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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE

SENSORS 2025年第7期25卷 2257-2257页

作者： Cao, Gangsheng Jia, Shen Wu, Qing Xia, Chunming East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Mechanomyography (MMG) is a non-invasive technique for assessing muscle activity by measuring mechanical signals, offering high sensitivity and real-time monitoring capabilities, and it has many applications in rehabilitation training. Traditional MMG-based motion recognition relies on feature extraction and classifier training, which require segmenting continuous actions, leading to challenges in real-time performance and segmentation accuracy. Therefore, this paper proposes an innovative method for the real-time segmentation and classification of upper limb rehabilitation actions based on the You Only Look Once (YOLO) algorithm, integrating the Squeeze-and-Excitation (SE) attention mechanism to enhance the model's performance. In this paper, the collected MMG signals were transformed into one-dimensional time-series images. After image processing, the training set and test set were divided for the training and testing of the YOLOv5s-SE model. The results demonstrated that the proposed model effectively segmented isolated and continuous MMG motions while simultaneously performing real-time motion category prediction and outputting results. In segmentation tasks, the base YOLOv5s model achieved 97.9% precision and 98.0% recall, while the improved YOLOv5s-SE model increased precision to 98.7% (+0.8%) and recall to 98.3% (+0.3%). Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. This method realizes the automatic segmentation of time-domain motions, avoids the limitations of manual parameter adjustment in traditional methods, and simultaneously enhances the real-time performance of MMG motion recognition through image processing, providing an effective solution for motion analysis in wearable devices.

关键词： mechanomyography upper limb motion segmentation pattern recognition target detection YOLOv5s-SE deep learning

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Detection of Moiré pattern in high-resolution images

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SIGNAL image AND VIDEO processing 2024年第1期18卷 561-568页

作者： Zhang, Yu Shen, Li-Yong Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Despite the improvements in the quality of digital images brought about by the development of digital cameras and smartphones, taking clean photographs remains a challenge due to the presence of noise such as moire patterns, especially for high-resolution images commonly used in today's real life. To more efficiently and accurately identify and remove moires in high-resolution images, this paper proposes an improved network based on the high-resolution network HRDN. We conducted experiments on the FHDMi dataset to evaluate the effectiveness of our method. The results demonstrate that the improved algorithm significantly enhances the performance of moire detection in high-resolution images, making it better suited to meeting the demands of real-world applications.

关键词： image process Demoireing High-resolution deep learning

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deep learning-based prediction of the remaining time and future distribution of pebble flow from real-scene images

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CHEMICAL ENGINEERING SCIENCE 2024年 283卷

作者： Wu, Mengqi Bin, Li Gui, Nan Yang, Xingtuan Tu, Jiyuan Jiang, Shengyao Tsinghua Univ Inst Nucl & New Energy Technol Collaborat Innovat Ctr Adv Nucl Energy Technol Adv Reactor Engn & Safety Key Lab Minist Educ Beijing 100084 Peoples R China RMIT Univ Sch Engn Melbourne Vic 3083 Australia

Pebble flow dynamics is a crucial issue for designing and operating pebble bed reactors. The existing experimental or simulation methods are often associated with high time, resource, and effort costs. Therefore, imagebased deep learning methods are explored to make real-time predictions of pebble flow dynamics directly from images. real-scene images, captured by the high-speed camera during pebble flow experiments are used as the dataset. This paper proposes an RT-Net model based on Convolutional Neural Network (CNN) to predict the remaining time of pebble flow from experimental images. The core of the RT-Net model is the mergeable multibranch convolutional component called ConvBlock, which effectively improves accuracy and reduces computational costs compared to traditional convolutional operators. Results show that this model is superior in both accuracy and efficiency metrics compared with typical convolutional neural networks (like AlexNet and VGGs). The proportion of test sets with prediction error within 0.05 s reaches 96.7 %, and the parameters count and inference time are 5.02 M and 1.99 ms respectively. Furthermore, to anticipate the pebble distribution at a given future time, a dual-input PreNet that combines CNN and Generative Adversarial Network (GAN) is designed, where the input includes the current pebble flow image and an arbitrarily chosen temporal displacement Delta t. Targeted evaluation metrics, such as Target Similarity (TS) and Equivalent Target Similarity (TSeq) are proposed to quantitatively evaluate the model's performance. Results indicate that the Pre-Net model can make quite satisfactory predictions of the future distribution of most data, while more efforts such as a task-specific loss function are encouraged to achieve better performance.

