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On the Performance Trade-off of Distributed Integrated Sensing and communication Networks

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arXiv 2023年

作者： Li, Xuran Guo, Shuaishuai Li, Tuo Zou, Xiaofeng Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University Jinan250061 China School of Control Science and Engineering Shandong University Jinan250061 China Shandong Provincial Key Laboratory of Wireless Communication Technologies China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. China

In this letter, we analyze the performance tradeoff in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models

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Computers in Biology and Medicine 2024年 179卷 108913-108913页

作者： Li, Yu Zeng, Guo-Hua Liang, Yong-Jia Yang, Hong-Rui Zhu, Xi-Liang Zhai, Yu-Jia Duan, Li-Xia Xu, Ying-Ying School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China Cancer Center Affiliated Hospital of Guangdong Medical University Zhanjiang524000 China Guangzhou Red Cross Hospital Medical College Jinan University Guangzhou510220 China

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics. © 2024 Elsevier Ltd

关键词： Proteins

Isomer Engineering for Red Thermally Activated Delayed Fluorescence Molecules: Luminescence Mechanism Study and Rational Molecular Design

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SSRN

SSRN 2023年

作者： Zhao, Xin Liu, Huanling Mu, Qingfang Xie, Zhen Song, Yuzhi Lin, Lili Xu, Yuanyuan Fan, Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China School of Science Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Guangdong Provincial Key Laboratory of Luminescence from Molecular Aggregates South China University of Technology Guangzhou510640 China

Through urgently needed, red thermally activated delayed fluorescence (TADF) molecules are still rare due to the energy-gap law, developing efficient red TADF molecules remains a great challenge and it is extremely urgent to reveal the luminescence mechanisms through theoretical perspectives. Herein, based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) methods coupled with the thermal vibration correlation function (TVCF) method, the photophysical properties of two pairs of red TADF isomers (PDCN-DPAC, PDCN-DPAC-2, PPDCN-DPAC and PPDCN-DPAC-2) in toluene and solid phase have been theoretically studied by the polarizable continuum model (PCM) and the combined quantum mechanics and molecular mechanics (QM/MM) method, respectively. Detailed parameters such as the adiabatic singlet-triplet energy gaps, natural transition orbital properties, reorganization energies, the rate constants of radiative, non-radiative, intersystem crossing (ISC) and reverse intersystem crossing (RISC) processes are studied, and excited state energy consumption processes are analyzed. Results indicate that, compared with trans-isomers (PDCN-DPAC and PPDCN-DPAC) in toluene, cis-isomers PDCN-DPAC-2 and PPDCN-DPAC-2 possess smaller energy gaps which bring fast RISC processes. While for molecules in solid phase, decreased energy gaps and increased spin orbit coupling (SOC) constants are determined, all these factors contribute to the remarkable RISC processes. Furthermore, based on the PPDCN-DPAC configuration, three new red TADF molecules (PPDCN-PAS, PPDCN-TPA and PPDCN-DMAC) are theoretically designed by linking different donor fragments phenazasiline (PAS), triphenylamine (TPA), dimethylacridine (DMAC) with acceptor unit PPDCN. Notably, small energy gaps and large SOC constants are obtained, remarkable triplet excitons harvest abilities and fast up-conversion processes are revealed. Moreover, oscillator strengths are significantly improved and promising

关键词： Isomers

Deep Learning Methods for Ship Classification: From Visible to Infrared images

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Deep Learning Methods for Ship Classification: From Visible ...

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Tianci Liu Hengjia Qin Zhuo Zhan Yunpeng Liu Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Laboratory of Image Understanding and Computer Vision Shenyang Liaoning Province China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Shenyang Liaoning Province China

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database.

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GazeGAN: A robust generative adversarial saliency model based on invariance analysis of human gaze during scene free viewing

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arXiv 2019年

作者： Che, Zhaohui Borji, Ali Zhai, Guangtao Min, Xiongkuo Guo, Guodong Le Callet, Patrick Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China MarkableAI United States Institute of Deep Learning Baidu Research Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States Equipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

—Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time-consuming and expensive. Most of current studies on human attention and saliency modeling have used high-quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label-preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label-preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified U-Net is proposed as the generator of the GazeGAN, which combines classic "skip connections" with a novel "center-surround connection (CSC)", in order to leverage multi-level features. We also propose a histogram loss based on Alternative Chi-Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. We also provide a comprehensive quantitative comparison of 22 state-of-the-art saliency models on distorted images, which contributes a robustness benchmark for saliency community. Our code and data are available at: https://***/CZHQuality/

关键词： Luminance

Niobium telluride absorber for a mode-locked vector soliton fiber laser

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arXiv 2023年

作者： Shang, XinXin Xu, NanNan Guo, Jia Sun, Shuo Zhang, HuaNian Wageh, S. Al-Ghamdi, Ahmed A. Zhang, Han Li, DengWang Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and Electronics Shandong Normal University Jinan250014 China Shenzhen University Shenzhen518060 China School of Physics and Optoelectronic Engineering Shandong University of Technology Zibo255049 China Department of Physics Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

