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TechRxiv 2023年

作者： Hamza, Anis Amazigh Guesmi, Amira Dayoub, Iyad Amrouche, Abderrahmane Alouani, Ihsen Univ. Polytechnique Hauts-de-France CNRS Univ. Lille UMR 8520 IEMN Institut d’Electronique de Microélectronique et de Nanotechnologie ValenciennesF-59313 France INSA Hauts-de-France ValenciennesF-59313 France Laboratory of Speech Communication and Signal Processing Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Bab Ezzouar Algiers16111 Algeria Queen’s University Belfast Belfast United Kingdom XLIM UMR 7252 Laboratory University of Limoges Limoges87000 France University of Limoges France

Machine Learning (ML) is becoming a cornerstone enabling technology for the next generation of wireless systems. This is mainly due to the high performance achieved by these data-driven models in addressing problems in communication that are challenging to solve using the classical methods. However, ML models are known to be vulnerable to adversarial attacks;maliciously crafted low-magnitude signals that are designed to mislead ML models. More specifically, the propagation nature of the electromagnetic signals makes the wireless domain even more critical compared to other applications like computer vision where the attacker is physically constrained by the victim’s immediate neighborhood to be efficient. While several works showed the practicality of these attacks in the wireless domain, the main countermeasure is adversarial training. However, this approach results in a considerable accuracy loss, which makes the very utility of ML questionable. In this paper, we address this problem with a new approach tailored to wireless communication contexts. Specifically, we propose a new defense that leverages the physical properties of the wireless propagation to enhance ML-based wireless communication systems against adversarial attacks. We propose Anti-adversarial Noise (AaN), where the Base Station (BS) broadcasts a carefully crafted defensive signal that is designed to counter the impact of any adversarial noise. We specifically focus on ML-based modulation recognition. However, the proposed method is not specific to this application and can be generalized to other ML-based communication use cases. Our results show that our proposed defense can enhance models’ robustness by up to 44% without loosing utility. © 2023, CC BY.

关键词： Machine learning

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Corrigendum to “Strong convergence theorem of a new modified Bregman extragradient method to solve fixed point problems and variational inequality problems in general reflexive Banach spaces [Communications in Nonlinear science and Numerical Simulation, 135, (2024): CNSNS 108051]

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Communications in Nonlinear science and Numerical Simulation 2024年 138卷

作者： Huilin Tan Qian Yan Gang Cai Qiaoli Dong School of Mathematical Sciences Chongqing Normal University College of Sciences Chongqing University of Technology Tianjin Key Laboratory for Advanced Signal Processing and College of Science Civil Aviation University of China

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Tract-based spatial statistics (TBSS): Application to detecting white matter tract variation in mild hypoxic-ischemic neonates

Tract-based spatial statistics (TBSS): Application to detect...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jie Gao Xianjun Li Xin Hou Abby Ding Kevin C. Chan Qinli Sun Ed X. Wu Jian Yang Department of Radiology the First Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China Department of Biomedical Engineering School of Life Science and Technology Xi'an Jiaotong University Xi'an China Laboratory of Biomedical Imaging and Signal Processing University of Hong Kong Hong Kong China Departments of Ophthalmology and Bioengineering Center for the Neural Basis of Cognition University of Pittsburgh Pittsburgh PA USA Department of Radiology the First Affiliated Hospital of Medical College Department of Biomedical Engineering School of Life Science and Technology Department of Radiology the First Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China