关键词： Pebble flow deep learning real-scene images Remaining time Future distribution

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Auto-BCS: A Hybrid System for real-time Breast Cancer Screening from Pathological images

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第4期37卷 1752-1766页

作者： Ekta, Vandana Bhatia, Vandana Netaji Subhas Univ Technol Delhi India

Breast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, rendering them unsuitable for deployment on low-processing-power mobile devices. In this paper, a real-time automated system "Auto-BCS" is introduced that significantly enhances the efficiency of early breast cancer screening. The system is structured into three distinct phases. In the initial phase, images undergo a pre-processing stage aimed at noise reduction. Subsequently, feature extraction is carried out using a lightweight and optimized deep learning model followed by extreme gradient boosting classifier, strategically employed to optimize the overall performance and prevent overfitting in the deep learning model. The system's performance is gauged through essential metrics, including accuracy, precision, recall, F1 score, and inference time. Comparative evaluations against state-of-the-art algorithms affirm that Auto-BCS outperforms existing models, excelling in both efficiency and processing speed. Computational efficiency is prioritized by Auto-BCS, making it particularly adaptable to low-processing-power mobile devices. Comparative assessments confirm the superior performance of Auto-BCS, signifying its potential to advance breast cancer screening technology.

关键词： Breast cancer screening deep learning Classification models Hybrid system On-device image recognition

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deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

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JOURNAL OF NEUROIMAGING 2024年第2期34卷 232-240页

作者： Estler, Arne Zerweck, Leonie Brunnee, Merle Estler, Bent Hauser, Till-Karsten Richter, Vivien Oergel, Anja Buerkle, Eva Becker, Hannes Hurth, Helene Stahl, Stephane Konrad, Eva-Maria Kelbsch, Carina Ernemann, Ulrike Gohla, Georg Univ Hosp Tuebingen Dept Radiol Diagnost & Intervent Neuroradiol Tubingen Germany Heidelberg Univ Hosp Neurol Univ Clin Dept Neuroradiol Heidelberg Germany Heidelberg Univ Hosp Dept Cardiol Angiol & Pneumol Heidelberg Germany Univ Tubingen Dept Neurosurg Tubingen Germany Centerplast Private Practice Saarbrucken Germany Univ Tubingen Univ Eye Hosp Ctr Ophthalmol Tubingen Germany Hoppe Seyler Str 3 D-72076 Tubingen Baden Wurttembe Germany

Background and Purpose: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital ***: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES) and DL TSE sequences (TSEDL) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert ***: TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated ***: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

关键词： acquisition time deep learning reconstruction deep resolve boost image processing image quality magnetic resonance imaging orbital imaging

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LDFNN: A lightweight dual-feedback neural network for space target component detection

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ADVANCES IN SPACE RESEARCH 2025年第7期75卷 5702-5717页

作者： Zhou, Wenbo Wang, Zebin Li, Ligang Liu, Bo Wu, Yijia Chinese Acad Sci Natl Space Sci Ctr Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Shanghai Acad Spaceflight Technol Shanghai 201109 Peoples R China Beijing Univ Chem Technol Beijing 100029 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan Peoples R China

As the number of spacecraft and the complexity of their missions increase, space target component detection technology is becoming critical for ensuring spacecraft safety in orbit. An effective detection system not only monitors and identifies potential collision threats in real-time, enabling timely obstacle avoidance or path adjustments, but also provides crucial localization and identification capabilities for autonomous spacecraft maintenance and repair. However, most existing methods overlook image quality degradation resulting from prolonged on-orbit operation. Although deep learning models have improved detection accuracy, they are often burdened by excessive network parameters and high computational complexity, which limits their applicability to low-power, lightweight, and resource- constrained on-board processing. In this study, we propose a lightweight dual-feedback neural network (LDFNN) for space target component detection and recognition. The network is designed to address the challenges posed by the space environment through a dual- feedback mechanism. The outer-feedback mechanism enhances recognition stability and accuracy by incorporating modulation transfer function (MTF) analysis and image quality assessment, while the inner-feedback mechanism is optimized for low-power on-orbit processing, improving both real-time performance and generalization. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Space target Component detection Dual-feedback neural network deep learning MTF

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A Graphical deep learning Approach to RF Fingerprinting in the time-Frequency Domain

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IEEE SENSORS JOURNAL 2024年第10期24卷 16984-16990页

