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International Conference on Computing and Networking Technology (ICCNT)

作者： Sruthy Anand Sethuraman N Rao A.D. Dev Nandan B.Madan Gopal Madhav A Das Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Disasters affect a large number of people yearly and recurrently at many locations. Emergencies compel the local community to participate in disaster response; they are mostly the first responders during any disaster. Hence, it is necessary to devise solutions to support the local community and enhance their resilience. This work proposes a comprehensive platform for the victims, administrators, and volunteers during a catastrophe. It encourages the participation of the local community in managing local resources, requesting help, and getting acknowledged for the service they lend. The platform helps recognize the local community’s strengths and provides a method to receive information from disaster managers. The platform will also bring more situational awareness to disaster managers and volunteers. It also helps disaster managers assess the disaster’s potential impact and vulnerability levels of the community at an early stage. The platform envisions providing a decision support system for the spontaneous volunteers to co-work with disaster managers in a regulated fashion by eliminating fake volunteers and messages. The research also focuses on the characteristics of technology adoption for the widespread acceptance of the solution.

关键词： Decision support systems Social networking (online) Disasters Urban areas Prototypes Disaster management Emergency services Real-time systems Mobile applications Resilience

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Polarization-Entangled Photon-Pair Sources Based on 3R-MoS2

Polarization-Entangled Photon-Pair Sources Based on 3R-MoS2

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Weissflog, Maximilian A. Fedotova, Anna Tang, Yilin Santos, Elkin A. Laudert, Benjamin Shinde, Saniya Abtahi, Fatemeh Afsharnia, Mina Pérez, Inmaculada Pérez Ritter, Sebastian Qin, Hao Janousek, Jiri Shradha, Sai Staude, Isabelle Saravi, Sina Pertsch, Thomas Setzpfandt, Frank Lu, Yuerui Eilenberger, Falk Institute of Applied Physics Abbe Center of Photonics Friedrich Schiller University Jena Jena07745 Germany Institute of Solid State Physics Friedrich Schiller University Jena Helmholtzweg 3 Jena07743 Germany School of Engineering College of Science and Computer Science The Australian National University CanberraACT Australia Institute for Condensed Matter Physics Technical University of Darmstadt Darmstadt64289 Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena07745 Germany

We demonstrate for the first time generation of polarization-entangled photon-pairs in a transition metal dichalcogenide. Using 3R-phase molybdenum disulfide, we experimentally show tunable, high-fidelity generation o... 详细信息

ISBN: (纸本)9781957171395

关键词： Polarization

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A biosensing system employing nanowell microelectrode arrays to record the intracellular potential of a single cardiomyocyte

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Microsystems & Nanoengineering 2022年第3期8卷 185-198页

作者： Yuting Xiang Haitao Liu Wenjian Yang Zhongyuan xu Yue Wu Zhajian Tang Zhjing Zhu Zhiyong Zeng Depeng Wang Tianxing Wang Ning Hu Diming Zhang Department of Obstetrics and Gynecology Affiliated Dongguan People's HospitalSouthern Medical UniversityDongguan 523058China Research Center for Inelligent Sensing Systems Zhejiang LaboratoryHangzhou 311100China Research Center for Humanoid Sensing Zhejiang LaboratoryHangzhou 311100China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-Link Care Meditech Co. LtdHangzhou 310011China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemitryZhejiang UniversityHangzhou 310058China

Electrophysiological recording is a widely used method to investigate cardiovascular pathology,pharmacology and developmental *** arrays record the electrical potential of cells in a minimally invasive and highthroughput ***,commonly used microelectrode arays primarily employ planar microelectrodes and cannot work in applications that require a recording of the intracellular action potential of a single *** this study,we proposed a novel measuring method that is able to record the intracellular action potential of a single cardiomyocyte by using a nanowell patterned microelectrode array(NWMEA).The NWMEA consists of five nanoscale wells at the center of each circular planar *** pulse electroporation was applied to the NWMEA to penetrate the cardiomyocyte membrane,and the intracellular action potential was continuously *** intracellular potential recording of cardiomyocytes by the NWMEA measured a potential signal with a higher quality(213.76±25.85%),reduced noise root-mean-square(~33%),and higher signal-to-noise ratio(254.36±12.61%)when compared to those of the extracellular *** to previously reported nanopillar microelectrodes,the NWMEA could ensure single cell electroporation and acquire high-quality action potential of cardiomyocytes with reduced fabrication *** NWMEA-based biosensing system is a promising tool to record the intracellular action potential of a single cell to broaden the usage of microelectrode arrays in electrophysiological investigation.

关键词： arrays potential record

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview

Wireless Sensor Networks and Green Networking for 6G communi...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Dibyahash Bordoloi Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Faculty of Engineering Research Center for AI and IoT University of Kyrenia Kyrenia Turkey Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun RS CSE Graphic Era University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Greater Noida India

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms.

