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arXiv 2024年

作者： Sikder, Niloy Khan, Al-Masrur Bairagi, Anupam Kumar Masud, Mehedi Tiang, Jun Jiat Nahid, Abdullah-Al Radboud University Medical Center Donders Institute for Brain Cognition and Behaviour Nijmegen Netherlands Faculty of Technology and Bionics Rhine-Waal University of Applied Sciences Kleve Germany Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Faculty of Engineering Multimedia University Cyberjaya63100 Malaysia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh

Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts’ living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses are the biggest biological threat faced by animal and plant species. It is often challenging to promptly detect the presence of a virus in a possible host’s body and accurately determine its type using manual examination techniques;however, it can be done using computer-based automatic diagnosis methods. Most notably, the analysis of Transmission Electron Microscopy (TEM) images has been proven to be quite successful in instant virus identification. Using TEM images collected from a recently published dataset, this article proposes a deep learning-based classification model to identify the type of virus within those images correctly. The methodology of this study includes two coherent image processing techniques to reduce the noise present in the raw microscopy images. Experimental results show that it can differentiate among the 14 types of viruses present in the dataset with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable way of virus identification subsidiary to the thorough diagnostic procedures. © 2024, CC BY.

关键词： computer aided diagnosis

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Radio-Frequency Pseudo-Null Induced by Light in an Ion Trap

arXiv

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arXiv 2025年

作者： Chung, Daun Cha, Yonghwan Shon, Hosung Park, Jeonghyun Lee, Woojun Lee, Kyungmin Cho, Beomgeun Choi, Kwangyeul Kim, Chiyoon Yoo, Seungwoo Kim, Suhan Jeong, Uihwan Kang, Jiyong You, Jaehun Kim, Taehyun Dept. of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo-null, a locus of points where the ion responds to light as if it were at the true rf null, despite being displaced from it. The phenomenon is fully explained by accounting for the general two-dimensional structure of micromotion and is experimentally verified under various potential configurations, with observations in great agreement with numerical simulations. The rf pseudo-null manifests as a line in a two-dimensional parameter space, determined by the geometry of the incident light and its overlap with the motional structure of the ion. The true rf null occurs uniquely at the concurrent point of the pseudo-null lines induced by different light sources. © 2025, CC BY.

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Observation of the symmetry-protected signature of 3-body interactions

arXiv

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arXiv 2024年

作者： Zhukas, Liudmila A. Wang, Qingfeng Katz, Or Monroe, Christopher Marvian, Iman Duke Quantum Center Departments of Electrical and Computer Engineering and Physics Duke University DurhamNC27708 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States

Identifying and characterizing multi-body interactions in quantum processes remains a significant challenge. This is partly because 2-body interactions can produce an arbitrary time evolution, a fundamental fact often called the universality of 2-local gates in the context of quantum computing. However, when an unknown Hamiltonian respects a U(1) symmetry such as charge or particle number conservation, N-body interactions exhibit a distinct symmetry-protected signature known as the N-body phase, which fewer-body interactions cannot mimic. We develop and demonstrate an efficient technique for the detection of 3-body interactions despite the presence of unknown 2-body interactions. This technique, which takes advantage of GHZ states for phase estimation, requires probing the unitary evolution and measuring its determinant in a small subspace that scales linearly with the system size, making it an efficient approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Arduino-Based Wearable Health Monitoring System for Elderly Care Using Internet of Things 1st

Arduino-Based Wearable Health Monitoring System for Elderly ...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Ather, Danish Singh, Anupam Khan, Rubina Liyakat Vijay, Richa Singh, Ramveer Kumar, Raj Amity University in Tashkent Tashkent Uzbekistan Department of Computer Science and Engineering Graphic Era Hill University Dehradun India The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Amity University Noida Uttar Pradesh Noida India Department of CSE Galgotias College of Engineering and Technology Greater Noida India Faridabad India

