664 results about "Re sampling" patented technology

A method and Electro-Optical (EO) system for processing imagery comprising selecting a first frame of data as a template frame; capturing a second frame of data using the EO system, for a plurality of pixels of the second frame, correlating the plurality of pixels of the second frame with pixels of the template frame to generate a plurality of shift vectors, one for each pixel of the plurality of pixels of the second frame, registering the second frame with the template frame by interpolating the second frame using the plurality of shift vectors and re-sampling at least a portion of the second frame to produce a registered frame, re-sampling the template frame, and combining the re-sampled template frame and the registered frame to generate an averaged frame.

A method, system and apparatus for real-time reservoir model updating using ensemble Kalman filters is described. The method includes a conforming step for bring bringing static and dynamic state variables into conformance with one another during a time step of the updating. Also, an iterative damping method is used in conjunction with the conformance step to account for nonGaussian and nonlinear features in a system. Also, a re-sampling method is described which reduces the ensemble size of reservoir models which are to be updated.

A method and apparatus are provided for identifying diffe rences between a stored pattern and a matching image subset, where variations in pattern position, orientation, and size do not give rise to false differences. The invention is also a system for analyzing an object image with respect to a model pattern so as to detect flaws in the object image. The system includes extracting pattern features from the model pattern; generating a vector-valued function using the pattern features to provide a pattern field; extracting image features from the object image; evaluating each image feature, using the pattern field and an n-dimensional transformation that associates image features with pattern features, so as to determine at least one associated feature characteristic; and using at least one feature characteristic to identify at least one flaw in the object image. The invention can find at least two distinct kinds of flaws: missing features, and extra features. The invention provides pattern inspection that is faster and more accurate than any known prior art method by using a stored pattern that represents an ideal example of the object to be found and inspected, and that can be translated, rotated, and scaled to arbitrary precision much faster than digital image re-sampling, and without pixel grid quantization errors. Furthermore, since the invention does not use digital image re-sampling, there are no pixel quantization errors to cause false differences between the pattern and image that can limit inspection performance.

A device for detecting a demodulated signal received by a spread spectrum receiver and converted into digital samples. The device is characterized by a matched filter for calculating the correlation between an incoming signal and at least one reference signal, an oscillator for generating a sampling frequency, and a sampling circuit for re-sampling the demodulated digital sample signal at the sampling frequency, which is such that the timing of samples of the references signals of the matched filter corresponds to the timing of a sample signal going from the sampling circuit to the matched filter. The device also includes a multiplier in which the sample signal is multiplied by a carrier replica generated locally before the sampling circuit or thereafter, to remove the carrier from the sample signal.

A full color display system comprised of: a) a display which is formed from a two-dimensional array of three or more differently colored light-emitting elements arranged in a repeating pattern forming a first number of full-color two-dimensional groups of light-emitting elements, each full-color group of light-emitting elements being formed by more than one luma-chroma sub-group of light-emitting elements, wherein the display has a peak white luminance and each luma-chroma sub-group comprising at least one distinct high-luminance light-emitting element having a peak output luminance value that is 40 percent or greater of the peak white luminance of the display device; and b) a processor for providing a signal to drive the display by receiving a three-or-more color input image signal, which specifies three-or-more color image values at each of a two-dimensional number of sampled addressable spatial locations within an image to be displayed; wherein the processor dynamically forms re-sampling functions for image spatial locations derived from the input image signal and corresponding to the spatial location of each luma-chroma sub-group in the display array based on an analysis of the spatial content of the three-or-more color input image signal and the display array repeating pattern, and applying the re-sampling functions to the three-or-more color input image signal to render a signal for driving each light-emitting element within each corresponding luma-chroma sub-group of light-emitting elements.

