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Bad data detection and identification is an important step in state estimation procedures. Finding the values of the state variables relies on real time measurements which are normally contaminated by noise or may suffer some error due to misconfiguration. Furthermore, the data is a target for hackers who try to change some measurement readings that lead operators to take wrong decisions. The need...
In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large area. The Nystrom algorithm has overcome the classical multidimensional scaling in a low and moderate signal to noise ratio, in addition it performs well as the number...
Mobile station (MS) localization which plays an important role in the Wireless Sensor Networks (WSNs) has received considerable attention. In this paper, a new framework based on subspace approach for positioning a MS at WSNs localization system with the use of time-of-arrival (TOA) measurements is introduced. Unlike ordinary multidimensional scaling (MDS) algorithm using eigendcomposition or inverse...
We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense. There are a variety...
This paper details a simple and useful method for obtaining sensor coordinates in a microphone array. The scheme described herein is based on a well known technique, the multidimensional scaling, which uses distances measured between pairs of microphones to estimate their coordinates in a three dimensional space. While the classical multidimensional scaling provides a solution having the same set...
In this paper, a novel multidimensional scaling (MDS) method based on arrival of angular (AOA) measurements is proposed for passive emitter location in wireless networks. Simulations are included to contrast the estimator performance with conventional MDS algorithm and least square algorithm designed for AOA measurements.
This study presents a fast speaker clustering method based on multidimensional scaling. Speech segments are trained as initial acoustic models. MDS is utilized to transform acoustic models to a space with the coordinate best preserve the distances or dissimilarity between models. Speaker clusters are clustered using vector quantization on the MDS coordinates and the acoustic speaker models are trained...
In wireless sensor networks, three-dimensional 3D localization is very crucial for the applications of target tracking, environmental monitoring and building inspection system, etc. This paper proposed a multi-dimensional positioning based on the method of multidimensional scaling (MDS) and wireless signal strength indicator value to locate the node, and established the dissimilarity matrix by combining...
Since coordinate-based methods for network distance prediction can estimate distances more accurately and effectively than previously proposed methods, they have been widely studied and used in Internet applications. However, there still exist at least three problems unsolved: to find a embedding low-dimensional Euclidean space best preserving distance information, to determine the dimension of embedded...
Sensor localization from only connectivity information is a highly challenging problem. To this end, our result for the first time establishes an analytic bound on the performance of the popular MDS-MAP algorithm based on multidimensional scaling. For a network consisting of n sensors positioned randomly on a unit square and a given radio range r = o(1), we show that resulting error is bounded, decreasing...
This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster principal coordinate analysis...
This paper presents an algorithm for the simultaneous localization and mapping (SLAM) problem. Inspired by the basic idea of the fast SLAM which separates the robot pose estimation problem and mapping problem, we use the particle filter (PF) to estimate the pose of individual robot and use the multi-dimensional scaling (MDS), one of the distance mapping method, to find the relative coordinates of...
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