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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Lei, Wentai Tan, Qianying Hou, Feifei Yang, Zhilin Liu, Gengye Gu, Qingyuan Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Cent S Univ Sch Software Changsha Hunan Peoples R China Time Varying Transmiss Co LTD Xiangtan Peoples R China

ISBN: (纸本)9781538653739

When using radar to recognize gesture, radar echo signals are susceptible to noise. In order to improve detection resolution and data interpretation quality of gesture radar, this paper proposes a new denoising algorithm based on weighted principal component analysis and improved wavelet threshold, named WPCA-IWT denoising algorithm. First, radar echo data are standardized and each echo data's variance are calculated. Each echo data is multiplied by it's corresponding variance to complete weighting operation. By using WPCA processing, the dimension of echo data is reduced and echo signal's principal components are extracted. Then, a new threshold function is proposed and an improved wavelet threshold denoising algorithm is designed to denoise principal components. Finally, denoised principal components are multiplied with an eigenvector matrix to reconstruct radar echo. The proposed WPCA-IWT denoising algorithm has obvious advantages at the aspects of efficiency and denoising effect. Both simulation and on-site radar data experiments verified the effectiveness of the proposed algorithm.

关键词： gesture radar weighted principal component analysis (WPCA) improved wavelet threshold denoising algorithm

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Efficacy of the SDGSAT-1 glimmer imagery in measuring sustainable development goal indicators 7.1.1, 11.5.2, and target 7.3

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REMOTE SENSING OF ENVIRONMENT 2024年 305卷

作者： Liu, Shaoyang Wang, Congxiao Chen, Zuoqi Li, Wei Zhang, Lingxian Wu, Bin Huang, Yan Li, Yangguang Ni, Jingwen Wu, Jianping Yu, Bailang East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Fuzhou Univ Natl & Local Joint Engn Res Ctr Satellite Geospati Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350108 Peoples R China Fuzhou Univ Acad Digital China Fuzhou 350108 Peoples R China Sun Yat Sen Univ Sch Geospatial Engn & Sci Zhuhai 519082 Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518060 Peoples R China

The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the Glimmer Imager (GLI), provides high -resolution nighttime light (NTL) data across multiple spectral bands, potentially facilitating the monitoring of sustainable development goals (SDGs). This study developed a denoising algorithm for the multispectral SDGSAT-1 GLI and demonstrated that its data capacity allows for the measurement of the SDG indicators 7.1.1, 11.5.2, and the achievement of target 7.3. The results indicate that (1) The denoising algorithm can effectively remove strips and salt -and -pepper noise from SDGSAT-1 GLI images, with the residual noise significantly reduced and almost little information loss. (2) SDGSAT-1 GLI data can accurately identify electrified areas at a finer spatial scale for calculating Indicator 7.1.1, compared to the traditional NASA ' s Black Marble Product. The findings show that highly urbanized cities exhibit a greater proportion of their population with access to electricity than underdeveloped cities. (3) SDGSAT-1 proficiently estimates economic losses resulting from nonnatural disasters for Indicator 11.5.2. Changes in SDGSAT-1 NTL intensity strongly correlate with pandemicinduced economic losses, with an R 2 exceeding 0.8. (4) When measuring Target 7.3 achievement, the SDGSAT-1 GLI multispectral bands classify streetlight types into light-emitting diode and high-pressure sodium lamps with acceptable overall accuracy (89.9%). Sequentially, the classification shows that Shanghai achieved a 13.09% energy-saving benefit. Overall, by leveraging the high spatial resolution, multiple spectra, and appropriate satellite overpass times of SDGSAT-1 GLI, the estimated SDG indicators in this study outperform those based on Black Marble products, and SDGSAT-1 GLI data have the potential to serve as a direct data source or reference factor for estimating at least 11 SDG indicators.

关键词： SDGSAT-1 SDGs Glimmer imager Nighttime light denoising algorithm

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Comparative signal to noise ratio as a determinant to select smartphone image sensor colour channels for analysis in the UVB

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SENSORS AND ACTUATORS A-PHYSICAL 2018年 272卷 125-133页

作者： Igoe, D. P. Parisi, A. V. Downs, N. J. Amar, A. Turner, J. Univ Southern Queensland Fac Hlth Engn & Sci Toowoomba Qld Australia James Cook Univ Sch Med & Dent Townsville Qld Australia Univ Southern Queensland Fac Hlth Engn & Sci Phys & Math Toowoomba Qld Australia

