Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

RSS-Based Indoor Localization Algorithm for Wireless Sensor Network Using Generalized Regression Neural Network

Abstract: Traditional received signal strength (RSS)-based localizations are often erroneous for the low-cost WSN devices. The reason is that the wireless channel is vulnerable to so many factors that deriving the appropriate propagation loss model for the WSN device is difficult. We propose a flexible location estimation algorithm using generalized regression neural network (GRNN) and weighted centroid localization. In the first phase of the proposed scheme, two GRNNs are trained separately for x and y coordinates, using RSS data gathered at the access points from the reference nodes. The networks are then used to estimate the approximate location of the target node and its close neighbors. In the second phase, the target node position is determined by calculating the weighted centroid of the Nc-closer neighbors. Performance of the proposed algorithm is compared with some existing RSS based techniques. Simulation and experimental results indicate that the location accuracy is satisfactory. The system performance is remarkably good in comparison with its simplicity and requiring no additional hardware.

Flare Plasma Dynamics Observed with the YOHKOH Bragg Crystal Spectrometer. I. Properties of the CA XIX Resonance Line

Abstract: Using data from the Bragg crystal spectrometer on the Yohkoh spacecraft we have computed measures of the total intensity, centroid position, and line width for the resonance line of Ca XIX during the rise phase and after maximum for 219 solar flares. The difference between the centroid positions early and late in each flare yields a measure of the line-of-sight velocity shift of the line centroid. We find a trend in the average value of the centroid shift with distance from Sun center suggesting radial mass motions with a characteristic velocity of 58 km s −1 . There is a correlation between the rise-phase line widths and the centroid shift

Statistics of Velocity from Spectral Data: Modified Velocity Centroids

Abstract: We address the problem of studying interstellar turbulence using spectral line data. We find a criterion when the velocity centroids may provide trustworthy velocity statistics. To enhance the scope of centroid applications, we construct a measure that we term "modified velocity centroids" (MVCs) and derive an analytical solution that relates the two-dimensional spectra of the modified centroids with the underlying three-dimensional velocity spectrum. We test our results using synthetic maps constructed with data obtained through simulations of compressible MHD turbulence. We show that the MVCs are complementary to the Velocity Channel Analysis technique. Employed together, they make the determination of the velocity spectral index more reliable for a wider variety of astrophysical situations.

Data clustering with modified K-means algorithm

Abstract: This paper presents a data clustering approach using modified K-Means algorithm based on the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm partitions the whole space into different segments and calculates the frequency of data point in each segment. The segment which shows maximum frequency of data point will have the maximum probability to contain the centroid of cluster. The number of cluster's centroid (k) will be provided by the user in the same manner like the traditional K-mean algorithm and the number of division will be k∗k (‘k’ vertically as well as ‘k’ horizontally). If the highest frequency of data point is same in different segments and the upper bound of segment crosses the threshold ‘k’ then merging of different segments become mandatory and then take the highest k segment for calculating the initial centroid (seed point) of clusters. In this paper we also define a threshold distance for each cluster's centroid to compare the distance between data point and cluster's centroid with this threshold distance through which we can minimize the computational effort during calculation of distance between data point and cluster's centroid. It is shown that how the modified k-mean algorithm will decrease the complexity & the effort of numerical calculation, maintaining the easiness of implementing the k-mean algorithm. It assigns the data point to their appropriate class or cluster more effectively.