Search results for "Covariance matrix"
showing 10 items of 73 documents
Systematic and statistical uncertainties of the hilbert-transform based high-precision FID frequency extraction method.
2021
Abstract Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is one of the methods that have been used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described, and it is briefly compared with other commonly used frequency extraction methods. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytical…
Complex network analysis of resting-state fMRI of the brain.
2016
Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation …
Pose classification using support vector machines
2000
In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues …
Thinking outside the box: effects of modes larger than the survey on matter power spectrum covariance
2012
Considering the matter power spectrum covariance matrix, it has recently been found that there is a potentially dominant effect on mildly non-linear scales due to power in modes of size equal to and larger than the survey volume. This {\it beat coupling} effect has been derived analytically in perturbation theory and while it has been tested with simulations, some questions remain unanswered. Moreover, there is an additional effect of these large modes, which has so far not been included in analytic studies, namely the effect on the estimated {\it average} density which enters the power spectrum estimate. In this article, we work out analytic, perturbation theory based expressions including…
An Analysis of Regional and Intra-annual Precipitation Variability over Iran using Multivariate Statistical Methods
1998
The temporal and spatial precipitation regime of Iran was analysed using multivariate analyses of monthly mean precipitation records for 71 stations. A Principal Component Analysis was applied to the correlation matrix in order to describe the intra-annual variations of precipitation. The Principal Component scores were mapped to visualize the spatial structure of the three derived precipitation regimes. By applying an agglomerative clustering (WARD) of the three Principal Component scores, five homogeneous spatial clusters, representing five precipitation regions, were developed. The intra-annual types of precipitation distribution, shown by the five clusters, are described and discussed.
Irrelevant Features, Class Separability, and Complexity of Classification Problems
2011
In this paper, analysis of class separability measures is performed in attempt to relate their descriptive abilities to geometrical properties of classification problems in presence of irrelevant features. The study is performed on synthetic and benchmark data with known irrelevant features and other characteristics of interest, such as class boundaries, shapes, margins between classes, and density. The results have shown that some measures are individually informative, while others are less reliable and only can provide complimentary information. Classification problem complexity measurements on selected data sets are made to gain additional insights on the obtained results.
Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor
2019
This work deals with the tuning of an Extended Kalman Filter for sensorless control of induction motors for electrical traction in automotive. Assuming that the parameters of the induction motor-load model are known, Genetic Algorithms are used for obtaining the system noise covariance matrix, considering the measurement noise covariance matrix equal to the identity matrix. It is shown that only stator currents have to be acquired for reaching this objective, which is easy to accomplish using Hall-effect transducers. In fact, the Genetic Algorithm minimizes, with respect to the system covariance matrix, a suitable measure of the displacement between the stator currents experimentally acquir…
The impact of sample reduction on PCA-based feature extraction for supervised learning
2006
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases
2011
Published version of an article in the journal: Pattern Analysis and Applications. Also available from the publisher at: http://dx.doi.org/10.1007/s10044-011-0199-9 We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing micro-aggregation techniques (MATs), it is expedient to incorporate information about the dependence between the random variable…
Finding condensed descriptions for multi-dimensional data.
1976
Abstract We describe two programs that may be used to find condensed descriptions for data available in a contingency table or in a covariance matrix in the case that these data follow a multinomial or a multivariate normal distribution, respectively. The programs perform a stepwise model search among multiplicative models by computing appropriate likelihood-ratio test statistics.