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Thin ceramic PZT dual-and multi-frequency pMUT arrays for photoacoustic imaging

Microsystems & Nanoengineering 2022年第6期8卷 23-34页

作者： Qincheng Zheng Haoran Wang Hao Yang Huabei Jiang Zhenfang Chen Yao Lu Philip X.-L.Feng Huikai Xie School of Integrated Circuits and Electronics Beijing Institute of Technology(BIT)100081 BeijingChina Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Medical Engineering University of South FloridaTampaFL 33620USA MEMS Engineering and Materials Inc. SunnyvaleCA 94086USA BIT Chongqing Institute of Microelectronics and Microsystems 400030 ChongqingChina

Miniaturized ultrasonic transducer arrays with multiple frequencies are key components in endoscopic photoacoustic imaging(PAI)systems to achieve high spatial resolution and large imaging depth for biomedical *** this article,we report on the development of ceramic thin-film PZT-based dual-and multi-frequency piezoelectric micromachined ultrasonic transducer(pMUT)arrays and the demonstration of their PAI *** chips sized 3.5mm in length or 10mm in diameter,square and ring-shaped pMUT arrays incorporating as many as 2520 pMUT elements and multiple frequencies ranging from 1 MHz to 8 MHz were developed for endoscopic PAI *** ceramic PZT with a thickness of 9μm was obtained by wafer bonding and chemical mechanical polishing(CMP)techniques and employed as the piezoelectric layer of the pMUT arrays,whose piezoelectric constant d_(31)was measured to be as high as 140 pm/*** from this high piezoelectric constant,the fabricated pMUT arrays exhibited high electromechanical coupling coefficients and large vibration *** addition to electrical,mechanical,and acoustic characterization,PAI experiments with pencil leads embedded into an agar phantom were conducted with the fabricated dual-and multi-frequency pMUT *** signals were successfully detected by pMUT elements with different frequencies and used to reconstruct single and fused photoacoustic images,which clearly demonstrated the advantages of using dual-and multi-frequency pMUT arrays to provide comprehensive photoacoustic images with high spatial resolution and large signal-to-noise ratio simultaneously.

关键词： ceramic piezoelectric dual

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Application and Realization of Three-dimensional Virtual Garment Technology Based on Two-dimensional Hand Drawing 2

Application and Realization of Three-dimensional Virtual Gar...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Meenakshi, R. Thomas Leonid, T. Celine Kavida, A. Dodmane, Radhakrishna Francis, Divya Muthu Krishnammal, P. Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of ECE KCG College of Technology KCG nagar karapakam Chennai India Department of Physics Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Karnataka India Department of ECE PSNA College of Engineering and Technology Tamilnadu Dindigul India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798350305418

The computer-aided design is the most important of the costume designing, and it is the power source of the contemporary costume design development. Through three-dimensional image analysis and simulation of costume design, can realize the enterprise mass customization production, enhancing the economic efficiency of enterprises. In this paper, the application and realization of three-dimensional virtual garment technology based on two-dimensional hand drawing is studied, and a quadrilateral mesh algorithm based on the regular grid method is applied to realize the transformation of two-dimensional clothing sheet to three-dimensional flexible surface, and the stitching process of three-dimensional virtual garments is given. Experimental results showed that the proposed method can accurately predict the rationality of different types of apparel design. © 2023 IEEE.

关键词： CAD Costume Design Three-dimensional Virtual Clothing Two-dimensional Hand Drawing

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Improved Pulse Coupled Neural Network based on Maximize SNR for Vessel Extraction

Improved Pulse Coupled Neural Network based on Maximize SNR ...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Singh, Davinder Paul Murat Singh, Ram Gopi, T. Deepa, D. Gandhi, K. Parimala Kumar, Divesh Pandit Deen Dayal Energy University Department of Computer Science and Engineering Gandhinagar India Woxsen University School of Technology Telangana India Annamacharya Institute of Technology and Sciences Department of Ece Hyderabad India St Joseph's College of Engineering Department of Ads Chennai India Nehru Institute of Technology Department of Chemistry Coimbatore India Graphic Era Hill University Department of Electronics and Communication Engineering Uttarakhand India

ISBN: (纸本)9798350363289

Aiming at the problems of branch loss and fracture in the fundus retinal vessel segmentation algorithm, with improved pulsed coupled neural network (PCNN) is proposed. As the matched filter can maximize the signal to noise ratio even though using the gaussian filter, therefore, it is used for reducing the noise. First, matching filters are used to enhance the retinal image, and then a segmentation algorithm is suggested using a combination of matching filter and PCNN. Finally, the evaluation on DRIVE database demonstrates that the sensitivity, specificity, and accuracy of the algorithm are 0.7310, 0.9640, and 0.9711 accordingly compared with other algorithms. Simulation experiments illustrate that the Matched-PCNN algorithm resolves the effect of low light and optic disc highlight on vessel segmentation, and demonstrates good connectivity between blood vessels. © 2024 IEEE.