The signal to noise ratio (SNR) is an important consideration for any scientific image sensor application, particularly the relatively low light involved with observations of the solar disc at a discrete ultraviolet-B (UVB) wavelength using an unmodified smartphone image sensor. In particular, the SNR of each of the primary image sensor colour channels (red, green and blue) is a critical step in determining which colour channel signal to analyse for any characterisation research. In each image, the solar disc appears as a very small pale-magenta dot. In this paper, the SNR of each colour channel response for solar UVB, alongside their chromatic transforms were analysed for a stacked, mosaic filtered, backside illuminated complementary metal oxide semiconductor (CMOS) image sensor. Using data visualisation techniques, it has become clear that specific colour channels, in this case - the red channel, provide the strongest SNR for use in characterisation and other analytical research. The effects of a straightforward adaptive threshold and de-noising algorithm (median filter) on each colour channel's SNR are also analysed. The variation of the colour channels' SNR with external factors, including irradiance, is modelled. The effects of the prevalence of noise features, such as hot pixels and dark noise, are also observed. It has been found that before the median filter is applied, most of the signal, particularly for the green colour channel, is from these noise features in some image sensors - representing a 'false positive' in these low-light conditions. A chrominance model using a weighted proportion of the red and blue colour channels that provides the best SNR when sensing in the UVB waveband for the sensor has been developed and evaluated. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Signal-to-noise ratio Ultraviolet-B Complementary metal oxide semiconductor denoising algorithm Adaptive threshold Image integrity

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Research on the processing and interpretation methods of distributed fiber optic vibration signal logging injection profiles

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Gan, Lixiong Dang, Lixia Wang, Dewei Wang, Yi Chen, Guangyao Liu, Qian Deng, Rui Yangtze Univ Key Lab Oil & Gas Resources & Explorat Technol Minist Educ Wuhan 430100 Hubei Peoples R China China Natl Logging Co Xian 710000 Shaanxi Peoples R China Sinopec Shengli Oilfield Branch Co Dongying 257000 Shandong Peoples R China

The oil and gas industry currently faces the dual challenges of improving extraction efficiency and reducing environmental impacts. Traditional well monitoring technologies are increasingly unable to meet the demands of modern oil and gas extraction due to technological limitations and environmental concerns. Distributed Acoustic Sensing (DAS) technology, which utilizes optical fibers as sensing elements, enables real-time and accurate monitoring of the CO2 injection process in wells. However, DAS technology encounters issues such as weak amplitude signals and noise interference in practical applications. In response, a comprehensive method for processing and interpreting DAS logging data for CO2 injection wells has been proposed. This method involves restoring amplitude signals through wavefront energy compensation and applying Gaussian filtering and wavelet threshold denoising algorithms for noise reduction. The processed results are classified using the K-Medoids clustering algorithm and the CO2 absorption volume in the injection layer is calculated using the integral area method. The accuracy and practicality of this method have been validated through comparative analysis with pulse neutron oxygen activation logging results. This approach aims to address the challenges faced by DAS technology in the oil and gas industry, thereby providing technical support for the efficient, environmentally friendly, and intelligent management of oil and gas fields.

关键词： Distributed fiber optic sensing technology DAS logging Wavefront energy compensation denoising algorithm K-medoids clustering Quantitative analysis

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The application of plug-and-play ADMM framework and BM3D denoiser for compressed sensing MR image reconstruction

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2021年第4期65卷 304-315页

作者： Yuan, Xiaojun Jiang, Mingfeng Zhu, Lingyan Li, Yang Li, Yongming Wang, Pin Li, Tie-Qiang Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China Zhejiang Univ Finance & Econ Dongfang Coll Haining Peoples R China Chongqing Univ Coll Commun Engn Chongqing Peoples R China Karolinska Inst Dept Clin Sci Intervent & Technol Stockholm Sweden

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is an effective technique to reduce MRI data acquisition time. There is currently growing interest in using Alternating Direction Method of Multiplier (ADMM) for CS-MRI reconstruction. In this paper, we propose a flexible plug-and-play framework to incorporate the block matching 3D (BM3D) denoising algorithm as prior into the plug-and-play ADMM reconstruction procedure for CS-MRI reconstruction, termed BM3D Plug-and-play ADMM (BPA) method. We investigated the performance of the proposed BPA method for the construction of highly under-sampled MRI data of two different sampling masks. Compared with other widely used CS-MRI reconstruction methods, such as, PANO, BM3D-IT, BM3D-MRI and BM3D-AMP-MRI, the proposed framework can reconstruct highly under-sampled CS-MRI data with improved gains in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).

关键词： MR image reconstruction plug-and-play ADMM denoising algorithm compressed sensing

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Quantitative evaluation of total variation noise reduction algorithm in CT images using 3D-printed customized phantom for femur diagnosis

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JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2022年第5期81卷 450-459页

作者： Kang, Seong-Hyeon Park, Minji Yoon, Myeong Seong Lee, Youngjin Gachon Univ Coll Hlth Sci Dept Radiol Sci 191 Hambakmoero Incheon South Korea Gachon Univ Dept Hlth Sci Gen Grad Sch 191 Hambakmoero Incheon South Korea Hanyang Univ Coll Med Dept Emergency Med 222 Wangwimniro Seoul South Korea

