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Improved image denoising based on Haar wavelet transform

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Improved image denoising based on Haar wavelet transform

2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017

作者： Pang, Jing Department of Electrical and Electronic Engineering Computer Engineering Program California State University Sacramento Sacramento United States

ISBN: (纸本)9781538604342

The wavelet decomposing levels and the selection of the thresholding function affect the performance of image denoising using the wavelet thresholding method. In this paper, a new method to identify the wavelet decomposing levels using the 2D Haar wavelet thresholding method is presented to denoise an image. It uses the standard deviation values of the sub-bands to find out if the signal energy is strong or weak in the high frequency sub-bands after the 2D Haar wavelet transform. In addition, a new thresholding function is proposed which achieves better denoising performance in terms of peak signal-To-noise ratio (PSNR) and mean squared error (MSE) than the soft thresholding method. Especially, at high noise levels, the proposed new thresholding method outperforms hard thresholding, soft thresholding and semi-soft thresholding methods. © 2017 IEEE.

关键词： Image denoising

Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

Pre-training of deep bidirectional protein sequence representations with structural information

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arXiv 2019年

作者： Min, Seonwoo Park, Seunghyun Kim, Siwon Choi, Hyun-Soo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon24341 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://***/mswzeus/PLUS. © 2019, CC BY-NC-ND.

关键词： Proteins

The limits of near Field Immersion Microwave Microscopy evaluated by imaging bilayer graphene Moiré patterns

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arXiv 2020年

作者： Ohlberg, Douglas A.A. Tami, Diego Gadelha, Andreij C. Neto, Eliel G.S. Santana, Fabiano C. Miranda, Daniel Avelino, Wellington Watanabe, Kenji Taniguchi, Takashi Campos, Leonardo C. Ramirez, Jhonattan C. do Rego, Cássio Gonçalves Jorio, Ado Medeiros-Ribeiro, Gilberto Microscopy Center Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Electrical Engineering Graduate Program Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Physics Department Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Instituto de Física Universidade Federal da Bahia Campus Universitário de Ondina Salvador - BA40170-115 Brazil 1-2-1 Sengen Tsukuba-cityIbaraki305-0047 Japan Department of Electronic Engineering School of Engineering Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Technology Innovation Graduate Program Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Computer Science Department Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil

Molecular and atomic imaging required the development of electron and scanning probe microscopies to surpass the physical limits dictated by diffraction [1]. Nano-infrared experiments [2] and pico-cavity tip-enhanced Raman spectroscopy imaging later demonstrated that radiation in the visible range [3] can surpass this limit by using scanning probe tips to access the near-field regime [4]. Here we show that ultimate resolution can be obtained by using scanning microwave imaging microscopy to reveal structures with feature sizes down to 1 nm using a radiation of 0.1 m in wavelength. As a test material we use twisted bilayer graphene, which is not only a very important recent topic due to the discovery of correlated electron effects such as superconductivity [5], but also because it provides a sample where we can systematically tune a superstructure Moiré patterńs modulation from below one up to tens of nanometers. By analyzing the tip-sample distance dynamics, we demonstrate that this ultimate 108 probe-to-pattern resolution can be achieved by using liquid immersion microscopy concepts and exquisite force control exerted on nanoscale water menisci. Copyright © 2020, The Authors. All rights reserved.

关键词： Moire fringes

Closed-loop sparse channel estimation for wideband millimeter-wave full-dimensional MIMO systems

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arXiv 2019年

作者： Liao, Anwen Gao, Zhen Wang, Hua Chen, Sheng Alouini, Mohamed-Slim Yin, Hao School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom King Abdulaziz University Jeddah21589 Saudi Arabia Electrical Engineering Program Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Makkah Province Saudi Arabia Institute of China Electronic System Engineering Corporation Beijing100141 China

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Millimeter waves

Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

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arXiv 2018年

作者： Oh, Dong Yul Yun, Il Dong Interdisciplinary Program in Bioengineering Seoul National University Korea Republic of Division of Computer and Electronic System Engineering Hankuk University of Foreign Studies Korea Republic of

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels;however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

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Biomedical journal 2024年 100820页

作者： Yu-Nong Gong Nai-Yu Kuo Ting-Syuan Yeh Shin-Ru Shih Guang-Wu Chen Research Center for Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan International Master Degree Program for Molecular Medicine in Emerging Viral Infections College of Medicine Chang Gung University Taoyuan Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. Medical Education Department Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan. Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Department of Medical Biotechnology and Laboratory Science College of Medicine Chang Gung University Taoyuan Taiwan Research Center for Chinese Herbal Medicine Research Center for Food and Cosmetic Safety and Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan. Department of Computer Science and Information Engineering College of Engineering Chang Gung University Taoyuan Taiwan. Electronic address: gwchen@mail.cgu.edu.tw.

