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Ultra-Low Concentration Biosensing Harnessing Aulter-Townes Doublets in Meta Waveguide Ring Resonators

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SSRN

SSRN 2025年

作者： Li, Wanxin Li, Jiewen Li, Rui Chen, Gina Jinna Shum, Perry Ping Zou, Yi Chen, Ray T. Dong, Yongkang Xu, Xiaochuan National Key Laboratory of Laser Spatial Information Guangdong Provincial Key Laboratory of Integrated Photonic-Electronic Chip Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Xili University Town Harbin Institute of Technology campus Guangdong Shenzhen518055 China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of EEE Southern University of Science and Technology Shenzhen518055 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering The University of Texas at Austin 10100 Burnet Rd. AustinTX78758 United States

Pushing sensitivity to ultra-low concentrations unlocks new possibilities in sample analysis. However, the limit of detection (LOD) in highly sensitive optical sensors is often constrained by noise. In this study, we address this limitation by leveraging Autler-Townes Splitting (ATS) within meta waveguide microring resonators (MWMRs) for self-referenced biological detection. This approach effectively suppresses noise, achieving a minimal baseline drift of 4.6 fm/min without the need for sophisticated environmental controls. By combining high sensitivity with robust noise suppression, ATS-based MWMRs enable the detection of streptavidin at an ultra low concentration of 0.01 pg/mL, with the blank solution serving as the baseline. These results highlight the significant potential of ATS-based MWMRs as a robust and versatile platform for biomolecule detection at ultra-low concentrations, paving the way for advancements in biological research and medical diagnostics. © 2025, The Authors. All rights reserved.

关键词： Optical resonators

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

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arXiv 2021年

作者： Khozeimeh, Fahime Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Alizadehsani, Roohallah Gorriz, Juan M. Hussain, Sadiq Sani, Zahra Alizadeh Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Department of Psychiatry University of Cambridge Cambridge United Kingdom Dibrugarh University Assam 786004 India Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Mathematics Faculty of Science University of Bojnord Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Selection of Material using IWDNN Algorithm for Multiphase Material Component Design

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Selection of Material using IWDNN Algorithm for Multiphase M...

2019 Global Conference for Advancement in Technology, GCAT 2019

作者： Sivaram, M. Karuppusamy, S. Anbu Porkodi, V. Manikandan, V. Lebanese French University Dept. of Computer Networking Erbil Kurdistan Region Iraq Excel Engineering College Department of ECE Namakkal India

ISBN: (纸本)9781728136943

A component is made of different materials in various regions, which is ascertained to satisfy the application requirements. The component of a material can be categorized into homogeneous with functionally graded materials and composites and heterogeneous with periodic microstructures. The design of such component is implemented from its function requirements to material properties, geometry, and microstructures or compositions. However, the presence of fractures in such material affects the properties of materials and reduces its lifetime. Hence, we propose an improved Intelligent Water Drop Neural Network (IWDNN) Algorithm to optimally select the microstructures and constituent compositions of material from a material database. The IWDNN algorithm is built with the material properties to identify the fractures present in microstructures and constituent compositions of materials. The proposed approach is tested using a special circular beam constructed with multiphase materials. © 2019 IEEE.

关键词： Microstructure

Guest Editorial: Sixth edition of the IEEE JSAC series on network softwarization and enablers

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IEEE Journal on Selected Areas in Communications 2020年第6期38卷 985-987页

作者： Taleb Aalto, Tarik Department of Communications and Networking School of Electrical Engineering Aalto University Espoo02150 Finland Computer and Information Security Department Sejong University Seoul05006 Korea Republic of

Reconfigurable Intelligent Computational Surfaces: When Wave Propagation Control Meets Computing

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arXiv 2022年

作者： Yang, Bo Cao, Xuelin Xu, Jindan Huang, Chongwen Alexandropoulos, George C. Dai, Linglong Debbah, Mérouane Poor, H. Vincent Yuen, Chau The Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore The College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University Hangzhou310007 China International Joint Innovation Center Zhejiang University Haining314400 China The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates The Department of Electronic Engineering Tsinghua University Beijing100084 China CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France Department of Electrical and Computer Engineering Princeton University NJ United States

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the ‘reconfigurable beamforming layer’ which is responsible for tunable signal reflection, absorption, and refraction, and the ‘intelligence computation layer’ that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted. Copyright © 2022, The Authors. All rights reserved.

