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arXiv 2019年

作者： Wu, Youqing Xiang, Youzhi Guo, Yutang Tang, Jin Yin, Zhaoxia The School of Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China The School of Computer Science and Technology Hefei Normal University Hefei230601 China

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image but not in small image blocks to reserve room for hiding data. Then the original image is encrypted with an encryption key and the parametric binary tree is used to label encrypted pixels into two different categories. Finally, one of the two categories of encrypted pixels can embed secret information by bit replacement. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original plaintext image and the secret information can be restored and extracted losslessly and separately. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary trees

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A Wideband Spectrum Sensing Based on ESPRIT Algorithm

A Wideband Spectrum Sensing Based on ESPRIT Algorithm

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2018 International Computers, signals and Systems Conference, ICOMSSC 2018

作者： Shen, Yanni Wan, Qun Xia, Changxiong Wan, Yihe University of Electronic Science and Technology of China School of Electronic Engineering Chengdu China Key Lab of Blind Signal Processing Chengdu China Technology Co. Ltd Jiangxi Province Engineering Research Center Special Wireless Communications Tongfang Electronic Jiujiang China

ISBN: (纸本)9781538667514

Spectrum sensing is a prerequisite for cognitive dio. This paper proposes a wideband spectrum sensing method based on ESPRIT algorithm for detecting active channels. In this method, it detects the occupied channels directly according to the relationship between the Fourier transform (FT) of the multicoset sampler's output sequences and the active channels, which can save a lot of sampling rate and reduce the computational complexity. And the performance of this method is evaluated by calculating the detection probabilities for different numbers of samples and different signals to noise ratios (SNRs). The simulation results show that the proposed method performs well in low SNR and less data samples. © 2018 IEEE.

关键词： signal to noise ratio

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Drugs with SMILES Similar to Coxibs

Drugs with SMILES Similar to Coxibs

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： George Kallergis Stelios Sfakianakis Michael Zervakis Marios Spanakis Digital Image and Signal Processing Laboratory School of Electrical and Computer Engineering Technical University of Crete Polytechnioupolis Chania Greece Computational Biomedicine Laboratory Institute of Computer Science Foundation for Research & Technology Hellas (FORTH) Heracklion Greece

Coxibs are a group of drugs with selective inhibition against cyclooxygenase-2 (COX-2) enzymes with increased interest from scientific community due to their side effects and potential other pharmacological mechanisms. The aim of this work is to utilize the chemical characteristics of coxibs in order to identify compounds with similar chemical structure. The approach is based on the assessment of the Simplified Molecular-Input Line-Entry System (SMILES) as adequate molecular structure representations for the identification of drug similarities. The similarity measurements are based on molecular fingerprints that were extracted from coxibs and the Maximum Consecutive Subsequence (MCS) algorithm. An ensemble of methods based on majority voting, weighting and equal weighting on the algorithms was further applied. Majority voting returned 200 similar compounds whereas weighting and equal weighting returned 53 and 27 compounds respectively. Interestingly, despite the independence of the methods, all three identified 20 common compounds. The identification of drugs with potential chemical similarity with coxibs, as revealed from similarity measurements of fingerprints and MCS scores could provide new insights for potential biological targets for coxibs or drugs that could interact with COX-2 or other biological targets of coxibs.

关键词： Drugs Fingerprint recognition Compounds Chemicals Biological information theory Inhibitors Digital images

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Dynamic range impact on 3D MIMO channel characteristics in rural-macro scenario at 3.5 GHz 12

Dynamic range impact on 3D MIMO channel characteristics in r...

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12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Zhang, Weite Tian, Lei Wu, Zhongyuan Zhang, Jianhua Zheng, Yi Cao, Jingmin Key Lab of Universal Wireless Communications Ministry of Education China State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Mailbox NO.92 Beijing100876 China China Mobile Research Institude Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781785617911

Rural-macro (RMa) is an important scenario for fifth generation (5G) deployment, and there is a strong diffraction in RMa making it difficult to distinguish between line-of-sight (LoS) and non-line-of-sight (NLoS) case. Since the environmental condition is closely related to the dynamic range of channel impulse response (CIR), this paper studies the dynamic range impact on characteristics of three dimensional (3D) multiple-input multiple-output (MIMO) channels based on 3.5 GHz measurement campaign in RMa. The space-alternating generalized expectation-maximization (SAGE) algorithm is employed to estimate multipath component parameters. The channel statistical characteristics, channel capacity and eigenvalues of CIR matrix are calculated for different CIR dynamic ranges. The results show that the delay spread, the angle spread, the channel capacity and the sub-channel correlation are linearly related to the dynamic range of CIR. Thus, the results give a new perspective to study channel characteristics based on CIR dynamic range in RMa scenario. © 2018 Institution of Engineering and *** Rights Reserved.

