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.

Focusing SAR data with time-varying Doppler centroid

Abstract: SAR data spatially sampled at the Nyquist limit can be correctly processed if the Doppler centroid is precisely known. Whenever the Doppler centroid shows rapid variations either with range or azimuth, more care is required in order to take advantage of the computational efficiency of frequency domain techniques. It is shown that such focusing techniques can still be exploited, provided that SAR raw data are previously modified and a space-varying nondimensional filter is applied to the focused image. The computational cost increases, but it is still smaller than time-space domain processing. Results obtained with simulated SIR-C/X-SAR data and SPOTlight geometries are presented. >

Weight Adjustment Schemes For a Centroid Based Classifier

Abstract: : In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intra-nets. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics or themes) of documents, is an important task that can help both in organizing as well as in finding information on these huge resources. Similarity based categorization algorithms such as k-nearest neighbor, generalized instance set and centroid based classification have been shown to be very effective in document categorization. A major drawback of these algorithms is that they use all features when computing the similarities. In many document data sets, only a small number of the total vocabulary may be useful for categorizing documents. A possible approach to overcome this problem is to learn weights for different features (or words in document data sets). In this report we present two fast iterative feature weight adjustment algorithms for the linear complexity centroid based classification algorithm. Our algorithms use a measure of the discriminating power of each term to gradually adjust the weights of all features concurrently. We experimentally evaluate our algorithms on the Reuters-21578 and OHSUMED document collections and compare it against Rocchio, Widrow-Hoff and SVM. We also compared its performance in terms of classification accuracy on data sets with multiple classes. On these data sets we compared its performance against traditional classifiers such as k-nn, Naive Bayesian and C4.5. Experiments show that feature weight adjustment improves the performance of the centroid-based classifier by 2- 5%, substantially outperforms Rocchio and Widrow-Hoff and is competitive with SVM. These algorithms also outperform traditional classifiers such as k-nn, naive bayesian and C4.5 on the multi-class text document data sets.

On the dynamic analysis of a manipulator and its end effector interacting with the environment

Abstract: Dynamic behavior of a manipulator arm and its end effector that interact with the environment is analyzed. Inertial properties of the arm and the end effector are represented with respect to a point of contact between the end effector and the environment. Virtual mass is then defined to be the equivalent mass of the arm and the end effector reflected to the point of contact, and is given by the ratio of a force acting on the point to the acceleration caused by the force at the point. Unlike a real mass, the virtual mass varies depending on the direction of the applied force and the location of the contact point. The maximum and minimum values of the virtual mass are then obtained and the physical meanings are discussed. Next, the rotational motion of the end effector is considered. A single rigid body possesses a centroid; a particular point at which rotation and translation of the rigid body are separated. The concept of the centroid is extended to the one for a system of rigid bodies such as arm links and the members of the end effector. The point is referred to as the generalized centroid, at which a linear force causes only a linear acceleration and a pure moment causes only an angular acceleration, hence separated. The virtual mass and the generalized centroid are then applied to task planning for chipping, hard surface contact, and dynamic insertion operations. The orientation of a tool and the configuration of the manipulator arm are optimized so that a desired dynamic behavior can be accomplished by having an appropriate virtual mass and the generalized centroid at an appropriate point.

Modulation-Constrained Clustering Approach to Blind Modulation Classification for MIMO Systems

Abstract: Blind modulation classification is a fundamental step before signal detection in cognitive radio networks where the knowledge of modulation scheme is not completely known. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships between different digital modulation schemes, the modulation classification is transformed into a number of clustering problems, one for each modulation scheme, with the final classification decision based on the maximum likelihood criterion. To improve the learning efficiency, centroid reconstruction is proposed to recover all cluster centroids through a limited number of parameters by exploiting the structural relationships in constellation diagrams. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the clustering algorithm for the enhanced convergence performance. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.

Sub-pixel accuracy location estimation from digital signals

Abstract: Identification of the spatial location of images in 1-D, 2-D, or even 3-D situations is often a requirement from sampled and digitized images which refer to regular independent linearly distributed pixels. The high-precision evaluation of the position requires interpolation calculations between the spatial samples to obtain a precision better than the geometrical pitch of the elementary pixels. Two calculation methods are discussed to attain such a subpixel accuracy: The first one proceeds from the well-known method of calculating the "centroid" of the samples in the direct space; the second one relies on the retrieval of the Fourier phase frequency dependence in the frequency domain. Both specifications are evaluated and the specific case of a 3-D signal is discussed. Both methods are independent of the analytical model for the function.