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A Data Augmentation Approach to 28GHz Path Loss modeling Using CNNs

A Data Augmentation Approach to 28GHz Path Loss Modeling Usi...

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international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Bokyung Kwon Youngbin Kim Hyukjoon Lee Dept. Computer Information Engineering Kwangwoon University Seoul Korea

Millimeter waves are easily influenced by the surrounding environment, making it difficult to predict path loss values for 28GHz communication systems. Recently, deep learning approaches have become popular mainly thanks to their superior performance in terms of prediction accuracy, generalizability as well as local adaptability. These deep learning approaches require a sufficient number of training data which often lacks variability with respect to the parameter values of base station configuration if not unavailable at all. This paper proposes to use the data augmentation approach to address these two issues by using a simulator to generate predicted data for the arbitrary values of base station parameters. It is shown that a Convolution Neural Network (CNN) trained with both measurement and augmented data outperforms a vanilla CNN model trained with measurement data only and that it can make accurate predictions for arbitrary base station configurations.

关键词： Deep learning Base stations Convolution Communication systems Neural networks Millimeter wave technology Training data

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SLFFormer: A Network for Remote Sensing Change Detection with Spatial-Temporal Feature Fusion and Local Residual Learning

SLFFormer: A Network for Remote Sensing Change Detection wit...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Wenting Fu Yaning Tian School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Detecting changes in images captured at various times of the same geographical region, known as Remote Sensing Change Detection (RSCD), is crucial for urban expansion, agricultural oversight, environmental and disaster tracking, and land use evolution. The progress in remote sensing technology, particularly the access to high-resolution satellite imagery, along with the evolution of deep learning methods, especially convolutional neural networks (CNNs) and transformers, has considerably improved RSCD's effectiveness. Nonetheless, RSCD encounters challenges like the requirement for substantial computational power, intricate data patterns, and the struggle to attain high precision and prompt execution in varied and dynamic conditions. To surmount these obstacles, we introduce the SLFFormer model with three key innovations: the Spatial-Temporal Feature Fusion (STFF) module for enhanced detection of changes in dual-temporal images; the Local Residual Learning (LRL) module to focus on significant changes by filtering out less relevant details; and the Feature Attention Fusion (FAF) module to dynamically emphasize important features and pixels, improving pattern representation. Our model outperforms several state-of-the-art change detection models on the LEVIR-CD dataset in terms of Precision, Recall, F1 Score, IoU, and OA, validating its effectiveness.

关键词： measurement Technological innovation Computational modeling Feature extraction Transformers Robustness Real-time systems Satellite images Remote sensing Testing

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Online Low-Cost Air Quality Monitoring System Using LoRa-Based Communication

Online Low-Cost Air Quality Monitoring System Using LoRa-Bas...

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international joint conference on computer Science and Software Engineering (JCSSE)

作者： Vittayasak Rujivorakul Surapol Vorapatratorn Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Air pollution poses significant challenges to public health and ecological equilibrium globally, with particulate matter (PM) 2.5 emerging as a major concern due to its adverse health effects. Traditional air quality monitoring systems face limitations in cost, coverage, and data latency, necessitating innovative solutions. This paper introduces M-Care, a low-cost air quality monitoring system designed to monitor PM 2.5 levels, temperature, humidity, and pressure in real-time with web and mobile applications, collectively known as Yakkaw. Leveraging LoRa technology and IoT advancements, M-Care offers a scalable and easily deployable solution, addressing existing monitoring system constraints. The system's hardware design, web application interface, and mobile app functionality are detailed, alongside a case study in Northern Thailand showcasing its efficacy. Evaluation through field testing validates M-Care's accuracy and reliability, enabling informed decision-making and proactive pollution mitigation measures. Additionally, experiments assessing communication range highlight the system's performance limits. Visual analyses of hourly and daily PM2.5 data reveal patterns and fluctuations, aiding in identifying pollution trends and guiding policy decisions. Furthermore, seasonal variations in PM2.5 concentrations underscore the impact of agricultural burning and forest fires, emphasizing the importance of effective monitoring and management strategies. The findings contribute to advancing air quality monitoring technologies, offering insights for addressing environmental challenges and safeguarding public health.

关键词： Accuracy Atmospheric measurements Decision making Air pollution Market research Real-time systems Pollution measurement

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Deep Transfer Learning for Kidney Disease Detection Using CT Scan Images

Deep Transfer Learning for Kidney Disease Detection Using CT...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Shahid Mohammad Ganie Pijush Kanti Dutta Pramanik Zhongming Zhao Department of Analytics AI Research Centre Woxsen University Hyderabad India School of Computer Applications and Technology Galgotias University Greater Noida India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Kidney disease is a significant health issue that leads to a high number of deaths worldwide. Accurate and timely detection of kidney diseases, including cysts, stones, and tumours, is critical for effective treatment and patient outcomes. Deep learning methodologies, specifically transfer learning, have recently been widely used in medical image analysis. This study comprehensively evaluates seven transfer learning models—Xception, DenseNet201, MobileNet, InceptionV3, VGG16, ResNet50, and EfficientNetB0—for multiclass kidney disease classification using CT scan images. The models are assessed based on key performance metrics such as accuracy, loss, precision, recall, F1-score, and AUC. Results indicate that ResNet50 delivers the best overall performance, making it a promising model for clinical applications. By comparing these state-of-the-art models, this research provides valuable insights into the most effective transfer learning approaches for kidney disease detection, paving the way for robust computer-aided diagnostic systems.

