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An integrated microfluidic platform for nucleic acid testing

Microsystems & Nanoengineering 2024年第3期10卷 181-194页

作者： Antao Sun Petra Vopařilová Xiaocheng Liu Bingqian Kou TomášŘezníček TomášLednický Sheng Ni JiříKudr Ondřej Zítka Zdenka Fohlerová Petr Pajer Haoqing Zhang Pavel Neužil Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical University127 West Youyi RoadXi’anShaanxi 710072P.R.China Department of Chemistry and Biochemistry Mendel University in BrnoZemědělská161300 BrnoCzech Republic ITD Tech s.r.o Osvoboditelů100573581 BohumínCzech Republic Central European Institute of Technology Brno University of TechnologyPurkyňova 123Brno 61200Czech Republic Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina Department of Microelectronics Faculty of Electrical Engineering and CommunicationBrno University of TechnologyTechnická3058/10Brno 61600Czech Republic Military Health Institute U Vojenskénemocnice 120016200 Praha 6Czech Republic The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and TechnologyXi’an Jiaotong UniversityXi’anShaanxi 710049P.R.China Bioinspired Engineering and Biomechanics Center(BEBC) Xi’an Jiaotong UniversityXi’an 710049P.R.China

This study presents a rapid and versatile low-cost sample-to-answer system for SARS-CoV-2 *** system integrates the extraction and purification of nucleic acids,followed by amplification via either reverse transcriptionquantitative polymerase chain reaction(RT–qPCR)or reverse transcription loop-mediated isothermal amplification(RT–LAMP).By meeting diverse diagnostic and reagent needs,the platform yields testing results that closely align with those of commercial RT-LAMP and RT‒qPCR *** advantages of our system include its speed and *** assay is completed within 28 min,including sample loading(5 min),ribonucleic acid(RNA)extraction(3 min),and RT-LAMP(20 min).The cost of each assay is≈$9.5,and this pricing is competitive against that of Food and Drug Administration(FDA)-approved commercial *** some RNA loss during on-chip extraction is observed,the platform maintains a potential limit of detection lower than 297 *** makes the system particularly useful in environments where centralized laboratories are either unavailable or inconveniently *** key feature is the platform’s versatility,allowing users to choose between RT‒qPCR or RT‒LAMP tests based on specific requirements.

关键词： testing maintain versatile

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical Recording systems

Certain Investigation of Optimization Methods of Sensor Node...

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International Conference on Advanced Computing and communication systems (ICACCS)

作者： N. Ashokkumar C. Kanmani Pappa Siva Kumar A Dhamodharan Srinivasan M. M. Vijay Department of ECE Mohan Babu University Tirupati India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of ECE SCAD College of Engineering and Technology Tirunelveli India

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. Downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence.

关键词： Wireless sensor networks Electrocardiography Sensor systems Biosensors Synchronization Biomedical monitoring Integrated circuit modeling

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Facial Emotion Recognition using YOLO based Deep Learning Classifier

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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Electronics, communication and Signal Processing (ICECSP), International Conference on

作者： Sri Vaibhav Mohan Dev Vanamoju Moturi Veda Vineetha Hitesh Tekchandani Pallavi Joshi Praveen Kumar Shukla Anita Khanna Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task.

关键词： YOLO Deep learning Emotion recognition Accuracy Neural networks Signal processing algorithms Signal processing Prediction algorithms Real-time systems Classification algorithms

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Matching Game for Optimized Association in Quantum communication Networks

Matching Game for Optimized Association in Quantum Communica...

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IEEE Conference on Global communications (GLOBECOM)

作者： Mahdi Chehimi Bernd Simon Walid Saad Anja Klein Don Towsley Mérouane Debbah Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Arlington VA USA Communication Engineering Lab Technische Universität Darmstadt Darmstadt Germany Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA USA Electrical Engineering and Computer Science Department Khalifa University of Science and Technology Abu Dhabi UAE

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

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Prediction of Alzheimer's Disease Using DHO-Based Pretrained CNN Model

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Mathematical Problems in Engineering 2023年第1期2023卷

作者： Venkatasubramanian, S. Dwivedi, Jaiprakash Narain Raja, S. Rajeswari, N. Logeshwaran, J. Praveen Kumar, Avvaru Department of Computer Science and Business Systems Saranathan College of Engineering Tamilnadu Trichy620012 India Electronics & Communication Engineering School of Engineering & Technology Lingaya's Vidyapeeth Haryana Faridabad India Managing Director Research & Development Mr. R BUSINESS CORPORATION Tamilnadu Karur India Department of Mechanical Engineering Surya Engineering College Tamilnadu Erode India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore641202 India Department of Applied Chemistry School of Applied Natural Science Adama Science and Technology University P.O. Box 1888 Adama Ethiopia