关键词： Matched filters

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An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

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computer Systems Science & engineering 2023年第7期46卷 701-717页

作者： Ahmed Ben Atitallah Mohamed Amin Ben Atitallah Yahia Said Mohammed Albekairi Anis Boudabous Turki MAlanazi Khaled Kaaniche Mohamed Atri Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Laboratory of Informatics Gaspard-MongeA3SIESIEE ParisCNRSGustave Eiffel UniversityFrance LETI ENISUniversity of SfaxSfaxTunisia Remote Sensing Unit College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) University of MonastirTunisia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from *** this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into *** system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been *** this study,an acceleration of the GCM algorithm(AGCM)is *** AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original *** assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech *** text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

关键词： Text recognition system GCM AGCM OCR color images graphical interface

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Natural Language Processing in Healthcare: Intelligent System for Extracting Insights from Clinical Text data

Natural Language Processing in Healthcare: Intelligent Syste...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Rashmi, C. Srinivas, Dava Prabu, S. Santosh Kumar, P.N. Siddiqua, Ayesha Malla Reddy Engineering College for Women It Department Hyderabad India Jyothishmathi Institute of Technology & Science Department of Computer Science and Engineeing Karimnagar India Mahendra Institute of Technology Department of Ece Namakkal India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The World Health Organization identified COVID-19 to be a new pandemic in March 2020. This fatal virus spread over the world, affecting many different countries. Massive volumes of useful data were provided by social media platforms like Twitter on the outbreak to help evaluate health-related decision-making. To predict disease occurrence and issue early warnings, thus implement that user's attitudes be evaluated through the use of efficient deep learning. The gathered tweets were pre-processed and divided into three categories like neutral, positive, and negative. In the second step, various features were extracted from the articles using a number of popular techniques to capture feature dataset, including TF-IDF, FastText, and Glove. The implemented TFIDF has combination of semantic features (Glove and FAsText) to accurately describe posts and enhance the classification process utilizing the deep learning method. The implemented TF-IDF-CNN method's performance is evaluated using Twitter dataset. Implemented method achieved better performance than the existing intrusion detection methods including NLP-BiLSTM, TF-IDF, SVM and CF and the metrics are 96% of accuracy, 0.89 of f-measure, 0.93 of precision, and 0.90 of recall. © 2023 IEEE.

关键词： COVID-19 Deep learning Feature extraction Sentiment analysis Twitter

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Enhancing Predictive Maintenance in Industrial IoT through Machine Learning Models 4

Enhancing Predictive Maintenance in Industrial IoT through M...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Rani, A. Jaya Mabel Prithivirajan, V. Shiney, S Arumai Babaiah, Ch. Swamy, Hemanth Sivagamidevi, G. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Dundigal Hyderabad India Motorola Solutions India Dhanalakshmi College of Engineering Department of Ai & Ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, Lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions. © 2024 IEEE.

关键词： Predictive maintenance

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Securing the Digital Data using SFLA based Deep Convolutional Neural Network in Medical Network Environment 2

Securing the Digital Data using SFLA based Deep Convolutiona...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Murthy, G. N. Keshava Achyutha Prasad, N. Pareek, Piyush Kumar Zanke, Pankaj Patil, Madhu Siddaganga Institute Of Technology Department Of Electronics And Instrumentation Engineering Karnataka Tumakuru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India Sapiens International Corporation Senior Technical Business Analyst Israel Bgs College Of Engineering And Technology Department Of Ece Bangalore India

ISBN: (纸本)9798350381665

As a result of the rapid development of multimedia in relation to network knowledge, accessing digital material has become relatively straightforward. Therefore, more emphasis on picture watermarking is required for IP protection. For this reason, many picture watermarking techniques were developed;nevertheless, they suffer from a lack of transparency and are not as sturdy. Multimedia copyright protection is very important in the field of digital watermarking. Digital picture watermarking is the process of secretly removing a watermark and inserting it into a carrier image. The success of digital watermarking in protecting digital data has attracted a lot of academic interest recently. There has been a lot of recent focus on using deep learning networks in tandem with wavelet-oriented strategies for picture watermarking. However, these benefits cannot be provided by traditional watermarking methods, which need manual methods for both extraction and embedding. In light of this problem, this research is driven to present a unique deep learning-based wavelet-based method for producing watermarked pictures with increased invisibility. A newly created Convolutional Neural Networks (CNN) model with medical pictures, and the deep features are retrieved utilising its fully connected layers. Next, the Shuffled frog-leaping algorithm (SFLA) is used to train the model, which ultimately results in higher quality classifications. The experimental results validate that the suggested model outperformed the baseline on a number of different measures, PSNR. In F (228 × 344) dataset thevWatermark1 ratio the speckle noise reach as 0.9950 and then gaussian noise reach as 0.9924 and then salt and peper noise reaches as 0.9972 respectively. And then the Watermark2 ratio the speckle noise reach as 0.9950 and then gaussian noise reach as 0.9924 and then salt and peper noise reaches as 0.9972 respectively. © 2024 IEEE.

