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Enhancing fitness action recognition with ResNet-TransFit: Integrating IoT and deep learning techniques for real-time monitoring

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 89-101页

作者： Li, Jijie Gong, Ruyao Wang, Gang Kunsan Natl Univ Phys Educ Gunsan 54150 Jeollabuk South Korea Linyi Univ Coll Sports & Hlth Linyi 276000 Shandong Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Peoples R China Imperial Coll London Dept Bioengn London SW7 2AZ England

With the growing popularity of fitness, the demand for real-time action recognition and feedback is increasing. Current research faces challenges in handling complex actions, real-time processing, and system integration. address these issues, we propose a novel fitness action recognition model that integrates ResNet, Transformer, and transfer learning techniques. Specifically, ResNet is used for image feature extraction, Transformer handles time-series data processing, and transfer learning accelerates the model's adaptation to new data. We evaluated our model on the NTU RGB+D action recognition dataset, achieving 48.5 ms latency, 29.1 fps throughput, and 93.7% accuracy, significantly outperforming other models. Our model achieved an accuracy improvement 5% over existing methods, demonstrating significant potential for real-time fitness monitoring. By incorporating IoT technology, our system enables real-time data processing and action recognition, making it ideal for smart fitness monitoring. Although the model has high complexity and memory usage, its efficiency and accuracy demonstrate its potential for widespread adoption. Future work will focus on optimizing the model structure and training methods to enhance applicability in resource-constrained environments, ensuring broader usability and efficiency in various real-world applications.

关键词： Action recognition image analysis ResNet Transformer Transfer learning

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image processing Technology and deep learning Application: In Relation to the Context of Laser Positioning

Image Processing Technology and Deep Learning Application: I...

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2020 Ibn Al-Haitham International Conference for Pure and Applied Sciences, IHICPS 2020

作者： Jwaid, Wasan M. Faculty of Administration and Economics Thi-Qar University Thi-Qar Iraq

In the current investigation, there was the use of the technology of machine vision. The usage of this technology was informed by the need to have the laser spot's highest energy positioned precisely, eventually allowing for the facilitation of further product work piece joining. Indeed, the joining occurred in laser welding machinery. Relative to the displacement phase, it is notable that it could aid in work piece placement into superposition areas, upon which there could be the joining of the parts. Training programs or models that were used involved convolutional neural network and deep learning, which allowed for the resultant system's enhancement of the accuracy with which the positioning could be achieved. Also, the aforementioned algorithms were insightful because they led to the enhancement of machine work efficiency. Similarly, in the study, there was the proposing of a bi-analytic deep learning localization technique. For the purpose of system monitoring in real time, there was the use of a camera. As such, the initial stage entailed the application of the convolutional neural network, which aided in the implementation of large-scale initial searchers before having the laser light spot zone located. In turn, the phase that followed entailed increasing the camera's optical magnification, which paved the way for the spot area's re-imaging, as well as the application of a template matching method to ensure that high-precision repositioning was achieved. When the parameter of the search result area's ratio was considered, it could be seen that the study was able to determine the target spot's integrity parameters. For the case of the complete laser spot, there was the performance of the centroid calculation. Also, in situations where an incomplete laser spot reflected the target, there was the performance of invariant moments' operation. From the findings, the study indicated that from incomplete laser spot images, the laser spot's highest energy could be pos

关键词： Computer vision

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Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework

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JOURNAL OF APPLIED REMOTE SENSING 2024年第2期18卷

作者： Sakr, Mohamed Saleh, Ahmed AbdElkader, Fathy Amer, Ghada AboElenean, Mohamed Mil Tech Coll Elect Engn Cairo Egypt October 6 Univ Fac Informat & Comp Sci Giza Egypt MUST Univ Fac Engn Dept Elect Engn Giza Egypt

A synthetic aperture radar (SAR) system is a notable source of information, recognized for its capability to operate day and night and in all weather conditions, making it essential for various applications. SAR image formation is a pivotal step in radar imaging, essential for transforming complex raw radar data into interpretable and utilizable imagery. Nowadays, advancements in SAR sensor design, resulting in very wide swaths, generate a massive volume of data, necessitating extensive processing. Traditional methods of SAR image formation often involve resource-intensive and time-consuming postprocessing. There is a vital need to automate this process in near-real-time, enabling fast responses for various applications, including image classification and object detection. We present an SAR processing pipeline comprising a complex 2D autofocus SARNet, followed by a CNN-based classification model. The complex 2D autofocus SARNet is employed for image formation, utilizing an encoder-decoder architecture, such as U-Net and a modified version of ResU-Net. Meanwhile, the image classification task is accomplished using a CNN-based classification model. This framework allows us to obtain near real-time results, specifically for quick image viewing and scene classification. Several experiments were conducted using real-SAR raw data collected by the European remote sensing satellite to validate the proposed pipeline. The performance evaluation of the processing pipeline is conducted through visual assessment as well as quantitative assessment using standard metrics, such as the structural similarity index and the peak-signal-to-noise ratio. The experimental results demonstrate the processing pipeline's robustness, efficiency, reliability, and responsivity in providing an integrated neural network-based SAR processing pipeline.

关键词： synthetic aperture radar (SAR) deep learning CNN-based model ResU-Net SAR autofocus

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A Regret Bound for the AdaMax Algorithm With image Segmentation Application

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第9期48卷 10208-10214页

作者： Jirakipuwapat, Wachirapong King Mongkuts Univ Technol North Bangkok KMUTNB Fac Sci Energy & Environm Rayong Thailand

The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well-known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real-time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.

