Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Gen...
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Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object- based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing （ESRGG） algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.
For the large-scale wireless mobile networks, the topology or global state information directly affects congestion control, traffic control, quality of service and so on. Due to the dynamic change of the opportunistic...
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ISBN:
(纸本)9781509038237;9781509038220
For the large-scale wireless mobile networks, the topology or global state information directly affects congestion control, traffic control, quality of service and so on. Due to the dynamic change of the opportunistic network structure, the node can not perceive the current state of the network, therefore, it is important to improve the routing quality by designing a topology aware algorithm that can predict the network topology or global state parameters. According to the theoretical deduction and experiment verification of opportunistic network, some conclusions of the average degree and eigenvalue curve is given. Based on these conclusions, an algorithm with multiple measured values for eigenvalue prediction is proposed. The results of the error analysis indicate that the prediction accuracy is obviously improved compared with the traditional Kalman filtering algorithm.
A point clouds filtering algorithm is presented based on Grid Partition using Dynamic Quad Tress and Moving Least Squares. First, points are partitioned reasonably and corresponding Dynamic Quad Trees indices are esta...
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A point clouds filtering algorithm is presented based on Grid Partition using Dynamic Quad Tress and Moving Least Squares. First, points are partitioned reasonably and corresponding Dynamic Quad Trees indices are established. Second, points in grids are utilized to fit a DEM reference plane using moving least squares technology. Finally, ground points are separated from those non-ground ones if they are positioned above the reference plane and have a distance to the plane exceeding threshold value. Experiments show that this filtering algorithm is of high precision and identify ground points effectively without losing detailed topography information.
When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occur...
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When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occurring. To solve this problem, this study evaluates a runner's risk of heat stroke injury by using a wearable heat stroke detection device (WHDD), which we developed previously. Furthermore, some filtering algorithms are designed to correct the physiological parameters acquired by the WHDD. To verify the effectiveness of the WHDD and investigate the features of these physiological parameters, several people were chosen to wear the WHDD while conducting the exercise experiment. The experimental results show that the WHDD can identify high-risk trends for heat stroke successfully from runner feedback of the uncomfortable statute and can effectively predict the occurrence of a heat stroke, thus ensuring safety.
Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds ...
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Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand;only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases. (C) 2015 Elsevier B.V. All rights reserved.
In the paper, we proposed big data novel filtering method – Local-loop Particle Filter Based on the Artificial Fish algorithm(LPF-AF) for nonlinear dynamic systems. Particle filtering algorithm has been widely used i...
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ISBN:
(纸本)9781510870604
In the paper, we proposed big data novel filtering method – Local-loop Particle Filter Based on the Artificial Fish algorithm(LPF-AF) for nonlinear dynamic systems. Particle filtering algorithm has been widely used in solving nonlinear/non Gaussian filtering problems. The proposal distribution is the key issue of the particle filtering, which will greatly influence the performance of algorithm. In the proposed LPF-AF, the local searching of AF is used to regenerate sample particles, which can make the proposal distribution more closed to the poster distribution. There are mainly two steps in the proposed filter. In the first step of LPF-AF, extended kalman filter was used as proposal distribution to generate particles, then means and variances of the proposal distribution can be calculated. In the second step, some particles move to toward the particle with the biggest weights. The proposed LPF-AF algorithm was compared with other several filtering algorithms and the experimental results show that means and variances of LPF-AF are lower than other filtering algorithms.
Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless ...
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ISBN:
(纸本)9781538632215
Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless higher accuracy and efficiency are obtained. We propose a novel scene parsing algorithm framework which integrates the object detection technologies into convolution neural network to improve the overall effectiveness. The framework consists of three components: i) a scene parsing network presenting primary semantic segmentation result. ii) an object detection network calculating the location and confidence of the targets in images. iii) an integration and filter module that cascades previous two results. Extensive experiments suggest that our model is capable of practical use and achieving more favorable scene parsing performance of mIoU score as 69.4% on CamVid dataset.
With the rapid development of wearable devices, this paper improves the main step counting algorithm, and adds the functions of computing and displaying the data such as velocity, mileage and calorie consumption. The ...
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ISBN:
(纸本)9781538635735
With the rapid development of wearable devices, this paper improves the main step counting algorithm, and adds the functions of computing and displaying the data such as velocity, mileage and calorie consumption. The algorithm mainly includes the step algorithm of human motion data acquisition and gesture analysis, the filter algorithm based on Fourier decomposition, the filter algorithm based on mean value algorithm and the energy consumption algorithm for calories. These algorithms are written through the core processor. And single chip microcomputer will collect the calculated movement steps, mileage, current calories consumption and other motion data through Bluetooth components to the mobile phone, to achieve the monitoring of human movement. It has the characteristics of high precision, low redundancy and rich function.
Shipping information-based management is rapidly developing, which results in increasingly research on location-sensing technologies for shipboard environment. However, these technologies have not been extensively app...
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ISBN:
(纸本)9781538604373
Shipping information-based management is rapidly developing, which results in increasingly research on location-sensing technologies for shipboard environment. However, these technologies have not been extensively applied on shipboard environment for two fatal limitations. Firstly, they waste resources of node when sometimes fewer nodes are asking for the positioning requirements. Secondly, they ignore the privacy protection while improving the positioning accuracy. In this paper, a new method called Multi-scale Localization Method is proposed, which is based on RSS information. A multi-precision location model is constructed from step level to room level to meet different requirements and protect privacy of people. Meanwhile, a filtering algorithm is utilized to reduce the localization error by decreasing RSSI noise. Computer simulations and real experiments have been conducted to evaluate the method on shipboard environment, the result shows that it performs well with better scalability, lower cost, and customized privacy protection.
The rapid adoption of wireless technologies has increased the interest of many laboratories about the field of Wireless Sensor Network (WSN) or the Radio-Frequency Identification (RFID) technology which has emerged as...
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ISBN:
(纸本)9781467393119
The rapid adoption of wireless technologies has increased the interest of many laboratories about the field of Wireless Sensor Network (WSN) or the Radio-Frequency Identification (RFID) technology which has emerged as a winning combination for the implementation of an advanced assistance system within smart environments. To fulfill the important mission of a technological assistance, an algorithm first had to identify the ongoing activities of its user by tracking everyday life objects in real time using, for example, passive RFID tags. To increase the quality of information extracted from the objects localization by properly using the Received Signal Strength Indicator (RSSI), this paper explores Kalman filter, particle filter and few others filtering algorithm that enhances the tracking performance. It also discusses three of the most interesting methods that can be applied for the localization of objects in smart environments without requiring the installation of references tags everywhere. Finally, to increase the value, we include experiments that were conducted within a real smart home infrastructure to review the positive and negative elements of each method.
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