The invention discloses a method for autonomously localizing robots on the basis of laser radar. The method includes randomly generating N particles to form particle swarms around initial locations ofthe robots, and updating the particle swarms according to robot real-time movement distances and real-time rotation angles measured by sensors of the robots at current operation moments of the robots; computing the superposition quantity of point cloud of the laser radar and obstacles of maps for each particle to use the superposition quantity as a score of the particle, computing weighted position and posture average values of the particle swarms by the aid of the score, which is used as a weight, of each particle and utilizing the weighted position and posture average values as AMCL (adaptive Monte Carlo localization) estimation positions and posture; utilizing the AMCL estimation positions and posture as initial values, acquiring scanned and matched positions and posture by the aid ofscanning and matching algorithms on the basis of Gauss-Newton iterative processes and utilizing the scanned and matched positions and posture as the optimal positions and posture of the robots at thecurrent operation moments; re-sampling the particle swarms by the aid of AMCL algorithms to ultimately obtain the global optimal positions and posture of the robots during operation. The global optimal positions and posture of the robots are used as localization results. The method has the advantage that the localization convergence rate can be greatly increased, and the localization precision andthe localization stability can be greatly enhanced.

A method and apparatus are provided for rapidly refining a given approximate location of a pattern to produce a more accurate location. The invention employs a multi-dimensional space that includes translation, orientation, and scale. The invention can serve as a replacement for the fine resolution phase of any coarse-fine system for pattern location. Patterns and images are represented by a feature-based description that can be translated, rotated, and scaled to arbitrary precision much faster than digital image re-sampling, and without pixel grid quantization errors. Thus, accuracy is not limited by the ability of a grid to represent small changes in position, orientation, or size (or other degrees of freedom). The invention determines an accurate object pose from an approximate starting pose in a small, fixed number of increments that is independent of the number of dimensions of the space, and independent of the distance between the starting and final poses, provided that the starting pose is within the “capture range” of the true pose. Thus, accuracy need not be sacrificed to keep execution time acceptable for practical applications. Specifying locations in four or more dimensions will often result in better matches between the pattern and image than two-dimensional location systems, thereby improving accuracy. Accuracy is not degraded if some portion of the object is missing or occluded, or if unexpected extra features are present.

The invention relates to a target localization and tracking system and a method. The system comprises a plurality of cluster localization modules and command control modules, each cluster localizationmodule comprises a plurality of sensor nodes and cluster head nodes, and each cluster head node comprises an initialization module, a particle filtration module, a particle weight calculation module,a re-sampling determination module and an estimation target state module. The method which combines Kalman and particle filtration is adopted for realizing the passive localization of a target, and the computation speed is far lower than the particle filtration method on the basis of realizing high-precision localization; the localization and the track of the target is finally realized by adopting multiple sensors and being based on the observation of the direction of the target, thereby being capable of overcoming the constraint that the traditional machine-mounted or ship-mounted single station system must carry out motorization during the observation period, having no need of observing the motorization of a platform, improving the flexibility of target localization, greatly increasingthe area of a region of target monitoring localization, avoiding the shortcoming of existence of localization blind regions and having very high effectiveness, accuracy and feasibility.

The subject matter discloses a method and apparatus for re-sampling a sequence of images in order to improve its resolution, fill-in missing pixels, or de-interlace it. The method operates locally in the images to be processed, comparing pixel values of pixels surrounding the target pixel to pixel values of substantially the same locations in neighboring images. The comparison results in assigning a weight value for each area compared with the area containing the reviewed pixel. The pixel value of the reviewed pixel is updated as a function of multiplying pixel values of the areas by the weight assigned to each area. In another embodiment, areas within the same image are compared to areas containing the reviewed pixel. The subject matter also discloses two possible penalty functions for improving the resolution of images.

The present invention discloses a laser and vision-based hybrid location method for a mobile robot. The mobile robot comprises a laser radar and a vision sensor. According to the technical scheme of the invention, the weight of each particle at a predicted position is updated based on the collected data of the laser radar and the collected data of the vision sensor. After that, particles of higher weights are re-sampled, so that the real location distribution of the mobile robot at the moment t can be obtained. Compared with the prior art, the above technical scheme integrates the high accuracy of the laser radar with the information integrity of the vision sensor, thus being wider in application range. Meanwhile, the defect that the visual location is unstable is overcome. In addition, in one embodiment of the present invention, a conventional particle filtering sampling model is improved, so that the diversity of particles is ensured.

