Article
Environmental Sciences
Zhiyuan Li, Shun Li, Jiandong Mao, Juan Li, Qiang Wang, Yi Zhang
Summary: A novel denoising method combining VMD, SSA, and SVD is proposed in this paper to reduce noise and extract useful signals from lidar return signals. The method has shown improved noise reduction over other existing methods and can eliminate complex noise while retaining signal details.
Article
Physics, Multidisciplinary
Jiandong Mao, Zhiyuan Li, Shun Li, Juan Li
Summary: This study proposes an efficient denoising method called VMD-SSA-SVD algorithm for noise reduction of ECG signals. The algorithm combines the sparrow search algorithm (SSA) and singular value decomposition (SVD) algorithm with the variational modal decomposition (VMD) algorithm. It decomposes the signal into finite modal components using VMD-SSA, eliminates components with baseline drift, extracts effective modalities using the mutual relation number method, applies SVD noise reduction to each effective modal, and finally reconstructs a clean ECG signal. Experimental results show that the VMD-SSA-SVD algorithm achieves the most significant noise reduction effect, suppressing noise and removing baseline drift interference while retaining the morphological characteristics of the ECG signals effectively.
Article
Computer Science, Information Systems
Wansoo Ha, Changsoo Shin
Summary: This study presents a method to attenuate seismic noise in the Laplace domain using singular value decomposition. By transforming seismic wavefields into the Laplace domain and applying various filtering and decomposition techniques, the accuracy of inversions was improved and the errors and uncertainties caused by noise were reduced.
Article
Medicine, General & Internal
Ryosuke Kasai, Hideki Otsuka
Summary: Coronary computed tomography angiography (CCTA) is widely used for its improved diagnostic performance in computed tomography (CT). It requires shorter rotation times of the X-ray tube, improving temporal resolution and enabling imaging of the beating heart. However, reconstructed CT images, including those of the coronary arteries, suffer from insufficient X-ray photons and significant noise. This study introduces an image-processing technique using singular value decomposition (SVD) and Jensen-Shannon (JS) divergence to reduce noise in CCTA images. Experiments showed that this method produced higher-quality images compared to conventional noise reduction methods.
Article
Green & Sustainable Science & Technology
Quanjie Zhu, Longkun Sui, Qingsong Li, Yage Li, Lei Gu, Dacang Wang
Summary: The denoising method proposed in this study aims to extract relevant signals from background interference in microseismic mine signals (MMS), enabling their utilization in various applications. Based on the Fourier transform, inverse transform, and singular value decomposition, the method effectively improves the signal-to-noise ratio (SNR) of MMS. Through analysis and processing of three types of microseismic waveforms and evaluation of four indicators, the results demonstrate the strong noise suppression capability and significance of this method in the analysis and processing of microseismic signals in mining.
Article
Chemistry, Multidisciplinary
Milan Brankovic, Eduardo Gildin, Richard L. Gibson, Mark E. Everett
Summary: The development of seismic data compression methods aims to reduce data volume and increase interpretation accuracy in real-time monitoring, while also being used for data denoising and signal detection.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Huan Liu, Zehua Wang, Changfeng Zhao, Jian Ge, Haobin Dong, Zheng Liu
Summary: A new noise-reduction algorithm based on multilinear SVD for FID signals is proposed in this study, showing significant improvement in SNR compared to traditional methods such as SVD and PCA, especially in high noise interference environments.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Chemistry, Analytical
Yuncheng Zhang, Xiang Gao, Jiawei Zhang, Jingpin Jiao
Summary: An ultrasonic reverse time migration imaging method is proposed in this study, which is based on high-order singular value decomposition to address the problems of low signal-to-noise ratio (SNR) and excessive artifacts in defect ultrasonic detection imaging results of materials with high noise levels. The method utilizes the 3D structural properties of the ultrasonic full-matrix capture data and directly performs higher-order singular value decomposition. It overcomes the difficulties in determining the number of singular values in the original noise-reduction algorithm and achieves one-step noise reduction processing of all signals. The method also conducts reverse time migration imaging in the frequency domain, resulting in high-quality acoustic images.