The purpose of this study was to evaluate the usefulness of the total variation (TV) noise reduction algorithm for improving image characteristics with 3D printing technique for the accuracy enhancement of femur fracture diagnosis with computed tomography (CT) images. For this purpose, 3D printing technique was applied to output customized phantom, which can copy a human femur, with iron-polylactic acid filaments that has similar density of human bones. Then, CT images of 3D printed customized femur phantom were obtained by applying the conditions for tube current of 100 mA, 200 mA, and 400 mA, respectively. In addition, a TV noise reduction algorithm applied with regularization parameters of 0.005 and 0.05, respectively, was modeled and applied to 100 mA CT images. To quantitative evaluate the degree of improvement for TV noise reduction algorithm, coefficient of variation (CV) and contrast to noise ratio (CNR), which are noise level evaluation parameters, and blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE), which are no-reference image quality assessment, were calculated. As a result, when the TV noise reduction algorithms with regularization parameters of 0.005 and 0.05 were applied to the 100 mA image, the noise level evaluation parameters showed similar or improved to the 200 mA and 400 mA images, respectively. On the other hand, the BRISQUE was most improved when the TV noise reduction algorithm with regularization parameters of 0.005 was applied, however, the 0.05 showed poorer results than the images without algorithm. In addition, the NIQE showed that when the TV noise reduction algorithm was applied, the results were improved compared to the CT images without the algorithm, and the regularization parameter of 0.005 was improved than 0.05. In conclusion, we demonstrated through experiment with 3D printing technique that TV noise reduction algorithm with an appropriate regularization parameter can solv

关键词： Computed tomography 3D printing technique Femur fracture denoising algorithm Total variation

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Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device

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SENSORS 2020年第16期20卷 4611.-4611.页

作者： Reljin, Natasa Lazaro, Jesus Hossain, Md Billal Noh, Yeon Sik Cho, Chae Ho Chon, Ki H. Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Zaragoza IIS Aragon Aragon Inst Engn Res I3A Biomed Signal Interpretat & Computat Simulat BSIC Zaragoza 50009 Spain CIBER Bioengn Biomat & Nanomed CIBER BBN Madrid 28029 Spain Univ Massachusetts Coll Nursing Amherst MA 01003 USA Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01002 USA

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. denoising was performed using the redundant convolutional encoder-decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70-100% vs. 34-97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7-19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 +/- 0.25 dB (mean +/- standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm imp

关键词： denoising algorithm redundant convolutional encoder-decoder (R-CED) wearable devices electrocardiogram (ECG) motion artifacts signal-to-noise ratio (SNR) ratio of power cross-correlation

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Speech enhancement with MAP estimation and ICA-based speech features

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ELECTRONICS LETTERS 2000年第17期36卷 1506-1507页

作者： Lee, JH Jung, HY Lee, TW Lee, SY Korea Adv Inst Sci & Technol Brain Sci Res Ctr Yusung Gu Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Yusung Gu Taejon 305701 South Korea Univ Calif San Diego Inst Neural Computat La Jolla CA 92093 USA

The authors present a denoising algorithm based on Gabor-like speech features extracted by independent component analysis, that demonstrates much improved signal-to-noise ratio and recognition rates.

关键词： interference suppression Gabor-like speech features recognition rate improvement Speech processing techniques Speech recognition signal-to-noise ratio MAP estimation feature extraction Speech recognition and synthesis SNR improvement ICA-based speech features denoising algorithm speech recognition parameter estimation Gaussian noise speech enhancement independent component analysis

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Estimation of Convolutional Interleaver Parameters in the Burst and BSC Channels

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2016年第2-4期40卷 93-102页

作者： Jamshidi, A. Keshavarz-Hadad, A. Zare, F. Shiraz Univ Commun & Elect Dept Elect & Comp Engn Sch Shiraz Iran

Blind estimation of interleaver parameters is one of the initial steps of non cooperative context. Some studies have been performed to obtain the convolutional interleaver parameters, however, in all existing works, it is assumed that the channel is error free or at least has a very low error rate. Recently, by considering received noisy data, Gauss-Jordan Elimination Through Pivoting (GJETP) algorithm is proposed to estimate the period of the block interleaver. In this paper, by considering the convolutional interleaver, at first, we derive the period of the convolutional interleaver by applying GJETP algorithm on the intercepted analytical matrix. Then by using the denoising method, the more accurate estimation of the code rate will be derived. Finally, dependent columns sets can be derived precisely and by computing the minimum distance of locations of dependent columns, the depth of the interleaver can be estimated. Compared with the case which does not use the denoising method, our proposed method estimates the convolutional interleaver more accurately. Simulation results show that the algorithm has good performance even if the channel error rate is high.

关键词： Convolutional interleaver Blind estimation Gauss-Jordan elimination through pivoting (GJETP) algorithm denoising algorithm

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Multi-Scale Feature Learning Convolutional Neural Network for Image denoising

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SENSORS 2023年第18期23卷 7713页

作者： Zhang, Shuo Liu, Chunyu Zhang, Yuxin Liu, Shuai Wang, Xun Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China Chinese Acad Sci Key Lab Space Based Dynam & Rapid Opt Imaging Tec Changchun 130033 Peoples R China

Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective.

关键词： multi-scale feature learning denoising algorithm convolutional neural network

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