The aim of this study is to employ tract-based spatial statistics (TBSS) to analyze the voxel-wise differences in DTI parameters between normal and mild hypoxic-ischemic (HI) neonatal brains. Forty-one full term neonates (24 normal controls and 17 with mild HI injury) and 31 preterm neonates (20 normal controls and 11 with mild HI injury) underwent T1 weighted imaging, T2 weighted imaging and diffusion tensor imaging (DTI) within 28 days after birth. The voxel differences of fractional anisotropy (FA), λ1, λ2, and λ3 values between mild HI group and control group were analyzed in preterm and full term neonates respectively. The significantly decreased FA with increased λ2, λ3 in corticospinal tract, genu of corpus callosum (GCC), external capsule (EC) and splenium of the corpus callosum (SCC) in mild HI neonates suggested deficits or delays in both myelination and premyelination. Such impaired corticospinal tract, in both preterm and term neonates, may directly lead to the subsequent poor motor performance. Impaired EC and SCC, the additional injured sites observed in full term neonates with mild HI injury, may be causally responsible for the dysfunction in coordination and integration. In conclusion, TBSS provides an objective, independent and sensitive method for DTI data analysis of neonatal white matter alterations after mild HI injury.

关键词： Pediatrics Diffusion tensor imaging Injuries Tensile stress Educational institutions

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A computer-aided system for mass detection and classification in digitized mammograms

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Biomedical Engineering - Applications, Basis and Communications 2005年第5期17卷 215-228页

作者： Yang, Sheng-Chih Wang, Chuin-Mu Chung, Yi-Nung Hsu, Giu-Cheng Lee, San-Kan Chung, Pau-Choo Chang, Chein-I. Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Chin Yi Institute of Technology Taichung Taiwan Department of Electrical Engineering Da-Yeh University Chunghua Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Administrative Office Suao Veterans Hospital Yilan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

关键词： Computer aided analysis

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HSIGene: A Foundation Model for Hyperspectral Image Generation

arXiv

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

arXiv

引用

arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) Image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

arXiv

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arXiv 2022年

作者： Zhao, Yue Zhang, Lingming Liu, Yang Meng, Deyu Cui, Zhiming Gao, Chenqiang Gao, Xinbo Lian, Chunfeng Shen, Dinggang Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China The Chongqing Key Laboratory of Signal and Information Processing Chongqing400065 China Department of Orthodontics Stomatological Hospital of Chongqing Medical University Chongqing401147 China Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing401147 China Faculty of Information Technology Macau University of Science and Technology China School of Mathematics and Statistics Xi'an Jiaotong University Xian710049 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China The School of Computer Science The University of Hong Kong 999077 Hong Kong School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation. Github: https://***/ZhangLingMing1/TSGCNet. © 2022, CC BY.

关键词： Scanning

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Coherent oscillations of driven rf SQUID metamaterials

arXiv

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arXiv 2017年

作者： Trepanier, Melissa Zhang, Daimeng Mukhanov, Oleg Koshelets, V.P. Jung, Philipp Butz, Susanne Ott, Edward Antonsen, Thomas M. Ustinov, Alexey V. Anlage, Steven M. Department of Physics CNAM University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Hypres Inc. 175 Clearbrook Road ElmsfordNY10523 United States Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow125009 Russia Physikalisches Institut Karlsruhe Institute of Technology Germany Russian Quantum Center National University of Science and Technology MISIS Moscow119049 Russia

Through experiments and numerical simulations we explore the behavior of rf SQUID (radio frequency superconducting quantum interference device) metamaterials, which show extreme tunability and nonlinearity. The emergent electromagnetic properties of this metamaterial are sensitive to the degree of coherent response of the driven interacting SQUIDs. Coherence suffers in the presence of disorder, which is experimentally found to be mainly due to a dc flux gradient. We demonstrate methods to recover the coherence, specifically by varying the coupling between the SQUID meta-atoms and increasing the temperature or the amplitude of the applied rf flux. Copyright © 2017, The Authors. All rights reserved.

关键词： Metamaterials

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

arXiv

引用

arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams

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An Audio-Visual Whisper Database in Chinese

引用

Journal of Physics: Conference Series 2019年第2期1237卷

作者： Jian Zhou Yuting Hu Hailun Lian Cong Pang Huabin Wang Liang Tao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei AnHui 230039 China School of Computer Science and Technology Anhui University Hefei AnHui 230601 China

Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database.

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