关键词： Metamaterials

Virtual Cluster Head Based PSO and Fuzzy Logic Approach for WSN to Extend Network Lifetime

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Virtual Cluster Head Based PSO and Fuzzy Logic Approach for ...

2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Manikandan, V. Mavaluru, DInesh Gobinathan, K. Rajanarayanan, S. Jenifer, M. Rohini, R. Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia Gnanamani Collge of Technology Department of CSE Namakkal India Arba Minch University Dept. of Business Administration and Information System Ethiopia Arba Minch University Arba Minch Institute of Technology Faculty of Computing and Software Engineering Ethiopia Vivekanandha College of Engineering for Women Department of CSE Namakkal India

ISBN: (纸本)9781728126807

In recent times, Wireless Sensor Networks (WSNs) are broadly utilized in targeted monitoring applications. Traditional target coverage techniques usually consider that environment is known completely and every target is covered by single sensor. However those algorithms are inflexible when the sensor is about to die, that is, lack of energy and therefore target has to be covered with more than a sensor which is termed as n coverage crisis. In general, n coverage problem is energy and time consuming process, as well sensor organization is needed all the time. To resolve this crisis, this work anticipates a n coverage model with VCH-PSO (Virtual Cluster Head based Particle Swarm Optimization and EFLM (Energy management based Fuzzy logic Model) to enlarge the lifetime of WSN. During the search of optimal area coverage, diverse factors like node position, energy consumption, and coverage range of sensors are considered significantly. Simulation was carried out in MATLAB environment and compared with prevailing techniques. The proposed model shows better trade off in terms of network lifetime and time needed for switching between various coverage regions. © 2020 IEEE.

关键词： Fuzzy logic

Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

Uncertainty-aware semi-supervised method using large unlabelled and limited labeled COVID-19 data

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arXiv 2021年

作者： Alizadehsani, Roohallah Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Gorriz, Juan M. Hussain, Sadiq Arco, Juan E. Sani, Zahra Alizadeh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Acharya, U. Rajendra Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

GGS: General Gradient Sparsification for Federated Learning in Edge Computing*

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GGS: General Gradient Sparsification for Federated Learning ...

IEEE International Conference on Communications (ICC)

作者： Shiqi Li Qi Qi Jingyu Wang Haifeng Sun Yujian Li F. Richard Yu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The Department of Systems and Computer Engineering Carleton University Canada

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Federated learning is an emerging concept that trains the machine learning models with the distributed datasets, without sending the raw data to the data center. But in an edge computing enviroment where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameters synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Gradient sparsification has received increasing attention, which only updates significant gradients and accumulates insignificant gradients locally. However, how to preserve the accuracy after a high ratio sparsification has always been ignored. In this paper, a General Gradient Sparsification (GGS) framework is proposed for adaptive optimizers, to correct the sparse gradient update process. It consists of two important mechanisms: gradient correction and batch normalization update with local gradients (BN-LG). With gradient correction, the optimizer can properly treat the accumulated insignificant gradients, which makes the model converge better. Furthermore, updating the batch normalization layer with local gradients can relieve the impact of delayed gradients without increasing the communication overhead. We have conducted experiments on LeNet-5, CifarNet, DenseNet-121, and AlexNet with adaptive optimizers. Results show that when 99.9% gradients are sparsified, validation datasets are maintained with top-l accuracy.

关键词： Training Edge computing Computational modeling Servers Convergence Machine learning Adaptation models