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Wireless recording and autoencoder denoising of intestinal activity in freely moving rats

JOURNAL OF PHARMACOLOGICAL SCIENCES 2025年第1期158卷 54-58页

作者： Ishii, Yamato Matsumoto, Nobuyoshi Ikegaya, Yuji Kashima, Tetsuhiko Univ Tokyo Grad Sch Pharmaceut Sci Tokyo 1130033 Japan Univ Tokyo Inst AI & Beyond Tokyo 1130033 Japan Natl Inst Informat & Commun Technol Ctr Informat & Neural Networks Suita Osaka 5650871 Japan

Conventional wired systems for recording intestinal motility using strain-gauge transducers physically limit animal movement and are not ideal for long-term studies. Here, we developed a wireless recording system that allows continuous monitoring of intestinal activity in freely moving rats. We also developed a denoising autoencoder that isolates intestinal motility signals from locomotor noise while maintaining a 10-s temporal resolution. The refined data revealed decreased intestinal motility while the rats were behaviorally active. This system has broad applications for in vivo physiological research.

关键词： intestinal activity Free-moving denoising autoencoder

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Open-Set Specific Emitter Identification Leveraging Enhanced Metric denoising autoencoders

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3453-3462页

作者： Huang, Shennan Guo, Lantu Fu, Xue Peng, Yang Guo, Yongan Wang, Yu Zhang, Qianyun Gui, Guan Sari, Hikmet Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China China Res Inst Radiowave Propagat Res Dept 5 Qingdao 266107 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Intelligent Informat Proc & Commun Nanjing 210003 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China

Specific emitter identification (SEI) is pivotal for ensuring the security of the Internet of Things (IoT). Traditional deep learning-based SEI techniques often falter in real-world applications, particularly when distinguishing between legitimate and rogue devices amid noisy conditions and low-signal-to-noise ratios (SNRs). To surmount these challenges, we propose a novel open-set SEI (OS-SEI) strategy that utilizes a metric-enhanced denoising autoencoder (MeDAE) architecture. This advanced framework incorporates a deep residual shrinkage network, significantly augmenting the denoising autoencoder's capability, thereby bolstering its resilience against noisy environments. Further, the integration of discriminative metrics, such as center loss, markedly enhances feature discrimination, resulting in heightened accuracy of device identification. Our comprehensive experimental assessments, conducted on an automatic dependent surveillance-broadcast (ADS-B) data set, underscore the superiority of our proposed OS-SEI method over existing models. The findings confirm our approach's enhanced robustness to noise and its superior accuracy in device identification within open-set scenarios.

关键词： Deep learning (DL) deep metric learning denoising autoencoder noise robustness open set specific emitter identification (SEI) Deep learning (DL) deep metric learning denoising autoencoder noise robustness open set specific emitter identification (SEI)

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Siamese denoising autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis

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IEEE Access 2025年 13卷 86333-86343页

作者： Shim, Jaesung Jo, Kyuri Chungbuk National University Department of Computer Engineering Cheongju 28644 South Korea

Deep learning models have achieved groundbreaking results in computer vision;however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep learning models. This paper proposes a simple, powerful, and efficient adversarial defense method: a Siamese network-based denoising autoencoder (Siamese-DAE). This method addresses the classification accuracy reduction caused by the denoising process. Experiments on Chest X-ray, Brain MRI, Retina, and Skin images, using FGSM, PGD, DeepFool, CW, SPSA, and AutoAttack adversarial algorithms, demonstrate that the Siamese-DAE, trained to remove noise, effectively eliminates perturbations, leading to improved classification accuracy compared not only to the standard classification model but also to relevant denoising defense models. © 2013 IEEE.

关键词： Adversarial Examples Deep Learning denoising autoencoder Siamese Network

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TrustEMG-Net: Using Representation-Masking Transformer With U-Net for Surface Electromyography Enhancement

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第4期29卷 2506-2520页

作者： Wang, Kuan-Chen Liu, Kai-Chun Yeh, Ping-Cheng Peng, Sheng-Yu Tsao, Yu Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA 01003 USA Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Chung Yuan Christian Univ Dept Elect Engn Taoyuan 320 Taiwan

Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remove sEMG contaminants, as non-invasive measurements render sEMG susceptible to various contaminants. However, these approaches often rely on heuristic-based optimization and are sensitive to the contaminant type. A more potent, robust, and generalized sEMG denoising approach should be developed for various healthcare and human-computer interaction applications. This paper proposes a novel neural network (NN)-based sEMG denoising method called TrustEMG-Net. It leverages the potent nonlinear mapping capability and data-driven nature of NNs. TrustEMG-Net adopts a denoising autoencoder structure by combining U-Net with a Transformer encoder using a representation-masking approach. The proposed approach is evaluated using the Ninapro sEMG database with five common contamination types and signal-to-noise ratio (SNR) conditions. Compared with existing sEMG denoising methods, TrustEMG-Net achieves exceptional performance across the five evaluation metrics, exhibiting a minimum improvement of 20%. Its superiority is consistent under various conditions, including SNRs ranging from -14 to 2 dB and five contaminant types. An ablation study further proves that the design of TrustEMG-Net contributes to its optimality, providing high-quality sEMG and serving as an effective, robust, and generalized denoising solution for sEMG applications.

