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浏览/检索结果: 共4条，第1-4条 帮助 限定条件 内容类型：会议论文    专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Detection and analysis of 1/f noise correlation in semiconductor laser diodes 会议论文 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, AOMATT 2014, April 26, 2014 - April 29, 2014, Harbin, China Fan H.-J.; Gao F.-L.; Cao J.-S.; Guo S.-X. 收藏  |  浏览/下载：14/0  |  提交时间：2015/04/27 Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 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：34/0  |  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.   Speech signal enhancement through wavelet domain MMSE filtering (EI CONFERENCE) 会议论文 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Fenghua Z.; Le Y.; Jian W.; Qiang S. 收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25 A new speech enhancement system that combine robust signal enhancement and minimum signal distortion is proposed in this paper. The proposed method introduces frequency depended  parametric  MMSE filtering techniques that involve wavelet packets. Voice activity detection (VAD) is used to further distinguish speech from noise and help to adaptively remove noise components from color noise eruptive noisy speech  while perceptual criteria is also taken into account. Experimental results and objective quality measurement test results validate the proposed speech enhancement system and illustrate the benefit of the proposed wavelet domain MMSE filtering as an excellent speech enhancement method to provide sufficient noise reduction and good intelligibility and perceptual quality  without causing considerable signal distortion and musical background noise method. 2010 IEEE.   Arc fault signatures detection on aircraft wiring system (EI CONFERENCE) 会议论文 6th World Congress on Intelligent Control and Automation, WCICA 2006, June 21, 2006 - June 23, 2006, Dalian, China Hongkun Z.; Tao C.; Wenjun L. 收藏  |  浏览/下载：18/0  |  提交时间：2013/03/25 In this paper an arc fault detection method Is proposed based on characteristics of the fault current of an electric arc. A localized signal processing method Is developed using wavelet analysis to decompose the differential current signal Into a series of wavelet components  each of which Is a time-domain signal that covers a specific frequency band. Thus  more distinctive signal features that represent arc faults and other fault transient phenomena are extracted. As a result  by quantifying the extracted features  an arc faults are distinguished from phenomena similar to arc and other fault transients using the differences In the quantified features. Simulation studies have demonstrated that the proposed method Is reliable and simple. 2006 IEEE.