Niobium telluride (NbTe2), an emerging transition metal dichalcogenide material, has been theoretically predicted with nonlinear absorption properties and excellent optical response, while deficient research about utilized in ultrafast photonics has been reported. In this work, a NbTe2-based saturable absorber was applied in an Er-doped fiber (EDF) as a mode-locked device and vector soliton was obtained based on NbTe2 for the first time. NbTe2 - PVA film SA was successfully prepared by liquid-phase exfoliation and spin-coating method, whose modulation depth is up to 10.87 %. The nonlinear absorption coefficient of NbTe2 SA film tested through the open-aperture Z-scan laser measurement is 0.62×10-11 m/W. Conventional soliton with pulse duration of 858 fs was generated based on NbTe2 SA, which was demonstrated to be a kind of polarization-locked vector soliton in the further investigation. Our experimental results fully suggest the nonlinear optical properties of NbTe2 and broaden its ultrafast applications as ultrafast photonic device. Copyright © 2023, The Authors. All rights reserved.

关键词： Fiber lasers

3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images

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arXiv 2023年

作者： Liu, Mengqing Shao, Xiao Jiang, Liping Wu, Kaizhi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China The First Affiliated Hospital of Nanchang University Nanchang University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Background: Segment prostates from transrectal ultrasound (TRUS) images plays an essential role in the diagnosis and treatment of prostate cancer. However, traditional segmentation methods are time-consuming and laborious. To address this issue, there is an urgent need to develop computer algorithms that can automatically segment prostates from TRUS images, which makes it become the direction and form of future development. Purpose: Automatic prostate segmentation in TRUS images has always been a challenging problem, since prostates in TRUS images have ambiguous boundaries and inhomogeneous intensity distribution. Although many prostate segmentation methods have been proposed, they still need to be improved due to the lack of sensibility to edge information. Consequently, the objective of this study is to devise a highly effective prostate segmentation method that overcomes these limitations and achieves accurate segmentation of prostates in TRUS images. Methods: A 3D edge-aware attention generative adversarial network (3D EAGAN)-based prostate segmentation method is proposed in this paper, which consists of an edge-aware segmentation network (EASNet) that performs the prostate segmentation and a discriminator network that distinguishes predicted prostates from real prostates. The proposed EASNet is composed of an encoder-decoder-based U-Net backbone network, a detail compensation module, four 3D spatial and channel attention modules, an edge enhance module, and a global feature extractor. The detail compensation module is proposed to compensate for the loss of detailed information caused by the down-sampling process of the encoder. The features of the detail compensation module are selectively enhanced by the 3D spatial and channel attention module. Furthermore, an edge enhance module is proposed to guide shallow layers in the EASNet to focus on contour and edge information in prostates. Finally, features from shallow layers and hierarchical features from the decod

关键词： Generative adversarial networks

Detection of brown adipose tissue in rats with acute cold stimulation using quantitative susceptibility mapping

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Chinese Medical Journal 2023年第17期136卷 2137-2139页

作者： Cuiling Zhu Yihao Guo Wenbin Si Qiaoling Zhong Yingjie Mei Yanqiu Feng Xiaodong Zhang Department of Radiology The Third Affiliated Hospital of Southern Medical University(Academy of Orthopedics Guangdong Province)GuangzhouGuangdong 510630China Department of Radiology Foshan Hospital of Traditional Chinese MedicineFoshanGuangdong 528000China School of Biomedical Engineering Southern Medical UniversityGuangzhouGuangdong 510515China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical UniversityGuangzhouGuangdong 510515China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical UniversityGuangzhouGuangdong 510515China Philips Healthcare GuangzhouGuangdong 510000China

To the Editor:Adipose tissue occurs in at least two different entities in mammals and humans:brown adipose tissue(BAT)and white adipose tissue(WAT).BAT is characterized by a unique uncoupling protein 1(UCP1)in the mitochondria that enables the uncoupling of the respiratory chain from adenosine triphosphate ***,energy is dissipated as heat to reduce fat *** is also considered a highly heterogeneous tissue with abundant oxygen,blood supply,and iron-rich mitochondria.[1,2]Activation of BAT via exposure to a cold environment is considered to be a means of reducing triglycerides to fight obesity.[3]The alterations in cells and tissues of activated BAT include increased iron content and UCP1 expression in mitochondrial,blood perfusion,and lipid utilization.[4]Therefore,accurate identification and quantitative analysis of inactive and activated BAT are of great significance for the treatment of metabolic diseases that target BAT,such as obesity.

关键词： stimulation alterations susceptibility