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Quantitative Electrostatic Potential Mapping in Dense Polycrystalline Functional Materials and Devices

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 280-281页

作者： Durham, Daniel B. Aabrar, Khandker Akif Ravindran, Prasanna Venkatesan Zaluzec, Nestor J. Stan, Liliana Khan, Asif Islam Datta, Suman Guha, Supratik Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Photon Sciences Directorate Argonne National Laboratory Lemont IL United States Center for Nanoscale Materials Argonne National Laboratory Lemont IL United States Pritzker School of Molecular Engineering University of Chicago Chicago IL United States

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Tackling Data Sparsity and Combinatorial Challenges in Rare Disease Matching with Medical Informed Machine Learning

Tackling Data Sparsity and Combinatorial Challenges in Rare ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Berger, Armin Lagones, Tom Anglim Grigull, Lorenz Fendrich, Lara Bell, Thiago Högl, Henriette Ernst, Gundula Schmidt, Ralf Bascom, David Sifa, Rafet Lübbering, Max Fraunhofer Iais Department of Media Engineering Germany University of Bonn Department of Computer Science Germany West-AI - Federal Ministry of Education and Research Germany Department of Health Queensland Australia Griffith University School of Medicine Australia University Hospital Bonn Center for Rare Diseases Germany Children's Network for Chronic Illnesses and Disabilities Germany Medical School Hannover Department of Medical Psychology Germany

ISBN: (纸本)9798350362480

With over 7,000 known rare diseases and a prevalence of less than one in a thousand, rare diseases pose substantial challenges to advanced medical support networks. This study investigates the efficacy of ***, a novel social networking platform designed for individuals affected by rare diseases, including patients, their family members, and medical professionals, addressing data sparsity and combinatorial complexities in user matching. We demonstrate that simple matching heuristics already serve as a decent basis for collecting user feedback on match quality. Leveraging over 10,000 user matching feedback scores from more than 2,000 active users, we evaluate algorithms including collaborative filtering and user embedding similarity with state-of-the-art Large Language Models (LLMs). With a top-10 and top-5 hit-rate of 55% and 37%, respectively, we show that a combination of medical data augmentation and embeddings significantly enhances performance beyond the initial heuristic baseline. © 2024 IEEE.

关键词： Collaborative filtering

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A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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Unsupervised optical flow estimation with dynamic timing representation for spike camera 23

Unsupervised optical flow estimation with dynamic timing rep...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Lujie Xia Ziluo Ding Rui Zhao Jiyuan Zhang Lei Ma Zhaofei Yu Tiejun Huang Ruiqin Xiong School of Computer Science Peking University and National Engineering Research Center of Visual Technology (NERCVT) National Engineering Research Center of Visual Technology (NERCVT) and Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology Peking University School of Computer Science Peking University and National Engineering Research Center of Visual Technology (NERCVT) and Institute for Artificial Intelligence Peking University

Efficiently selecting an appropriate spike stream data length to extract precise information is the key to the spike vision tasks. To address this issue, we propose a dynamic timing representation for spike streams. Based on multi-layers architecture, it applies dilated convolutions on temporal dimension to extract features on multi-temporal scales with few parameters. And we design layer attention to dynamically fuse these features. Moreover, we propose an unsupervised learning method for optical flow estimation in a spike-based manner to break the dependence on labeled data. In addition, to verify the robustness, we also build a spike-based synthetic validation dataset for extreme scenarios in autonomous driving, denoted as SSES dataset. It consists of various corner cases. Experiments show that our method can predict optical flow from spike streams in different high-speed scenes, including real scenes. For instance, our method achieves 15% and 19% error reduction on PHM dataset compared to the best spike-based work, SCFlow, in Δt = 10 and Δt = 20 respectively, using the same settings as in previous works. The source code and dataset are available at https://***/Bosserhead/USFlow.

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A Comprehensive Analysis of Deep Learning Frameworks to Mitigate the Impact of Varied Lighting and Weather Conditions

A Comprehensive Analysis of Deep Learning Frameworks to Miti...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Anshuman Mohanty Balasundaram A Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Center for Cyber Physical Systems Vellore Institute of Technology Chennai India

Object detection is an extensively researched field in computer vision and deep learning-related use cases. Popular frameworks such as YOLO, RCNN, SSD, FPN, RetinaNet, etc., and other cutting-edge detection systems are extensively used in various applications, such as autonomous vehicle driving scenarios and surveillance systems. However, in practical applications, the performance of the generic object detection systems suffers when subject to images with insufficient or poor lighting due to adverse weather conditions and various external factors. Several frameworks utilizing different deep learning and image processing-based approaches have been presented to enhance object detection considering recent advancements in this field. This paper provides a comprehensive overview of several contemporary techniques under various challenging lighting and weather situations. This study also talks about several public benchmark datasets and applications where varying illumination, or adverse weather conditions play a crucial role in estimating the efficacy of detection. Finally, the study summarizes the future scope and research direction.

关键词： Deep learning Solid modeling computer vision Surveillance Lighting Object detection Detectors

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy

A Study on Coherence Length and Interferometry in Digital Ho...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Takahiro Koga Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do Republic of Korea

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object.

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