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Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

Light(Science & Applications) 2025年第4期14卷 931-947页

作者： Jiehua Zhou Liye Mei Mingjie Yu Xiao Ma Dan Hou Zhuo Yin Xun Liu Yan Ding Kaining Yang Ruidong Xiao Xiandan Yuan Yueyun Weng Mengping Long Taobo Hu Jinxuan Hou Yu Xu Liang Tao Sisi Mei Hui Shen Yaxiaer Yalikun Fuling Zhou Liang Wang Du Wang Sheng Liu Cheng Lei The Institute of Technological Sciences Wuhan UniversityWuhan 430072China School of Computer Science Hubei University of TechnologyWuhan 430068China Division of Materials Science Nara Institute of Science and TechnologyTakayama-cho 8916-5Japan School of Science Hubei University of TechnologyWuhan 430068China Department of Pathology Peking University Cancer HospitalBeijing 100142China Department of Breast Surgery Peking University People’s HospitalBeijing 100044China Department of Thyroid and Breast Surgery Zhongnan HospitalWuhan UniversityWuhan 430071China Department of Radiation and Medical Oncology Zhongnan HospitalWuhan UniversityWuhan 430071China People’s Hospital of Anshun City Guizhou Province Anshun 561000China Department of Hematology Zhongnan HospitalWuhan UniversityWuhan 430071China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China Suzhou Institute of Wuhan University Suzhou 215000China Shenzhen Institute of Wuhan University Shenzhen 518057China

Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental ***,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell *** this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image *** flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and *** capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal *** work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.

关键词： real time data processing flow cytometryoffering spatial resolution imaging flow cytometry ifc combines high throughput cell analysis cell analysis imaging flow cytometry optical time stretch imaging

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Co-Mask R-Cnn: Collaborative Learning-Based for Tooth Instance Segmentation

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SSRN 2023年

作者： Yang, Jingyu Liu, Hongzhi Wang, Chen Yu, Peng Jiang, Xijun The School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China A Department of Cariology and Endodontology Peking University School Hospital of Stomatology Beijing100081 China National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing Key Laboratory of Digital Stomatology Beijing China

Traditional tooth image analysis methods primarily focus on feature extraction from individual images, often overlooking critical tooth shape and position information. This paper presents a novel computer-aided diagnosis method, Co-Mask R-CNN, designed to enhance tooth image analysis by leveraging the integration of complementary information. Firstly, it employs image enhancement to generate an edge-enhanced tooth edge image. Then, it introduces a collaborative learning strategy combined with Mask R-CNN, where the original image and edge image are simultaneously inputted, and an two-stream encoder extract feature maps from complementary images. By utilizing an attention mechanism, the output features from the two branches are dynamically fused, quantifying the relative importance of the two complementary images in different spatial positions. Finally, the fused feature map is utilized for tooth instance segmentation. To evaluate the effectiveness of Co-Mask R-CNN, extensive experiments are conducted using a proprietary dataset, with a comparison against alternative segmentation network. The results demonstrate that Co-Mask R-CNN outperforms other networks in terms of both segmentation accuracy and robustness. Consequently, this method holds considerable promise in providing medical professionals with precise tooth segmentation results, thereby establishing a reliable foundation for subsequent tooth disease diagnosis and treatment. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Ultra-Compact Magnetoelectric Sensors for Femto-Tesla Vlf Signal Reception

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SSRN 2024年

作者： Dong, Cunzheng Sun, Changxing Chen, Lei He, Yifan Liu, Yisi Luo, Bin Sun, Nian-Xiang Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Key Lab of Computer Vision and Intelligent Information System Chongqing University of Arts and Sciences Chongqing402160 China

Very low-frequency (VLF) electromagnetic waves can penetrate dense conductive media, such as earth and saltwater, with minimal attenuation, allowing for long-distance signal transmission via ionospheric reflection. These characteristics make VLF waves ideal for applications in submarine navigation, subterranean mapping, underground communication, and ionospheric remote sensing. Conventional VLF signal reception has relied on magnetic loop antennas due to their low noise performance;however, their large size and reduced sensitivity due to low quality factors (Q) limit their use in portable and compact applications, particularly in underwater and underground environments. To address these challenges, we propose an ultra-compact room-temperature extremely sensitive femto-tesla magnetic sensor based on a strain-mediated high-Q Metglas/Quartz magnetoelectric (ME) resonator operating at its electromechanical resonance (EMR) at 24.55 kHz for VLF signal reception. The Metglas/Quartz ME sensor demonstrates a sensitivity and magnetic noise performance enhancement by an order of magnitude compared to conventional Metglas/PZT ME sensors, achieving an ultra-low equivalent magnetic noise level of 5 fT/Hz1/2, owing to high magnetic permeability and magnetostriction of Metglas and the high-quality factor of Quartz at EMR. Moreover, the Metglas/Quartz ME VLF receiver exhibits overwhelming near-field and far-field VLF signal reception capability, realizing a successful reception of VLF signal 400 km away from the NAA VLF Transmitter Cutler. The demonstrated ultra-compact high-Q Metglas/Quartz ME sensor capable of femto-tesla VLF signal reception shows significant improvements in magnetic sensing capability, size, power consumption, and cost compared to traditional magnetic loop antennas, making it a promising solution for portable VLF signal reception in challenging environments. © 2024, The Authors. All rights reserved.

