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We consider tracking of a target with elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the known ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to...
In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of...
We consider a number of sensors deployed over a large geographical area for tracking a target with linear constraints on its motion dynamics which are specified by Kalman filter conditions. The state estimates from the sensors are sent over long-haul networks to a remote fusion center, where they are fused to improve the tracking accuracy. The mismatches among the sensors in incorporating the target...
Sobol' sensitivity analysis method is widely used as a classical approach in global sensitivity analysis. this paper improves the integral used for calculating conditional variance, and proves that it can reduce error in the view of Monte Carlo method; besides, Sobol' low discrepancy sequence is replaced by another quasi-random numbers with lower star discrepancy generated by uniform design to increase...
We consider long-haul sensor networks where sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network...
Demand for large-scale data mining and data analysis has led both industry and academia to design highly scalable data-intensive computing platforms. MapReduce is a well-known programming model to process large amount of data. However, current implementations perform poorly and are inefficient, even to run a single MapReduce job. To manage and process enormous data, multi-jobs instead of single job,...
We propose a neural network based approach for estimating the total wirelength of a digital circuit, mapped onto an FPGA, before circuit placement and routing. A 3-layer MLP neural network is trained to learn the behavior of a placement tool and then quickly predicts the wirelength of a circuit design with the accuracy similar to one obtained after placement. A priori knowledge about the wirelength...
It is crucial to estimate the exterior orientation elements accurately when carrying out geometric positioning based on geometric imaging model. Currently, least-squares method is widely adopted for the estimation of exterior orientation elements. However, in the absence of sufficient ground control points, least-squares estimation is not convergent. Therefore, we proposed a Bayesian estimation model...
Class Point is a method of system-level size estimation for Object-Oriented (OO) products, including measurement of CP1 and CP2. In this paper, we extend this method in two ways. First, we propose a size measurement named CP3 based the Class Point Approach to predict the maintenance effort. Second, in order to improve the precision of estimation, we take more impact factors into account. In the phase...
Reliability modeling has become an important issue in some special areas such as military and aerospace. Most existing reliability estimation methods are usually limited in these areas because of the requirement of large sample. Furthermore, the large sample test is impractical in terms of implementation expense and time, since most product have the characteristics of high reliability, high cost and...
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