption Ozone formation potential Secondary organic aerosol formation potential

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 24

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent systems

作者： Xiaoqiang Wu Qingling Zhu Qiuzhen Lin Weineng Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400704864

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method.

关键词： evolutionary algorithm evolutionary reinforcement learning reinforcement learning

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