Abstract: A computer-program product, a system, and a computer-implemented method include a processor(s) obtaining a configuration of a network including configurations of multiple network nodes and configurations of the network communication devices. The program code automatically models the network to generate a system model. The program code derives, from the system model, a loop-free Bayesian inference model, by generating a loop-free Bayesian network from the network.

Abstract: Examples are disclosed that relate to computing devices, head-mounted display (HMD) devices, and methods for displaying an image via a display device. In one example, a method for displaying an image via a display device comprises receiving an original frame of reference, determining a rotated device orientation of the display device, and modifying the original frame of reference to a rotated frame of reference based on the rotated device orientation. The method further comprises sampling data describing a virtual world at the rotated frame of reference to produce an intermediate image frame, generating a final image frame by back-rotating the intermediate image frame to the original frame of reference, and displaying the final image frame via the display device.

Abstract: Examples are disclosed that relate to culling of polygons for efficient rendering of a high-density polygon mesh using one or more compute shaders. Examples include monoscopic and stereoscopic rendering, foveated and non-foveated rendering, and selectively utilizing one or more computer shaders to rasterize very small triangles instead of using a regular rendering pipeline for increased performance.

Abstract: Examples are disclosed that relate to culling of polygons for efficient rendering of a high-density polygon mesh using one or more compute shaders. Examples include monoscopic and stereoscopic rendering, foveated and non-foveated rendering, and selectively utilizing one or more computer shaders to rasterize very small triangles instead of using a regular rendering pipeline for increased performance.

Abstract: Examples are disclosed that relate to computing devices, head-mounted display (HMD) devices, and methods for displaying an image via a display device. In one example, a method for displaying an image via a display device comprises receiving an original frame of reference, determining a rotated device orientation of the display device, and modifying the original frame of reference to a rotated frame of reference based on the rotated device orientation. The method further comprises sampling data describing a virtual world at the rotated frame of reference to produce an intermediate image frame, generating a final image frame by back-rotating the intermediate image frame to the original frame of reference, and displaying the final image frame via the display device.

Abstract: A signal separating device includes an iterative estimator, a repeating calculator, a result output unit, and a repetition controller. The repeating calculator repeatedly causes the iterative estimator to iteratively perform independent component analysis on an observed signal matrix, and to further perform independent component analysis on the source signal matrix obtained as a result. The result output unit outputs the product of the respective mixing matrices obtained during each repetition as a mixing matrix with respect to the observed signal matrix, while also outputting the source signal matrix obtained during the final repetition as a source signal matrix with respect to the observed signal matrix. The repetition controller causes the repeating calculator to repeat the calculation control until all mixing matrices and all source signal matrices satisfy a convergence condition. The iterative estimator may perform a fixed number of iterations, or perform iterations until convergence is obtained.

Abstract: A mobile body control device includes: a brain activity detecting unit that detects brain activity information of a user; a brain signal separating unit that separates an artifact component from the brain activity information detected by the brain activity detecting unit; a control signal generating unit that slides a sampling period for extracting brain data, at predetermined intervals in an overlapped manner in the brain activity information from which the artifact component is separated, successively calculates feature values for the brain data within each of the sampling periods obtained by sliding, and generates a control signal based on the feature values calculated; and a drive control unit that drives and controls a mobile body with a user riding thereon, based on the control signal generated.

Abstract: A signal separating device includes an iterative estimator, a repeating calculator, a result output unit, and a repetition controller. The repeating calculator repeatedly causes the iterative estimator to iteratively perform independent component analysis on an observed signal matrix, and to further perform independent component analysis on the source signal matrix obtained as a result. The result output unit outputs the product of the respective mixing matrices obtained during each repetition as a mixing matrix with respect to the observed signal matrix, while also outputting the source signal matrix obtained during the final repetition as a source signal matrix with respect to the observed signal matrix. The repetition controller causes the repeating calculator to repeat the calculation control until all mixing matrices and all source signal matrices satisfy a convergence condition. The iterative estimator may perform a fixed number of iterations, or perform iterations until convergence is obtained.

Abstract: In a multimedia information providing system, responding to a preview request from a terminal equipment, a server device generates a chaotic random number sequence from an initial value allocated to the terminal equipment, mixes this sequence into a set of multimedia information as a noise signal at a first mixing ratio, and then transmits the result as a first set of multimedia information for distribution. Responding to a quality improvement request, the server device generates a chaotic number sequence from the initial value allocated to the terminal equipment, mixes this sequence into a set of multimedia information as a noise signal at a second mixing ratio, and then transmits the result as a second set of multimedia information for distribution. The terminal equipment performs component analysis on the first and second sets of multimedia information, removing the noise and restoring a high-quality version.

Abstract: When M observed signals xi(k) are sequentially inputted into a noise removing part 12 via M channels 11a of an input part 11 in time series, processing is sequentially performed on the observed signals xi(k) by singular value decomposition units 12a of N stages cascaded to one another. Specifically, the singular value deposition unit 12a of each stage separates M input signals into a signal subspace and a noise subspace by a singular value decomposition and extracts M output signals, which are signals over a time region, by orthonormal projection of the M input signals onto the separated signal subspace.