A system and method for representing quasi-periodic electrocardiography waveforms, wherein system employs a hybrid-coding scheme combining linear predictive coding techniques based upon Algebraic Code Excited Linear Prediction with a discrete wavelet transforms.

The invention relates to methods, devices, systems and computer program products where input image data is received for processing in a computer system, a first transform is carried out to the input image data, the transform being done in a first direction with respect to the input image data, to obtain first transformed image data, a transposing operation is carried out to the first transformed image data to obtain first transposed image data, and a second transform is carried out to the first transposed image data, the second transform being done in a second direction with respect to the input image data, to obtain second transformed image data. The first transform and the second transform may be carried out in a processor in a parallel manner, and the transposing operation may be carried out at least partially using registers of the processor.

A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

A method and device for reducing the computational complexity of a processing algorithm, of a discrete signal, in particular of the spectral estimation and analysis of bio-signals, with minimum or no quality loss, which comprises steps of (a) choosing a domain, such that transforming the signal to the chosen domain results to an approximately sparse representation, wherein at least part of the output data vector has zero or low magnitude elements; (b) converting the original signal in the domain chosen in step (a) through a mathematical transform consisting of arithmetic operations resulting in a vector of output data; (c) reformulating the processing algorithm of the original signal in the original domain into a modified algorithm consisting of equivalent arithmetic operations in the domain chosen in step (a) to yield the expected result with the expected quality quantified in terms of a suitable application metric; (d) combining the mathematical transform of step (b) and the equivalent mathematical operations introduced in step (c) for obtaining the expected result within the original domain with the expected quality; (e) selecting a threshold value based on the difference in the mean magnitude value of the elements of the output data vector of the transform said in step (b) and the preferred complexity reduction and degree of output quality loss that can be tolerated in the expected result within the target application; (f) pruning a number of elements the magnitude of which is less than the threshold value selected in step (e); and/or eliminating arithmetic operations associated with the pruned elements of step (f) either in the mathematical transform of step (b) and/or in the equivalent algorithm of step (c).

A realtime evaluation method and system for a virtual reality (VR) immersion effect are provided. An electroencephalogram signal is collected while a VR video is played, a degree of emotional arousal and a degree of cognitive absorption are calculated in real time based on energy of frequency bands α, β, and θ that is obtained after wavelet transform, and finally, an immersion effect index is dynamically monitored for objective evaluation on an immersion effect. The method and the system realize realtime measurement and analysis, dynamically monitors a VR video immersion effect, adopts a multi-dimensional comprehensive calculation strategy and takes individual differences into account when calculating an index, effectively resolves problems such as after-fact reporting, social desirability biases, and strong subjectivity in questionnaire measurement and other measurement means, and has a broad market application prospect.

A network of motion sensors employs sensitive accelerometers to issue time-domain measurements of building movement from multiple locations within and between buildings and other structures. The time-domain measurements from the various motion sensors are synchronized and converted into frequency-domain measurements of building movement. Individual motion sensors can be equipped with the requisite processor and memory to synchronize and covert the time-domain measurements. The motions sensors can classify detected events into various event types, such as earthquakes, wind events, or bipedal locomotion. The sensors can also communicate with one another or other resources to calculate event probabilities. A motion sensor may, for example, receive an earthquake-verification signal responsive to an earthquake-verification request. The network of motion sensors can calculate local soil stiffness and financial loss estimations responsive to their individual or collective frequency-domain measurements.

This application discloses example image generation methods. One example method includes obtaining a target vector. The target vector can then be separately input to a first generator and a second generator to correspondingly generate a first sub-image and a second sub-image, where the first generator is obtained by a server by training, based on a low-frequency image and a first random noise variable that satisfies normal distribution, a first generative adversarial network (GAN), the second generator is obtained by the server by training, based on a high-frequency image and a second random noise variable that satisfies the normal distribution, a second GAN, and a frequency of the low-frequency image is lower than a frequency of the high-frequency image. The first sub-image and the second sub-image can then be synthesized to obtain a target image.

A network of motion sensors employs sensitive accelerometers to issue time-domain measurements of building movement from multiple locations within and between buildings and other structures. The time-domain measurements from the various motion sensors are synchronized and converted into frequency-domain measurements of building movement. Individual motion sensors can be equipped with the requisite processor and memory to synchronize and covert the time-domain measurements. The motions sensors can classify detected events into various event types, such as earthquakes, wind events, or bipedal locomotion. The sensors can also communicate with one another or other resources to calculate event probabilities. A motion sensor may, for example, receive an earthquake-verification signal responsive to an earthquake-verification request. The network of motion sensors can calculate local soil stiffness and financial loss estimations responsive to their individual or collective frequency-domain measurements.

Methods, systems and programs for denoising a signal using an undecimated discrete wavelet transformation are provided. The methods use signal location windows. The signal location windows may be used before or after thresholding. When signal location windows are used after thresholding coefficients within the signal location windows may be restored to their original values. When signal location windows are used before thresholding, coefficients within the signal location windows may be unchanged by the thresholding. The signal location windows may be used only on a subset of decomposition levels that are thresholded. The signal location windows may be used on both the Detail components and the highest decomposition level for thresholding of the Approximation component.

Systems and methods are described for performing dynamic time warping using diffusion wavelets. Embodiments of the inventive concept integrate dynamic time warping with multi-scale manifold learning methods. Certain embodiments also include warping on mixed manifolds (WAMM) and curve wrapping. The described techniques enable an improved data analytics application to align high dimensional ordered sequences such as time-series data. In one example, a first embedding of a first ordered sequence of data and a second embedding of a second ordered sequence of data may be computed based on generated diffusion wavelet basis vectors. Alignment data may then be generated for the first ordered sequence of data and the second ordered sequence of data by performing dynamic time warping.