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2005 IEEE International Conference on Granular computing

作者： Chen, Wan L. Cheng, Jia X. Key Lab. of Intelligent Computing and Signal Processing Ministry of Education AnHui University HeiFei 230039 AnHui China

ISBN: (纸本)0780390172

Quotient space theory of problem solving, a formal model of granular computing, is generalized in the sense that topological structure is replaced by Cech's closure space. Some basic issues of granular computing, such as the representation of real world at different levels of granularity, property preserving and the construction of granular world, are discussed in detail. It turns out that most of conclusions of the classical quotient space theory keep being valid, so intension and applicable fields are enriched and enlarged respectively. © 2005 IEEE.

关键词： Problem solving

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Compact Broadband Absorptive Bandstop Filter Based on Microstrip 2

Compact Broadband Absorptive Bandstop Filter Based on Micros...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Lu, Qi Wu, Xianliang Wang, Chao Key Lab of Intelligent Computing and Signal Processing Ministry of Education AnHui University AnHui University Heifei AnHui230000 China

A compact broadband absorptive band rejection filter (ABSF) based on microstrip line is proposed. The filter consists of a pair of coupled wires and is loaded with a resistor. The coupling line of the filter has an open stub. The transmission zero point can be adjusted by adjusting the width of the coupling line. The center frequency can be adjusted by changing the length of the intermediate coupling line. Absorption performance can be achieved through loaded resistors. In this way, the signal not input in the filter will be dissipated in the filter and will not be reflected. When F0=3.8 GHz, 26% of the 20 dB bandwidth covers 3.1. GHz-4.2 GHz, the rejection depth of the stopband is less than-40 dB, and the in-band absorption is less than-1. dB. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Notch filters

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 1.% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Teaching reform and innovation of the course - Digital image processing experiments

Teaching reform and innovation of the course - Digital image...

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International Conference on Computer Science and education

作者： Zhao, Haifeng Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424460052

Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of the course "Digital Image processing Experiments" is discussed in this paper. The combined platform of experiments includes TI DSP experimental box and three different kinds of programming languages. The six experimental projects cover the basic theories of digital image processing. The result can offer a significant reference for the teaching innovations in the other related specialties. ©201. IEEE.

关键词： MATlab

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Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption and Multi-MSB Prediction 10th

Reversible Data Hiding for 3D Mesh Model Based on Block Modu...

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1.th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Fu, Zihao Gao, Yang Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819787456

Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, while research on 3D mesh models is still immature. This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction. Initially, the original mesh model is partitioned into non-overlapping subblocks of equal size, and then the vertices in each subblock are encrypted with the same key for additive modulus encryption, ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks. Subsequently, the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security. The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices, where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex. Finally, the secret information is embedded into the allocated room. Since the proposed method almost completely preserves the spatial correlation within each subblock, the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption (VRAE). The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex (bpv), surpassing the state-of-the-art method that achieves 25.63 bpv. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mesh generation

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Segmentation of brain MR images based on t-mixture model

Segmentation of brain MR images based on t-mixture model

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3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

作者： Haifeng, Zhao Xingming, Xu Sibao, Chen Bin, Luo Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424429028

As Magnetic Resonance Imaging (MRI) is an important technology of radiological evaluation and computeraided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation. ©2009 IEEE.

关键词： Magnetic resonance imaging

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Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection

Semi-supervised geometric mean of Kullback-Leibler divergenc...

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201. 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 201., Jointly with the 201. 7th International Conference on Natural Computation, ICNC'1.

作者： Chen, Si-Bao Wang, Hai-Xian Zhang, Xing-Yi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781612841816

Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MGMD (SSMGMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMGMD. © 201. IEEE.

关键词： Newton-Raphson method

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Adversarial Example Generation Method Based on Probability Histogram Equalization 42

Adversarial Example Generation Method Based on Probability H...

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42nd Chinese Control Conference, CCC 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wan-Li School of Computer Science and Technology Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

ISBN: (纸本)9789887581543

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Iterative methods

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New algorithm of target classification in polarimetric SAR

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Journal of Systems Engineering and Electronics 2008年第2期19卷 273-279页

作者： Wang Yang Lu Jiaguo Wu Xianliang Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China

The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine （SVM）, as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar （SAR） data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.

关键词： polarimetric synthetic aperture radar target decomposition support vector machine target classification kernel function.

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Adaptive beamforming passive radar based on FM radio transmitter

Adaptive beamforming passive radar based on FM radio transmi...

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RADAR 2007 - The Institution of Engineering and Technology International Conference on Radar Systems

作者： Zhu, Jiabing Hong, Yi Tao, Liang East China Research Institute of Electronic Engineering China Education Ministry Key Lab. of Intelligent Computing and Signal Processing Anhui University China

ISBN: (纸本)9780863418488

One of the major problems in continuous wave bistatic radar based on FM radio transmitter is direct path interference(DPI). This is the signal received directly from a transmitter by the receive channel antenna. The DPI and the reflected signal are coherent, have similar structure except for the mutual delay and Doppler frequency shift. To detect targets it is necessary to suppress or remove the DPI signal. First of all, the DPI signal based on FM radio transmitter is analyzed. Secondly, the approach based on adaptive nulling array processing is introduced in detail to solve this problem. Finally, associated signal processing schemes of the FM-radio-based passive radar is discussed. Simulation results by applying real collected data show the proposed method is effective.

关键词： Beamforming

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