A method for creating a mother wavelet function. The method includes preparing a plurality of vectors, extracting a kernel from the plurality of vectors, and extracting the mother wavelet function from the kernel. The kernel includes a mode value of a vector of the plurality of vectors.

Disclosed are a nondestructive detection system and a method for an internal defect of a fruit. The system comprises an aluminum profile frame, a conveyor belt, a tray, a pulse type gas spray device, and a laser Doppler vibrometer; when a piece of fruit passes a detection station, the pulse type gas spray device excites the fruit to vibrate, and the laser Doppler vibrometer evaluates a vibration response signal of the fruit; a time-domain vibration characteristic parameter and a frequency-domain vibration characteristic parameter are acquired by means of a wavelet transform and a fast Fourier transform; and a prediction model for an internal defect of the fruit is established on the basis of the acquired time-domain and frequency-domain vibration characteristic parameters.

A method of authenticating security documents and a mobile device, especially a smartphone, programmed to carry out the method, based on an analysis of features which are produced by intaglio printing, which analysis involves a decomposition of sample images of a candidate document to be authenticated based on Wavelets, each sample image being digitally processed by performing a Wavelet transform of the sample image in order to derive a set of classification features. The method is based on an adaptive approach, which includes the following steps: prior to carrying out the Wavelet transform, defining a categorization map containing local information about different intaglio line structures that are found on the security documents; carrying out a Wavelet selection amongst a pool of Wavelet types based on the categorization map; and performing the Wavelet transform of the sample image on the basis of the selected Wavelet.

An apparatus and method for detecting a bio-signal feature are provided. The apparatus according to one aspect may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal.

A method of sensing including the steps of, (a) acquiring measurement values using a distributed optical fibre sensor; (b) arranging the measurement values in a matrix having at least two dimensions; (c) transforming each measurement value in the matrix to a corresponding pixel value on a predefined scale of pixel values and forming an image with pixels having the corresponding pixel values, wherein each pixel in the image is positioned at a position in the image corresponding to the position of the measurement value in the matrix; (d) processing the image using an image processing algorithm so as to reduce noise in the image to provide processed image; (e) transforming each pixel value of pixels in the processed image to values to provide measurement values with reduced noise.

Disclosed is a technique of determining a measure of proximity between two devices (4, 6). A method implementation of the technique comprises obtaining a first device signature comprising an indication of a first point in time and a first parameter characteristic of a first measurement performed by a first sensor (10) comprised in the first device (4); obtaining a second device signature comprising an indication of a second point in time and a second parameter characteristic of a second measurement performed by a second sensor (12) comprised in the second device (6); and determining, based on the first device signature and the second device signature, the measure of proximity between the first device (4) and the second device (6).

Method and apparatus for compressed sensing yields acceptable quality reconstructions of an object from reduced numbers of measurements. A component x of a signal or image is represented as a vector having m entries. Measurements y, comprising a vector with n entries, where n is less than m, are made. An approximate reconstruction of the m-vector x is made from y. Special measurement matrices allow measurements y=Ax+z, where y is the measured m-vector, x the desired n-vector and z an m-vector representing noise. A is an n by m matrix, i.e. an array with fewer rows than columns. A enables delivery of an approximate reconstruction, x.sup.#, of x. An embodiment discloses approximate reconstruction of x from the reduced-dimensionality measurement y. Given y, and the matrix A, approximate reconstruction x.sup.# of x is possible. This embodiment is driven by the goal of promoting the approximate sparsity of x.sup.#.

A system and method allow for extracting target signals from the overall measurement data without knowing how and where these data are collected, the locations and characteristics of target sources as well as those of random background noise sources. In essence, it uses an innovative and advanced signal processing technique to reveal certain critical information from a mixture of data that is hard to obtain otherwise. In particular, it allows for denoising the measured data that have been contaminated by various interfering and background noise, making it possible to extract certain target information that may be otherwise difficult to observe. The only assumption made in this method is that the target signal is incoherent with respect to all interfering signals and background noise. The more information about a target signal is available, the more complete the extracted signal becomes.

A GIS mechanical fault diagnosis method and the device are disclosed. The method includes: collecting vibration signals to be measured of various excitation sources of GIS in mechanical operation; performing wavelet packet-feature entropy vector extraction on the vibration signals to be measured, when it is determined that the vibration signals to be measured are abnormal according to standard vibration signals in the normal state; inputting the extracted wavelet packet-feature entropy vectors into the pre-trained BP neural network for GIS mechanical fault identification, and outputting the corresponding fault. The disclosure integrates the vibration signals under the action of various excitation sources, extracts the feature entropy vectors according to the entropy theory, and constructs and trains a BP neural network that can classify and recognize various GIS mechanical faults, so as to perform comprehensive and effective GIS mechanical faults diagnose.