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11th International symposium on computational intelligence and Design (ISCID)

作者： Dong, Shijia Yu, Chunyu Sun, Bin Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Microelect Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538685273

This paper proposes a noise reduction method of blind source separation based on image sequence. In the image, the signal and the noises can be considered as the independent components, so the multi-frame images can be considered as the multiple linear combinations of one signal and a lot of noises for the noise's randomness. Due to that, the noises can be removed from the sampled multi-images based on blind source separation (BSS). During the separation calculation, the common Gaussian noise is taken as the object to be eliminated, and the nonlinear principal component analysis (NLPCA) is used as the BSS method and the analysis of the noise reduction is based on changing either the noise degree or the image sampling numbers. This proposed algorithm is compared with the multi-frame average (MFA) that is a famous noise reduction algorithm based on image sequence. The analysis results show that the noise reduction properties by using the proposed method will be affected by the noise degree or the image sampling numbers when eliminating the Gaussian noise;and this method can recover the images which are heavily noise polluted, and the suppression effect on strong noises is obviously better than MFA algorithm.

关键词： image noise reduction image sequence blind source separation principal component analysis multi-frame averaging

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Full-Body Visible Human Project® Female computational Phantom and Its Applications for Biomedical Electromagnetic Modeling

Full-Body Visible Human Project® Female Computational Phant...

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ieee signal processing in Medicine and Biology symposium

作者： Yanamadala, J. Rathi, V. K. Maliye, S. Win, H. A. Tran, A. L. Zagalskaya, M. Noetscher, G. M. Makarov, S. N. Kozlov, M. K. Nazarian, A. Worcester Polytech Inst Elect & Comp Engn Dept Worcester MA 01604 USA Max Plank Inst Human Cognit & Brain Sci D-04103 Leipzig Germany Harvard Med Sch Beth Israel Deaconess Med Ctr Boston MA 02215 USA

ISBN: (纸本)9781479981847

This study describes the development to date of a computational full-body human phantom based on the VHP female dataset. Its unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D. Applications for low-frequency and radio-frequency electromagnetic modeling are considered.

关键词： image segmentation Visible Human Project (R) (VHP) computational phantom MATLAB (R) Low-frequency electromagnetic modeling RF modeling

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Greedy Steep Slope Finder: the Fast Impulsive Noise Rejection for Compressed Measurement image signals

Greedy Steep Slope Finder: the Fast Impulsive Noise Rejectio...

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ieee International symposium on Intelligent signal processing and Communications Systems (ISPACS)

作者： Suwanwimolkul, Suwichaya Sermwuthisarn, Parichat Auethavekiat, Supatana Chulalongkorn Univ Dept Elect Engn Bangkok Thailand

ISBN: (纸本)9781467350839

Impulsive noise in a compressed measurement signal can be detected by the energy distribution of the reconstructed sparse signal. In this paper, the fast preprocessing for impulsive noise rejection for a compressed measurement image signal is proposed. An image is sparsified by octave-tree wavelet transform with db8 as the mother wavelet. The impulsive noise is detected via the change in the ratio of the energy leaking out of the third level subband to the total energy of the sparse signal. Since the information in the image is redundant, the number of the noisy element is estimated within + g of the actual value. g is defined in the unit of the percent of the size of the compressed measurement signal. The experiment shows that the proposed method effectively rejected noise with large magnitude. It provided the comparable performance to the method that rejected the exact number of noisy elements but required less computational time.

关键词： Compressed Sensing (CS) preprocessing impulsive noise energy ratio binary search

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A DCT-SVD Domain Watermarking for Color Digital image Based on Compressed Sensing Theory and Chaos Theory 7

A DCT-SVD Domain Watermarking for Color Digital Image Based ...

