Papers by Keyword: De-Noising

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Authors: Ping Guo, Yi Wen Deng, Hai Yan Zhang
Abstract: Using CAPTCHA is a simple but convenient way to ensure user data security. It’s widely used in user authentication and user interaction. In this paper, CAPTCHA images from several typical websites were used as the research objects. The paper shows the whole process on image binarization, de-noising, dilation, splitting characters. Gives out the CAPTCHA images recognition algorithm based on edit distance which defines the string similarity. Experiments show that the proposed algorithm is simple, fast, robust performance and has a high recognition accuracy rate.
Authors: Zhi Zhou, Xing Man Yang, Gang Chen
Abstract: As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.
Authors: Jing Rong Sun, Gui Ying Zhang
Abstract: In order to denoise the pulsar signal, a variable step NLMP algorithm was introduced under the-stable distribution. The algorithm introduced a step update factor. By adjusting parameters and error information, the algorithm can adjust the incremental direction of the adaptive filter weight vector accurately, and improve the convergence performance. Simulation results show that the variable step-size NLMP algorithm is better than the NLMP algorithm in the denoising effect in-stable distribution noise environments.
Authors: Lu Gan, Long Zhou, Shan Mei Liu
Abstract: Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.
Authors: Yan Hai Shang
Abstract: The paper proposed a de-noising method for non-stationary signals received by a small-size antenna array. By taking into consideration of the correlation of signals received by antennae which compose the antenna array, we could improve greatly the discovery probability of signal coefficients in time-frequency domain through taking advantage of the method of integration for signal detection. Meanwhile, under the assumption of Gaussian noise environment, the probability density functions of noise and signal after integration were also presented. In order to extract the coefficients for signal and reduce the Type I error further, an algorithm based False Discovery Rate (FDR) was put forward. Finally, a comparison between the detection performances before and after integration was made: under same rate of Type I error, the detection performance of signal after integration is improved significantly. And the effectiveness of the method was showed by experimental results as well.
Authors: Lin Lin, Xiao Huan Wu, Jia Jin Qi, Hong Xin Ci, Zhi Yong Yu
Abstract: The noise component in power quality signals affects the accuracy of analysis result. This paper presents a new approach for power quality signals de-noising. Firstly, the original signals are transformed by S-transform method. Then, the matrix which is get from S-transform result is processed as a two-dimension image. The global thresholding de-noising method is used to filtering the noise component in the power quality signals. The simulation results showed the effectiveness of the new approach.
Authors: Jie Zhao, Yong Mei Qi, Jian Ying Pei
Abstract: A novel model which is about the image denoising and enhancement is proposed in this article, the image denoising and enhancement increasingly becomes a bottleneck restricting the follow-up image of a series of processing On the basis of anisotropic diffusion model, an edge stopping function is introduced, which can make up the drawback that solely relies on detecting the gradient information to control the diffusion process .Using the edge stopping function position accurately on the edge so as to achieve the purpose of the noise reduction fully in the non-edge zone, but it inevitably will blur the edge information. Therefore, the further use of the shock filter in the edge enhancement is essential. Experiments show that the model can well remove the image noise and achieve good visual effect.
Authors: Can Zhao, Yun Ling Shi, Jun Ting Cheng
Abstract: For the mass point cloud, which is generated by large work piece for free surface, the point cloud noise removal is the most important step, after denoising point cloud which quality will directly influence the follow-up point normal vector estimation and curvature estimation. Therefore, this paper presents a simple and efficient algorithm for near-point denoising. Firstly, it using the bounding box for messy point cloud data differentiate space topology structure, then, traverse all points, for each point looking for its K neighborhood, and fitting quadric surface using the K neighbors; finally, using Z value method for calculation of the distance that point to the secondary surface distance. Setting threshold, and if the distance beyond the threshold, then the point that noise points and delete. Experiments show that this algorithm compared with the traditional algorithm, not only improve efficiency, and can be a very good retain the original model data, but also for the follow-up process provides high quality raw data, there is a wide useful in three dimensions scanning, projective measurement reverse design and other fields.
Authors: Wen Jie Zhu, Guang Long Wang, Zhong Tao Qiao, Feng Qi Gao
Abstract: A novel noise reduction algorithm combined with compressive sensing (CS) and lifting wavelet transform (LWT) is proposed in this paper. This algorithm can overcome the limitations of traditional noise reduction methods based on Kalman filtering and wavelet threshold filtering. The characteristics of wavelet time-frequency distribution of the microelectromechanical system (MEMS) gyroscope are discussed to illustrate the demerit of the classical filtering methods. Noise reduction algorithm of MEMS gyroscope signal is studied in detail by combining CS theory with lifting wavelet transform. De-noising effect, time-consumption of computation as well as traditional CS reconstruction algorithms are analyzed. The results show that the signal reconstruction algorithm of conventional matching pursuit (MP) greedy algorithms contains more glitches and computation time-consumption, the basis pursuit de-noising (BPDN) algorithm is better and it has advantages of high computational efficiency and ease of implementation.
Authors: Zhi Qiang Xu, Jian Hua Zhang, Jing Fang Ji, Xiang Jun Yu
Abstract: Due to gearbox is one of the high failure rate component in the wind turbine, the research of it has been paid wide attention in recent years. This paper reviewed the two aspects about the wind turbine gearbox. First, some signal process methods including how to determine the threshold were summarized. Then, the condition monitoring and fault diagnosis of gearbox were reviewed using the measured signals. These researches are benefited for reducing economic losses which is caused by the gearbox failure. Based on the above reviews, this paper gives some developmental direction.
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