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第41.中国控制会议

作者： Fei-Fei Qian Nuo Xu Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Interpolation technology has evolved into a powerful tool for reversible data hiding in the image processing ***,existing interpolated algorithms only have a trivial impact on image *** this paper,an innovative interpolation and matrix-based algorithm is proposed.A novel concept of the difference between interpolated pixels is represented to dramatically improve the visual quality of the image,which lays a solid foundation for the subsequent data hiding *** is growing evidence that the Tetris matrix plays a vital role in improving embedding *** is worth mentioning that our scheme can intensely resist different attacks of various *** experimental findings demonstrate that the effect of our proposed scheme is unprecedentedly perfect even though a higher capacity is embedded than with traditional steganography approaches.

关键词： Data hiding interpolation image processing

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation

Improving Transferability Reversible Adversarial Examples Ba...

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

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.

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An Implementation of Membrane Algorithms on GPU

An Implementation of Membrane Algorithms on GPU

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The Second Asian Conference on Membrane computing(ACMC201.)

作者： Xingyi Zhang Dong Qin Zhuanlian Ding Jin Tang Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University

Membrane algorithms are a new class of heuristic algorithms, which attempt to incorporate some components of membrane computing models (also called P systems) for designing efficient optimization algorithms, such as the structure of P systems, the way of communication between cells, etc. Membrane algorithms are a kind of parallel methods, where many operations can be performed in parallel. Although the importance of the parallelism of such algorithms is recognized, membrane algorithms were often implemented on the serial computing device Central processing Unit (CPU), which makes the algorithms cannot work in a more efficient way. In this work, we consider the implementation of membrane algorithms on the parallel computing device Graphics processing Unit (GPU). Under such implementation, all cells of membrane algorithms can work simultaneously. Experiment results on two classical intractable problems, point set matching problem and TSP, show that GPU implementation of membrane algorithms is much more efficient than CPU implementation in terms of runtime, especially for solving the problems with a high complexity.

关键词： Membrane computing Membrane algorithm Parallel computing device GPU

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The structure of the sparse basis function in compressive sensing for fast analysis of wide-angle monostatic scattering problems

The structure of the sparse basis function in compressive se...

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201. International Conference on Microwave and Millimeter Wave Technology, ICMMT 201.

作者： Du, Hong-Mei Chen, Ming-Sheng Wu, Xian-Liang Key Lab. of Intelligent Computing and Signal Processing Anhui University Ministry of Education Hefei 230029 China Department of Physics and Electronic Engineering Hefei Normal University Hefei 230601 China University of Science and Technology of China Hefei 230026 China

ISBN: (纸本)9781467321839

In the theory of compressive sensing, the selection of the basis functions directly affects the sparse transformation, observation number and reconstruction accuracy. In this paper, we introduce the structure of three sparse basic functions, which are used to analyze the electromagnetic scattering problems over a wide incident angle by the method of moment and compressive sensing. According to the characteristics of the induced current of wide angle, the best basic function is selected. Compared with the DCT, the accuracy of the reconstruction for different shape object are improved and the observation number reduces about 50%. © 201. IEEE.

关键词： Method of moments

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Compressive Sensing for Wide-Angle Analysis

Compressive Sensing for Wide-Angle Analysis

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第九届国际天线、电波传播及电磁理论学术研讨会(ISAPE201.)

作者： Hong-Mei Du Yi-Cai Mei Si-Long Lu Ming-Sheng Chen Xian-Liang Wu Key Lab of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei Department of Physics and Electronic Engineering Hefei Normal University Hefei 230601 China Schoo Key Lab of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei

A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM） is independent from incident angles, the equivalent relationship between induced current and the measured CS-current was build, while the CS-current can be computed directly under the new incident source. Finally, we can reconstruct the induce current by utilizing the theory of compressive sensing (CS). Compared with traditional MOM, the computational complexity can be greatly reduced.

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On dynamic weighting of data in clustering with K-alpha means

On dynamic weighting of data in clustering with K-alpha mean...

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201. 20th International Conference on Pattern Recognition, ICPR 201.

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab. of Child Development and Learning Science Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9780769541099

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances. © 201. IEEE.

关键词： Clustering algorithms

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Accelerated Plasma Simulations using the FDTD Method and the CUDA Architecture

Accelerated Plasma Simulations using the FDTD Method and the...