The invention discloses a wind turbine generator bearing fault diagnosis method under the variable speed. According to the method, a rotation angle change curve is drafted according to the bearing rotation speed; equal angle division for the rotation angle change curve is carried out, and an equal angle re-sampling time sequence is determined; interpolation for a bearing vibration signal is carried out according to the time sequence, a random Gauss white noise sequence is added to an angle domain vibration signal, and the signal added with the white noise is processed by utilizing an empirical mode decomposition (EMD) algorithm to acquire multiple sets of IMF; a kurtosis value of each IMF component is calculated; the IMF with the largest kurtosis value is selected and taken as a sensitive IMF; Hilbert envelope demodulation for the sensitive IMF is carried out to obtain an envelope signal, the envelope signal is processed by utilizing Fourier transform to obtain order envelope spectrum of the sensitive IMF, the fault characteristic frequency is extracted, and fault diagnosis on the wind turbine generator bearing is realized. The method is advantaged in that influence of rotation speed change on vibration signal analysis can be eliminated, and accuracy and validity of fault diagnosis can be improved.

A method of and system for reducing complexity for transcoding Advanced Video Coding (AVC) videos is described herein. Transcoding from higher resolution signals to lower resolution signals or to signals for a lower resolution display is implemented. The complexity is reduced by decoding the AVC video at reduced horizontal and/or vertical resolution. This results in the reduction of computation cost for decoding and re-sampling the AVC video to lower resolution.

The invention discloses a particle filtering method based on a Gaussian mixture model and variational Bayes, comprising the following steps: (1) modeling observation noise using a Gaussian mixture model, and initializing the initial state; (2) randomly generating N initial particles based on the probability density function of the initial state; (3) initializing the super parameters of the unknown parameters in the Gaussian mixture model of observation noise; (4) generating sampling particles from a chosen importance reference function; (5) updating measurement, and calculating the particle weights according to the latest observation value and a particle weight iteration formula; (7) using a variational Bayesian method to get the distribution of the unknown parameters in the Gaussian mixture model by means of loop iteration; and (7) normalizing the particle weights, and re-sampling a particle set in view of particle degradation. Through the scheme, the filtering precision and the target state estimation performance are improved effectively.

An analysis and synthesis system for sound is provided that can independently modify characteristics of audio signals such as pitch, duration, and timbre. High-quality pitch-scaling and time-scaling are achieved by using a technique for sinusoidal phase compensation adapted to a sinusoidal representation. Such signal modification systems can avoid the usual problems associated with interpolation-based re-sampling so that the pitch-scaling factor and the time-scaling factor can be varied independently, arbitrarily, and continuously. In the context of voice modification, the sinusoidal representation provides a means with which to separate the acoustic contributions of the vocal excitation and the vocal tract, which can enable independent timbre modification of the voice by altering only the vocal tract contributions. The system can be applied to efficiently encode the pitch in sinusoidal models by compensating for pitch quantization errors. The system can also be applied to non-speech signals such as music.

A method for estimating vehicle battery parameters that uses two different sampling rates. The method samples a battery terminal voltage and current at a high sampling rate to estimate the battery open circuit voltage and high frequency resistance. The battery state of charge (SOC) is derived from the open circuit voltage. Next, the battery terminal voltage and current are re-sampled at a low sampling rate. Other battery parameters can be extracted from the low-rate sampled signals. Next, all of the battery parameters that were obtained from the two sampling rates are used together to predict battery power.

