A system processes an audio signal using spectrally orthogonal sound components. The system includes a circuitry that generates a mid component and a side component from a left channel and a right channel of the audio signal. The circuitry generates a hyper mid component including spectral energy of the side component removed from spectral energy of the mid component. The circuitry filters the hyper mid component, such as to provide spatial cue processing including panning or binaural processing, dynamic range processing, or other types of processing. The circuitry generates a left output channel and a right output channel using the filtered hyper mid component.

An apparatus includes a processor programmed to define an input matrix and kernel matrix based upon the encrypted data, identify an algebraic structure of an encryption method applied to the encrypted data, determine a primitive root of unity in the algebraic structure in response to an input matrix size and a kernel matrix size, transform the input matrix and kernel matrix utilizing the primitive root of unity into a transformed input matrix and a transformed kernel matrix, compute an element-wise multiplication of the transformed input matrix and transformed kernel matrix, apply a reverse discrete Fourier transformation, and output a convolution of the input matrix and the kernel matrix based upon the encrypted data.

An automotive radar using combinations of the techniques of alternating transmit-receive bursts of digitally frequency modulated millimeter wave carriers; sparse MIMO antenna arrays with sidelobe-suppressive coarse and fine beamforming; frequency hopping; range-walking-compensated Doppler analysis and successive, and subtractive target detection in signal strength order.

A co-processor is configured for performing vector matrix multiplication (VMM) to solve computational problems such as partial differential equations (PDEs). An analog Discrete Fourier Transform (DFT) can be implemented by invoking VMM of input signals with Fourier basis functions using analog crossbar arrays. Linear and non-linear PDEs can be solved by implementing spectral PDE solution methods as an alternative to massively discretized finite difference methods, while exploiting inherent parallelism realized through the crossbar arrays. The analog crossbar array can be implemented in CMOS and memristors or a hybrid solution including a combination of CMOS and memristors.

A system for determining the frequency coefficients of a one or multi-dimensional signal that is sparse in the frequency domain includes determining the locations of the non-zero frequency coefficients, and then determining values of the coefficients using the determined locations. If N is total number of frequency coefficients across the one or more dimension of the signal, and if R is an upper bound of the number of non-zero ones of these frequency coefficients, the systems requires up to (O (R log(R) (N))) samples and has a computation complexity of up to O (R log.sup.2(R) log (N). The system and the processing technique are stable to low-level noise and can exhibit only a small probability of failure. The frequency coefficients can be real and positive or they can be complex numbers.

The present invention is suitable for easily properly setting control parameters in short time. The simulation device of the present invention comprises: a frequency response function computing part (53) computing a frequency response function according to a first command value and a measured value of a mechanical system; an impulse response computing part (41) computing an impulse response by performing inverse Fourier transform on the frequency response function obtained according to the frequency response function and the control parameters; and a time response outputting part (44) executing time response simulation of the mechanical system (7) according to a second command value and the impulse response.

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).

Various embodiments of the present technology comprise a method and apparatus for an encoder. In various embodiments, the encoder is configured to perform offset and gain correction. The encoder includes a first correction circuit to perform offset and gain correction and a second correction circuit to perform additional offset and gain correction.

Methods, systems, computer-readable media, and apparatuses for determining one or more attributes of at least one target based on eigenspace analysis of radar signals are presented. In some embodiments, a subset of eigenvectors to use for forming a signal or noise subspace is identified based on principal component analysis. In some embodiments, the subset of eigenvectors is identified based on estimating the total number of targets using a discrete Fourier transform (DFT) or other spectral analysis technique. In some embodiments, a DFT is used to identify areas of interest in which to perform eigenspace analysis. In some embodiments, a DFT is used to estimate one attribute of a target, and eigenspace analysis is performed to estimate a different attribute of the target, with the results being combined to generate a multi-dimensional representation of a field of view.

Methods, apparatuses, decoders, and computer programs for replacing decoded parameters in a received multichannel signal are provided. Multichannel parameters of a frame of the signal are decoded. Responsive to a bad frame being indicated, it is determined that a parameter memory is corrupted. Responsive to a bad frame not being indicated: responsive to the parameter memory not being corrupted, a location measure is derived of a reconstructed sound source based on decoded multichannel parameters. Responsive to the parameter memory being corrupted, it is determined, based on the location measure, whether the reconstructed sound source is stable and predominantly concentrated in a subset of channels of multichannels of the received multichannel signal. Responsive to the reconstructed sound source being concentrated in the subset of channels of the multichannels and being stable, parameter recovery is activated to replace decoded multichannel parameters with stored multichannel parameters.