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Reduced Shell Model for the simulation of Crosstalk in Piezoelectric Micromachined Ultrasound Transducer Arrays

Reduced Shell Model for the Simulation of Crosstalk in Piezo...

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Thermal, Mechanical and Multiphysics simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Philipp Möhrle Aleskander Bajt Gabriele Schrag Technical University Munich Munich Germany Fraunhofer Institute for Electronic Microsystems and Solid State Technologies Munich

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Piezoelectric micromachined ultrasonic transducers (PMUTs) offer a promising approach for generating ultrasonic waves across various applications. Typically utilized in large arrays, this poses significant challenges on design and simulation, particularly if crosstalk induced by surface acoustic waves (SAWs) are to be modeled, leading to extensive computation times and a large amount of random-access memory needed. To overcome these challenges, we developed a Finite Element Method (FEM)-based shell model of a PMUT cell, which we benchmarked against an FEM solid model. This shell model significantly reduces simulation time while maintaining high accuracy in predicting sound radiation, eigenfrequency and inplane stress. When applied to large arrays, this model enables the exploration of acoustic radiation with phase-varied arrays and the mutual interactions of PMUT cells through surface acoustic waves.

关键词： Phased arrays Micromechanical devices Ultrasonic transducers Solid modeling Ultrasonic imaging Transducers Surface acoustic waves Crosstalk Acoustic arrays Finite element analysis

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Advanced Parkinson's Disease Classification Using Hybrid Model: SVM and Random Forest

Advanced Parkinson's Disease Classification Using Hybrid Mod...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Archana S H Rajeshkannan M Anish M J Pasupuleti Sandeep Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Parkinson's disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector Machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyper-parameter tuning further optimizes each classifier, bolstering the model's accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study high-lights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care.

关键词： Support vector machines Analytical models Accuracy Parkinson's disease Computational modeling Data models Robustness Random forests Tuning Principal component analysis

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Advancements in Brain Tumor Segmentation: A Comprehensive Review of the BraTS Challenges

Advancements in Brain Tumor Segmentation: A Comprehensive Re...

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Advancement in Computation & Computer technologies (InCACCT), international conference on

作者： Ravi Ranjan Kumar Sheifali Gupta Kanegonda Ravi Chythanya Rahul Singh Haeedir Mohameed Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Brain tumor segmentation is a critical task in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of gliomas, including high-grade and lower-grade gliomas. The Brain Tumor Segmentation (BraTS) challenges, held annually from 2017 to 2022, have significantly advanced the field by providing standardized datasets, evaluation frameworks, and fostering innovation in segmentation methodologies. In review examines the progression of BraTS challenges, highlighting key developments in data complexity, tasks, and evaluation metrics. Starting with BraTS 2017, which introduced standardized segmentation tasks and datasets with 285 subjects, subsequent iterations expanded the dataset size to 1,251 subjects by 2021 and introduced survival prediction tasks, advanced preprocessing, and enhanced segmentation algorithms. Each iteration addressed the unique challenges posed by LGG, such as diffuse boundaries and infiltrative patterns, by adopting cutting-edge architectures like 3D U-Net, nnU-Net, and Transformer-based models. These advancements have improved segmentation accuracy, robustness, and generalizability across diverse clinical settings. The review underscores the transformative role of the BraTS challenges in shaping the future of brain tumor segmentation, bridging the gap between research innovations and practical clinical applications.

关键词： Measurement Technological innovation Image segmentation Accuracy Reviews Brain tumors Brain modeling Transformers Robustness Complexity theory

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modeling in Industry 4.0: Key Tools and Techniques for Optimization

Modeling in Industry 4.0: Key Tools and Techniques for Optim...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Hanae Cherradi Asmaa Elasri Driss Lamrani Soufiane Hamida Bouchaib Cherradi Ahmed Rebbani EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco 2IACS Laboratory ENSET of Mohammedia UH2C Mohammedia Morocco GENIUS Laboratory SupMTI of Rabat Rabat Morocco STIE Team CRMEF Casablanca Settat EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The advent of Industry 4.0 has revolutionized production and maintenance systems, driving unprecedented levels of efficiency, sustainability, and reliability. However, this review also considers the evolving landscape with the emergence of Industry 5.0, where human ingenuity and machine capabilities collaborate more seamlessly. This review examines the transformative tools and technologies integral to Industry 4.0, including digital twins, federated learning, agent AI, and cyber-physical systems. By synthesizing insights from peer-reviewed articles, technical reports, and case studies, the study provides a comprehensive exploration of these advanced methodologies, emphasizing their practical applications in intelligent manufacturing. The analysis highlights the significant benefits— such as enhanced operational performance, predictive maintenance, and real-time decision-making—while addressing challenges like data privacy, computational demands, and system scalability. It offers insights into solutions that can pave the way for smoother transitions into Industry 5.0. The findings aim to establish a foundation for the development of innovative, adaptive models tailored to the dynamic, interconnected ecosystems of Industry 4.0.

