A method for calibrating the position and/or orientation of a camera, in particular a camera mounted to a vehicle such as a truck, relative to a calibration pattern includes the steps of: A] acquiring an image of the calibration pattern by means of the camera; B] determining at least one parameter of the image and/or of the calibration pattern or a sub-pattern of the calibration pattern as it appears in the image; C] transforming the image based on the at least one parameter; D] identifying characteristic points or possible characteristic points of the calibration pattern within the transformed image of the calibration pattern; E] deriving the position and/or orientation of the camera relative to the calibration pattern from the identified characteristic points or possible characteristic points; F] in dependence of a confidence value of the derived position and/or orientation of the camera and/or in dependence of the number of iterations of steps B to F so far, repeating steps B to F, wherein in step B the derived position and/or orientation of the camera are taken into account for determining the at least one parameter; and G. outputting the position and/or orientation of the camera derived in the last iteration of step E.

In some embodiments, a style transfer computing system receives, from a computing device, an input text and a request to transfer the input text to a target style combination including a set of target styles. The system applies a style transfer language model associated with the target style combination to the input text to generate a transferred text in the target style combination. The style transfer language model comprises a cascaded language model configured to generate the transferred text. The cascaded language model is trained using a set of discriminator models corresponding to the set of target styles. The system provides, to the computing device, the transferred text.

A motion processing method and apparatus are provided. The motion processing method includes obtaining a base-level motion by applying a linear Gaussian model to an input motion, obtaining a controllable motion displacement vector and a residual motion displacement vector by applying the linear Gaussian model to a displacement vector between the input motion and the base-level motion, and synthesizing an output motion based on the base-level motion and the controllable motion displacement vector.

A detection system 1 includes a control device 10 and a monitoring device 20 communicably connected to the control device 10. An acquisition unit 10A of the control device 10 acquires a target’s observation value by a sensor 30. A first-noise-output unit 10B outputs a first-noise-value changing with time and less than a resolution of the sensor 30. An integration unit 10C outputs an integrated value obtained by integrating the first-noise-value and the observation value. A transmission unit 10D transmits the integrated value to the monitoring device 20. A separation unit 20A of the monitoring device 20 separates the integrated value from the control device 10 into the observation value and the first-noise-value. A second-noise-output unit 20B outputs a second-noise-value as the first-noise-value. A detection unit 20C detects whether the integrated value is a replay attack using the spatial distance between the first-noise-value and the second-noise-value.

An information handling system may include at least one processor, and a non-transitory memory communicatively coupled to the at least one processor. The information handling system may be configured to: receive a data stream of data points indicative of a parameter of a monitored system; determine local maxima and minima based on the data stream; determine relative amplitudes of the local maxima and minima based on an absolute value of differences between consecutive ones of the local maxima and minima; partition the relative amplitudes into a plurality of clusters; and determine at least one of the plurality of clusters as at least one noise cluster.

In some embodiments, a style transfer computing system generates a set of discriminator models corresponding to a set of styles based on a set of training datasets labeled for respective styles. The style transfer computing system further generates a style transfer language model for a target style combination that includes multiple target styles from the set of styles. The style transfer language model includes a cascaded language model and multiple discriminator models selected from the set of discriminator models. The style transfer computing system trains the style transfer language model to minimize a loss function containing a loss term for the cascaded language model and multiple loss terms for the multiple discriminator models. For a source sentence and a given target style combination, the style transfer computing system applies the style transfer language model on the source sentence to generate a target sentence in the given target style combination.

The present disclosure provides a fingerprint detection device and method. The fingerprint detection device includes a detection substrate and a signal converter. The detection substrate includes pixels arranged in rows and columns. Each pixel includes a sensing circuit configured to receive an optical signal and output a sensing electrical signal according to the received optical signal. The signal converter includes A/D converters each coupled to one column of sensing circuits. The fingerprint detection device further includes a control circuit coupled to the sensing circuits and the A/D converters and configured to obtain common mode component of sensing electrical signals output by sensing circuits of at least part of the pixels and provide information about the common mode component to the A/D converters. The A/D converter is configured to perform analog-to-digital conversion on difference between the sensing electrical signal from corresponding sensing circuit and the common mode signal.

A method to detect the presence and location of submarines in a complex marine environment by wavelet denoising, wavelet signal enhancement, by autocorrelation and signal source identification a convolutional neural network.

A method for extracting a sought signal from a noisy signal. The method includes sampling a plurality of samples in a series of cycles of the noisy signal wherein each sample having an n-bit sampled value (n≥1), giving rise to a plurality of samples each associated with a respective cycle of the series, wherein each sample is sampled at time T relative to the origin of the respective cycle. The method further includes associating data indicative of the plurality of n-bit samples to N bins according to the corresponding sampled values, wherein N is a function of n, and calculating data indicative of a number of samples for each bin, giving rise to data indicative N-bins histogram or normalized N-bins histogram. The method further includes determining the signal value based on the data indicative of the N-bins histogram or normalized N-bins histogram.

A method for determining noise associated with a received signal in a telecommunications network, the received signal being sampled beforehand in the form of a succession of data. The method includes: selecting a portion of the data in the received signal and determining a given number of possible partitions of the selected portion of the data in the received signal, the number of partitions being greater than or equal to two; partitioning all of the data in the received signal into the number of partitions; estimating mean energies of the signal in each of the partitions and identifying a partition from among the partitions having a minimum mean, the identified partition being a noise partition; and estimating a variance of the noise partition, the noise associated with the received signal being a function of the variance of the noise partition.