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CLEO: QELS_Fundamental Science, QELS 2022

作者： Seshadri, Suparna Lu, Hsuan-Hao Leaird, Daniel E. Weiner, Andrew M. Lukens, Joseph M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We demonstrate a simple and versatile scheme to generate all four two-dimensional frequency-bin Bell states using single and dual spectral-line pumps and passive filtering. Experimentally, we measure ≥97% fidelity fo... 详细信息

ISBN: (纸本)9781557528209

关键词： Spectroscopy

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Characterization of plain and ZnO coated D-shaped plastic optical fiber sensor of different diameters 8

Characterization of plain and ZnO coated D-shaped plastic op...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. Saidin, Norazlina Bte Khalaf, Ahmed Lateef Roslan, F.S. Haider, Muhammad Luqman Universiti Kuala Lumpur British Malaysian Institute Communication Technology Section Kuala lumpur53100 Malaysia International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur53100 Malaysia Al-ma'Moon University College Department of Computer Engineering Techniques Baghdad Iraq University of Texas at Dallas Department of Electrical Engineering TX United States

ISBN: (纸本)9781665491068

The sensitivity and simple fabrication of fiber sensor are major concerns now a days. In this work, the effect of sensor diameter and zinc oxide (ZnO) sensing layer on the intensity of D-shaped multimode plastic optical fiber (POF) sensor was experimentally investigated. A simple side polishing method was used to prepare a fiber sensor of various diameters having a length of 30 cm. The sensor is coated with ZnO using hydrothermal method. The behavior of both plain and coated fiber sensor in terms of intensity were recorded using spectrometer. After analysis, it was observed that plain and coated sensor having a diameter of 500 generated strong evanescent waves, which resulted in a lower intensity and higher absorbance. The proposed method is efficacious for improving the sensitivity of optical fiber sensors. © 2022 IEEE.

关键词： Zinc oxide

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Quantum delay metrology with complete frequency-bin Bell basis synthesizer

Quantum delay metrology with complete frequency-bin Bell bas...

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Frontiers in Optics, FiO 2022

We generate all four frequency-bin Bell states via single and dual spectral-line pumping of spontaneous parametric down-conversion and demonstrate their sensitivity to the sum and difference of entangled-photon delays... 详细信息

ISBN: (纸本)9781557528209

关键词： Quantum entanglement

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Smart Sampling and Residual Bottleneck Dense Network for Reliable and Energy-Efficient Routing in WSNs

Smart Sampling and Residual Bottleneck Dense Network for Rel...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Asma Anjum Dattathreya Salman Arafath Mohammed Dhaval Rabadiya K. B. Venkata Brahma Rao Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Alva’s Institute of Engineering and Technology Moodbidri Karnataka Department of Electrical Engineering Computer Engineering Section College of Engineering King Khalid University Abha Asir Region Saudi Arabia Faculty of Management Studies Parul University Dist. Vadodara Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The fundamental requirement for WSNs made up of power-efficient and inexpensive sensor nodes depends on short-distance multi-hop communication. The potential of WSNs expands through new digital electronics and wireless communications technology despite the ongoing critical challenge related to energy efficiency. The main energy issues in WSNs stem from the excessively high control signal overhead between base stations and sensor nodes together with packet losses that require retransmissions to reduce energy efficiency. The proposed routing technique solves energy inefficiencies by employing RBDN-HMOA as an energy-efficient routing method. The proposed approach lowers system power consumption through its algorithm which picks distance and energy residual-based optimum forwarding nodes and adapts sampling rates automatically. The algorithm adopts Human Memory Optimization Algorithm (HMOA) to optimize network parameters. Research data shows that experimental results demonstrate both power efficiency and enhanced data transfer rate. The network demonstrates scalability because both throughputs reach 1100 kbps and energy usage reduces to 0.08 J when using 10 nodes. The RBDN-HMOA creates a practical method which extends network duration in practical deployments by diminishing WSN energy utilization without compromising data integrity.

