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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Design of three-dimensional gesture recognition and motion tracking human-computer intelligent interaction system based on PAJ7620

Design of three-dimensional gesture recognition and motion t...

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2021 International Conference on Information technology and Intelligent Control, CITIC 2021

作者： Wang, Jian-Liang Wei, Ye Li, Yang Meng, Yuan Li, Ming-Yu Xu, Jiang-Yan Jiao, Wan-Ming Ban, Lan Liu, Jun-Pei Zhang, Hong-Feng Zhang, Wei Wang, Li-Na Luo, Fu-Chen Wang, Yu-Duo Tianjin College University of Science and Technology Beijing Tianjin China Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communication and Radio Energy Transmission Tianjin China Binhai College Nankai University Tianjin China Tianjin Tianshi College Tianjin China

With the increasing degree of informatization and intelligence in modern society, human-computer interaction technology has become one of the commonly used scientific and technological achievements. "Gesture" can be used as inputing image information, and make more intuitive and convenient for perception of user interface. To a certain extent, it can complete the intelligent interaction function between human and computer. The design is a human-computer intelligent interaction system for 3D gesture recognition and motion tracking based on gesture sensor PAJ7620. STM32 chip is used as the main control unit to recognize accurately, analyze and process different gestures collected by the gesture sensor, and control the OLED screen to display the gesture information. It can further drive the carrier to take corresponding actions and realize the intelligent human-computer interaction recognition of gestures. The experimental test results show that the recognition rate is lose to 100% when the distance is within 10cm. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Gesture recognition

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Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis

arXiv

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arXiv 2024年

作者： Tang, Kunming Jiang, Zhiguo Shi, Jun Wang, Wei Wu, Haibo Zheng, Yushan Beijing Advanced Innovation Center on Biomedical Engineering School of Engineering Medicine Beihang University China Image Processing Center School of Astronautics Beihang University China Tianmushan Laboratory China School of Software Hefei University of Technology China Department of Pathology The First Affiliated Hospital of USTC China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China China

Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods. © 2024, CC BY.

关键词： WSI circuits

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Measuring the Orbital-Angular-Momentum Complex Spectrum of Light with the Fast Fourier Transform

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Physical Review Applied 2023年第1期20卷 014010-014010页

作者： Jialong Zhu Yihua Wu Le Wang Hailong Zhou Shengmei Zhao Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications (NUPT) Nanjing 210003 China School of Information Engineering Suqian University Suqian 223800 China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing China National Laboratory of Solid State Microstructures Nanjing University Nanjing 210093 China

Measuring the orbital-angular-momentum (OAM) complex spectrum of light is very important in OAM-based applications. In this paper, we propose and experimentally demonstrate a simple scheme to measure the OAM complex spectrum of light with the fast Fourier transform (FFT). The light with input OAM modes coaxially interferes with a Gaussian light beam that is generated locally to achieve the intensity distribution of the interference. By subtracting the intensity distribution of the input OAM light from this interference intensity distribution, the OAM complex spectrum can be extracted by employing the FFT transform on the result of the subtraction. In addition, we also present an approach to enlarge the range of detectable OAM modes (from 1 to 50) and analyze the effects of optical alignment and the number of superimposed OAM modes on the measurement results. The proposed scheme is demonstrated in a proof-of-principle experiment, where six superimposed OAM modes can be measured without error, with their average deviation of amplitude and phase being 0.07 and 0.308, respectively. The proposed scheme can exploit applications in some advanced domains, including OAM sensing, OAM imaging, optical communication decoding, and various other OAM-based systems.

关键词： Angular momentum of light Metrology Spatial profiles of optical beams Optical sources & detectors Optical spectroscopy

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Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior

Intelligent Reflecting Surface OFDM Communication with Deep ...

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IEEE International Conference on Communications (ICC)

作者： Tomer Fireaizen Gal Metzer Dan Ben-David Yair Moshe Israel Cohen Emil Bjö rnson Signal and Image Processing Laboratory (SIPL) Faculty of Electrical &amp Computer Engineering Technion &#x2013 Israel Institute of Technology Haifa Israel Faculty of Engineering Tel Aviv University Tel Aviv Israel Dept. of Electrical Engineering (ISY) Link&#x00F6 ping University and KTH Royal Insitute of Technology Sweden

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683484

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

关键词： Wireless communication Training Deep learning OFDM image processing Neural networks Generators

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Slide-based Graph Collaborative Training for Histopathology Whole Slide image Analysis

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IEEE Transactions on Medical Imaging 2025年