关键词： AdaMax deep learning image segmentation regret bound YOLO

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A deep-learning Empowered, real-time processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1220-1230页

作者： Xia, Yunjia Chen, Jianan Li, Jinchen Gong, Tingchen Vidal-Rosas, Ernesto E. Loureiro, Rui Cooper, Robert J. Zhao, Hubin Univ Coll London UCL Div Surg & Intervent Sci HUB Intelligent Neuroengn HUBIN CREATe London WC1E 6BT England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England UCL Dept Med Phys & Biomed Engn DOT HUB London WC1E 6BT England

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

关键词： real-time systems US Department of Transportation Hemodynamics Functional near-infrared spectroscopy Optical imaging Three-dimensional displays Feedback amplifiers Jacobian matrices image reconstruction Electroencephalography Functional near-infrared spectroscopy (fNIRS) diffuse optical tomography (DOT) brain-computer interface (BCI) neurofeedback (NFB) motion artifacts deep learning real-time processing

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Detection of weeds in vegetables using image classification neural networks and image processing

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FRONTIERS IN PHYSICS 2025年 13卷

作者： Jin, Huiping Han, Kang Xia, Hongting Xu, Bo Jin, Xiaojun Nanjing Forestry Univ Engn Training Ctr Nanjing Jiangsu Peoples R China Peking Univ Shandong Lab Adv Agr Sci Weifang Inst Adv Agr Sci Weifang Shandong Peoples R China Jurong Inst Smart Agr Zhenjiang Jiangsu Peoples R China

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex distribution creates difficulties in rapid and accurate weed detection. In this study, instead of directly applying deep learning to identify weeds, we first created grid cells on the input images. image classification neural networks were utilized to identify the grid cells containing vegetables and exclude them from further analysis. Finally, image processing technology was employed to segment the non-vegetable grid images based on their color features. The background grid cells, which contained no green pixels, were identified, while the remaining cells were labeled as weed cells. EfficientNet, GoogLeNet, and ResNet models achieved overall accuracies of over 0.956 in identifying vegetables in the testing dataset, demonstrating exceptional identification performance. Among these models, the ResNet model exhibited the highest computational efficiency, with a classification time of 12.76 ms per image and a corresponding frame rate of 80.31 fps, satisfying the requirement for real-time weed detection. Effectively identifying vegetables and differentiating weeds from soil significantly reduces the complexity of weed detection and improves its accuracy.

关键词： weed detection deep learning image classification neural networks image processing weed management

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information real-time application Improved pulse coupled neural network image segmentation

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real-time Moving Object Detection for 3-D LiDAR Using Occlusion Accumulation in Range image

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kim, Junha Kim, Haram Oh, Changsuk Kim, Changhyeon Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul 08826 South Korea 42dot Seoul 13449 South Korea Seoul Natl Univ Artificial Intelligence Inst AIIS Seoul 08826 South Korea Samsung Res Seoul 06765 South Korea Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea

deep learning methods have been applied to detect moving objects from 3-D LiDAR data, but they require extra computing devices, such as graphics processing units (GPUs), and often struggle to operate at real-time LiDAR frame rates. In addition, retraining is necessary for new environments, which demands significant resources. Nonlearning methods that operate online without prior maps and rely solely on a central processing unit (CPU) often fail to achieve real-time performance. Furthermore, they are limited in applicability due to their dependence on specific pose estimation algorithms. To address these issues, we propose a novel occlusion accumulation framework in the range image domain for real-time 3-D LiDAR moving object detection on a CPU. By incorporating compensation strategies that account for LiDAR artifacts and measurement sparsity, the proposed method reduces false positives (FPs) and improves detection accuracy. Our approach is also flexible, integrating seamlessly with different pose estimation algorithms without performance degradation. Extensive experiments on KITTI, Apollo, and CARLA datasets demonstrate that the proposed method achieves competitive results compared to state-of-the-art (SOTA) learning-based methods on KITTI and outperforms them in cross-validation on Apollo and CARLA. Moreover, the proposed method operates four times faster than SOTA nonlearning-based methods while offering higher detection accuracy, making it highly suitable for real-world applications in autonomous driving and robotic navigation. We provide all the source code and datasets used in this article to the public at: https://***/JunhaAgu/Mapless_Moving.

关键词： 3-D LiDAR moving object detection occlusion accumulation occlusion accumulation range image range image range image

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image processing framework for in-process shaft diameter measurement on legacy manual machines

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期135卷 4323-4338页

作者： Choudhari, Sahil J. Singh, Swarit Anand Kumar, Aitha Sudheer Desai, Kaushal A. Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur 342030 Rajasthan India

In-process dimension measurement is critical to achieving higher productivity and realizing smart manufacturing goals during machining operations. Vision-based systems have significant potential to serve for in-process dimensions measurements, reduce human interventions, and achieve manufacturing-inspection integration. This paper presents early research on developing a vision-based system for in-process dimension measurement of machined cylindrical components utilizing image-processing techniques. The challenges with in-process dimension measurement are addressed by combining a deep learning-based object detection model, You Only Look Once version 2 (YOLOv2), and image processing algorithms for object localization, segmentation, and spatial pixel estimation. An automated image pixel calibration approach is incorporated to improve algorithm robustness. The image acquisition hardware and the real-time image processing framework are integrated to demonstrate the working of the proposed system by considering a case study of in-process stepped shaft diameter measurement. The system implementation on a manual lathe demonstrated robust utilities, eliminating the need for manual intermittent measurements, digitized in-process component dimensions, and improved machining productivity.

关键词： Dimension measurement In-process inspection Machine vision image processing Object segmentation deep learning

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deep Convolution Neural Networks for image-Based Android Malware Classification

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Computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

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