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	Engineering nanoparticles to overcome the mucus barrier for drug delivery: Design, evaluation and state-of-the-art 期刊论文 OAI收割 Medicine in Drug Discovery, 2021, 卷号: 12, 页码: 100110 作者: Liu, Chang; Jiang, Xiaohe; Gan, Yong; Yu, Miaorong \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2024/03/14 Mucus barrier Mucosal drug delivery Nanoparticles Particle tracking experiments Mucus-penetrating nanoparticles
	An improved tracking method for large-acceptance spectrometers in intermediate-energy RIB experiments 期刊论文 OAI收割 NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2021, 卷号: 985, 页码: 5 作者: Sun, Y. Z.; Wang, S. T.; Sun, Z. Y.; Zhang, X. H.; Zhao, Y. X. \| 收藏 \| 浏览/下载：10/0 \| 提交时间：2021/12/13 Tracking method Drift chamber Particle identification RIB experiments
	An automatic pedestrian detection and tracking method: Based on mach and particle filter (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Network Computing and Information Security, NCIS 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China Han Q.; Yao Z. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 This paper introduces a pedestrian detecting and tracking approach. Correlation filters present the composite properties which have been successively used in target detection. Particle filter are combined to locate the targets in real-time. Our contribution is proposing a general algorithm that is able to detect and track pedestrians in clutter environments. We also create a different view pedestrian dataset. Experiments show our algorithm is comparative when there is block and occlusion in tracking. 2011 IEEE.
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.