作者： Li, Bo Cetin, Ediz Macquarie Univ Sch Engn Sydney NSW 2109 Australia

The emergence of the Internet of Things (IoT) as a global infrastructure of interconnected network of heterogeneous wireless devices and sensors is opening new opportunities in myriad of applications. This growing pervasiveness of IoT devices, however, is leading to growing concerns regarding security and privacy. Radio Frequency (RF) fingerprinting techniques operating at the physical layer can be used to provide an additional layer of protection to ensure trustworthy communications between devices to address these concerns. We present a graphical deep learning approach in the time-frequency (TF) domain based on short-time Fourier transform (STFT) where the intensity information of the STFT is used to generate 2-D image inputs for training and testing of the deep learning models for RF fingerprinting and identification. The performance of the proposed approach is evaluated and compared with the baseline approach operating in the waveform domain, using the same neural network architecture based on over-the-air captured datasets from 12 Zigbee devices. The experimental results show that the proposed approach outperforms both baseline approach, achieving nearly 100% identification accuracy based on data captured in a makeshift RF chamber, and accuracy of 98% on the dataset which was captured under real-life conditions, demonstrating the robustness of the proposed approach. Furthermore, the impact of the STFT-parameter selection on the identification performance of the proposed approach is also evaluated.

关键词： deep learning Radio frequency Fingerprint recognition time-frequency analysis Object recognition Sensors Internet of Things Internet of Things (IoT) radio frequency (RF) fingerprinting software-defined radio wireless device identification

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Online Microscopic Imaging Combined with Machine learning image processing for Online Monitoring of Mineral Froth Flotation Process 8

Online Microscopic Imaging Combined with Machine Learning Im...

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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Dong, Xianfa Wang, Xuezhong Beijing Institute of Petrochemical Technology College of New Materials and Chemical Engineering Beijing China

ISBN: (纸本)9798331506230

Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for real-time monitoring of the process. Previous limited work explored imaging the foam at the top surface layer of the froth flotation process. The new process imaging system in this work uses a corrosion-resistant online real-time imaging probe that can be put into the inside of the slurry and capture real-time images of bubbles. The acquired images are analyzed online using deep learning algorithms to automatically obtain key parameters of the bubbles, providing valuable data for froth flotation research and process control. © 2024 IEEE.

关键词： Froth flotation

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A comprehensive study on enhanced QR extraction techniques with deep learning-based verification

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APPLIED INTELLIGENCE 2025年第7期55卷 1-22页

作者： Alam, Nur Sagar, A. S. M. Sharifuzzaman Zhang, Wenqi Jin, Taicheng Dosset, Arailym Dang, L. Minh Moon, Hyeonjoon Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam

In the digital age, Quick Response (QR) codes have become essential in sectors such as digital payments and ticketing, propelled by advancements in Internet of Things (IoT) and deep learning. Despite their growing use, there are significant challenges in the accurate extraction and verification of QR codes, particularly in dynamic environments. Traditional methods struggle with issues like variable lighting, complex backgrounds, and counterfeits, which degrade the performance of QR code extraction and verification processes. This paper introduces a comprehensive approach that refines QR code extraction using enhanced adaptive thresholding techniques and incorporates a deep learning framework specifically tailored for robust QR code verification. Our methodology integrates dynamic window size adjustment, statistical weighting, and post-thresholding refinement to ensure precise QR code extraction under varying conditions. The verification process employs the ShuffleNetV2 network to ensure high performance with significantly low processing times suitable for real-time applications. Additionally, our deep learning model is trained on a comprehensive dataset comprising 28,523 images across 24 distinct QR code pattern classes, including variations in lighting, noise, and backgrounds to simulate real-world conditions. Experimental results demonstrate that our proposed methodology outperforms competing techniques in both processing speed and recognition accuracy, achieving a processing time of 0.08 seconds and a validation accuracy of 99.99% in constrained scenarios. Our approach shows an exceptional ability to distinguish real QR codes from counterfeits and highlights the significance and efficacy of our method in addressing contemporary challenges.

关键词： QR code extraction deep learning Feature extraction Verification

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A computational method for real-time roof defect segmentation in robotic inspection

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2025年

作者： Zhao, Xiayu Jebelli, Houtan Univ Illinois Dept Civil & Environm Engn Champaign IL USA

Roof inspections are crucial but perilous, necessitating safer and more cost-effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due to computational constraints and scarce specialized data. This study presents real-time roof defect segmentation network (RRD-SegNet), a deep learning framework optimized for mobile robotic platforms. The architecture features a mobile-efficient backbone network for lightweight processing, a defect-specific feature extraction module for improved accuracy, and a regressive detection and classification head for precise defect localization. Trained on the multi-type roof defect segmentation dataset of 1350 annotated images across six defect categories, RRD-SegNet integrates with a roof damage identification module for real-time tracking. The system surpasses state-of-the-art models with 85.2% precision and 76.8% recall while requiring minimal computational resources. Field testing confirms its effectiveness with F1-scores of 0.720-0.945 across defect types at processing speeds of 1.62 ms/frame. This work advances automated inspection in civil engineering by enabling efficient, safe, and accurate roof assessments via mobile robotic platforms.

关键词： image segmentation

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