作者： Shi, Jun Shu, Tong Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan Hefei University of Technology School of Software Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China School of Astronautics Beihang University Image Processing Center Beijing102206 China Beihang University Tianmushan Laboratory Zhejiang Hangzhou311115 China University of Science and Technology of China Division of Life Sciences and Medicine Department of Pathology the First Affiliated Hospital of USTC Hefei230036 China University of Science and Technology of China Division of Life Sciences and Medicine Intelligent Pathology Institute Hefei230036 China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center on Biomedical Engineering Beijing100191 China

The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research focuses on the inner-contextual information within each WSI yet ignores the possible inter-correlations between slides. As the development of tumors is a continuous process involving a series of histological, morphological, and genetic changes that accumulate over time, the similarities and differences between WSIs across various stages, grades, locations and patients should potentially contribute to the representation of WSIs and deserve to be taken into account in WSI modeling. To verify the advancement of introducing the slide inter-correlations into the representation learning of WSIs, we proposed a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks and improve their performance. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow, which concurrently initializes and refines the slide representation, as a guide for message passing in the slide-based graph. Extensive comparisons and experiments are conducted to validate the effectiveness and robustness of the proposed pipeline across 4 different tasks, including cancer subtyping, cancer staging, survival prediction, and genemutation prediction, with 8 representative SOTA WSI analysis frameworks as backbones. © 1982-2012 IEEE.

关键词： image correlation

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Causal Invariant Hierarchical Molecular Representation for Out-of-distribution Molecular Property Prediction

Causal Invariant Hierarchical Molecular Representation for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery Data mining Compounds

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Design of Miniaturized UWB Low Noise Amplifier Based on 65nm CMOS technology

Design of Miniaturized UWB Low Noise Amplifier Based on 65nm...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Qing Guo Yuting Chen Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

An X-band CMOS low noise amplifier (LNA) with flat gain and noise Figure (NF) is proposed in this paper. To achieve sufficient gain, the LNA adopts two-stage topology. A feed-back transformer is used at the input terminal to implement noise and wideband input matching. Transformer-based inter-stage and output matching networks are adopted to achieve well isolation and compact size. By introducing inductive peaking technique, the proposed LNA obtain lower NF and extend the bandwidth. Post simulation results show that the LNA achieves a gain of 14.6± 2dB and NF of 2. 5-3.3dB across a bandwidth from 8.5 GHz to 12.5 GHz. With the supply voltage of 1 V at the first stage and 1.8 V at the second stage, the dc power dissipation is 20.8 mW. The simulated input 1dB compression point ($\mathrm{P}_{\mathrm{l}\mathrm{d}\mathrm{B}}$) is -27.3dBm. The core chip area is only 0.41× 0.87mm 2 .

关键词： Low-noise amplifiers Impedance matching Simulation Voltage Transformer cores Transformers CMOS technology

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RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and Applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

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Novel explanation of the Active Galactic Nuclei. The Power Source of Quasars as a result of vacuum polarization by the gravitational singularities on the distributional BHs horizon 9

Novel explanation of the Active Galactic Nuclei. The Power S...

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9th International Conference on Mathematical Modeling in Physical sciences, IC-MSQUARE 2020

作者： Foukzon, Jaykov Men'Kova, Elena R Potapov, Alexander A Department of Mathematics Israel Institute of Technology Haifa3200003 Israel All-Russian Research Institute for Optical and Physical Measurements Moscow119361 Russia Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences Moscow125009 Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China

In this paper we argue that the current paradigm for AGN and quasars is essentially incomplete and a rivision is needed. Remind that the current paradigm for AGN and quasars is that their radio emission is explained by synchrotron radiation from relativistic electrons that are Doppler boosted through bulk motion. In this model, the intrinsic brightness temperatures cannot exceed 1011 to 1012 K. Typical Doppler boosting is expected to be able to raise this temperature by a factor of 10. The observed brightness temperature of the most compact structures in BL Lac, constrained by baselines longer than 5.3G, must indeed exceed 2 × 1013K and can reach as high as 3 × 1014K. This is difficult to reconcile with current incoherent synchrotron emission models from relativistic electrons, requiring alternative models such as emission from relativistic protons. However the proton, as we know, is 1836 times heavier than an electron and absolutely huge energy is required to accelerated it to sublight speed. These alternative models such as emission from relativistic protons can be suported by semiclassical gravity effect finds its roots in the singular behavior of quantum fields on curved distributional space- times presented by rotating gravitational singularities. © Published under licence by IOP Publishing Ltd.

关键词： Synchrotron radiation

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