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IEEE International Conference on Electro-Information Technology

作者： Akhila Sai Sree Cherukuri Poulami Samaddar Alejos Aguero Carlos Keerthy Gopalakrishnan Sunil Gaddam Lucia T. Valenzuela Perez Hyun Se Kim Lee Shuvashis Dey Sayan Roy Dipankar Mitra Petra Hirsova Shivaram P. Arunachalam Microwave Engineering & Imaging Laboratory (MEIL) Division of Gastroenterology & Hepatology Mayo Clinic Rochester MN USA GIH Artificial Intelligence Laboratory (GAIL) Division of Gastroenterology and Hepatology Mayo Clinic Rochester MN USA Department of Electrical and Computer Engineering North Dakota State University Fargo ND USA Department of Electrical Engineering and Computer Science South Dakota Mines Rapid City USA Department of Computer Science & Computer Engineering University of Wisconsin-La Crosse La Crosse WI USA Department of Medicine Mayo Clinic Rochester MN USA Department of Radiology Mayo Clinic Rochester MN USA

Nonalcoholic steatohepatitis (NASH) is a chronic liver disease that can progress to cirrhosis, liver failure, and hepatocellular cancer. Currently, liver biopsy is the gold standard for diagnosis, but it is invasive and risky. Therefore, there is a need for non-invasive diagnostic tools. Recent studies have shown that microwaves and dielectric spectroscopy can differentiate between healthy and diseased liver tissue and can provide information on the severity of NASH. In this study, we used ex vivo NASH mouse model tissues to diagnose NASH using dielectric spectroscopy, a non-invasive technique. According to our study results, the novelty of the study lies in differentiating the various NASH diseased tissue and analyzing them using cole-cole parameters, suggesting the potential development of a noninvasive diagnostic and treatment monitoring tool for NASH. If successful, the use of microwaves could revolutionize the field of NASH diagnosis and monitoring, providing a safer and more convenient alternative to liver biopsy. However, additional research is required to validate this approach and optimize its sensitivity and specificity.

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Rotational Doppler effect using ultra-dense vector perfect vortex beams

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Photonics Research 2025年第2期13卷 468-476页

作者： JIANBO GAO XINGYUAN LU XUECHUN ZHAO ZHUOYI WANG JUNAN ZHU ZHIQUAN HU JINGJING HE QIWEN ZHAN YANGJIAN CAI CHENGLIANG ZHAO School of Physical Science and Technology Jiangsu Key Laboratory of Frontier Material Physics and DevicesSoochow University School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shandong Provincial Engineering and Technical Center of Light Manipulations&Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal University Shandong Joint Research Center of Light Manipulation Science and Photonics Integrated Chip of East China Normal University and Shandong Normal University East China Normal University

The rotational Doppler effect holds significant potential for remote sensing of rotating objects due to its real-time performance and non-contact advantages.A single-ring beam is used to measure rotation *** enhance the signal-to-noise ratio and measure additional parameters,multiple rings are introduced in the context of a rotational Doppler ***,the interference between these rings poses a challenge for multitasking detection *** this study,cross-polarization superposition was applied to generate an ultra-dense vector perfect vortex beam that exhibited sensitivity to spatial position and object size,and flexibility in designing topological charge combinations for generating frequency combs.A proof-of-principle experiment was conducted to demonstrate its capability in improving the signal-to-noise ratio,and accurately perceiving both the radius of rotation and radial *** ultra-dense vector perfect vortex beam provides a general strategy for beam construction and the multi-parameter perception of rotating objects,thereby enabling potential applications in the measurement of velocity gradient measurement of fluids.

关键词： Doppler effect Fiber optic communications Frequency combs Optical tweezers Phase modulation Remote sensing

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization

arXiv

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arXiv 2024年

作者： Ji, Zhong Yang, Shuo Liu, Jingren Pang, Yanwei Han, Jungong School of Electrical and Information Engineering Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin University Tianjin300072 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Computer Science The University of Sheffield United Kingdom

Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data. However, current approaches inadequately address the intrinsic optimization of the co-occurrence matrix Ā based on cosine similarity, failing to achieve zero base-novel regions and adequate sparsity in base and novel domains. To address these deficiencies, we propose a Non-Negative Generalized Category Discovery (NN-GCD) framework. It employs Symmetric Non-negative Matrix Factorization (SNMF) as a mathematical medium to prove the equivalence of optimal K-means with optimal SNMF, and the equivalence of SNMF solver with non-negative contrastive learning (NCL) optimization. Utilizing these theoretical equivalences, it reframes the optimization of Ā and K-means clustering as an NCL optimization problem. Moreover, to satisfy the nonnegative constraints and make a GCD model converge to a near-optimal region, we propose a GELU activation function and an NMF NCE loss. To transition Ā from a suboptimal state to the desired Ā∗, we introduce a hybrid sparse regularization approach to impose sparsity constraints. Experimental results show NN-GCD outperforms state-of-the-art methods on GCD benchmarks, achieving an average accuracy of 66.1% on the Semantic Shift Benchmark, surpassing prior counterparts by 4.7%. Copyright © 2024, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Xiong, Zehui Kang, Jiawen Ai, Bo Han, Zhu Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore China Ministry of Education Guangzhou510006 China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing. © 2024, CC BY.

关键词： Artificial intelligence

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： Fan Wang Ruihong Jiang Yuan Liu Tingnan Bao Mugen Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Zhejiang Laboratory Research Center for Space Computing System Hangzhou China School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios.

