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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Sensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognition is employed in surveillance systems, patient health monitoring, and many other systems involving the interaction between human and intelligent wearable devices, including smartphones and smartwatches. The primary objective of this study work is to identify human behavior in the actual world. We proposed an improved long short-term memory network called RLSTM that uses a squeeze-and-excitation module to efficiently identify human actions and enhance action identification systems' interpretation. A publicly available real-world dataset known as REALWORLD16 was used to train and validate the model five times to analyze the proposed network. The proposed RLSTM achieved the highest accuracy of 98.04% and F1-score of 97.76%, as determined by several investigations. © 2022 IEEE.

关键词： Wearable sensors

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The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model

The Effect of Sensor Placement for Accurate Fall Detection b...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

The development of inertial sensor technology and the growing utilization of wearable electronics (such as smartwatches, smart bands, and other intelligent gadgets) have facilitated the advancement of studies into automated Fall Detection Systems (FDSs). In the last decade, there has been significant scientific interest in maintaining FDSs. Focused on assessing the data acquired by wearable inertial sensors, machine learning (ML) techniques have demonstrated high efficacy in distinguishing falls from typical motions or activities of daily living (ADLs). In most research, unfortunately, the effectiveness of machine learning classifiers was constrained by feature extraction and selection processes that relied on human-made decisions. Recently, deep learning (DL) model findings s how their effectiveness for FDS. One of these effective DL models is the ResNeXt model, a deep neural network that operates based on convolutional layers with aggregated residual transformation. This study investigates the influence of sensor placement on various body locations for the fall detection issue. The ResNeXt model was assessed and compared to other baseline deep learning algorithms using the public UMAFall dataset for fall detection. Employing sensor data on waist location, the suggested model attained the most significant classification ac curacy of 97.275% when classifying falls. © 2022 IEEE.

关键词： Wearable sensors

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Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network 13

Badminton Activity Recognition and Player Assessment based o...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

With the fast expansion of digital technologies and sporting events, interpreting sports data has become an immensely complicated endeavor. Internet-sourced sports big data exhibit a significant development trend. Big data in sports offer a wealth of information on sportspeople, coaching, athletics, swimming, and badminton. Today, various sports data are freely accessible, and incredible data analysis tools based on wearable sensors have been established, allowing us to investigate the usefulness of these data thoroughly. In this research, we investigate the detection of badminton action and player evaluation based on movement data captured by wearable sensors. Movement data captured by an accelerometer, gyroscope, and magnetometer are utilized for training and validating a classification model for badminton actions. In addition, the movement signals are used to train a player evaluation model employing a deep residual network. To assess our suggested technique, we utilized a publicly available benchmark dataset consisting of inertial measurement unit (IMU) sensors attached to every investigator's dominant wrist, palm, and both legs. The experimental findings indicate that the proposed deep residual network obtained good performance with a maximum accuracy of 98.00% for identifying badminton activities and 98.56% for evaluating badminton players. © 2022 IEEE.

关键词： Wearable sensors

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A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals 19

A Deep Residual-based Model on Multi-Branch Aggregation for ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2022

作者： Mekruksavanich, Sakorn Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665485845

Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. © 2022 IEEE.

关键词： Emotion Recognition

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A Multi-input Deep Neural Network Framework for Non-invasive Detection of Anemia using Finger Nail images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIELAB color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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Aluminum nitride waveguide beam splitters for integrated quantum photonic circuits

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Photonics Research 2023年第7期11卷 1196-1202页

作者： HYEONG-SOON JANG DONGHWA LEE HYUNGJUN HEO YONG-SU KIM HYANG-TAG LIM SEUNG-WOO JEON SUNG MOON SANGIN KIM SANG-WOOK HAN HOJOONG JUNG Center for Quantum Information Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Electrical and Computer Engineering Ajou UniversitySuwon 16499Republic of Korea Division of Nano and Information Technology KIST SchoolKorea University of Science and TechnologySeoul 02792Republic of Korea

We demonstrate integrated photonic circuits for quantum devices using sputtered polycrystalline aluminum nitride(Al N)on ***-chip Al N waveguide directional couplers,which are one of the most important components in quantum photonics,are fabricated and show the output power splitting ratios from 50:50 to 99:*** beam splitters with an extinction ratio of more than 10 d B are also realized from the Al N directional *** the fabricated Al N waveguide beam splitters,we observe Hong–Ou–Mandel interference with a visibility of 91.7%±5.66%.

关键词： waveguide quantum beam

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PDCapsNet: A Convolution Neural Network Method for COVID-19 Detection from Chest X-Ray images 13

PDCapsNet: A Convolution Neural Network Method for COVID-19 ...

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13th International Conference on information technology in Medicine and Education, ITME 2023

作者： Liang, Zhihao Lu, Huijuan You, Cunqian Zhu, Wenjie Xie, Li Yao, Yudong College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology Zhejiang Province Hangzhou China College of Information Engineering China Jiliang University Drore Information and Technology Co. LTD. Zhejiang Province Hangzhou China College of Information Engineering China Jiliang University Modern Educational Technology Center Zhejiang Province Hangzhou City China College of Biomedical Engineering and Instrumentation Science Zhejiang University Institute of Advanced Digital Technologies and Instrumentation Zhejiang Province Hangzhou China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350319156

The COVID-19 epidemic started in 2019 and it is still important to develop intelligent and efficient diagnostic methods for the detection of COVID-19. In the task of COVID-19 X-ray image recognition, conventional deep neural networks encounter challenges in effectively discerning distinctive features within pneumonia images, resulting in limited generalization capability when confronted with novel samples. To address this issue, we introduce a novel model in this paper, denoted as PDCapsNet. The model effectively exploits Pyconv's multi-scale fusion technique and leverages the capsule network's capacity to encapsulate diverse attributes of specific entities within COVID-19 X-ray images, resulting in enhancements in both accuracy and generalization. Experimental results show that the proposed model outperforms other previous related works. © 2023 IEEE.

关键词： COVID-19

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Navigating the future:Embracing resilience and interoperability in the industrial internet

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Fundamental Research 2024年第1期4卷 1-2页

作者： Chaojie Gu Chunxiao Jiang Jiming Chen Yang Cong Bin Xiao College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China

In the midst of the fourth industrial revolution,the convergence of the digital,physical,and biological realms is propelling industrial innovation to new *** the heart of this transformative era lies the Industrial In... 详细信息

关键词： internet network shaping

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Silicon Nitride Integrated Photonics with Intrinsic Quantum Emitters

Silicon Nitride Integrated Photonics with Intrinsic Quantum ...

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Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Senichev, Alexander Martin, Zachariah O. Peana, Samuel Yesilyurt, Omer Matthiessen, Owen M. Sychev, Demid Lawrie, Benjamin Lagutchev, Alexei S. Boltasseva, Alexandra Shalaev, Vladimir M. Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center Department of Energy a National Quantum Information Science Research Center of the U.S. Oak RidgeTN37931 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We create intrinsic quantum emitters in silicon nitride, study their structure and temperature-dependent optical properties, and demonstrate monolithic integration with photonic waveguides to evaluate the potential of... 详细信息

ISBN: (纸本)9781957171272

关键词： Integrated photonics Optical properties Photonic integrated circuits Quantum information Quantum light sources Silicon nitride

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Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Mueang Phayao Thailand Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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