关键词： Surface electromyography sEMG contam-inant removal denoising autoencoder U-Net U-Net transformer transformer transformer

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Intelligent cognitive internet of things-based spectrum sensing algorithm for future communication

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INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES 2025年第1期21卷 18-41页

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Keshta, Ismail Soni, Mukesh Kumar, Pankaj Bhadla, Mohit Khan, Muhammad Attique Damasevicius, Robertas Inje Univ Dept Digital Antiaging Healthcare Gimhae 50834 South Korea Al Maarif Univ Coll Dept Comp Sci Al Anbar 31001 Iraq Intervent Treatment Inst Houston TX USA AlMaarefa Univ Coll Appl Sci Comp Sci & Informat Syst Dept Riyadh Saudi Arabia Lovely Profess Univ Div Res & Dev Phagwara India Dr DY Patil Vidyapeeth Dr DY Patil Sch Sci & Technol Pune India GL Bajaj Inst Technol & Management Dept Comp Sci & Engn Greater Noida 201306 Uttar Pradesh India Swarnim Startup & Innovat Univ Gandhinagar India Prince Mohammad Bin Fahd Univ Dept AI Dhahran Saudi Arabia Kaunas Univ Technol Kaunas Lithuania

The emergence of fifth-generation (5G) mobile communication technologies has propelled the advancement of the internet of things (IoT). Nevertheless, the intricate nature of the IoT mobile communication environment and the fluctuating characteristics of the signal's present substantial obstacles to current spectrum detection techniques for future communication. Hence, an artificial intelligent spectrum sensing technique is introduced, which integrates artificial intelligent, IoT and denoising autoencoder (DAE) with an enhanced long-short-term memory (LSTM) neural network. The DAE utilises encoding and decoding to retrieve the fundamental structural characteristics of mobile signals, while the enhanced LSTM spectrum sensing classifier model incorporates previous moment information features to classify the time-series signal sequences. This method has demonstrated a 45% improvement in perception performance compared to SVM, RNN, LeNet5, LVQ, and Elman algorithm.

关键词： future communication internet of things IoT spectrum sensing artificial intelligent long-short-term memory LSTM denoising autoencoder DAE

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Self-supervised fusion of deep soft assignments for multi-view diagnosis of machine faults

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第4期36卷 2493-2507页

作者： Li, Chuan Wu, Yifan Xiong, Manjun Yang, Shuai Bai, Yun Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Res Ctr Syst Hlth Maintenance Chongqing 400067 Peoples R China

Fault patterns are often unavailable for machine fault diagnosis without prior knowledge. This makes it challenging to diagnose the existence of machine faults and their types. To address this issue, a novel scheme of deep soft assignments fusion network (DSAFN) is proposed for the self-supervised multi-view diagnosis of machine faults. To enhance the robustness of the model and prevent overfitting, random noise is added to the collected signals. In each view, vibration features are extracted by a denoising autoencoder. Using the extracted deep features, a soft assignment fusion strategy is proposed to fully utilize both the public and complementary information of multiple views. Critical diagnosis missions, including novel fault detection and fault clustering, are accomplished through binary clustering and multi-class clustering of DSAFN, respectively. Two diagnostic experiments are conducted to validate the proposed method. The results indicate that the proposed method performs better than state-of-the-art peer methods in terms of diagnostic accuracy and noise robustness.