关键词： Adaptation models Reviews Federated learning Computational modeling Ecosystems Real-time systems Fourth Industrial Revolution Digital twins Sustainable development Fifth Industrial Revolution

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Image Forgery Detection Using Deep Learning

Image Forgery Detection Using Deep Learning

引用

Disruptive technologies (ICDT), international conference on

作者： Satyam Kumar Saloni Lodhi Kamini Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Images playa significant role in our life. With the growing Images are playing a pivotal role in our daily life. By the growing era images are becoming more and more popular as it contains lot of informatic content as well as memorable too. The problem arises from here, as the images are becoming popular day by day there are also increase in counterfeiting of the images or we can say that image forgery. It leads everyone to believe the content that's been forged. Not everyone is able to detect whether the image is forged or not. There are several applications that help people forge the image. This is a serious problem that has been arising and it should be addressed. There are many algorithms that can detect forgery, thorough the study, the most reliable approach or algorithm for detecting forgery is Convolutional Neural Network (CNN), but the singular approach of CNN is not much reliable. We hereby present a deep learning hybrid approach of Error Level Analysis with convolutional neural network for detecting Image manipulation. To detect whether an image is manipulated, the ELA from the image is extracted and will send the information to CNN. The training of the above algorithm will be performed on the dataset CASIA V2.0 which contains many forged images as well as authentic image. Transfer learning with UNET (encoder-decoder model) has also been applied for image segmentation. The experimental result on the dataset used are quite good, and these methodologies have been duly evaluated. The outcome of the proposed model outperforms the other well-known Transfer learning approaches compare to the findings. The study suggests that proposed model can get up to 0.910 and 0.930 accuracy and precision, respectively. The comprehensive analysis comparatively presented in this study provides valuable information that can guide the development of precise forgery detection technologies, safeguarding the honor of digital content and lessening the consequences of picture frame-u

关键词： Deep learning Training Image segmentation Accuracy Computational modeling Transfer learning Computer architecture Forgery Convolutional neural networks Reliability

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3D Fine Electrothermal modeling of IGBT Power Device for Reliability applications

3D Fine Electrothermal Modeling of IGBT Power Device for Rel...

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Thermal, Mechanical and Multiphysics simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Augustin Empeyrou Merouane Ouhab Nicolas Degrenne Mitsubishi R&D Centre Europe Health Management Technologies Rennes France

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Power Modules (PM) with a standard packaging technology are subject to degradations after long operation time due to thermomechanical fatigue. Top electrical interconnections, i.e. wire bonds, are the most critical components prone to degradation leading to the device failure. Finite Element Methods (FEM) serve as a valuable tool for examining the effects of Power Module (PM) degradations through multi-physics simulations, thus they can significantly contribute to the development of semiconductor device health monitoring strategies. However, the applicability of FE models to reliability issues is conditional on their capability to yield results that accurately reflect experimental observations in degraded device conditions. This requires a well-defined electrothermal coupling that accurately represents the underlying physics, enabling the study of a wide range of degradation scenarios using a single model. In this paper, a physics-based 3D electrothermal model of an IGBT PM is developed and examined against experimental measurements in different scenarios under conduction and switching operation, to evaluate its ability to reflect experimentally observed uneven temperature fields owing to wire bonds degradations.

关键词： Degradation Temperature measurement Insulated gate bipolar transistors Solid modeling Three-dimensional displays Switching loss Mathematical models Finite element analysis Wire Load modeling

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges

A Critical Study on Hybrid Computing and Its Current Influen...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Praveen K V Joe Prathap P. M L Sherin Beevi W Vinil Dani Department of Information Technology St. Peter’s College of Engg. and Tech. Chennai India Department of Computer Science and Engineering R.M.D. Engineering College India Department of EEE St. Peter’s College of Engg. and Tech. Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computing Program processors Scalability Computational modeling Biological system modeling Standards organizations Machine learning Organizations Hardware Optimization

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An Iterative Analysis of Deep Learning Models Including LSTM and CNN-GRU for Real-Time Seizure Prediction

An Iterative Analysis of Deep Learning Models Including LSTM...

