中国科学院机构知识库网格系统: 检索

Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes 期刊论文 OAI收割
ENTROPY, 2023, 卷号: 25, 期号: 10, 页码: 16
作者: Dai, Rui; Wang, Yibo; Zhang, Da; Ji, Hu

|

收藏 | 浏览/下载：25/0 | 提交时间：2023/12/27

microseisms P-phase onset time picking STA/LTA method AIC method skew and kurtosis method wavelet coefficient threshold denoising in time-frequency domain

A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2021, 卷号: 29, 页码: 841-851
作者: Shan, Xiaocai; Huo, Shoudong; Yang, Lichao; Cao, Jun; Zou, Jiaru

|

收藏 | 浏览/下载：38/0 | 提交时间：2021/10/19

Electroencephalography Time-frequency analysis Transforms Wavelet transforms Oscillators Signal resolution Physiology EEG transient connectivity cross-spectrum Hilbert Huang transform

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review 期刊论文 OAI收割
APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 7, 页码: 22
作者: Rhif, Manel; Ben Abbes, Ali; Farah, Imed Riadh; Martinez, Beatriz; Sang, Yanfang

|

收藏 | 浏览/下载：63/0 | 提交时间：2019/09/24

wavelet transform non stationary time series time-frequency decomposition applied sciences

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review 期刊论文 OAI收割
APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 7, 页码: 22
作者: Rhif, Manel; Ben Abbes, Ali; Farah, Imed Riadh; Martinez, Beatriz; Sang, Yanfang

|

收藏 | 浏览/下载：52/0 | 提交时间：2019/09/24

wavelet transform non stationary time series time-frequency decomposition applied sciences

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review 期刊论文 OAI收割
APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 7, 页码: 22
作者: Rhif, Manel; Ben Abbes, Ali; Farah, Imed Riadh; Martinez, Beatriz; Sang, Yanfang

|

收藏 | 浏览/下载：24/0 | 提交时间：2019/09/24

wavelet transform non stationary time series time-frequency decomposition applied sciences

Multi-scale signal transform and application of solar time series through phase analysis 会议论文 OAI收割
9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016, Datong, China, 2016-10-15
作者: Deng LH(邓林华)

收藏 | 浏览/下载：22/0 | 提交时间：2017/04/28

statistical signal processing time-frequency data analysis wavelet coherence transform

Study on time registration method for photoelectric theodolite data fusion (EI CONFERENCE) 会议论文 OAI收割
10th World Congress on Intelligent Control and Automation, WCICA 2012, July 6, 2012 - July 8, 2012, Beijing, China
Yang H.-T.; Gao H.-B.

收藏 | 浏览/下载：18/0 | 提交时间：2013/03/25

In range measurement theodolite and radar constitute a real-time tracking system at different sites to track the same target in the air and get useful information exactly and timely. As the optical theodolite and radar have different sampling frequency and measurement system the data is sent to the fusion center is asynchronous. This paper proposed a time registration method based on multi-sensor data using Wavelet neural network algorithm which not only better solved the basic problems of theodolite fusion tracking but also improve the efficiency of data fusion. Simulation experiment and comparison with other time registration method have shown the advantage of this method. 2012 IEEE.

Phase-locked and non-phase-locked event-related oscillations and channel power spectra analysis during motor imagery with speed parameters for BCRI 会议论文 OAI收割
5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011, Wuhan, China, May 10-12, 2011
作者: Fu YF(伏云发); Xu BL(徐保磊); Pei LL(裴立力); Li HY(李洪谊)

收藏 | 浏览/下载：35/0 | 提交时间：2012/06/06

Bioinformatics Biomedical engineering Feature extraction Frequency bands Investments Power spectrum Robots Time domain analysis Wavelet transforms

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：78/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Real-time matching algorithm of navigation image based on corner detection (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Imaging Detectors and Applications, June 17, 2009 - June 19, 2009, Beijing, China
作者: Zhang T.

收藏 | 浏览/下载：46/0 | 提交时间：2013/03/25

In order to meet requirement of real-time and high accuracy in image matching aided navigation SSDA algorithm is used to match remote sensing image and template image coarsely a fast and effective algorithm of remote sensing image matching based on corner detection is put forward. With the combination of rough and fine match when the matching result is bigger than one to count absolute value sum of energy difference of characteristic point energy to realize fine match of remote sensing image and template image to locate the position of template image in remote sensing image accurately. Simulation experiment proves that the matching of a remote sensing image resolution of 1018*1530 and a template image resolution of 150*90 can be fulfilled within 2.392 second wavelet transform is used to acquire low frequency component to realize image compression to decrease calculation work and increase matching speed. Harris corner detection algorithm is used to detect corner of remote sensing image and template image and energy of every corner is calculated the algorithm is robust and effective real time image navigation can be achieved. 2009 SPIE.