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	古尔班通古特沙漠4种草本植物叶片与土壤的化学计量特征[J] 期刊论文 OAI收割应用生态学报, 2015, 卷号: 3, 页码: 659-665 作者: 陶冶; 张元明; 安庆师范学院生命科学学院; 中国科学院新疆生态与地理研究所/中国科学院干旱区生物地理与生物资源重点实验室 \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2017/12/29 化学计量学土壤养分养分限制荒漠草本生活型 Stoichiometry Soil Nutrient Nutrient Limitation Desert Herb Life Form.
	Trajectory tacking control of a quad-rotor based on active disturbance rejection control (EI CONFERENCE) 会议论文 OAI收割 2012 IEEE International Conference on Automation and Logistics, ICAL 2012, August 15, 2012 - August 17, 2012, Zhengzhou, China Gong X.; Tian Y.; Bai Y.; Zhao C. 收藏 \| 浏览/下载：32/0 \| 提交时间：2013/03/25 The objective of this paper is to deal with a trajectory tracking of a Quad-rotor unmanned aerial vehicle (UAV). For the model uncertainty the external disturbance and the coupling factor are considered an active disturbance rejection control (ADRC) algorithm is introduced into the designing procedure. The aircraft dynamic model is proposed in this article based on which the closed-loop control system is divided into four independent channels with the coupling factor compensated by the extended state observer (ESO). The nonlinear state error feedback (NLSEF) algorithm is designed in each channel to improve the closed-loop dynamics. In this article the ADRC controller is expressed in the discrete form. And finally the simulation results show that the proposed control algorithm achieves a favourable tracking performance. 2012 IEEE.
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：44/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.