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Research on CMUTs Design with Different Cell Arrangements

Recent Patents on engineering 2024年第1期18卷 110-118页

作者： Cao, Xuewen Yu, Yuanyu Liu, Xin Ma, Jingyi Zhang, Hua Wang, Lijia Intelligent Manufacturing Institute Hebei Vocational University of Industry and Technology Shi Jiazhuang050091 China School of Artificial Intelligence Neijiang Normal University Neijiang641100 China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau 999078 China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau 999078 China

Background: Capacitive micromachined ultrasonic transducers (CMUTs) is a promising component of mechanical-electrical-acoustical conversion, which shows valuable applications in non-distructive testing and obstacle detection fields. In order to study the influence of dimension parameters on the directivity of CMUTs, the three-dimensional multi-physics coupling model is proposed for CMUTs in this paper. Methods: The model combines multiple physical fields through finite element method (FEM), which is more efficient and accurate. The influence of dimension parameters on the transducer is studied from two aspects: the pitch between adjacent cells and cell number, respectively. Results: When increasing the pitch, the directivity becomes better if the pitch is less than half of the wavelength, the grating lobe and side lobe are aroused. However, the bandwidth at-3dB is reduced by up to 71.1% at the same time. When increasing the cell number, the better the direcitvity, while the change of the cell number has little effect on the bandwidth of the array. However, 5.4% frequency shift was caused due to the effective mass change of the transducer. Conclusion: In this study, a 3D model based on FEM is proposed for the influence of dimension parameter on CMUTs. The simulation results indicate that the directivity can be enhanced by chang-ing the dimension. At the same time, it also introduces other performance issues, such as bandwidth decrease and frequency shift through this approach. Therefore, the simulation proposed the guidance of CMUTs performance optimization and patented technology application work in the future. © 2024, Bentham science Publishers. All rights reserved.

关键词： Finite element method

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Mobile medical Assistant System for Laboratory Report Analysis and medical Drug Identification 4

Mobile Medical Assistant System for Laboratory Report Analys...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Warnakulasuriya, Deshani Dewangi, Tharushi Sewwandi, Navodya Rathnayake, Minoli Kodagoda, Nuwan Suriyawansha, Kushanra Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month's results of the medical lab report of the patient using machine learning. © 2022 IEEE.

关键词： Hospitals

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Eco-Friendly Production Forecasting in Industrial Pollution Control with IoT and Logistic Regression

Eco-Friendly Production Forecasting in Industrial Pollution ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sriram, S. Chandrakala, D. Kokulavani, K. Mohankumar, N. Vanitha, S. Murugan, S. Department of Electronics and Communication Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India J.J. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Maharashtra Nagpur India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Modern industrial pollution requires proactive measures to mitigate its environmental effects. This paper advances eco-friendly production forecasting using IoT and Logistic Regression (LR). Optimizing output, reducing pollution, and minimizing industrial activities' environmental imprint are goals. Industrial IoT sensors keep track of energy consumption, raw material use, and pollution in real-time. Processing and analyzing this data identifies production patterns and trends. LR is used to predict production factors and pollution levels. The model's use has several advantages. First, it provides real-time production environmental effect data. This lets firms alter production on the go, optimizing resource use and reducing emissions. The system can estimate future pollution levels based on projected production scenarios, enabling proactive pollution management and environmental compliance. LR and IoT enable sustainable industrial production. Company eco-friendly policies, waste reduction, and manufacturing efficiency may be improved by recognizing pollution drivers. This method supports global sustainability and environmental preservation. This technology is shown to revolutionize industrial pollution management, decrease ecological damage, and encourage environmentally aware production forecasting. It's a promising move toward eco-friendly production and meets the rising need for a responsible and sustainable industry. © 2024 IEEE.

关键词： Pollution control

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Framework for detecting the patients affected by COVID-19 at early stages using Internet of Things along with Machine Learning approaches with improved Accuracy 12

Framework for detecting the patients affected by COVID-19 at...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Devi, T. Priya, J. Sathya Deepa, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Velammal Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9781665480352

COVID-19 has been affecting the entire world from the year 2019 and in order to handle this pandemic situation, it is necessary to follow the measures till valid medicine has been found. The proposed approach helps in detecting as well as monitoring the COVID-19 on real time basis. Data is collected with the help of IoT devices for detecting this disease at the early stage. The components of the system are, (i) Collection of symptom data, (ii) Center of Isolation, (iii) Machine Learning approaches for analysis, (iv) Healthcare analysts and (v) Cloud. The three algorithms in machine learning used for detection of the virus are Decision tree, Support vector machine and Neural Network. These three algorithms are tested with the real time dataset and it is observed that all these algorithms have accuracy greater than 91%. Identifying the disease accurately with the three machine learning algorithms, effective results are produced. The response of treatment for every person who gets affected by the virus is then documented. © 2022 IEEE.

关键词： COVID-19

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Optimal Utilization of the Electric Vehicle in a Various Market Approach 5

Optimal Utilization of the Electric Vehicle in a Various Mar...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Mišljenović, Nemanja Knežević, Goran Žnidarec, Matej Dubravac, Marina Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In order to reduce greenhouse gases, it is necessary to transit to renewable energy sources and electric vehicles. The electricity consumer becomes a prosumer by installing a distributed generation from renewable energy sources or providing ancillary services to the system. The prosumer can buy energy from the grid when needed and sell the excess energy to the grid. However, current market conditions encourage prosumers to maximize self-consumption because the price when buying electricity is significantly higher than the price of selling electricity to the grid. This paper proposes and compares two market models for end customers with their own electricity generation currently applied in Croatia. The first model consists of electricity generation, an electric vehicle, and stationary battery storage. The second model features electricity generation and an electric vehicle, and the grid operates similarly to energy storage. The research is aimed at reducing investment and operating costs. The results show that savings can be achieved without additional investment in energy storage to increase flexibility depending on the chosen market model. © 2022 IEEE.