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7th International symposium on computational intelligence and Design (ISCID)

作者： Li, Mengmeng Han, Chao Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei Peoples R China

ISBN: (纸本)9781479970056

In this paper, it is presented that a new color image watermarking algorithm based on compressed sensing theory and chaos theory in discrete cosine transform (DCT) and singular value decomposition (SVD) domain. Initially, a color watermarking image is preprocessed by four steps i.e. being divided into red, green and blue channels, being sparse by wavelet transform, scrambling by 1-D chaos sequence and measurement through Gaussian random matrix. Here, the chaos sequence is produced by 1-D Logistic mapping. Then the red, green and blue channel images of a color host image are divided into 8x8 blocks and then transformed by DCT. Next the first DCT coefficient of each block is selected to compose a new matrix, and the new matrix is decomposed by SVD. The singular values are the regions of watermarking embedment. Thirdly, three sets of measured coefficients as new watermarking are embedded into the corresponding singular values. Finally, the Iterative Hard Threshold (IHT) algorithm is used to reconstruct the watermarking image. The method proposed can not only enlarge the information capacity of watermarking embedment but also strengthen the security of watermarking in the condition of the good imperceptibility. The experiments show that the scheme is feasible and has good anti-attack ability for JPEG compression, Gaussian white noise, rotating and median filtering.

关键词： color digital watermarking compressed sensing chaos theory singular value decomposition

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Defects Detection of Catenary Suspension Device Based on image processing and CNN

Defects Detection of Catenary Suspension Device Based on Ima...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li, Yongguang Wei, Dehua Wei, Xiukun Wu, Kuan Liang, Yong Shen, Xing Shengzhou Tongzhen Technol Co Beijing Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China CRRC Changchun Rail Transit Bus Co Ltd Changchun Peoples R China

ISBN: (纸本)9781728124858

Catenary suspension device (CSD) is a critical part of the pantograph-catenary system, which is a fundamental power equipment to supply electricity to urban rail transit vehicles. Whether the CSD is in a normal state is of great significance to the safe operation of operating vehicles. Therefore, it is important to detect the defects of CSD in time and automatically. In this paper, innovative and intelligent methods using image processing technologies and convolutional neural network (CNN) are proposed. Firstly, the insulators and bolts of CSD are extracted in the detected images using template matching algorithm. After that, an improved Bag of Features (BOF) model is proposed for the defect detection of CSD. Furthermore, in order to further improve the detection efficiency, AlexNet is trained for the defect detection and identification of CSD. The experimental results show that the proposed methods can detect the defects of CSD in time, with high robustness and accuracy.

关键词： defect detection catenary suspension device image processing alexNet CNN

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Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities 25

Poster: Cloud Computing with AI-empowered Trends in Software...

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25th ieee International symposium on a World of Wireless, Mobile and Multimedia Networks (ieee WoWMoM)

作者： Sharma, Ekta Deo, Ravinesh C. Davey, Christopher P. Carter, Brad D. Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Astrophys Toowoomba Qld 4350 Australia Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Spain

ISBN: (纸本)9798350394665;9798350394672

Artificial intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices.

关键词： cloud computing artificial intelligence software-defined radio modulation GNU Radio signal classification

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Segmentation of hippocampus in MRI images based on the improved level set

Segmentation of hippocampus in MRI images based on the impro...

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2011 4th International symposium on computational intelligence and Design, ISCID 2011

作者： Zhao, Shuying Zhang, Dan Song, Xiangman Tan, Wenjun College of Information Science and Engineering Northeastern University Shenyang Liaoning 110819 China

ISBN: (纸本)9780769545004

For hippocampus of MRI demonstrating the low contrast, low signal to noise ratio(SNR), boundary discrete, intensity in homogeneities features, this paper proposed an improved level set model that based on regional and edge information. Refer to the intensity in homogeneities image, the global image information of external energy item is joined, to optimize the segmentation result, adopt edge information that is extracted by wavelet, which is used as an edge constraint stop item. Experimental results of many times segmentation of the hippocampus of MRI image show that this algorithm can precisely segment intensity in homogeneities image and improve the segmentation speed, so this algorithm can be applied to the complex structure segmentation, such as the hippocampus. © 2011 ieee.

