5038 results about "Principal component analysis" patented technology

A blind locating system for finding and tracing buried objects such as utility lines, conductive pipes and sondes. A sensor array is coupled to a signal processor, which determines a field vector for one or more buried objects by producing a data signal representing a covariance matrix corresponding to the covariances of the time-varying sensor array signals over a selected frequency band and accumulation interval. The covariance matrix is characterized by eigenvalues and associated eigenvectors and a user interface (UI) indicates the field vector associated with the eigenvector having the largest eigenvalue. Using several different frequency bands, a plurality of underground objects may be simultaneously detected and indicated in the UI without foreknowledge of their existence or characteristics.

Fluorescence spectral data acquired from tissues in vivo or in vitro is processed in accordance with a multivariate statistical method to achieve the ability to probabilistically classify tissue in a diagnostically useful manner, such as by histopathological classification. The apparatus includes a controllable illumination device for emitting electromagnetic radiation selected to cause tissue to produce a fluorescence intensity spectrum. Also included are an optical system for applying the plurality of radiation wavelengths to a tissue sample, and a fluorescence intensity spectrum detecting device for detecting an intensity of fluorescence spectra emitted by the sample as a result of illumination by the controllable illumination device. The system also include a data processor, connected to the detecting device, for analyzing detected fluorescence spectra to calculate a probability that the sample belongs in a particular classification. The data processor analyzes the detected fluorescence spectra using a multivariate statistical method. The five primary steps involved in the multivariate statistical method are (i) preprocessing of spectral data from each patient to account for inter-patient variation, (ii) partitioning of the preprocessed spectral data from all patients into calibration and prediction sets, (iii) dimension reduction of the preprocessed spectra in the calibration set using principal component analysis, (iv) selection of the diagnostically most useful principal components using a two-sided unpaired student's t-test and (v) development of an optimal classification scheme based on logistic discrimination using the diagnostically useful principal component scores of the calibration set as inputs.