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Green Buildings (ATiGB), Applying New Technology in

作者： Viet Nguyen Kim Huyen Nguyen Thi Ngoc Hieu Dam Minh Nguyen Do Hoang Khoi Tien-Dung Do Cao Truong Dung Department of Information Technology Posts and Telecommunication Institute of Technology Ha Noi Vietnam Department of Computer Science Swinburne University Ha Noi Vietnam Department of Computer Science Posts and Telecommunication Institute of Technology Ha Noi Vietnam Faculty of Information Posts and Telecommunication Institute of Technology Ha Noi Vietnam Department of Electronic Engineering Posts and Telecommunication Institute of Technology Ha Noi Vietnam

ISBN: (数字)9798331505042

ISBN: (纸本)9798331505059

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper.

关键词： Adaptation models Error analysis Green buildings Scalability Diversity reception Self-supervised learning Phonetics Robustness Computational efficiency Automatic speech recognition

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Brain computer Interaction Framework for Speech and Motor Impairment Using Deep Learning

Brain Computer Interaction Framework for Speech and Motor Im...

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

作者： Somnathe, Abhijit T. Tayubi, Iftikhar Aslam Reddy, Pundru Chandra Shaker Sharma, Nipun Sharma, Vikas Yesubabu, Mannava Electronics and Telecommunication Engineering Smt. Kashibai Navale College of Engineering Pune India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia School of Computer Sicence and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Electronics and Communication Engineering Presidency University Karnataka Bangalore India School of Computer Science & Applications Iimt University Uttar Pradesh Meerut India Computing Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India

ISBN: (纸本)9798350357769

Some people may have trouble communicating with others because of motor and speech difficulties brought on by accidents, strokes, or diseases. Paralyzed people often struggle to communicate their demands, which can make even the simplest tasks challenging. People with disorders like dysarthria and amyotrophic lateral sclerosis may have trouble following conversations. The proposed method for automatic detection of everyday basic desires will help those with dysarthria and quadriplegic paralysis has more fulfilling lives. The device accomplishes this by monitoring brain impulses, which it then converts into either perceptible voice commands or messages that can be delivered to a healthcare provider's smart devices, depending on the user's preferences. The suggested method randomly displays an image of one of six basic requirements while using event-related-potentials(ERPs) detected from the Electroencephalogram (EEG) data to determine which need to fulfill. The dataset utilized for train, test, & validation was built using input from 10 participants. The proposed method had a 96.3|% success rate. © 2023 IEEE.

关键词： Brain-computer Interface CNN Deep Learning EEG ERP

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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Alternately Coupled Magnetoinductive Waveguide with Nulls-Free Characteristic for Two-Way Wireless Power Transfer Application

Alternately Coupled Magnetoinductive Waveguide with Nulls-Fr...

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2022 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2022

作者： Rakluea, Chawalit Worapishet, Apisak Chaimool, Sarawuth Zhao, Yan Akkaraekthalin, Prayoot Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Mahanakorn Institute of Innovation Mahanakorn University of Technology Bangkok Thailand Khon Kaen University Faculty of Engineering Department of Electrical Engineering Khon Kaen Thailand International School of Engineering Chulalongkorn University Faculty of Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (数字)9781665432382

ISBN: (纸本)9781665432382

Two-way alternately coupled magnetoinductive waveguide for wireless power transfer is presented in this paper. With the use of the alternate coupling polarities between consecutive resonator cells and quadrature phase termination at the last cell, the nulls-free characteristics were clearly achieved for all locations. The power is transferred to a single receiver with 74% from the input power. For two receivers placed separately on each path, a small variation of power transfer percentage ranging from 40% to 43% is obtained. Finally, the proposed configuration model can be developed to power and charge multiple electronic devices, robots and EVs without nulling effect. © 2022 IEEE.

关键词： Energy transfer

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Unsupervised Image Region of Interest Detection using Discrete Cosine Transform 4

Unsupervised Image Region of Interest Detection using Discre...

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4th International Conference on Information Communication and Signal Processing, ICICSP 2021

作者： Olaode, Abass Naghdy, Golshah Utaminingrum, Fitri School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong Australia Computer Vision Research Groups Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9781665407571

Spatial domain analysis of images has been recognised as an important task in image retrieval and computer vision, however, its application in the determination of image Region of Interests often require the inclusion of supervised training and similarity computations, both of which can be difficult to implement during the modelling of the images in a large number of images. however, unsupervised image Region of Interest determination via Frequency domain analysis can be implemented without training or similarity computation therefore it is more suitable for the modelling of images in a large set. This paper demonstrates image frequency domain analysis via Discrete Cosine Transform and presents its application in the unsupervised determination of Region of Interests for the elimination of the spatial incoherency often associated with the Bag-of-Visual Word Modelling. © 2021 IEEE.

