中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
机构
采集方式
内容类型
发表日期
学科主题
筛选

浏览/检索结果: 共25条，第1-10条 帮助

条数/页： 排序方式：
Ratiometric fluorescence sensor for sensitive detection of inorganic phosphate in environmental samples 期刊论文  OAI收割
ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2022
作者:  Zhang, Zhao;  Tao, Huihui;  Cao, Qiao;  Li, Lingfei;  Xu, Shihao
  |  收藏  |  浏览/下载：41/0  |  提交时间：2022/03/28
离子液体物理化学性质研究及其在毒品分析中的应用 学位论文  OAI收割
北京: 中国科学院大学, 2016
作者:  王瑞峰
  |  收藏  |  浏览/下载：145/0  |  提交时间：2017/12/07
Retrieval of snow depth in Northeast China using FY-3B/MWRI passive microwave remote sensing data (EI CONFERENCE) 会议论文  OAI收割
Satellite Data Compression, Communications, and Processing VIII, August 12, 2012 - August 13, 2012, San Diego, CA, United states
Ren R.; Gu L.; Chen H.; Cao J.
收藏  |  浏览/下载：138/0  |  提交时间：2013/03/25
Comparing with optical remote sensing techniques  passive remote sensing data have been proved to be effective for observing snowpack parameters such as snow depth and snow water equivalent  which can penetrate snowpack without clouds interferences. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3B (FY-3B) satellite is gradually used in the global environment research through November  2011. In this paper  we proposed a snow depth retrieval algorithm to estimate snow depth in Northeast China using MWRI passive microwave remote sensing data. A decision tree method of snow identification was firstly designed to distinguish different snow cover conditions in order to eliminate other interference signals. After using the proposed decision tree method  the processing results were further used to retrieve the snow depth in Northeast China. Finally  the practical snow depth data and the MODIS data were collected for the accuracy assessment of the proposed snow depth retrieval method. The experimental results demonstrated that the RMSE of snow depth used the proposed method was approximately 3 cm in Northeast China. 2012 SPIE.  
Correction of baseline drifts due to the pressure changes between attenuated total reflection prism and human skin for noninvasive blood glucose sensing with Fourier Transform infrared spectroscopy (EI CONFERENCE) 会议论文  OAI收割
2011 International Conference on Smart Materials and Nanotechnology in Engineering, SMNE 2011, September 17, 2011 - September 18, 2011, Wuhan, China
作者:  Wang D.
收藏  |  浏览/下载：48/0  |  提交时间：2013/03/25
Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文  OAI收割
2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China
作者:  Gao X.;  Yu P.;  Yu P.
收藏  |  浏览/下载：22/0  |  提交时间：2013/03/25
Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.  
Evaluation of spatial upscaling methods based on remote sensing data with multiple spatial resolutions (EI CONFERENCE) 会议论文  OAI收割
Satellite Data Compression, Communications, and Processing VIII, August 12, 2012 - August 13, 2012, San Diego, CA, United states
Ren R.; Gu L.; Cao J.; Chen H.; Sun J.
收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25
In most applications of remote sensing data  special spatial information is required from a finer to a coarser spatial resolution with appropriate upscaling methods. The purpose of this paper is to compare and evaluate current spatial upscaling methods using MODIS remote sensing data with multiple spatial resolutions. In the research  Northeast China was selected as the study area. MODIS data with spatial resolutions of 250 m (2 bands) and 500 m (7 bands) were used as the test data. Through using the selected upscaling methods  the Band 1 and Band 2 data of MODIS were scaled up from 250 m to 500 m spatial resolution. On the basis of land cover characteristics of Northeast China  the MODIS data located in the study area was classified into the five land cover types  including water  grasslands  forests  farmlands and bare lands using maximum likelihood method. The land cover classification results were further compared with MODIS Land Cover Type product. Finally  Structural Similarity (SSIM) was selected to evaluate the effects of these upscaling methods. The research can provide more useful information for spatial scaling transformation in remote sensing data applications. 2012 SPIE.  
Filter paper-templated preparation of ZnO thin films and examination of their gas-sensing properties 期刊论文  OAI收割
PARTICUOLOGY, 2011, 卷号: 9, 期号: 3, 页码: 253-259
作者:  Wang, Bao;  Han, Ning;  Meng, Dong;  Yue, Renliang;  Yan, Jinghui
收藏  |  浏览/下载：64/0  |  提交时间：2013/11/01
Design of motion compensation mechanism of satellite remote sensing camera (EI CONFERENCE) 会议论文  OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Gu S.; Yan Y.; Xu K.; Jin G.
收藏  |  浏览/下载：47/0  |  提交时间：2013/03/25
With the development of aerospace remote sensing technology  the ground resolution of remote sensing camera enhances continuously. Since there is relative motion between camera and ground target when taking pictures  the target image recorded in recording media is moved and blurred. In order to enhance the imaging quality and resolution of the camera  the image motion had to be compensated. In order to abate the effect of image motion to image quality of space camera and improve the resolution of the camera  the compensation method of image motion to space camera is researched. First  the reason of producing drift angle and adjustment principle are analyzed in this paper. This paper introduce the composition and transmission principle of image motion compensation mechanism. Second  the system adopts 80C31 as controller of drift angle  and adopts stepping motor for actuators  and adopts absolute photoelectric encoder as the drift Angle measuring element. Then the control mathematical model of the image motion compensation mechanism are deduced  and it achieve the closed-loop control of the drift angle position. At the last  this paper analyses the transmission precision of the mechanism. Through the experiment  we measured the actual precision of the image motion compensation mechanism  and compared with the theoretical analysis. There are two major contributions in this paper. First  the traditional image motion compensation mechanism is big volume and quality heavy. This has not fit for the development trend of space camera miniaturization and lightweight. But if reduce the volume and quality of mechanism  it will bring adverse effects for the precision and stiffness of mechanism. For this problem  This paper designed a image motion compensation that have some advantages such as small size  light weight at the same time  high precision  stiffness and so on. This image motion compensation can be applicable to the small optics cameras with high resolution. Second  the traditional mechanism control need to corrected  fitting and iterative for the control formula of mechanism. Only in this way  we can get the optimal control mathematical model. This paper has high precision of the control formula derived. It can achieve the high precision control without fitting  It also simplify the difficulty of control mathematical model establishment. This paper designed the range of adjusting of image motion compensation mechanism between -5 +5. Based on choosing-5  -4  -3  -2  -1  0  +1  +2  +3  +4  +4 as the expectation value of the imaginary drift angle  we get ten groups of the fact data in adjusting drift angle measured. The test results show that the precision of the drift angle control system can be achieved in 1. It can meet the system requirements that the precision of the control system is less than 3'  and it can achieve the high-precision image motion compensation. 2011 SPIE.  
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.  
Laser beacon adaptive optics ophthalmoscope for retinal multilayer imaging (EI CONFERENCE) 会议论文  OAI收割
2nd International Symposium on Bioelectronics and Bioinformatics, ISBB 2011, November 3, 2011 - November 5, 2011, Suzhou, China
作者:  Xuan L.;  Zheng X.;  Li D.
收藏  |  浏览/下载：24/0  |  提交时间：2013/03/25