Detecting Alzheimer's disease (AD) early on allows patients to take preventative measures before the onset of irreversible brain damage, which is a critical factor in the treatment of Alzheimer's patients. Most machine detection methods are constrained by congenital observations, although computers have been utilized in several recent research studies to diagnose AD. In AD, the hippocampus is usually the first part of the brain to be affected. Structural magnetic resonance imaging (SMRI) can be used to assist in diagnosing AD by measuring the hippocampus's form and volume (MRI). The information encoded by these attributes is restricted and may be affected by segmentation problems. These traits are also extracted independently of the classification, which could result in lower-than-desired classification accuracy. Researchers in this study used structural MRI data to develop a deep learning framework for combined automatic hippocampus segmentation and AD categorization. Multi-task deep learning (MTDL) is used to learn hippocampus segmentation simultaneously. The hyperparameter optimization of the CNN model (capsule network) for illness classification is then carried out using the deer hunting optimization (DHO) technique. ADNI-standardized MRI datasets have been used to test the suggested method, and it is accurate. Suggested MTDL achieved 97.1% accuracy and 93.5% of Dice coefficient, whereas the proposed MTDL model achieved an accuracy of 96% for binary classification and 93% for multi-class classification. © 2023 S. Venkatasubramanian et al.

关键词： Magnetic resonance imaging

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Quantum Artificial Intelligence: Unlocking the Future of Technology

Quantum Artificial Intelligence: Unlocking the Future of Tec...

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Next Generation communication & Information Processing (INCIP), International Conference on

作者： G. Ignisha Rajathi L.R. Priya A. Vegi Fernando R. Vedhapriyavadhana S. Sasindharan J. Mohanalin Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India Department of Electronics and Communication Engineeing Farncis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India School of Computing Engineering and Physical Sciences University of the west of Scotland London UK Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Electrical and Electronics Engineering College of Engineering Perumon Kerala India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Quantum Artificial Intelligence (QAI) stands for the combination of the exercise of ‘quantum computing’ and ‘artificial intelligence’ that drives breakthrough transformation across various industries. In this paper, it is discussed on how QAI is bringing change across industries including pharmaceuticals, finance, weather prediction, supply chain, security, smart cities, agriculture, telecommunication, and education. With quantum mechanics conceptions, QAI enhance computation ability to address more data, and enhance the algorithms. Quantum technologies have potential for attachment to AI systems and make the previously unachievable innovations with the help of overgrowing the classical models. As a conclusion, this paper also discusses its result on the methodological difficulties and ethical issues of QAI, the need to build strategic partnerships and the need for well-regulated legislation. The results extend our understanding of QAI to apprehend the future of technology and its application for solving some of the greatest problems that the world faces today.

关键词： Drugs Ethics Technological innovation Quantum computing Smart cities Finance Weather forecasting Security Artificial intelligence Optimization

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A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

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computers, Materials & Continua 2021年第10期69卷 679-694页

作者： Ayman Abdulhadi Althuwayb Fazirulhisyam Hashim Jiun Terng Liew Imran Khan Jeong Woo Lee Emmanuel Ampoma Affum Abdeldjalil Ouahabi Sébastien Jacques Department of Electrical Engineering Jouf UniversitySakakaAljouf72388Kingdom of Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)Serdang43400Malaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan School of Electrical and Electronics Engineering Chung-Ang UniversitySeoul06974Korea Electrical and Electronic Department Kwame Nkrumah University of Science and TechnologyKumasiGhana UMR 1253 iBrainUniversitéde ToursINSERMToursFrance LIMPAF Laboratory Department of Computer ScienceUniversity of BouiraBouira10000Algeria University of Tours(France) GREMAN UMR 7347CNRSINSA Centre Val-de-LoireTours37100France

With the rapid development of the mobile internet and the internet of things(IoT),the fifth generation(5G)mobile communication system is seeing explosive growth in data *** addition,low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency *** wave(mmWave)technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless ***,it has an abundant resource spectrum,which can significantly increase the communication rate of a mobile communication *** such,it is now considered a key technology for future mobile *** communication technology also has a more open network architecture;it can deliver varied services and be applied in many *** contrast,traditional,all-digital precoding systems have the drawbacks of high computational complexity and higher power *** paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy *** primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition(GMD).In this process,the objective function of the spectral efficiency is derived,then the basic tracking principle and least square(LS)techniques are deployed to design the proposed hybrid *** results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45%compared to traditional algorithms.

关键词： 5G mmWave phased array algorithm antenna beamforming

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on computer communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels systems architecture User interfaces Mobile applications

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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GUEST EDITORIAL

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China communications 2021年第12期 4-7页

作者： Yingchang Liang Yue Gao Liang Xiao Yulong Zou Guoru Ding the University of Electronic Science and Technology of China the Center for Intelligent Networking and Communications the Artificial Intelligence Research Institute the School of Computer Science and Electronic Engineering Institute for Communication Systems University of Surrey Engineering and Physical Sciences Research Council the IEEE Com Soc Wireless Communication Technical Committee the Department of Information and Communication Engineering Xiamen University the NUPT the College of Communications Engineering PLA Army Engineering University

To meet the rapid growth of mobile communications services, the continuously upgraded wireless networks significantly increase the demand for radio spectrum ***, the spectrum supply is limited, thus triggering the spectrum demand-supply ***, dynamic spectrum sharing emerges as a promising paradigm of enhancing spectrum usage, which has been widely recognized and adopted in commercial wireless standards,

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