关键词： NOMA Satellites Closed-form solutions computer network reliability Low earth orbit satellites Reconfigurable intelligent surfaces Reliability theory Throughput Multiaccess communication Satellite communications

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Atomic Localization Fluorescent Microscopy

arXiv

引用

arXiv 2025年

作者： Duan, Yuqin Gu, Qiushi Hu, Yong Chen, Kevin C. Trusheim, Matthew E. Englund, Dirk R. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States DEVCOM U.S. Army Research Laboratory AdelphiMD United States

Super-resolution microscopy has revolutionized the imaging of complex physical and biological systems by surpassing the Abbe diffraction limit. Recent advancements, particular in single-molecular localization microscopy (SMLM), have pushed localization below nanometer precision [1–5], by applying prior knowledge of correlated fluorescence emission from single emitters [6]. However, achieving a refinement from 1 nm to 1 Ångström demands a hundred-fold increase in collected photon signal [6–8]. This quadratic resource scaling imposes a fundamental barrier in SMLM, where the intense photon collection is challenged by photo-bleaching, prolonged integration times, and inherent practical constraints [9]. Here, we break this limit by harnessing the periodic nature of the atomic lattice structure. Specifically, applying this discrete grid imaging technique (DIGIT) in a quantum emitter system, we observe an exponential collapse of localization uncertainty once surpassing the host crystal’s atomic lattice constant. We further applied DIGIT to a large-scale quantum emitter array, enabling parallel positioning of each emitter through wide-field imaging. These results showcase that DIGIT unlocks a potential avenue to applications ranging from identifying solid-state quantum memories in crystals to the direct observation of optical transitions in the electronic structure of molecules. Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice constants

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Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

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Nano-Micro Letters 2022年第12期14卷 630-644页

作者： Shen Shen Jia Yi Zhongda Sun Zihao Guo Tianyiyi He Liyun Ma Huimin Li Jiajia Fu Chengkuo Lee Zhong Lin Wang Jiangsu Engineering Technology Research Center for Functional Textiles Jiangnan UniversityNo.1800 Lihu AvenueWuxiP.R.China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-Nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 100083P.R.China China National Textile and Apparel Council Key Laboratory of Natural Dyes Soochow UniversitySuzhou 215123People’s Republic of China School of Material Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332-0245USA

Letter handwriting,especially stroke correction,is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the *** this study,a biodegradable and conductive carboxymethyl chitosan-silk fibroin(CSF)film is prepared to design wearable triboelectric nanogenerator(denoted as CSF-TENG),which outputs of V_(oc)≈165 V,I_(sc)≈1.4μA,and Q_(sc)≈72 mW cm^(−2).Further,in vitro biodegradation of CSF film is performed through trypsin and *** results show that trypsin and lysozyme have stable and favorable biodegradation properties,removing 63.1%of CSF film after degrading for 11 ***,the CSF-TENG-based human-machine interface(HMI)is designed to promptly track writing steps and access the accuracy of letters,resulting in a straightforward communication media of human and *** CSF-TENG-based HMI can automatically recognize and correct three representative letters(F,H,and K),which is benefited by HMI system for data processing and *** CSF-TENG-based HMI can make decisions for the next stroke,highlighting the stroke in advance by replacing it with red,which can be a candidate for calligraphy practice and ***,various demonstrations are done in real-time to achieve virtual and real-world controls including writing,vehicle movements,and healthcare.

关键词： Letter handwriting Triboelectric nanogenerator Biodegradable Human-machine interface Calligraphy practice

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SalienTR: A closer look at multi-modal transformer for RGB-T salient object detection

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Expert Systems with Applications 2025年 286卷

作者： Guo, Ruohao Yue, Wenzhen Qu, Liao Qi, Yanyu Niu, Dantong Ying, Xianghua School of Intelligence Science and Technology State Key Laboratory of General Artificial Intelligence Peking University Beijing100871 China Electrical and Computer Engineering department Carnegie Mellon University PA15213 United States College of Information and Electrical Engineering China Agricultural University Beijing100091 China BAIR Lab University of California BerkeleyCA94709 United States

Recently, transformer-based methods show impressive performance on challenging dense prediction tasks, yet far from perfect. In this paper, we present SalienTR, a novel and effective transformer-based model for high-quality RGB-T salient object detection. First, it builds on two parallel Swin Transformer encoders to extract hierarchical appearance representations of color maps and spatial information of thermal infrared maps. Next, a well-designed cross-modal fusion transformer, namely ComFormer, is devised to mine and fuse two-modality features. Specifically, the proposed ComFormer consists of three consecutive components: (1) A local cross-modal multi-head self-attention (LoC-MSA) to capture local correlations along space-modal dimensions;(2) A global cross-modal multi-head self-attention (GLoC-MSA) to build long-range dependencies between different modalities;(3) A uni-modal convolution (Uni-Conv) that exploits fused information and further learn latent semantics. Besides, we develop a dual-stream decoder that integrates multi-level features to generate both the final salient map and its corresponding edge map. Extensive experiments show that our SalienTR performs well on widely used RGB-T benchmarks and surpasses other state-of-the-art approaches. In addition, SalienTR exhibits high robustness under various challenging scenarios and transferable generalization on RGB-D benchmark datasets. © 2025 Elsevier Ltd

关键词： Object detection

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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