关键词： image segmentation

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A flexible fully-multiplicative orthogonal-group based ICA algorithm

A flexible fully-multiplicative orthogonal-group based ICA a...

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ieee symposium on computational intelligence in Bioinformatics and computational Biology

作者： Ye, Yalan Wan, Jing Zhang, Zhi-Lin Jia, Chen Lei, Wu Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610054 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

ISBN: (纸本)9781424407101

In this paper, we propose a flexible fully-multiplicative orthogonal-group based ICA (FlexibleOgICA) algorithm, which can instantaneously separate the mixture of sub-Gaussian and super-Gaussian source signals. It adopts a self-adaptive nonlinear function, which adjusts its parameter to achieve better performance based on the estimation of the kurtosis of super-Gaussian source signals. We also have successfully applied the algorithm to obtain the fetal electrocardiogram (FECG) signal, showing its fast convergence speed and high separation performance.

关键词： Gaussian distribution

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Lightweight Deep Neural Network Model With Padding-Free Downsampling

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ieee signal processing LETTERS 2024年 31卷 865-869页

作者： Liu, Dengfeng Guo, Xiaohe Wang, Ning Wu, Qin Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jiangnan Univ Engn Res Ctr Intelligent Technol Healthcare Minist Educ Wuxi 214122 Peoples R China Wuxi Machinery & Electron Higher Profess & Tech Sc Wuxi 214028 Peoples R China

Deep neural networks have achieved impressive performance in image classification tasks. However, due to limitations in hardware resources, including computing units and storage capacity, deploying these networks directly on resource-constrained devices such as mobile and edge devices is challenging. While lightweight network models have made significant advancements, the downsampling stage has received little attention. As the feature map is reused multiple times, the reduction of its size during the downsampling stage not only reduces the computational cost of the downsampling module itself but also lowers the computational burden of subsequent stages. This letter addresses this gap by proposing a padding-free downsampling module that effectively reduces computational costs and can seamlessly integrates into various deep learning models. Furthermore, we introduce a hybrid stem layer to obtain competitive accuracy. Extensive experiments were conducted on CIFAR-100, Stanford Dogs, and imageNet datasets. On the CIFAR-100 dataset, the results show that the proposed module reduces computational costs by approximately 20% and improves inference speed on resource-constrained devices by around 10%.

关键词： Deep learning image classification model architecture design neural network lightweighting

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SLIDE - SUBSPACE-BASED LINE DETECTION

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 1994年第11期16卷 1057-1073页

作者： AGHAJAN, HK KAILATH, T Dept. of Electr. Eng. Stanford Univ. CA USA

An analogy is made between each straight line in an image and a planar propagating wavefront impinging on an array of sensors so as to obtain a mathematical model exploited in recent high resolution methods for direction-of-arrival estimation in sensor array processing. The new so-called SLIDE (Subspace-Based Line Detection) algorithm then exploits the spatial coherence between the contributions of each line in different rows of the image to enhance and distinguish a signal subspace that is defined by the desired line parameters. SLIDE yields closed-form and high resolution estimates for line parameters, and its computational complexity and storage requirements are far less than those of the standard method of the Hough transform. If unknown a priori, the number of lines is also estimated in the proposed technique. The signal representation employed in this formulation is also generalized to handle grey-scale images as well. The technique has also been generalized to fitting planes in 3-D images. Potential application areas of the proposed technique include road tracking in robotic vision, mask-wafer alignment and linewidth measurement in semiconductor manufacturing, aerial image analysis, text alignment in document analysis, particle tracking in hubble chambers, and similar applications.

关键词： LINE FITTING EDGE DETECTION SUBSPACE FITTING ARRAY processing OBJECT RECOGNITION PROJECTION HOUGH TRANSFORM

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