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Luo Q.,Harbin Institute of Technology | Luo Q.,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments | Peng Y.,Harbin Institute of Technology | Li J.,Harbin Institute of Technology | Peng X.,Harbin Institute of Technology
IEEE Sensors Journal | Year: 2016

When localizing the position of an unknown node for wireless sensor networks, the received signal strength indicator (RSSI) value is usually considered to fit a fixed attenuation model with a corresponding communication distance. However, due to some negative factors, the relationship is not valid in the actual localization environment, which leads to a considerable localization error. Therefore, we present a method for improved RSSI-based localization through uncertain data mapping. Starting from an advanced RSSI measurement, the distributions of the RSSI data tuples are determined and expressed in terms of interval data. Then, a data tuple pattern matching strategy is applied to the RSSI data vector during the localization procedure. Experimental results in three representative wireless environments show the feasibility and effectiveness of the proposed approach. © 2016 IEEE. Source

Wu J.,Guilin University of Electronic Technology | Wu J.,Guangxi Colleges and Universities Key Laboratory of Optoelectronic Information Processing | Rao Y.,Guilin University of Electronic Technology | Hu Y.,Guilin University of Electronic Technology | And 3 more authors.
Yaogan Xuebao/Journal of Remote Sensing | Year: 2016

This paper presents our research on registering single aerial image to a LiDAR point cloud. Given its high spatial resolution, spatial positioning accuracy, and efficiency in capturing data of physical surfaces, LiDAR has been influenced by and has significantly changed photogrammetry. The fusion of LiDAR data with aerial images offers various applications, such as DOM generation, virtual reality, city modeling, and military training, because of the complementary nature of the information provided by the two systems. However, the two datasets should be geo-registered into a common coordinate frame prior to such integration, which proves to be quite challenging in terms of either automation or accuracy. Such a challenge may be partly caused by inefficiency in the feature measurement or detection stage. For example, the identification of point of interest or straight line feature is viable and reliable in optical images but is difficult to achieve in LiDAR point clouds because of its poor discontinuity measurements. To this end, an automatic geo-registration approach based on “pin-hole” imaging simulation and iterative gradient mutual information computation is proposed to align single aerial image to discrete LiDAR point clouds. The proposed approach takes photogrammetry collinear equation as strict mathematic mode and involves three stages. First, a virtual “pin-hole” imaging process restored from aerial image orientation parameters is established on urban LiDAR point clouds to generate simulated, gray, LiDAR-depth images. The generated LiDAR-depth images are geometrically similar to aerial images. Hence, difficulties in registration caused by distinct differences in spatial resolution, perspective distortion, and size between the two types of data sources can be greatly alleviated. Second, the geometric transform parameters between LiDAR depth images and aerial images are successfully estimated with the gradient mutual information as the similarity measurement. Moreover, the image pyramid partitioning strategy is implemented to accelerate the search for parameter space. In this stage, LiDAR laser feet points can be roughly mapped on aerial image pixels on the basis of the estimated geometric transform parameters and the known projection relations between LiDAR point clouds and their depth images. Third, the photogrammetry space resection algorithm is implemented using all the mapped aerial image pixels as observed values and their gradient mutual information as weight to improve image orientation parameters. The three stages are repeated until the given iterative calculation condition is met and the LiDAR point clouds are registered with single aerial image. Selected airborne LiDAR data and an aerial image with different initial parameter values are tested with the proposed approach. Approximately 0.5 pixel is obtained, indicating a higher registration precision compared with the ICP algorithm. (1) The “pin-hole” simulation imaging and iterative gradient mutual information calculation successfully resolve the difficult heterologous correspondence problem between LiDAR point clouds and optical aerial images; (2) The photogrammetry space resection algorithm can obtain registration parameters with minimum projection errors and reliable precision evaluation by maximizing the use of intensive space information from LiDAR data and recovering optical bundles of laser beams directly. © 2016, Science Press. All right reserved. Source

Peng Z.,Guilin University of Electronic Technology | Peng Z.,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments | Wu J.,Guilin University of Electronic Technology
Optik | Year: 2015

In this paper, we developed the adaptive fast iris capture device, and proposed a new online iris image quality assessment algorithm which combines pupil coarse location. On ARM embedded system of iris image capture, iris quality is real-time assessment, combined with feedback control to adjust light source, focal length, pupil contraction LED, etc. It does not require the user actively move to cooperate with the device during iris capture. The qualified iris image is transmitted through the USB interface to the host computer. Finally, we establish the iris library (has captured more than 1000 iris samples), and the capture device and iris quality assessment algorithm has been tested. The average capture can be completed within 1 s. The test of iris recognition proved that the iris capture device and iris image quality assessment algorithm is valid. © 2015 Elsevier GmbH. All rights reserved. Source

Wu J.,Guilin University of Electronic Technology | Peng Z.,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

This paper presents our research on automatic shadow detection from high-resolution aerial image through the hybrid analysis of RGB and HIS color space. To this end, the spectral characteristics of shadow are firstly discussed and three kinds of spectral components including the difference between normalized blue and normalized red component - BR, intensity and saturation components are selected as criterions to obtain initial segmentation of shadow region (called primary segmentation). After that, within the normalized RGB color space and HIS color space, the shadow region is extracted again (called auxiliary segmentation) using the OTSU operation, respectively. Finally, the primary segmentation and auxiliary segmentation are combined through a logical AND-connection operation to obtain reliable shadow region. In this step, small shadow areas are removed from combined shadow region and morphological algorithms are apply to fill small holes as well. The experimental results show that the proposed approach can effectively detect the shadow region from high-resolution aerial image and in high degree of automaton. © 2015 SPIE. Source

Zhu A.,Xidian University | Xu C.,Guilin University of Electronic Technology | Xu C.,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments | Li Z.,Xidian University | And 5 more authors.
Journal of Systems Engineering and Electronics | Year: 2015

A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evolution (HGWO). Because basic grey wolf optimization (GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE's strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. © 1990-2011 Beijing Institute of Aerospace Information. Source

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