The invention discloses a network advertisement effect estimating method, which comprises the following steps of: classifying original data according to an impact factor of an advertisement effect; extracting an impact sub-factor of the advertisement effect from the impact factor of the advertisement effect, and establishing at least one estimating model by adopting regression analysis and a re-sampling algorithm (Bagging); selecting an optimal estimating model from the estimating models by a tenfold crossed validation method; and estimating the advertisement effect within a next prediction period by utilizing the optimal estimating model so as to obtain an advertisement estimated effect. By the application of an embodiment of the invention, the high-precision prediction of an advertisement delivery effect can be performed, and the estimated effect is diversified.

The invention relates to a multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification. Based on point set characteristics, high-precision classification of four kinds of common ground features including pedestrians, trees, buildings and automobiles and the like in a scene is realized. Firstly, point sets are constructed and a point cloud is re-sampled into a point cloud of different scales and thus point sets which are different in size and provided with layered structures are formed through clustering and characteristics of each point in the point sets are obtained; next, an LDA (Latent Dirichlet Allocation ) method is adopted to synthesizing point-based characteristics of all points in each point set into shape characteristics of the point sets; and at last, based on the shape characteristics of the point set, an Adaboost classifier is adopted to train the point sets of different levels so as to obtain a classification result of the whole point cloud. The multi-level-point-set characteristic extraction method has a higher classification precision and has a classification precision, which is far higher than that of point-based characteristics, Bag-of-Word-based characteristics and characteristics based on probabilistic latent semantic analysis (PLSA), in aspect of pedestrians and vehicles.

A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and/or enhanced parameter information.

An asynchronous sample rate converter interpolates and filters a digital audio input signal to produce a filtered, up-sampled first signal. A FIFO memory receives the first signal and stores samples thereof at locations determined by a write address and presents stored samples from locations determined by a read address. The presented samples are passed through an interpolation and resampling circuit to produce a continuous-time signal which is re-sampled to produce a signal that is up-sampled relative to a desired output. That signal then is filtered and down-sampled to produce the output signal. Sample rate estimating circuitry computes a difference signal representative of a time at which a data sample of the audio input signal is received and a time at which a corresponding audio output sample is required, and address generation circuitry generates the read and write addresses. A coefficient calculation circuit calculates filter coefficients for the interpolation and resampling circuit in response to the difference signal.

Methods, systems and mediums are disclosed for aligning mass spectrometry data before the analysis of the mass spectrometry data. The mass spectrometry data may be received from a mass spectrometry machine, and re-sampled using a smooth warping function. To estimate the warping function, a synthetic signal is build using, for example, Gaussian pulses centered at a set of reference peaks. The reference peaks may be designated by users or calculated after observing a group of spectrograms. The synthetic signal is shifted and scaled so that the cross-correlation between the mass spectrometry data and the synthetic signal reaches its maximum value.

A probabilistic digital signal processor for medical function is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and/or a blood pressure.

A camera with dynamic calibration and a method thereof is provided. The camera is first subject to an initial calibration. Then, a motion amount of the camera is calculated, and a plurality of motion amount estimation samples of the camera is generated according to the motion amount. Then, a weight of each of the motion amount estimation samples is calculated. Thereafter, the plurality of motion amount estimation samples is re-sampled based on the weights, and the camera is calibrated by the re-sampled estimated motion samples.

The embodiments of the invention provide a fault diagnosis method and apparatus for a rolling bearing. The fault diagnosis method for the rolling bearing comprises the following steps: acquiring vibration signals of the rolling bearing, and solving a time-frequency spectrum of the vibration signals; according to the time-frequency spectrum, determining an optimal order component of the vibration signals; according to the optimal order component, performing angle re-sampling on the vibration signals to obtain angle domain signals; and performing envelope analyzing on the angle domain signals, and determining fault positions of the rolling bearing, wherein determination of the optimal order component comprises: setting an equivalent frequency conversion scope and searching for all order components from the time-frequency spectrum; and calculating order energy of the order components and determining the optimal order component from the equivalent frequency conversion scope. The fault diagnosis method and apparatus for the rolling bearing realize automatic and rapid determination of the optimal order component so as to provide a basis for fault diagnosis of the rolling bearing, improve the fault diagnosis accuracy and avoid interference of manual factors.