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IEEE Symposium Series on Computational Intelligence

作者： Zhun Fan Wenji Li Xinye Cai Hui Li Han Huang Zhaoquan Cai Caimin Wei Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Department of Electronic Engineering Shantou University 515063 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi 710049 China Department of Mathematics Shantou University Guangdong 515063 China Department of Electronic Engineering Shantou University Guangdong 515063 China School of Software Engineering South China University of Technology Guangdong Guangzhou 510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu 210016 China Department of Computer Science Huizhou University Guangdong 516007 China

ISBN: (纸本)9781509042418

This paper proposes an improved epsilon constraint handling method embedded in the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). More specifically, it dynamically adjusts the epsilon level, which is a critical parameter in the epsilon constraint method, according to the feasible ratio of solutions in the current population. In order to verify the effect of the improved epsilon constraint handling method, three algorithms - MOEA/D-CDP, MOEA/D-Epsilon, and MOEA/D-IEpsilon (MOEA/D with the improved epsilon constraint handling mechanism) are tested on nine CMOPs (CMOP1-CMOP9). The comprehensive experimental results indicate that the proposed epsilon constraint handling method is very effective on the performance of both convergence and diversity.

关键词： improved epsilon constraint

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21-cm foreground removal using AI and frequency-difference technique

arXiv

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arXiv 2023年

作者： Shi, Feng Chang, Haoxiang Zhang, Le Shan, Huanyuan Zhang, Jiajun Zhou, Suiping Jiang, Ming Wang, Zitong School of Aerospace Science And Technology Xidian University Xi’an710126 China Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China School of Physics and Astronomy Sun Yat-sen University 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area Zhuhai519082 China Nandan Road 80 Shanghai200030 China University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710126 China

The deep learning technique has been employed in removing foreground contaminants from 21-cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input. Combining with the frequency difference, we refer to our method as the UNet-fd (UNet frequency-difference), where the U-Net structure is the same as that in deep21. Based on our tests, we demonstrate that this frequency-difference preprocessing technique can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of k 0.3 hMpc−1 in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent auto-correlation and cross-correlation power spectrum ratios at the 1σ level across scales k 0.1 hMpc−1, with only a 10% reduction observed in the cross-correlation power spectrum at k 0.2 hMpc−1. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching with the target truth. In order to test how thermal noise affects the performance of our method, we simulated various white noise levels in the map. This shows the mean cross-correlation ratio R¯cross 0.8 when the level of the thermal noise is smaller than or equal to that of the HI signal. In comparison, our method outperforms the traditional Principal Component Analysis (PCA) method. The PCA-derived cross-correlation ratios are underestimated by

关键词： Power spectrum

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Application of Fractional Calculus for Impact Oscillator Modeling

Application of Fractional Calculus for Impact Oscillator Mod...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Sergo Rekhviashvili Arsen Pskhu A.А. Potapov Tianhua Feng Viktor Narozhnov Department of Theoretical and Mathematical Physics Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan University Guangzhou China Department of Fractional Calculus Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia Institute of Radioengineering and Electronics Moscow Russia Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Laboratory of Measurement Automation Institute of Applied Mathematics and Automation KBSC RAS Nalchiky Russia

A new theoretical model for describing an impact oscillator that is based on the use of a fractional derivative is constructed. The acoustic signals that occur when the probe strikes the surface of metals are experime... 详细信息

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

关键词： fractional calculus impact oscillator damped vibrations energy dissipation mathematical modeling natural experiment Fractional calculus mathematical modeling Energy dissipation audio signals Oscillators

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Techniques for Automatic Magnetic Resonance Image Classification

Techniques for Automatic Magnetic Resonance Image Classifica...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.

关键词： Classification algorithms Training Image segmentation Brain Skull Software algorithms Support vector machines

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AaN: Anti-adversarial Noise - A Novel Approach for Securing Machine Learning-based Wireless Communication Systems

TechRxiv

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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...

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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

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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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