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

关键词： SARS-CoV-2 data science genomic surveillance next-generation sequencing phylodynamics

Unveiling long COVID symptomatology, co-occurrence trends, and symptom distress post SARS-CoV-2 infection

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Journal of infection and public health 2024年第7期17卷 102464页

作者： Shikha Kukreti Chun-Yin Yeh Yi-Jhen Chen Meng-Ting Lu Ming-Chi Li Yi-Yin Lai Chung-Yi Li Nai-Ying Ko Department of Nursing College of Medicine National Cheng Kung University Tainan Taiwan Department of Public Health College of Medicine National Cheng Kung University Taiwan. Department of Computer Science and Information Engineering National Cheng Kung University Taiwan. Department of Internal Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan. Centre of Infection Control College of Medicine National Cheng Kung University and Hospital Tainan Taiwan. Department of Public Health College of Medicine National Cheng Kung University Taiwan Department of Public Health College of Public Health China Medical University Taichung Department of Healthcare Administration College of Medical and Health Science Asia University Taichung Taiwan. International Doctoral Program in Nursing Department of Nursing College of Medicine National Cheng Kung University Tainan Taiwan. Electronic address: nyko@mail.ncku.edu.tw.

BACKGROUND:Long COVID, an emerging public health issue, is characterized by persistent symptoms following SARS-CoV-2 infection. This study aims to explore the relationship between post-COVID-19 symptomatology and patient distress employing Latent Class Analysis to uncover symptom co-occurrence patterns and their association with distress. METHODS:A cross-sectional study was conducted using an online survey among 240 participants from a university and affiliated hospital of southern Taiwan. The survey quantified distress due to persistent symptoms and assessed the prevalence of Long COVID, symptom co-occurrence, and latent symptom classes. Latent Class Analysis (LCA) identified distinct symptom patterns, and multiple regression models evaluated associations between symptom patterns, distress, and demographic factors. RESULTS:The study found that 80 % of participants experienced Long COVID, with symptoms persisting for over three months. Individuals with multiple COVID-19 infections showed a significant increase in general (β = 1.79), cardiovascular (β = 0.61), and neuropsychological symptoms (β = 2.18), and higher total distress scores (β = 6.35). Three distinct symptomatology classes were identified: "Diverse", "Mild", and "Severe" symptomatology. The "Mild Symptomatology" class was associated with lower distress (-10.61), while the "Severe Symptomatology" class showed a significantly higher distress due to symptoms (13.32). CONCLUSION:The study highlights the significant impact of Long COVID on individuals, with distinct patterns of symptomatology and associated distress. It emphasizes the cumulative effect of multiple COVID-19 infections on symptom severity and the importance of tailored care strategies.

关键词： Co-occurrence Distress Latent class Long COVID

Corrigendum to "Dynamic functional brain network connectivity during pseudoword processing relates to children's reading skill" [Neuropsychologia 168 (2022) 108181]

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Neuropsychologia 2023年 183卷 108531页

作者： E J Panda J Kember Z Emami C Nayman T A Valiante E W Pang Department of Child and Youth Studies Brock University 1812 Sir Isaac Brock Way St. Catharines ON L2S 3A1 Ontario Canada Epilepsy Research Program of the Ontario Brain Institute Toronto Ontario Canada Division of Neurology/Neurosciences and Mental Health The Hospital for Sick Children/SickKids Research Institute Toronto Ontario Canada. Electronic address: epanda@brocku.ca. Department of Child and Youth Studies Brock University 1812 Sir Isaac Brock Way St. Catharines ON L2S 3A1 Ontario Canada. Division of Neurology/Neurosciences and Mental Health The Hospital for Sick Children/SickKids Research Institute Toronto Ontario Canada. Krembil Brain Institute University Health Network and Toronto Western Hospital Toronto Ontario Canada Division of Neurosurgery Department of Surgery Institute of Biomedical Engineering Electrical and Computer Engineering University of Toronto Toronto Ontario Canada.