关键词： Loop antennas

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A Novel Methodology for Design Optimization of Solar Water Pumping systems

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SSRN 2023年

作者： Ben Atitallah, Ahmed Romdhane, Jaouher Ben Atitallah, Mohamed Amin Boudabous, Anis Department of Electrical Engineering College of Engineering Jouf University Sakaka Saudi Arabia Research and Developpemen Unit Departement of Electrical Electronic and System Engineering TRISKELL Consulting 32 Rue Arago Puteaux92800 France Laboratory of Informatics Gaspard-Monge A3SI ESIEE Paris CNRS Gustave Eiffel University France LETI ENIS University of Sfax Sfax Tunisia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

It is still hard to design a solar PV water pumping system. It is a complicated system with a lot of moving parts on different levels, including architecture, component technologies, sizing, and control. It can be seen that there aren't any tools available right now that help explore the whole design space and find the best solution for all of these levels. In this paper, a novel methodology tries to satisfy a part of this need. The proposed methodology begins with the project specification and stakeholder requirements and then autonomously generates all possible architecture graphs using constraint programming techniques. The different candidates are filtered and analyzed, and the best ones are selected. The performance of the generated solutions is calculated and evaluated. The most promising solutions are integrated into an optimization process;a genetic algorithm is used on the sizing component. A case study is carried out, and the capability of the methodology is evaluated. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Attention-Driven Graph Convolutional Networks for Deadline-Constrained Virtual Machine Task Allocation in Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ahmad Ullah, Inam Singh, Sushil Kumar Jiang, Weiwei Alturise, Fahad Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen 518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Beijing University of Posts and Telecommunications Beijing China Qassim University College of Computer Department of Cybersecurity Buraydah Saudi Arabia

Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： attention consumer electronics Edge computing load balancing machine learning resource utilization virtual machine

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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An Improved Q-RRT^(*) Algorithm Based on Virtual Light

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computer systems Science & engineering 2021年第10期39卷 107-119页

作者： Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao Department of Computer Information and Cyber Security Jiangsu Police InsituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InsituteNanjing210031China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Jiangsu Police InsituteNanjing210031China University of Technology Sydney Sydney2007Australia

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random *** RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal ***,it has been proven to take an infinite time to do *** improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this *** virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the *** this way,the tree can find the initial solution ***,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal ***^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling *** efficiency of the algorithm is verified by comparison experiments in different simulation *** results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

关键词： Path planning RRT^(*) Q-RRT^(*) LQ-RRT^(*) virtual light

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A Review of some LEACH Optimization Protocols for Wireless Sensor Network Clustering

A Review of some LEACH Optimization Protocols for Wireless S...

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Energy Transition and Security (ICETS), International Conference on

作者： Mohammed Benhadji Mohammed Kaddi Mohammed Omari Aakila Lagouch Laboratory of Energy Environment and Information System (LEESI) Ahmed Draïa University Adrar Algeria LDDI Laboratory Ahmed Draia University of Adrar Adrar Algeria Computer Science and Engineering Department American University of Ras Al Khaimah United Arab Emirates Laboratory of Energy Environment and Information System (LESSI) Ahmed Draïa University Adrar Algeria

The range of using wireless sensor networks is very wide as its application includes most fields. Because of the impact of communication costs on node power consumption, this sort of network has restricted node capacity and a short lifespan. Sensor nodes in wireless sensor networks rely on their mostly non-rechargeable batteries to monitor, collect information and send it back to the Sink, via various techniques for routing. remarkably efficient and extensively used routing protocol is LEACH. as it is based on a hierarchical grouping of nodes. However, it suffers from some drawbacks. In this article, we attempt to address the LEACH protocol and a comparative presentation of some of the improvements that have come in the literature. Finally, we offer proposals for enhancing this technique.

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