ISBN: (纸本)9783031843938

Concerns regarding the health and well-being of the old have been raised globally due to the elderly population’s rapid rise. In order to overcome these difficulties, this study introduces an Internet of Things (IoT)-based wearable health monitoring system for senior care. The suggested system combines a variety of sensors with wireless communication capabilities to continuously monitor the health and physical activities of elderly people. The central element for data collection, processing, and transmission is the Arduino platform, which is renowned for its simplicity and adaptability. In addition to an accelerometer for tracking movement and fall detection, the wearable gadget has sensors for assessing vital signs like heart rate, blood pressure, body temperature, and oxygen saturation. The Arduino board processes the data locally, and it is then wirelessly transferred to a centralized monitoring system using Wi-Fi or Bluetooth Low Energy (BLE) communication. Caretakers or medical experts can access the central monitoring system, which offers real-time visualization and analysis of the gathered data. It makes it possible to discover aberrant health situations early, to act quickly, and to perform remote monitoring. Additionally, the system may produce warnings and notifications in the event of crises, ensuring quick access to medical care. The suggested wearable health monitoring system built on Arduino has a number of benefits, including affordability, mobility, and simplicity of use. It gives elderly people the ability to preserve their independence while offering a safety net through ongoing health monitoring. Additionally, the system enables healthcare professionals to give individualized and timely interventions based on real-time health data and simplifies remote caregiving. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Advances in solar energy harvesting integrated by van der Waals graphene heterojunctions

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RSC Advances 2023年第44期13卷 31273-31291页

作者： Le, Top Khac Mai, The-Hung Iqbal, Muhammad Aamir Vernardou, Dimitra Dao, Van-Duong Ponnusamy, Vinoth Kumar Rout, Chandra Sekhar Pham, Phuong V. Faculty of Materials Science and Technology University of Science Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan School of Materials Science and Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering School of Engineering Hellenic Mediterranean University Heraklion71410 Greece Faculty of Biotechnology Chemistry and Environmental Engineering Phenikaa University Hanoi100000 Viet Nam Department of Medicinal and Applied Chemistry and Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan Department of Chemistry National Sun Yat-sen University Kaohsiung80424 Taiwan Centre for Nano and Material Sciences Jain University Bangalore562112 India

Graphene has garnered increasing attention for solar energy harvesting owing to its unique features. However, limitations hinder its widespread adoption in solar energy harvesting, comprising the band gapless in the molecular orbital of graphene lattice, its vulnerability to oxidation in oxidative environments, and specific toxic properties that require careful consideration during development. Beyond current challenges, researchers have explored doping graphene with ionic liquids to raise the lifespan of solar cells (SCs). Additionally, they have paid attention to optimizing graphene/Si Schottky junction or Schottky barrier SCs by enhancing the conductivity and work function of graphene, improving silicon's reflectivity, and addressing passivation issues at the surface/interface of graphene/Si, resulting in significant advancements in their power conversion efficiency. Increasing the functional area of graphene-based SCs and designing efficient grid electrodes are also crucial for enhancing carrier collection efficiency. Flaws and contaminants present at the interface between graphene and silicon pose significant challenges. Despite the progress of graphene/Si-based photovoltaic cells still needs to catch up to the efficiency achieved by commercially available Si p-n junction SCs. The low Schottky barrier height, design-related challenges associated with transfer techniques, and high lateral resistivity of graphene contribute to this performance gap. To maximize the effectiveness and robustness of graphene/Si-based photovoltaic cells, appropriate interlayers have been utilized to tune the interface and modulate graphene's functionality. This mini-review will address ongoing research and development endeavors using van der Waals graphene heterojunctions, aiming to overcome the existing limitations and unlock graphene's full potential in solar energy harvesting and smart storage systems. © 2023 The Royal Society of Chemistry.