关键词： Discrete cosine transforms

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Performance LoRa Technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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Advanced Training Set Generator for Use in Self-Organizing Neural Networks 32

Advanced Training Set Generator for Use in Self-Organizing N...

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32nd IEEE International Conference on Microelectronics, MIEL 2021

作者： Pasturczak, N. Dlugosz, R. Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Ul Bydgoszcz85-796 Poland

ISBN: (纸本)9781665445283

Success in training of artificial neural networks (ANNs) depends on properly selected training data set. The problem is not trivial, especially in situations in which the structure of data to be classified by the neural network (NN) after completing the learning phase is not known. To cope with this problem, one of the solutions is to conduct comprehensive tests of the ANN with training it with data sets that differ in the distribution of the learning patterns in the input data space. In order to prepare such sets, appropriate tools are required that introduce the ability of a broad parameterization. In the proposed work, an advanced data generator has been developed, which based on a specific variability of particular predefined parameters, generates a collection of learning data sets. The obtained sets are then used to optimize the learning process of selected algorithms of self-organizing ANNs. The NN then automatically retrieves subsequent data sets from specific location, and as a response generates reports with an assessment of the quality of the learning process. The aim of the proposed investigations is to optimize the structure of selected ANNs, so as to obtain their minimum complexity, important in case of their hardware implementation. © 2021 IEEE.

关键词： Neural networks

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Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection

Using Explainable AI for EEG-based Reduced Montage Neonatal ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dinuka Sandun Udayantha Kavindu Weerasinghe Nima Wickramasinghe Akila Abeyratne Kithmin Wickremasinghe Jithangi Wanigasinghe Anjula De Silva Chamira U. S. Edussooriya Department of Electronic and Telecommunication Engineering University of Moratuwa Sri Lanka Department of Electrical and Computer Engineering University of British Columbia Canada Department of Paediatrics Faculty of Medicine University of Colombo Sri Lanka

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The neonatal period is the most vulnerable time for the development of seizures. Seizures in the immature brain lead to detrimental consequences, therefore require early diagnosis. The gold -standard for neonatal seizure detection currently relies on continuous video-EEG monitoring; which involves recording multi-channel electroencephalogram (EEG) alongside real-time video monitoring within a neonatal intensive care unit (NICU). However, video-EEG monitoring technology requires clinical expertise and is often limited to technologically advanced and resourceful settings. Cost-effective new techniques could help the medical fraternity make an accurate diagnosis and advocate treatment without delay. In this work, a novel explainable deep learning model to automate the neonatal seizure detection process with a reduced EEG montage is proposed, which employs convolutional nets, graph attention layers, and fully connected layers. Beyond its ability to detect seizures in real time with a reduced montage, this model offers the unique advantage of real-time interpretability. By evaluating the performance on the Zenodo dataset with 10-fold cross-validation, the presented model achieves an absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall, respectively.

关键词： Deep learning Training Pediatrics Explainable AI Brain modeling Feature extraction Real-time systems Electroencephalography Monitoring Medical diagnostic imaging

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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A Deep Learning-Based System for Detecting Defects in Printed Circuit Boards

A Deep Learning-Based System for Detecting Defects in Printe...

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International Electrical engineering Congress (iEECON)

作者： Aekkarat Suksukont Jakkrit Onshaunjit Jakkree Srinonchat Dept. of Computer Engineering Faculty of Industrial Education Rajamangala University of TechnologySuvarnabhumi (RMUTSB) Suphanburi Thailand Dept. of Electronics and Telecommunication Engineering Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Traditional approaches to defect detection, relying on rule-based algorithms or human visual examination, are inadequate and ineffectual in handling intricate fault patterns. Implementing computer vision techniques in PCB defect detection can improve inspection processes' accuracy, reliability, and effectiveness. Deep learning is now used in those processes to explore important technology. This study presents a novel automated method for defect identification using deep learning methods. The system is built utilizing AlexNet, U-Net, ResNet 101, and Inception-v3, prominent frameworks known for their outstanding capacity to extract pertinent visual data components. The proposed technique comprehensively composes PCB photos, including faulty and defect-free samples. This training enhances the system's capacity to precisely differentiate between conventional PCB designs and six additional categories of errors. The suggested architecture of the CNN models consists of double process layers represented as ConV I, Pooling I, ConV II, and Pooling II. The validation procedure is carried out by using a range of learning rate data. The implementation findings indicate that the ResNet-101 variant achieves the highest testing accuracy of 99.286%. Indeed, incorporating this technology into the PCB manufacturing process enables the prompt identification of defective PCBs, leading to less waste, enhanced product quality, and heightened satisfaction among end-users.

关键词： Deep learning Visualization Accuracy Printed circuits Inspection Product design Quality assessment Circuit faults Defect detection Testing

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