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201. International Symposium on Antennas and Propagation(201.天线与传播国际会议)

作者： Wei Meng Yufa Sun Key Lab or Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei P.R.China

This letter presents the graphic processor unit (GPU)implementation of the finite-difference time-domain (FDTD)method for the solution of the two-dimensional electromagnetic fields inside dispersive *** improved Z-transform-based finite-difference time-domain (ZTFDTD) method was presented for simulating the interaction of electromagnetic wave with unmagnetized *** using the newly introduced Compute Unified Device Architecture (CUDA) technology, we illustrate the efficacy of GPU in accelerating the FDTD computations by achieving significant speedups with great ease and at no extra hardware *** effect of the GPU-CPU memory transfers on the speedup will be also studied.

关键词： GPU FDTD Z-transform CUDA

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FastICA based single-channel radar signal separation algorithm

FastICA based single-channel radar signal separation algorit...

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IET International Radar Conference (IRC 2023)

作者： Xinyu Wang Jin Zhao Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei People's Republic of China

ISBN: (数字)9781837240982

Aiming at the traditional blind source separation for the received single-channel mixed radar signal makes it difficult to accurately recover the source signal, a single-channel radar signal comprehensive separation method based on the combination of the FastICA algorithm and the variational modal decomposition (VMD) algorithm is proposed. The method utilizes VMD to first perform modal separation of single-channel mixed Linear Frequency Modulation (LFM) signal, then selects the modal component signal with the largest correlation coefficient with the mixed signal and expands it with single-channel mixed LFM signal into a virtual multi-channel mixed signal, and finally inputs it into FastICA algorithm to obtain the source signal. The simulation experiment results show that the comprehensive separation algorithm can accurately separate the LFM signal, overcoming the limitation that the FastICA algorithm cannot directly separate the single-channel mixed radar signal.

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An Improved FTRLS Filtering Algorithm and Its Simulation Analysis

An Improved FTRLS Filtering Algorithm and Its Simulation Ana...

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201. 1.th IEEE International Conference on Communication Technology (ICCT 201.)

作者： Jun Zhu Jingjing Zhang Qiang Chen Key Lab of Intelligent Computing and Signal Processing Ministry of Education China School of Electronics and Information Engineering Anhui University

Due to the large error of LMS algorithm and the slow convergence rate, the recursive least squares(RLS) algorithm is proposed. Although the recursive estimation error is greatly reduced, the convergence rate is one order of magnitude higher than that of the general LMS filter. When the order N increases, the amount of calculation for a single iteration of the RLS algorithm is increased significantly. Aiming at these problems, this paper proposes an improved FTRLS filtering algorithm, which is to find out the amount of large error and accumulate the error, and then make the error feedback to make the algorithm more stable. The analysis of MATlab simulation results show that the improved algorithm can improve the convergence speed and stability of the algorithm, and effectively reduce the convergence of the noise.

关键词： adaptive filtering Least Mean Squares(LMS) Recursive Least Squares(RLS) Fast Transverse Recursive Least Squares(FTRLS) improved FTRLS algorithm convergence rate

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Separable reversible data hiding based on MSB prediction for encrypted 3D mesh models?

arXiv

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arXiv 2019年

作者： Xu, Na Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei

Reversible data hiding in encrypted domain (RDH-ED) has received tremendous attention from the research community because data can be embedded into cover media without exposing it to the third party data hider and the cover media can be losslessly recovered after the extraction of the embedded data. Although, in recent years, exten- sive studies have been carried out about images based RDH-ED, little attention is paid to RDH-ED in 3D mesh models due to its complex data structure and irregular geometry. We propose a separable RDH-ED method for 3D mesh models based on MSB (most significant bit) predic- tion. Firstly, we divide all the vertices of the mesh into the "embedded" set and "reference" set according to topological information between ver- tices, and calculate the MSB prediction errors for the vertices of the "em- bedded" set. Then, bit-stream encryption is used to encrypt the mesh. Finally, data is embedded by replacing the MSB of the encrypted vertex coordinates of the "embedded" set by the additional data bits. Recipient with data hiding key can correctly extract the additional data from the marked encrypted mesh. Recipient with encryption key can recover the mesh perfectly by using MSB prediction. Experimental results show that the proposed method has higher embedding capacity and higher quality of the recovered meshes compared to the existing RDH-ED method for 3D meshes based on symmetric encryption. Copyright © 201., The Authors. All rights reserved.

关键词： Forecasting

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