关键词： Graphene

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Optimizing Electricity Theft Detection using Neural Turing Machines and Machine Learning

Optimizing Electricity Theft Detection using Neural Turing M...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： Erdal Büyükbiçakci Selcuk Bulat C Senthilkumar A Rajesh Malatthi Sivasundaram Rama Raju Svsv Palla Department of Computer Technologies University of Applied Sciences Information Technologies Vocational Highschool Sakarya Turkey Department of Nanoscience and Nanoengineering Institute of Natural Sciences Sakarya University Turkey 2 Turkish Standards Institution Istanbul Turkey Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of CSE Erode Sengunthar Engineering College Erode India Department of Computer Science and Design KSR College of Engineering Tiruchengode India Department of CSE Vignan’s Institute of Information Technology Duvvada India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Electricity theft detection (ETD) is crucial for smart grids to maintain their cost-effectiveness. Current approaches for identifying theft struggle with large datasets of electricity use owing to problems with missing values, data variance, and nonlinear patterns in the data; furthermore, an integrated infrastructure to coordinate the study of electricity load data is not currently in *** suggested method trains models after feature extraction and stage preprocessing. Data normalization, missing value imputation, and data cleansing are all components of data preprocessing. Utility data sets often suffer from inconsistent and missing data as a result of manual and unsynchronized meter reading and registering processes; however, this can be mitigated through the feature extraction process. All the way through training, the proposed approach stuck with the NTM paradigm. The average accuracy of this state-of-the-art approach is $\mathbf{94.22\%}$, which is higher than RNN and ELM.

关键词： Training Accuracy Turing machines Electricity Biological system modeling Feature extraction Imputation Smart grids Meter reading Synchronization

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Users Acceptance Online Attendance Application Using Barcode Scanner: The Case Study in al Buraimi University College (BUC) Students

Users Acceptance Online Attendance Application Using Barcode...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Alsideiri, Abir Alsharida, Rawan A. Hammood, Maytham Shakir, Mohanaad Binti Cob, Zaihisma Rashid Al Shamsi, Ibrahim Muttar, Ahmed Kh. Abood, Rehab Sheker, Mustefa Buraimi Information Technology Department Oman Tikrit University Computer Science Department Tikrit Iraq Cob University of Buraimi College of Business Buraimi Oman Universiti Tenaga National College of Computing and Informatics Malaysia Applied Science University College of Administrative Sciences Manama Bahrain Al-Fallujah Secondary School for Girls General Directorate of Anbar Education Fallujah Iraq Department of the Sewage of Fallujah Ministry of Municipalities and Public Works Fallujah Iraq

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Marking students' attendance within a class session is the prime task of the subject requirements since marking the attendance will regulate the students to attend the session. Moreover, it verifies how many students are attending the class. Previously, attendance was recorded manually by pen and paper. This method takes a long time, especially if the number of students is significant and adds workload to the teacher, and sometimes the data may be prone to error. This paper presents a mobile application for student attendance to avoid these issues. This application will ease the process of attending to the students in the registration instead of the traditional ones where the teacher spends time calling students to know who is present or absent. The students will use their mobile to log in to the application and select the current class to open the camera to scan the code provided by the teacher, and automatically their attendance will be registered. This application is mainly designed for Buraimi University College lecturers and students. © 2022 IEEE.

关键词： E-learning

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Esophageal Cancer Diagnosis with a Bilinear Pooling and Attention-Based Convolutional Neural Network 1st

Esophageal Cancer Diagnosis with a Bilinear Pooling and Atte...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Raina, Vikas Byeon, Haewon Bhende, Manisha Yogi, K. Sri Keshta, Ismail Sardana, Kanishka CSE SET Mody University of Science and Technology NH-52 Lakshmangarh Sikar332311 India Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Pune Tathawade Pune India Faculty Department of Operations Management Symbiosis Institute of Business Management Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9783031860683

