We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solutions for crowd-p...
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We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solutions for crowd-powered filtering exist, they make a range of implicit assumptions and restrictions, ultimately rendering them not powerful enough for real-world applications. We describe two approaches to discard these implicit assumptions and restrictions: one, that carefully generalizes prior work, leading to an optimal, but often-times intractable solution, and another, that provides a novel way of reasoning about filtering strategies, leading to a sometimes sub-optimal, but efficiently computable solution (that is provably close to optimal). We demonstrate that our techniques lead to significant reductions in error of up to 30-40% for fixed cost over prior work in a novel crowdsourcing application: peer evaluation in online courses.
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.
Airborne LiDAR is a new kind of surveying technology of remote sensing eveloped rapidly during recent years. Raw laser scanning point clouds data include terrain points, building points, vegetation points, outlier poi...
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ISBN:
(纸本)9783037855072
Airborne LiDAR is a new kind of surveying technology of remote sensing eveloped rapidly during recent years. Raw laser scanning point clouds data include terrain points, building points, vegetation points, outlier points, etc.. In order to generate digital elevation model (DEM) and three-dimensional city model,these point clouds data must be filtered. Mathematical morphology based filtering algorithm, slope based filtering algorithm, TIN based filtering algorithm, moving surface based filtering algorithm, scanning lines based filtering algorithm and so on several representative filtering algorithms for LiDAR point clouds data have been introduced and discussed and contrasted in this paper. Based on these algorithms summarize the studying progresss about the filtering algorithm of airborne LiDAR point clouds data in home and abroad. In the end, the paper gives an expectation which will provides a reference for the following relative study.
The AtMostNValue global constraint, which restricts the maximum number of distinct values taken by a set of variables, is a well known NP-Hard global constraint. The weighted version of the constraint, AtMostWValue, w...
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The AtMostNValue global constraint, which restricts the maximum number of distinct values taken by a set of variables, is a well known NP-Hard global constraint. The weighted version of the constraint, AtMostWValue, where each value is associated with a weight or cost, is a useful and natural extension. Both constraints occur in many industrial applications where the number and the cost of some resources have to be minimized. This paper introduces a new filtering algorithm based on a Lagrangian relaxation for both constraints. This contribution is illustrated on problems related to facility location, which is a fundamental class of problems in operations research and management sciences. Preliminary evaluations show that the filtering power of the Lagrangian relaxation can provide significant improvements over the state-of-the-art algorithm for these constraints. We believe it can help to bridge the gap between constraint programming and linear programming approaches for a large class of problems related to facility location.
In this paper, the filtering issues are investigated for wireless networked control systems (WNCSs) with Markovian packet losses. In WNCSs, Markovian packet losses often occur in both control and feedback channels, an...
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In this paper, the filtering issues are investigated for wireless networked control systems (WNCSs) with Markovian packet losses. In WNCSs, Markovian packet losses often occur in both control and feedback channels, and User Datagram Protocol (UDP) is usually used to ensure real-time control and energy saving. First, the optimal filtering algorithm for WNCSs with Markovian packet losses is obtained. However, it cannot be applied in practice as its running time increases exponentially with time. Then a computationally efficient filtering algorithm is developed by approximating the probability density function of the estimated value to a Gaussian distribution, where the approximation is obtained by minimising the Kullback-Leibler divergence (KLD). Finally, the upper and lower bounds of the performance of the KLD-based approximate filter and sufficient conditions of its stability are obtained. Numerical examples are given to verify the effectiveness of the theoretical results.
In this paper, the filtering issues are investigated for event-based Wireless Network Control Systems (WNCSs) with User Datagram Protocol (UDP). Because the optimal estimation of WCNSs with packet loss in both control...
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In this paper, the filtering issues are investigated for event-based Wireless Network Control Systems (WNCSs) with User Datagram Protocol (UDP). Because the optimal estimation of WCNSs with packet loss in both control and observation channels via UDP cannot be applied in practice due to the exponential increase of the computation, and in order to reduce the energy consumption of wireless communication nodes, an efficient recursive filter is developed for the event-based WNCSs with UDP. The state estimation and the filtering error covariance are derived from the interacting multiple model (IMM) methods, and the filter gain is determined by minimising an upper bound of the filtering error covariance. It is proved that the stability of the minimum upper bound of the filtering error covariance is only related to the actual arrival rate of the observation packet, and the minimum upper bound converges to the error covariance of the optimal estimation of the corresponding system with Transmission Control Protocol (TCP) if both the trigger threshold and the control input tend to zero. Numerical examples are given to verify the effectiveness and feasibility of the theoretical results.
This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to ...
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This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.
This paper presents the program KALMOD which has been developed to enable the execution of the integration of the Kalman filtering and the numerical groundwater flow model MODFLOW on microcomputers. The program can be...
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This paper presents the program KALMOD which has been developed to enable the execution of the integration of the Kalman filtering and the numerical groundwater flow model MODFLOW on microcomputers. The program can be applied to quantify and reduce the uncertainty of the groundwater flow model, and to analyse and design groundwater monitoring networks. KALMOD consists of a preprocessor, a processor and a postprocessor. The preprocessor acts as an interface between the user and the processor. The processor manipulates the measurement processes and carries out the filtering tasks. The filtering algorithm is implemented so that it is relatively efficient with respect to computer memory and execution time. The postprocessor was designed to present the model results in graphics. The program is suitable for small scale problems and for educational purposes.
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.
To solve the problem of multiple noise interference when tunable diode laser absorption spectroscopy (TDLAS) technology is used for gas concentration detection, the principles of Kalman, moving average, wavelet transf...
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To solve the problem of multiple noise interference when tunable diode laser absorption spectroscopy (TDLAS) technology is used for gas concentration detection, the principles of Kalman, moving average, wavelet transform, and singular value decomposition four filtering algorithm principles and system noise sources are analyzed. MATLAB simulation software is used to simulate the relationship curve of thermal noise, shot noise, relative intensity noise of lasers, and the characteristic value of harmonic signals. Four filtering algorithms are used to process the noise, and the best filtering algorithm is selected. Moreover, a TDLAS experimental system with ethylene gas as the detection object is designed in this paper. The experimental results show that the singular value decomposition method among the four filtering algorithms has the best effect in removing thermal noise, shot noise, and laser noise. After filtering is performed, the accuracy of the continuous detection of the system is improved, and the purpose of reducing or even eliminating system noise can be achieved. After processing occurs, the signal-to-noise ratio of the detected signal of the system is improved by 57 dB, and the noise removal rate of the system reaches 66.7%.
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