关键词： Investments

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Design of hybrid photovoltaic wind energy systems for residential and commercial applications for environmentally sustainable production

Design of hybrid photovoltaic wind energy systems for reside...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Ghali, Fatima Hassan, Ahmed R. Ahmed, Ibrahem Kalaw, Haider Saleem, Munqith Al-Tameemi, Ahmed Raed Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Energy is the potential that can be exerted via material or chemical means. Primitive energy comes from wind and sun power, fossil fuels, nuclear power, and hydropower. Primary energy comes from resources found in the environment. Photovoltaic wind energy is critical to any country's economic and social development. As the price of traditional energy sources continues to rise and its negative effects on the environment worsen, the adoption of renewable energy systems is likely to expand. Regarding production and industry, the cyclical nature of wind and photovoltaic renewable energy sources and their high investment cost are two key concerns. The Internet of Things-based Particle Swarm Optimization Algorithm algorithm(IoT-PSOA) has been proposed in this research to control and monitor PV wind energy systems in the green manufacturing sector. Applications of the Internet of Things in renewable energy production revolve around sensors attached to generation, transmission, and distribution machinery, allowing for remote monitoring and management of this equipment by companies. IoT lowers running expenses and lessens our already limited reliance on fossil fuels. A power control circuit that responds to variations in load provides a steady supply. The Internet of Things has shown that evaluation procedures are more productive, with more precise reporting and higher quality assurance. © 2023 IEEE.

关键词： Electric loads

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Metaheuristic Lightweight Cryptography for Security Enhancement in Internet of Things

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computers, Materials & Continua 2022年第8期72卷 3009-3023页

作者： Mahmoud Ragab Ehab Bahaudien Ashary Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia

The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different ***,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure *** this background,the current paper proposes a novel lightweight cryptography method for enhance the security in *** proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)*** the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography *** projected encryption algorithm works in line with encryption and decryption *** key selection is performed with the help of Artificial Intelligence(AI)tool named BWO *** the combination of AI technique and CCC operation,optimal security operation is improved in *** different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption *** results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.

关键词： Security images encryption time correlation coefficient black widow optimization artificial intelligence decryption time

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Application of Mobile Health in Mental Health Interventions

Application of Mobile Health in Mental Health Interventions

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Hussien, Naseer Ali Brayyich, Mohammed Hussian, Ahmed Dawood, Mustafa Nazar Majed, Safa Jawad, Israa Abed Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Mobile health services may effectively mitigate the obstacles to mental health treatment due to their ability to meet people where they are and provide instant assistance. M-Health allows for modifying and facilitating psychological support for well-being when the likelihood of success is greatest. The potential translation advantages for boosting mental health must be acknowledged. Yet, insufficient delivery of mental health technologies in the present world shows that novel means of increasing the accessibility to healthcare measures are necessary. Exploratory methods like randomized trials are used to improve and modify m-health services. Evidence gathered from this method may be used to develop and improve m-health programs. The data will be utilized to establish the specific locations and types of m-health operations required to integrate accurate therapy into ordinary practice. This study proposes and discusses Mobile Mental Health Interventions (MMHI), which consider issues during an MRI and provide a manual for psychiatric intervention developers. In comparison to existing methods, the suggested MMHI performs well in simulation © 2023 IEEE.

关键词： mHealth

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Point Cloud Completion Method Assisted by Projected Image

Point Cloud Completion Method Assisted by Projected Image

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Shujin Li, Zhaowen Wu, Runxun Zhou, Fan U-niversity of Sun Yat-sen Faculty of Journalism and Communication Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China School of Computer Science and Engineering University of Sun Yat-sen Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China

ISBN: (纸本)9781665410205

Image-guided point cloud completion task aims to utilize image information to address the uncertainties in point cloud completion inference. Although acquiring 2D image data is relatively simpler than 3D data, it is still ineffective in scenarios with occlusions where image data cannot be reliably obtained as a reference. Therefore, we propose a point cloud completion model assisted by projected image data, which addresses the limitations of acquiring 2D images by constructing projected images of the point cloud. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other advanced methods. © 2024 IEEE.

关键词：

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Screening of Autism Spectrum Disorder using Machine Learning Approach in Accordance with DSM-5 7

Screening of Autism Spectrum Disorder using Machine Learning...

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7th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2023

作者： Bose, Shreea Seth, Prakriti St Xavier's College Department of Post Graduate and Research Computer Science Kolkata India National Institute of Technology Department of Biotechnology and Medical Engineering Rourkela India

ISBN: (纸本)9798350305517

Autism Spectrum Disorder (ASD) is a specific category of neurodevelopmental disorder that can be associated with several behavioral conditions and has no known cure to date. ASD can be detected from a very early stage in childhood and upon successful detection can be ameliorated. There have been several clinical diagnosis procedures and they can be error-prone and time-consuming. Thus, machine learning-based prediction models for early-stage ASD as well as in adolescents and adults are being developed over the years. In our study, several parameters of ASD detection were implemented with open-source ASD datasets and analysed using several machine learning models like Logistic regression, XGboost, SVC and Naive Bayes. Among these XGboost showed the best performance. The outcomes of such analytical approaches demonstrate that, when suitably optimized, machine-learning techniques can offer robust predictions of Autism Spectrum Disorder (ASD) status. These findings imply that it may be feasible to employ these models for the early ASD detection, thereby enhancing the prospects of timely and effective intervention. XGBoost has given best results throughout all datasets, including cross validation. An accuracy of 100% has been achieved, making the model best for prediction. © 2023 IEEE.

关键词： Diagnosis

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