Phase Spectra Analysis for Signal Recognition and Sequencing Applications

Phase Spectrum for Imaging Information Analysis This paper is aimed at developing phase spectrum methods and technology to analyze information for recognition static and dynamic images produced by serially organized data. These include voice, sound, serial business data, etc. Phase Spectrum Background Spectrum analysis plays a central role in the area of voice recognition and similar applications dealing with time dependent series. It takes in the time series and subjects it to a Fourier transform, either in its analytical form or in computer digitized form, using the Fast Fourier Transform (FFT). FFT discover information that could define sound's harmonics, their power spectra used in voice and other sources of sound analysis and recognition technology. Similarly, power spectra found applications in chemistry to discover chemical components, in earth science, communications, electronics, etc., [1]-[5]. Phase Spectrum Technology Phase spectrum is the other by-product of the FFT. It provides the relative position of the source data's components, their harmonics' phases as a function of frequency. Phase spectrum requires additional transformation into a readable and applicable characterization of the original information. This paper will address research of the phase spectrum methodology, phase spectrum attributes, and its measurable characterization. This paper presents phase spectra derived from FFT of various serial data that provides basis for development and application of phase spectrum technology, and discuss phase technology applications to various sequencing data, such as voice, sound, and image recognition. The phase spectrum for this serial data displays tense concentration at the sidebands and fast alternating divergence at the center frequencies. In contrast, the phase spectrum of the second set of serial data displays only moderate concentration at the side bands, and moderate variation at all other frequencies.