关键词： Channel capacity

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation

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Computers in Biology and Medicine 2025年 194卷 110527页

作者： Xuesheng Bian Jia Liu Sen Xu Weiquan Liu Leyi Mei Chaoshen Xiao Fan Yang School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China College of Computer Engineering Jimei University Yinjiang Road No. 183 Xiamen 361005 Fujian China National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility) Key Laboratory of Genetic Evolution & Animal Models and National Resource Center for Nonhuman Primates Kunming Institute of Zoology Chinese Academy of Sciences East Jiaochang Road No. 32 Kunming 650221 Yunnan China National Key Laboratory of Radar Signal Processing Xidian university No. 2 South Taibai Road Xian 710071 Shaanxi China School of Resources and Environmental Engineering Wuhan University of Science and Technology 947 Heping Avenue Qingshan District Xian 430081 Wuhan China

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

关键词： Attention mechanism Breast lesion segmentation Deep learning Feature fusion Ultrasound images

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MOF-based high-entropy-alloy evaporator featuring enhanced interband transitions for efficient solar steam and green electricity generation

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Chemical Engineering Journal 2025年 517卷

作者： Dai, Chenyang Zhang, Jia-Han He, Cheng-Yu Dai, Rongrong Pan, Likun Yamauchi, Yusuke Zhang, Jing Li, Zhengtong Xu, Xingtao Marine Science and Technology College Zhejiang Ocean University Zhoushan316022 China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing School of Electronic Information Engineering Inner Mongolia University Hohhot010021 China Research Center of Resource Chemistry and Energy Materials State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou China Shanghai Key Laboratory of Magnetic Resonance School of Physics and Electronic Science East China Normal University Shanghai200026 China Department of Materials Process Engineering of Engineering Nagoya University Nagoya464-8603 Japan The University of Queensland BrisbaneQLD4072 Australia Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin University Zhejiang Ningbo315201 China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Yangtze Institute for Conservation and Development Hohai University Nanjing210098 China

Photothermal materials exhibiting broadband optical absorption and high energy efficiency are highly sought after in the field of solar-driven evaporation. Herein, we present a novel photothermal material derived from polymetallic metal–organic frameworks (MOFs) through a d-d interband transition (d-d IBT) engineering strategy. This approach enables comprehensive occupation of electronic states within ±4 eV relative to the Fermi level through 3d transition metal coordination, achieving an exceptional average solar absorption efficiency of over 97 % across the full solar spectrum (250–2500 nm). This breakthrough effectively overcomes the inherent wide bandgap limitation of MOFs that traditionally restricts their full-spectrum solar utilization. Leveraging its remarkable broadband sunlight harvesting capacity and ultrafast localized heating performance, this evaporator engineered with high-entropy alloy nanoparticles, achieves an evaporation efficiency surpassing 95% under one sun irradiation while maintaining a high evaporation rate of 2.80 kg m−2 h−1. Moreover, this evaporator demonstrates exceptional desalination efficiency even with high-salinity seawater (20 wt%), while the waste heat generated during operation can be strategically repurposed to produce green electricity, enhancing its sustainability profile. Thus, the FeCoNiMnCuAlZn high-entropy alloy nanoparticle-based photothermal evaporator offers innovative solutions for addressing the scarcity of clean water resources and the need for green energy. © 2025

关键词： Zinc alloys

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Monaural speech enhancement using deep multi-branch residual network with 1-D causal dilated convolutions

arXiv

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arXiv 2019年

作者： Zhang, Qiquan Nicolson, Aaron Wang, Mingjiang Paliwal, Kuldip K. Wang, Chenxu School of Electronic & Information Engineering Harbin Institute of Technology Shenzhen518055 China Signal Processing Laboratory Griffith University BrisbaneQLD4111 School of Information Science & Engineering Harbin Institute of Technology Weihai264209 China

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by its ability to capture the long-term effective history information. Long short-term memory (LSTM) model is limited by its high computational complexity and the exploding/vanish gradients problem. In this paper, we propose a novel convolution neural network (CNN) architecture, called multi-branch residual network (MB-ResNet), for monaural speech enhancement. The MB-ResNet exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity. The combination of causal dilated one dimensional CNN and residual connection significantly expands receptive fields, which allows for capturing extremely long-term temporal contextual information. Our extensive experimental investigation suggest that the MB-ResNets outperform the residual long short-term memory networks (ResLSTMs) and the CNN networks that employ residual or dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-ResNet model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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A Imaging Passive Localization Method for Wideband signal Based on SAR

A Imaging Passive Localization Method for Wideband Signal Ba...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Yuqi Wang Guang-Cai Sun Jixiang Xiang Mengdao Xing Liang Guo Jun Yang National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China College of Geomatics Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

A imaging passive localization method for wideband signal is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging method, the location of the signal emitter is directly given in SAR image. The key of this method is to focus unknown wideband data in range and azimuth domain. To focus the data in range domain without signal parameter, a new pulse compress method is proposed by constructing reference signal from raw data. To focus the data in azimuth domain without knowing range, a range-searching azimuth focus method is proposed by constructing azimuth focus functions with different range. Simulation result validate the effectiveness of the proposed method.

关键词：

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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Space object attitude and position determination based on astrometry and photometric data fusion 8

Space object attitude and position determination based on as...

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8th International Congress of Information and Communication technology, ICICT 2018

作者： Qin, Jidong Peng, Huafeng Dang, Tongxin National Digital Switching System Engineering and Technology R and D Center No. 61 Science Road Zhengzhou Henan450001 China National Key Laboratory of Science and Technology on Blind Signal No. 31 Dashi South Road Chengdu Sichuan610041 China

The astronomical observation model, light curve model and solar radiation pressure force model are established to allow estimation of the positions and attitudes of space objects using a fusion of photometric and astronomical data. Existing algorithms for this purpose involve large errors. The proposed method firstly estimates the attitude of the object by using photometric and astronomical data fusion. Secondly, the position of the object is filtered by the estimated attitude. Theory and numerical simulations show the efficiency of the proposed method. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Orbits

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