关键词： Machine fault Multi-view diagnosis denoising autoencoder Self-supervised fusion Deep soft assignment

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Near-surface defect detection in ultrasonic testing using domain-knowledge-informed self-supervised learning

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ULTRASONICS 2025年 147卷 107528页

作者： Jeon, Minsu Choi, Minseok Choi, Wonjae Ha, Jong Moon Oh, Hyunseok Gwangju Inst Sci & Technol GIST Sch Mech Engn Gwangju 61005 South Korea Ajou Univ Dept Mech Engn Suwon 16499 South Korea Korea Res Inst Stand & Sci KRISS Nondestruct Metrol Grp Daejeon 34113 South Korea

Recently, significant research efforts have been made to enhance ultrasonic testing (UT) by employing artificial intelligence (AI). However, collecting an extensive amount of labeled data across various testing environments to train the AI model poses significant challenges. Moreover, conventional UT typically focuses on detecting deep- depth defects, which limits the effectiveness of such methods in detecting near-surface defects. To this end, this paper proposes a novel near-surface defect detection method for ultrasonic testing that can be employed without collecting labeled data. We propose a self-supervised anomaly detection model that incorporates domain knowledge. First, synthetic faulty samples are generated by fusing the measured UT signals with the back-wall UT reflection signals, to simulate real faulty features. Unlike the CutPaste method used for computer vision applications, this synthesis method adds the back-wall echo signal to random locations by incorporating the physical principles of the superposition of ultrasonic signals. Next, a de-anomaly network is devised to isolate subtle defect features within the measured UT signals. The presence of defects was determined using the three- sigma rule of the mean absolute value of the residual output. The defect depth is determined by a time-of-flight calculation from the residual output. The effectiveness of the proposed method was evaluated through the UT of aluminum blocks with near-surface defects of varying depths under different surface conditions. Both qualitative and quantitative comparison studies demonstrated that the proposed method outperformed existing methods in detecting the presence and depth of near-surface defects.

关键词： Ultrasonic testing Diagnostics Data synthesis denoising autoencoder Self-supervised learning

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Wavelet Integrated Convolutional Neural Network for ECG Signal denoising 31st

Wavelet Integrated Convolutional Neural Network for ECG Sign...

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31st International Conference on Multimedia Modeling

作者： Terada, Takamasa Toyoura, Masahiro Univ Yamanashi 4-3-11 Takeda Kofu Yamanashi 4008511 Japan

ISBN: (纸本)9789819620708;9789819620715

Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise reduction difficult. Hence, it is necessary to provide a mechanism that changes the characteristics of the noise based on its intensity and type. This study proposes a convolutional neural network (CNN) model with an additional wavelet transform layer that extracts the specific frequency features in a clean ECG. Testing confirms that the proposed method effectively predicts accurate ECG behavior with reduced noise by accounting for all frequency domains. In an experiment, noisy signals in the signal-to-noise ratio (SNR) range of -10-10 are evaluated, demonstrating that the efficiency of the proposed method is higher when the SNR is small.

关键词： Electrocardiogram Wavelet transform denoising autoencoder Convolutional neural network

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denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation

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ENERGY AND BUILDINGS 2018年 158卷 1461-1474页

作者： Bonfigli, Roberto Felicetti, Andrea Principi, Emanuele Fagiani, Marco Squartini, Stefano Piazza, Francesco Univ Politecn Marche Dept Informat Engn Via Brecce Bianche I-60131 Ancona Italy

Non-Intrusive Load Monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the Deep Neural Networks. In particular, the NILM task is treated as a noise reduction problem addressed by using denoising autoencoder (dAE) architecture, i.e., a neural network trained to reconstruct a signal from its noisy version. This architecture has been initially proposed by Kelly and Knottenbelt (2015), and here is extended and improved by conducting a detailed study on the topology of the network, and by intelligently recombining the disaggregated output with a median filter. An additional contribution of this paper is an exhaustive comparative evaluation conducted with respect to one of the reference work in the field of Hidden Markov Models (HMM) for NILM, i.e., the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The experiments have been conducted on the AMPds, UK-DALE, and REDD datasets in seen and unseen scenarios both in presence and in absence of noise. In order to be able to evaluate AFAMAP in presence of noise, an HMM model representing the noise contribution has been introduced. The results showed that the dAE approach outperforms the AFAMAP algorithm both in seen and unseen condition, and that it exhibits a significant robustness in presence of noise. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Non-Intrusive Load Monitoring (NILM) Active power denoising autoencoder Deep Neural Network (DNN) Factorial Hidden Markov Model (FHMM)

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denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035024页

作者： Bacon, Philippe Trovato, Agata Bejger, Michal Univ Paris cite CNRS Astroparticule & Cosmol F-75013 Paris France Univ Trieste Dipartimento Fis I-34127 Trieste Italy Sez Trieste INFN I-34127 Trieste Italy INFN Sez Ferrara Via Saragat 1 I-44122 Ferrara Italy Polish Acad Sci Nicolaus Copernicus Astron Ctr Bartycka 18 PL-00716 Warsaw Poland

The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ('glitches'), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.

关键词： denoising autoencoder convolutional neural network data analysis gravitational waves

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