In the context of esophageal cancer diagnosis through gastrointestinal endoscopy, this study aims to examine the issues that are related with variability and misinterpretation of lesions observed under white-light endoscopy. To overcome these obstacles, the study introduces a convolutional neural network (CNN) that combines bilinear pooling and attention mechanisms. The CNN, built on the ResNet50 architecture, incorporates a novel global channel attention module and utilizes bilinear pooling to enhance feature representation by fusing multiple feature layers. The results, based on a dataset of 2101 cases from various clinical centers and white-light endoscopy images, demonstrate the effectiveness of this approach. It achieves high accuracy in classifying esophageal lesions, with classification rates of 94.2% at the image level and 96.9% at the patient level. In terms of esophageal cancer detection, it shows a sensitivity and specificity of 95.4% and 98.8% at the image level and 98.7% and 95.9% at the patient level. Notably, this approach outperforms recent models and methods in comparative experiments. These results show that the suggested network greatly raises the diagnostic accuracy for esophageal cancer under white-light endoscopy while maintaining its robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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AI-Driven VANETs for IoT-Enabled Transportation Systems 1st

AI-Driven VANETs for IoT-Enabled Transportation Systems

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Singh, Ramveer Kumar, Raj Singh, Anupam Vijay, Richa Khan, Rubina Liyakat Ather, Danish Department of CSE Galgotias College of Engineering and Technology Greater Noida India Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Amity University Uttar Pradesh Noida India The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Amity University in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9783031843938

This paper presents an innovative approach to address traffic congestion and safety challenges in smart cities by leveraging Artificial Intelligence (AI)-driven Vehicular Ad-Hoc Networks (VANETs) within IoT-enabled transportation systems. The integration of AI algorithms, such as machine learning and deep learning, enables seamless communication among connected vehicles and IoT infrastructure. Real-time data analysis facilitates effective traffic flow control, congestion detection, and accident prediction at the same instance security and privacy concerns are addressed through robust solutions. The current work showcases simulations and case studies, highlighting significant improvements in traffic efficiency, reduced travel time, and enhanced transportation safety. The study emphasizes the transformative potential of AI-driven VANETs in creating intelligent transportation systems for future smart cities, fostering more sustainable and liveable urban environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Vehicular ad hoc networks

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Methodological insights from unmanned system technologies in a rock quarry environment and geomining heritage site: coupling LiDAR-based mapping and GIS geovisualisation techniques

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Arabian Journal of Geosciences 2023年第11期16卷 1-18页

作者： Pires, Ana Dias, André Silva, Pedro Ferreira, António Rodrigues, Paulo Santos, Tiago Oliveira, Alexandre Freitas, Liliana Martins, Alfredo Almeida, José Silva, Eduardo Chaminé, Helder I. INESCTEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Laboratory of Autonomous Systems Department of Electrotechnical Engineering School of Engineering (ISEP) Polytechnic of Porto Porto Portugal Laboratory of Cartography and Applied Geology (Labcarga) Department of Geotechnical Engineering School of Engineering (ISEP) Polytechnic of Porto Porto Portugal Centre GeoBioTec|UA Aveiro Portugal

The main goal of the research is to present a methodological approach based on geotechnologies to acquire, process and analyse high-resolution 3D data to support georesources monitoring and geoheritage site preservation. The work is focused on the unmanned aerial vehicle (UAV) mapping technology with applications in a challenging framework, i.e. rock quarry and geomining heritage sites. The investigation interconnects several ground fields: engineering geosciences, rock engineering, sensing and aerial technology, and data processing systems for acquiring high-resolution imagery. The study couples two technologies: (1) robotic systems and (2) LiDAR-based mapping and GIS geovisualisation techniques. The approach presented herein shows the complementarity between techniques to improve the rock quarry’s geotechnical assessment and geomining heritage site. It was also possible to operate 3D recording technologies to preserve and record ancient mines’ cultural heritage and surroundings. The methodology shows a common ground for an integrated vision for rocky georesources monitoring by applying UAV mapping technologies. The final outputs allowed the image processing, geospatial analysis and 3D ground modelling of the ‘Malaposta’ gneiss quarry (Lourosa) and the Roman mine ‘Fojo das Pombas’ (Valongo), both sites in NW Portugal.

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