关键词： measurement Analytical models Accuracy Computational modeling Computed tomography Biological system modeling Transfer learning Kidney Diseases Residual neural networks

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Spinocerebellar Ataxia Type 12 classification through Structural MRI: Insights from Regional Brain Volume and Cortical Thickness Analysis

Spinocerebellar Ataxia Type 12 classification through Struct...

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computer Vision and Machine Intelligence (CVMI), international conference on

作者： Dhruv Chandra Lohani Bharti Rana S Senthil Kumaran Achal Kumar Srivastava Snigdha Agrawal Pankaj Department of Computer Science University of Delhi Delhi India Department of NMR AIIMS New Delhi Delhi India Department of Neurology AIIMS New Delhi Delhi India School of Computer and Systems Science Jawaharlal Nehru University Delhi India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Spinocerebellar ataxia (SCA) type 12 is a neurodegenerative hereditary disorder of progressive nature with a particularly high prevalence in India. This investigation evaluates the potential application of structural magnetic resonance imaging (sMRI) data in the automated classification of SCA12. We acquired 32 SCA12 patients and 28 healthy controls for binary classification. Feature extraction was performed via three brain atlases - Automated Anatomical Labeling (AAL3), Neuromorphometrics atlas, and Destrieux atlas. Subsequently, the ReliefF feature selection algorithm was employed to select the most informative features for analysis. Finally, two machine learning models, a linear Support Vector Machine (SVM) and radial basis kernel SVM (RBSVM), were implemented. Both models underwent training with a forward feature selection approach combined with leave-one-out cross-validation. The model achieved an optimal classification accuracy of $88.33 \%$ when features were constructed using gray matter volumes using the AAL3 atlas. It was observed that gray matter, white matter, and cortical thickness are better biomarkers than CSF volume for differentiating SCA12 patients. Additionally, biomarkers identified via feature selection may enhance the efficiency of clinical diagnosis.

关键词： Support vector machines measurement Accuracy Machine learning algorithms Biological system modeling Grey matter Biomarkers Feature extraction Brain modeling Classification algorithms

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Development of Skin Cancer Detection Using Deep Spiking Neural Networks

Development of Skin Cancer Detection Using Deep Spiking Neur...

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IEEE international conference on Smart Instrumentation, measurement and Applications (ICSIMA)

作者： Mohamed Hadi Habaebi Muhammad Amin Zulkarnain Teddy Surya Gunawan Helmy Fitriawan Mira Kartiwi Faridah Abd.Rahman Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Department of Electrical Engineering Universitas Lampung Bandar Lampung Indonesia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

The limitations of conventional techniques necessitate the development of new diagnostic strategies for early detection of skin cancer, the most prevalent form of cancer. This investigation investigates the application of Spiking Neural Networks (SNNs) in creating a dependable and automated skin cancer detection system. The primary goal is to evaluate the feasibility and efficacy of SNNs for this purpose. The methodology entails using the SpikingJelly framework to create and assess an SNN-based model, specifically a modified Spiking VGG-11. Focusing on images classified as melanoma or non-melanoma, the investigation employs the ISIC 2019 dataset. The Spiking VGG-11 model, which features approximately 15 million parameters, achieved a test accuracy of 81.3%, specificity of 81.7%, and precision of 81.5%. This performance is noteworthy in light of its reduced complexity compared to models such as the Spiking VGG-13, which has approximately 134 million parameters. The model's high specificity is especially advantageous in clinical environments, as it minimizes false positives and unnecessary testing. These results emphasize the potential of SNNs to offer diagnostic tools that are both efficient and effective in the detection of skin cancer. These findings underscore the potential of SNNs to enhance the early detection and diagnosis of skin cancer, offering a promising alternative to conventional diagnostic methods.

关键词： Training Accuracy Sensitivity Computational modeling Spiking neural networks Robustness Real-time systems

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Poster: Empowering IoT-Driven Remote ECG Monitoring: The Role of AI Spread-out

Poster: Empowering IoT-Driven Remote ECG Monitoring: The Rol...

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Mobility, Operations, Services and Technologies (MOST), IEEE international conference on

作者： Adarsha Bhattarai Dongming Peng Department of Electrical and Computer Engineering University of Nebraska–Lincoln

ISBN: (数字)9798350307733

ISBN: (纸本)9798350307740

The integration of artificial intelligence (AI) into IoT systems using federated learning (FL) enhances decision-making by processing nodes. This integration also upgrades safety in the learning process. In FL, the diverse computational strengths of neighboring nodes and the optimal execution of an already mostly learned AI model in diverse IoT environments are prime concerns. This poster introduces an AI spread-out approach to deploying AI models. AI spread-out addresses the hardware specifications of nodes in remote Electrocardiogram (ECG) monitoring. Automatic State Activation (ASA) uses AI partitioning for anomaly detection. It achieves 98.8% accuracy of abnormality detection in remote ECG monitoring. Further, ASA proves to be energy-efficient and reliable. In summary, these enhancements significantly improve AI-based edge computing for remote patient monitoring.

