The Experts below are selected from a list of 23775 Experts worldwide ranked by ideXlab platform
C C J Kuo - One of the best experts on this subject based on the ideXlab platform.
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A Statistical Approach to Contour Line Estimation in Wireless Sensor Networks With Practical Considerations
IEEE Transactions on Vehicular Technology, 2009Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:Contour Line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate Contour points for Contour Line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed Contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guideLines on the design of the WSN system.
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Contour Line extraction with wireless sensor networks
International Conference on Communications, 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
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GLOBECOM - Contour Line extraction in a multi-modal field with sensor networks
GLOBECOM '05. IEEE Global Telecommunications Conference 2005., 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:A distributed approach to Contour Line extraction in a multi-modal field using wireless sensor networks is proposed in this work. In contrast with previous work on edge or boundary estimation, which is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon. To effectively utilize the limited resources of sensors, a cluster-based data fusion technique is adopted so that the phenomenon of interest can be monitored in the base station (BS) without demanding excessive raw data transmission. Simulation results are given to demonstrate the efficiency of the proposed approach. Furthermore, some analysis is conducted to understand the impact of the sensor density and noise on the performance of the proposed approach.
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ICC - Contour Line extraction with wireless sensor networks
IEEE International Conference on Communications 2005. ICC 2005. 2005, 1Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
Peikai Liao - One of the best experts on this subject based on the ideXlab platform.
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A Statistical Approach to Contour Line Estimation in Wireless Sensor Networks With Practical Considerations
IEEE Transactions on Vehicular Technology, 2009Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:Contour Line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate Contour points for Contour Line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed Contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guideLines on the design of the WSN system.
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Contour Line extraction with wireless sensor networks
International Conference on Communications, 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
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GLOBECOM - Contour Line extraction in a multi-modal field with sensor networks
GLOBECOM '05. IEEE Global Telecommunications Conference 2005., 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:A distributed approach to Contour Line extraction in a multi-modal field using wireless sensor networks is proposed in this work. In contrast with previous work on edge or boundary estimation, which is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon. To effectively utilize the limited resources of sensors, a cluster-based data fusion technique is adopted so that the phenomenon of interest can be monitored in the base station (BS) without demanding excessive raw data transmission. Simulation results are given to demonstrate the efficiency of the proposed approach. Furthermore, some analysis is conducted to understand the impact of the sensor density and noise on the performance of the proposed approach.
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ICC - Contour Line extraction with wireless sensor networks
IEEE International Conference on Communications 2005. ICC 2005. 2005, 1Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
Minkuan Chang - One of the best experts on this subject based on the ideXlab platform.
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A Statistical Approach to Contour Line Estimation in Wireless Sensor Networks With Practical Considerations
IEEE Transactions on Vehicular Technology, 2009Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:Contour Line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate Contour points for Contour Line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed Contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guideLines on the design of the WSN system.
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Contour Line extraction with wireless sensor networks
International Conference on Communications, 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
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GLOBECOM - Contour Line extraction in a multi-modal field with sensor networks
GLOBECOM '05. IEEE Global Telecommunications Conference 2005., 2005Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:A distributed approach to Contour Line extraction in a multi-modal field using wireless sensor networks is proposed in this work. In contrast with previous work on edge or boundary estimation, which is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon. To effectively utilize the limited resources of sensors, a cluster-based data fusion technique is adopted so that the phenomenon of interest can be monitored in the base station (BS) without demanding excessive raw data transmission. Simulation results are given to demonstrate the efficiency of the proposed approach. Furthermore, some analysis is conducted to understand the impact of the sensor density and noise on the performance of the proposed approach.
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ICC - Contour Line extraction with wireless sensor networks
IEEE International Conference on Communications 2005. ICC 2005. 2005, 1Co-Authors: Peikai Liao, Minkuan Chang, C C J KuoAbstract:An algorithm to extract Contour Lines using wireless sensor networks is proposed for environmental monitoring in this work. In contrast to previous work on edge detection that is primarily concerned with the region of a certain phenomenon, Contour Lines offer more detailed information about the underlying phenomenon such as signal amplitude, density and source location. A distributed algorithm to extract the Contour Line information from local measurements is developed so that the phenomenon can be monitored in the basestation without demanding excessive raw data transmission. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.
Xiaojie Zhai - One of the best experts on this subject based on the ideXlab platform.
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a Contour Line color layer separation algorithm based on fuzzy clustering and region growing
Computers & Geosciences, 2016Co-Authors: Tiange Liu, Qiguang Miao, Yubing Tong, Jianfeng Song, Ge Xia, Yun Yang, Xiaojie ZhaiAbstract:The color layers of Contour-Lines separated from scanned topographic map are the basis of Contour-Line extraction, but it is difficult to separate them well due to the color aliasing and mixed color problems. This paper will focus us on Contour-Line color layer separation and presents a novel approach for it based on fuzzy clustering and Single-prototype Region Growing for Contour-Line Layer (SRGCL). The purpose of this paper is to provide a solution for processing scanned topographic maps on which Contour-Lines are abundant and densely distributed, for example, in the condition similar to hilly areas and mountainous regions, the Contour-Lines always occupy the largest proportion in Linear features and the Contour-Line separation is the most difficult task. The proposed approach includes steps as follows. First step, Line features are extracted from the map to reduce the interference from area features in fuzzy clustering. Second step, fuzzy clustering algorithm is employed to obtain membership matrix of pixels in the Line map. Third step, based on the membership matrix, we obtain the most-similar prototype and the second-similar prototype of each pixel as the indicators of the pixel in SRGCL. The spatial relationship and the fuzzy similarity of color features are used in SRGCL to overcome the inaccurate classification of ambiguous pixels. The procedure focusing on single Contour-Line layer will improve the accuracy of Contour-Line segmentation result of SRGCL relative to general segmentation methods. We verified the algorithm on several USGS historical maps, the experimental results show that our algorithm produces Contour-Line color layers with good continuity and few noises, which verifies the improvement in Contour-Line color layer separation of our algorithm relative to two general segmentation methods. A single-prototype region growing method is proposed for Contour-Line extraction.There are only a few parameters needed in this method.The first and the second most similar prototypes are employed in region growing.
Caihong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Robust wavelet network control for a class of autonomous vehicles to track environmental Contour Line
Neurocomputing, 2011Co-Authors: Tairen Sun, Hailong Pei, Yongping Pan, Caihong ZhangAbstract:We address the problem of environmental Contour Line tracking for a class of autonomous vehicles. A reference velocity is designed for the autonomous vehicles to do Contour Line tracking. Based on Lashall invariance principle, an ideal controller is designed for the vehicle with ideal model and ideal information about the environmental concentration function to track the desired Contour Line. For the vehicle with possibly modeling uncertainty, we combine a neural controller containing a wavelet neural network (WNN) identifier with a robust control to construct a robust adaptive WNN control for the vehicle to track the desired environmental Contour Line. Then we give theoretical proof of the efficiency of the designed robust adaptive WNN control. Simulation results and conclusion are presented and discussed.