Article
Computer Science, Information Systems
Abdulrahman B. B. Abdelaziz, Mohammad A. A. Rahimi, Muhammad R. R. Alrabeiah, Ahmed B. B. Ibrahim, Ahmed S. S. Almaiman, Amr M. M. Ragheb, Saleh A. A. Alshebeili
Summary: Biometric-based authentication is crucial in modern technologies, but using photoplethysmography (PPG) for authentication in crowded places like airport checkpoints can lead to heavy signal traffic. This paper proposes a compression-decompression scheme (Codec) based on truncated singular value decomposition (T-SVD) that meets the requirements of high-fidelity signal reconstruction and lightweight encoding. The proposed Codec achieves a 95% compression ratio and a 99% coefficient of determination, and the encoder can encode a PPG signal in 300 milliseconds on a Raspberry Pi 3.
Article
Multidisciplinary Sciences
Guanglei Yang, Guoxing Zhang, Dongqin Cao, Donglan Zha, Bin Su
Summary: Cities in China are playing an increasingly important role in mitigating climate change. A panel dataset on renewable energy transition in Chinese cities was developed to assess the CO2 emissions reduction. The study found that city-level renewable energy transition only reduced 446 million tonnes of CO2 emissions from 2005 to 2019. However, in scenarios with policy constraints or technology breakthrough, the reduction could significantly increase, potentially reaching the 2030 carbon peak target.
Article
Engineering, Electrical & Electronic
Xiangdong Peng, Huaqiang Zhu, Xiao Zhou, Congcheng Pan, Zejun Ke
Summary: In this article, a novel method called ST-Res U-net is proposed for the detection of QRS complexes and R-peaks in electrocardiogram (ECG) signals. The method uses an improved U-net model to extract spatiotemporal features and a threshold screening algorithm to locate the R-peaks. Experimental results demonstrate that the proposed method is effective for the automatic classification and annotation of ECG signals and greatly improves the accuracy of diagnosis of arrhythmia diseases.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Remote Sensing
Shun Li, Jiandong Mao, Zhiyuan Li
Summary: This paper proposes a new joint denoising method EEMD-GWO-SVD to improve the signal-to-noise ratio and extract useful signals in atmospheric lidar. The results show that the proposed method has better noise reduction effect than the other four methods. It can eliminate complex noise in the lidar return signal while retaining all the details of the signal.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Article
Engineering, Industrial
Taha J. Alhindi, Jaeseung Baek, Young-Seon Jeong, Myong K. Jeong
Summary: For automobile manufacturers, reducing vehicle interior noise is crucial for improving customer satisfaction. This paper proposes a novel automated windshield wiper fault-detection system that outperforms existing methods in accurately identifying faulty wipers. By utilizing a binarization approach and matrix-factorization technique, meaningful features are extracted from sound signals to classify the wiper's condition.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Optics
Liu-Ya Chen, Yi-Ning Zhao, Lin-Shan Chen, Chong Wang, Cheng Ren, De-Zhong Cao
Summary: In this letter, a scheme of color ghost imaging is proposed to reduce the impact of ambient noise on image quality. The measurement matrix is optimized with low-pass filters, and the patterns of random speckles are filtered using four different filters. By using the TSVD method, the pseudo-inverse matrix of the optimized measurement matrix is obtained and used for image reconstruction. The experimental results show that the image quality is greatly improved and the point spread functions are optimized after filtering the random speckles.
OPTICS AND LASER TECHNOLOGY
(2024)
Article
Engineering, Biomedical
Bozo Tomas, Mijo Grabovac, Karlo Tomas
Summary: This paper presents a novel algorithmic solution for locating noise in ECG signals by detecting false R-peaks to indicate noise-contaminated segments. The algorithm is easy to follow, and its performance is demonstrated in the results.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