关键词： measurement Patient monitoring Computational modeling Electrocardiography Energy efficiency Hardware Safety

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Optimizing Motion Capture Data in Animation Sequences Using Machine Learning Techniques

Optimizing Motion Capture Data in Animation Sequences Using ...

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IEEE international conference on Power, Intelligent Computing and systems (ICPICS)

作者： Aihua Zhang Yufei Li School of Art & Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

During the early 20 th Century, producing a real-world animated sequence motion was a process done by hand. Various techniques have emerged to compute and complete multiple actions in animation. Traditional MoCap systems, reliant on extensive hardware and physical markers, face significant limitations in accuracy and efficiency. We employ a two-tier machine learning framework comprising Deep Convolutional Neural Networks (DCNN) and Generative Adversarial Networks (GAN). The DCNN model's role is to process raw video footage to extract motion data initially. While the first part of this process provides sufficient visual attention, the GAN moves it to the next level, enhancing realism and creating a more accurate feel of animation sequence. The experiment gets proved by the fact that there is a huge improvement in the joint position accuracy, flowing natural motion of animated sequences, as well the overall realism of animated sequences. All these are evidenced by Mean Squared Error (MSE) and Structural Similarity Index (SSI) metrics. These advancements indicate that ML can significantly enhance traditional MoCap systems. Also, it offers a more accessible and less equipment-intensive approach that broadens the potential applications in animation, virtual reality, and biomechanical analysis.

关键词： measurement Solid modeling Accuracy Machine learning Virtual reality Animation Generative adversarial networks Hardware Indexes Convolutional neural networks

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joint estimation of SOH and RUL for lithium-ion batteries 25

Joint estimation of SOH and RUL for lithium-ion batteries

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Proceedings of the 2025 international conference on Intelligent systems, Automation and Control

作者： Wanting Liu Yingmin Ye Yiwei Yuan Xi'an University of Technology School of Mechanical Engineering Xi'an Shaanxi China Xi'an University of Technology School of Electrical Engineering Xi'an Shaanxi China Xi'an University of Technology School of Computer Science and Technology Xi'an Shaanxi China

ISBN: (纸本)9798400715198

With the rapid development of new energy vehicles and renewable energy storage systems, the safety and reliability of lithium-ion batteries (LIBs) have become the key challenges hindering their widespread adoption. In order to solve the challenges of insufficient feature extraction, difficulty in multi-objective collaborative optimization, and inaccurate long-term time-series dependency modeling of existing lithium-ion battery state of health (SOH) and remaining useful life (RUL) prediction methods, this paper proposes a multi-scale prediction framework based on adaptive BiGRU-Transformer neural network. Experimental results show that the proposed method is better than the traditional network, Transformer and single BiGRU models in terms of SOH estimation error and RUL prediction accuracy. The research provides a high-precision and robust intelligent solution for the health management of lithium-ion batteries, which is of great significance for promoting the safe operation and maintenance of new energy power systems.

关键词： Adaptive Mechanism

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Real-Time Ergonomic Risk Assessment Using Inertial measurement Units: A Case Study in the Manufacturing Industry

Real-Time Ergonomic Risk Assessment Using Inertial Measureme...

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IEEE international conference on Industrial Engineering and Engineering Management

作者： N. Salem S. Baklouti M.A. Kammoun T. Rezgui Z. Hajej S. Bennour Computer Engineering Production and Maintenance Laboratory (LGIPM) Lorraine University Metz France Mechanical Laboratory of Sousse (LMS) National Engineering School of Sousse University of Sousse Sousse Tunisia Tunisia Polytechnic School Applied Mechanics and Systems Research Laboratory (LASMAP) La Marsa Tunisia

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This study presents a novel method utilizing wearable technology for ergonomic risk assessment, specifically targeting the mitigation of work-related musculoskeletal disorders (WMSDs) within industrial settings. It employs Inertial measurement Units (IMUs) to capture and analyze joint angles in real time, with a detailed focus on the shoulder, elbow, and wrist during repetitive tasks. A significant advancement involves converting IMU data into a comprehensive 3D orientation model using quaternions, significantly aiding in visualizing limb movements and detecting potential ergonomic risks. A case study from the automotive industry highlights the importance of closely monitoring wrist movements to manage ergonomic risks effectively. By advancing the understanding of ergonomic risks and facilitating targeted interventions, this study contributes to integrating innovative solutions for improving productivity and worker well-being, in line with Industry 5.0 principles. Additionally, we propose future directions for integrating AI and machine learning to enhance predictive ergonomic risk assessment.

关键词： Wrist Musculoskeletal system measurement units Three-dimensional displays Ergonomics Employment Machine learning Real-time systems Vehicle dynamics Monitoring

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