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： Ramula Uttham Sai Latha Parthiban Department of Computer Science Pondicherry University Puducherry Department of Computer Science Pondicherry University Community College Lawspet Puducherry

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Epilepsy is the most common of all neurological conditions, affecting over 50 million people in the global population. Timely prediction of seizures may reduce mortality rates and improve outcomes for affected patients. The existing seizure detection and prediction methodologies rely on machine learning algorithms that need handcrafted features, have poor adaptability to EEG signals, and inadequate generalization capability. Additionally, past reviews have primarily focused on isolated aspects of seizure prediction and not on the comprehensive evaluation of the advancement in DL models along with their integration with the emergent technologies like IoT and transfer learning. The current review bridges this gap by providing an empirical analysis of 15 state-of-the-art DL methodologies for real-time seizure prediction. Some techniques addressed include LSTM, CNN-GRU-AM-based models because they can extract spatiotemporal features automatically and hybrid optimization frameworks such as WCA-PSO-EELM that result in more accurate classifications. New ideas include adaptation for concept drift and transfer learning for adaptability to different patients and lower computations. The review is made on multimodal approaches by integrating EEG and MRI data with IoMT applications for remote monitoring regarding the aspects of accessibility and cost-effectiveness of healthcare. This work is presented, reporting that models such as CNN-GRU-AM and WCA-PSO-EELM offer superior accuracy (>99%) and specificity, while the transfer learning approach proves to be a robust solution to data-scarce scenarios. This review provides a roadmap for future research and clinical deployment by systematically comparing these methods using performance metrics. The results are of significant relevance for personalized medicine, advancing real-time monitoring systems, and developing AI-driven innovations in neurological healthcare sets.

关键词： Adaptation models Reviews Transfer learning Epilepsy Medical services Predictive models Brain modeling Feature extraction Real-time systems Long short term memory

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Numerical Methods for Extracting Parameters in Single-Diode Models: A Comparative Study

Numerical Methods for Extracting Parameters in Single-Diode ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Fatima Ezzahrae Souaidi Mustapha Elyaqouti El Hanafi Arjdal Driss Saadaoui Imade Choulli Brahim Ydir Abdelfattah Elhammoudy Ismail Abazine Dris Ben Hmamou Souad Lidaighbi Chahir Omar Lahboub Ayoub Bendriouich Youssouf Laboratory of Materials Signals Systems and Physical Modeling Faculty of Sciences of Agadir Ibn Zohr University Agadir Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The accurate determination of parameters in the single-diode model (SDM) is crucial for the effective simulation, optimization, and control of photovoltaic (PV) systems. This paper compares three numerical methods: Villalva, Wang, and Vika. These methods are evaluated based on their precision, computational complexity, and adaptability to various PV technologies, including monocrystalline, polycrystalline, thin-film photovoltaic solar cells, and RTC France silicon cells. For polycrystalline, monocrystalline PV panels and crystalline silicon cells, Vika’s method strikes an optimal balance between computational efficiency and precision. Wang’s method, on the other hand, excels in modeling the intricate charge transport phenomena observed in thin-film photovoltaic solar cells PVM 752 GaAs. These findings offer practical insights and clear guidelines for researchers and practitioners, emphasizing the strengths and limitations of each approach in real-world photovoltaic applications.

关键词： Photovoltaic systems Adaptation models Accuracy Reviews Photovoltaic cells Computational modeling Gallium arsenide Silicon Numerical models Parameter extraction

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Methodology to Improve Interaction in Educational Processes Based on Virtual Reality Techniques

Methodology to Improve Interaction in Educational Processes ...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Elizabeth Arrieta-Amaya Wilver Auccahuasi Haymin Raez-martinez Nelly Caceres-Tovar Daniel Yucra-Sotomayor Martha Romero-Echevarria Universidad Cesar Vallejo Lima Perú Universidad Cientifica del Sur Lima Perú Universidad Autónoma del Perú Lima Perú Universidad Nacional Federico Villarreal Lima Perú

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Information and communication technologies are helping to improve experiences in many areas, such as engineering, simulation, education. It is in the area of education, that thanks to the help of hardware and software, are allowing to improve the educational experiences, in the present work, we resort to the use of the Virtual Reality glasses of the Oculus Gest 2 model, with the Spatial application, applied to the learning of music. We present the architecture of the solution so that it can be replicated and scaled to other courses and subjects of study, we present how to configure each of the tools as well as the alternatives that can be worked in order to optimize resources, as a result we present the advantages and disadvantages of using Virtual Reality technology and recommendations to optimize its application so that it can be replicated and scaled to other applications.

关键词： Performance evaluation Solid modeling Sentiment analysis Education Virtual reality Glass Mobile handsets Software Hardware Information and communication technology

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