关键词： Wireless sensor networks Energy consumption Fluctuations Heuristic algorithms Throughput Routing Energy efficiency Real-time systems Delays Optimization

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Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios

arXiv

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arXiv 2024年

作者： Spanos, Thodoris Fabra, Fran López-Salcedo, José A. Seco-Granados, Gonzalo Kanistras, Nikos Lapin, Ivan Paliouras, Vassilis Dept. of Electrical and Computer Engineering University of Patras Greece Loctio Greece Spain Radio Navigation Systems and Techniques Section European Space Agency Netherlands

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and practical tests on a developed dedicated testbed featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT), and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the environment where the measurements are conducted, has been constructed. Simulation results using this model are presented, along with the actual measurements. The obtained results affirm the effectiveness of our implementation. © 2024, CC BY-NC-ND.

关键词： Testbeds

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems Science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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A novel hybrid CNN-KNN ensemble voting classifier for Parkinson’s disease prediction from hand sketching images

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Multimedia Tools and Applications 2024年 1-33页

作者： Saleh, Shawki Ouhmida, Asmae Cherradi, Bouchaib Al-Sarem, Mohammed Hamida, Soufiane Alblwi, Abdulaziz Mahyoob, Mohammad Bouattane, Omar EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco STIE Team CRMEF Casablanca-Settat Provincial Section of El Jadida El Jadida24000 Morocco College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia 2IACS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco Department of Computer Science Applied College Taibah University Medina Saudi Arabia Dept. of Languages and Translation College of Arts and Humanities Taibah University Medina Saudi Arabia Technical Community College Taiz Yemen

Parkinson's disease is a progressive neurodegenerative disorder that causes significant physical disabilities and reduces the quality of life. This disease is caused by the loss of dopamine-producing cells in the brain. Its symptoms are Speech disorders, muscle rigidity, bradykinesia and tremors that cause involuntary shaking or trembling, typically starting in the hands, fingers, or limbs at rest. In this work, we focused on predicting this disease via the hand tremor, which appears in the speed and pen pressure that vary between healthy and affected people during sketching spiral and waves. To enhance the medical services, improve lifestyle and for early detection of people with Parkinson’s disease, we proposed an ensemble voting classifier that combines the Convolutional Neural Network and K-Nearest Neighbours (KNN) to decide whether or not a person has Parkinson's disease based on predicting spiral and wave sketching separately. Contrary to the traditional Convolutional Neural Network, the proposed architecture offers better flexibility in scenarios where data may be small and imbalanced to avoid overfitting or when capturing the nuanced relationships between data points (by considering their neighbours) can be beneficial. Moreover, the proposed system has been built to automate the extraction of features from images and perform classification. This work represents several approaches, such as image processing, developing Convolutional Neural Networks models, hyper-parameters tuning, transfer learning, feature extraction and developing a hybrid classifier that combines deep learning and machine learning to enhance the performance of the prediction. We have developed six models to predict Parkinson's disease using a Spiral-Wave dataset and provided a detailed explanation and comparison between their performances. Based on these models, we built the hybrid ensemble voting CNN-KNN classifier that reached 96.67% accuracy and 93.33% and 100% sensitivity and precision,

关键词： Feature extraction

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Incorporating Onboard Visual Sensing and Geographic Information for Lane-Level Vehicle Localization

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IEEE Sensors Journal 2025年

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-Information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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A Mathematical Model for the Selection of Software Requirements Elicitation Techniques

A Mathematical Model for the Selection of Software Requireme...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Virat Raj Saxena Rajeev Yadav Azra Parveen Mohd. Sadiq Department of Computer Science and Engineering Mangalayatan University Aligarh Uttar Pradesh India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi India

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model.

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Design and key technology research of virtual training system for new coronavirus detection 16

Design and key technology research of virtual training syste...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Yi, Zhongxin Yang, Yuting He, Nan Lou, Yan Dalian Ocean University Information Engineering College Dalian China ChinaMedicalUniversity Computer Teaching and Research Section ShenYang China

ISBN: (纸本)9781665414685

Aiming at the problems of large consumption of new coronavirus detection materials and high risk of infection, a virtual training system for new coronavirus detection is designed and developed based on virtual reality technology. The system establishes a 3D model of new coronavirus detection scene and medical equipment, and simulates the process of pre detection, diagnosis, wearing protective clothing and nucleic acid collection. Combined with the system evaluation function, it can effectively improve the learning interest and *** results show that the irreversible medical operation with high risk of infection and the practice that is difficult to practice on site at ordinary times can be converted into the operation that can be practiced *** has a certain reference significance to explore the application of virtual reality technology in medical training. © 2021 IEEE.

关键词： Coronavirus

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