关键词： Image watermarking

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Examining 6G Infrastructure Capabilities: Paving the Way for Future Connectivity

Examining 6G Infrastructure Capabilities: Paving the Way for...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rawat, Devyani Chaubey, Anurag Kumar, Prashant Hemelatha, S. Gajendran, P. Wankhede, D.S. Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India School of Commerce and Management Iimt University Meerut India Tmu Department of Ece India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

6G structures are anticipated to be a major driver for the smart data society, providing functionality that is better to 5G and satisfying innovative features and apps. In this piece, we outline utilization situations and specifications for non - linear and non per third and sophisticated 6G networks as well as our view of what 6G will be. That can provide all-encompassing and limitless wireless communication, we show a massive and independent structured cabling that combines land, air, sea, and space communications. For network interfaces and creative vapour style, we also address artificial intelligence and machine learning. The Internet of Graphene, microwave frequency connectivity, very highly directional arrays myriad, numerous different, large erudite textures and holograms directional antennas, orbital rotational frequency - division, laser and that can be seen comms, nanotech, genomic messaging, and Ethereum - based frequency hopping are a few of the systems we identify as enticing for the 6F I environment. © 2023 IEEE.

关键词： 6G infrastructure Artificial intelligence Autonomous networks Directional arrays Holograms directional antennas Machine learning Microwave frequency connectivity Nanotech Quantum communications and computing SDN Smart data society Wireless communication

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Enhancing Data Transmission Reliability in Cluttered Environments using Adaptive Wireless Sensors 2

Enhancing Data Transmission Reliability in Cluttered Environ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Dinesh, R. Vanathi, P. Susitha, N. Priya, P. Ambikavathi, C. Ali, Syed Sumera Excel Engineering College Department of Ece Namakkal India Adhiyamaan College of Engineering Department of Information Technology Hosur India School of Arts and Science Vinayaka Mission's Chennai Campus Vinayaka Mission's Resarch Foundation Du Dept. of Computer Science India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India School of Arts and Sciences Vinayaka Mission Research Foundation Department of Computer Science Tamilnadu Paiyanoor India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India

ISBN: (纸本)9798350369960

Wireless data transmission in complicated terrains like cities or deep woodlands sometimes raises dependability concerns. These issues are caused by barriers, multipath fading, and signal interference. Our study uses adaptive wireless sensors to improve data transmission reliability to solve this problem. These sensors are meant to dynamically modify gearbox parameters in real time, unlike standard sensors. This distinguishes them. The study uses key research methods. Dynamic Path Selection chooses the best transmission paths for data packets. Adaptive Modulation and Coding (AMC) alters modulation and coding rates based on real-time environmental information. The study also uses Spatial Diversity using Beamforming. This method concentrates energy using multi-antenna systems, increasing signal intensity and decreasing interference. Practical experiments in clutter-filled regions showed improved gearbox reliability. Compared to standard systems, adaptive sensors reduced packet loss by 45% and increased throughput by 30%. Adaptive wireless sensors increase connection reliability in difficult conditions. These sensors may be utilised for many applications, including urban IoT frameworks and autonomous navigation. © 2023 IEEE.

关键词： Cluttered Environments Data Transmission Dynamic Path Selection Wireless Sensors

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Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine 2

Identification of Driver Drowsiness Detection using a Regula...

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2nd International Conference on electronics and Renewable Systems, ICEARS 2023

作者： Mohan, Ravi Chalasani, Srinivas Suma Christal Mary, S. Chauhan, Amit Parte, Smita Athanere Anusuya, S. Shri Ram Institute of Technology Ece Department Madya Pradesh Jabalpur India Eluru Vatluru Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Information Technology Tamilnadu Chennai India Deemed to Be University Christ Department of Life Sciences Karnataka Bengaluru India Mits Gwalior Department of Computer Science and Engineering Madhya Pradesh India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350346640

In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. © 2023 IEEE.

关键词： Video cameras

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