A terminal number estimating system includes: a plurality of receivers that receive a wireless signal transmitted from a plurality of terminal devices by frequency-division multiple access; a Fourier transform unit that performs a discrete Fourier transform on each wireless signal received by the plurality of reception means and outputs a discrete Fourier coefficient; and a terminal number estimation unit that estimates a position of each terminal device by clustering a resource block of each wireless signal based on each discrete Fourier coefficient.

In an approach, a process stores a matrix of multibit values for a computation in an analog multiply-accumulate unit including at least one crossbar array of binary analog memory cells connected between respective pairs of word- and bit-lines of the array, where: bits of each multibit value are stored in cells connected along a word-line, and corresponding bits of values in a column of the matrix are stored in cells connected along a bit-line. In each of one or more computation stages for a cryptographic element, the process supplies a set of polynomial coefficients of an element bitwise to respective word-lines of the unit to obtain analog accumulation signals on the respective bit-lines. The process converts the analog signals to digital. The process processes the digital signals obtained from successive bits of the polynomial coefficients in each of the stages to obtain a computation result for the cryptographic element.

A computer-implemented method of Condition Monitoring (CM) for rotating machines like motors, a corresponding computer program, computer-readable medium and data processing system for CM for rotating machines as well as a system including the data processing system for CM for rotating machines. M accumulator variables are updated in real-time based on L samples including a current sample sn and at least one preceding sample Sn−1 of input data. Based on the M accumulator variables N spectral features are computed in real-time. A condition of the rotating machine is determined based on the N spectral features.

A communication system includes a communication apparatus and a base station. The communication apparatus includes a Discrete Fourier Transform (DFT) transformer which transforms a time-domain signal into a frequency-domain signal with a DFT size that is a product of powers of a plurality of values; a mapper which maps the frequency-domain signal on a plurality of frequency bands, each frequency band being located at a position separate from position(s) of other(s) of the plurality of frequency bands; and a signal generator which generates a single carrier-frequency division multiple access (SC-FDMA) time-domain signal from the mapped signal. The base station includes a receiver which receives the SC-FDMA time-domain signal; a combiner which generates the frequency-domain signal from the SC-FDMA time-domain signal; and a transformer which transforms the frequency-domain signal into the time-domain signal with an inverse Discrete Fourier Transform (IDFT) having the DFT size.

Provided is a fast Fourier transform device for analyzing specific frequency components of an input signal. The fast Fourier transform device includes an address generator that generates an address, based on a first frequency index corresponding to a first frequency, an FFT coefficient table that outputs a first Fourier transform coefficient corresponding to the generated address among Fourier transform coefficients of the first frequency index, and an operator that calculates a frequency characteristic of an input signal associated with the first frequency, based on the input signal and the first Fourier transform coefficient.

Transformer systems and methods of using such transformer systems including computer programs encoded on a computer storage medium, for performing a deep learning task on an input sequence to generate an encoded output. In one aspect, one of the transformer systems includes an encoder architecture block, comprising: a spectral transform mixing layer that receives input embeddings of input tokens and generates, as output, a spectral transform output along a sequence dimension of the input embeddings; and a feed forward layer that receives an input based on the input embeddings of input tokens and the spectral transform output and generates an output for a subsequent processing block.

An information processing device for processing a signal with a neural network includes: a Fourier transform layer for performing Fourier transform on an input signal and outputting a first amplitude signal and a first phase signal; an amplitude connection layer for multiplying the first amplitude signal by a first weight matrix for which a value is to be updated through training, and outputting a second amplitude signal; a phase connection layer for multiplying the first phase signal by a second weight matrix for which a value is to be updated through training, and outputting a second phase signal; a complex activation layer for updating at least the second amplitude signal, of the second amplitude signal and the second phase signal, using a complex activation function f in a spatial frequency domain; and an inverse Fourier transform layer for combining the updated signals and performing inverse Fourier transform thereon.

The present invention is related to Discrete Fourier Transform (DFT) Twiddle Factors (TFs) generation algorithms. It presents New Projection Algorithms (NPAs) to generate any required TF of the DFT matrix by providing only a small stored portion of the TFs that are stored in a Memory Storage (MS). The present invention presents how to exploit the existence of symmetries and the existence of similarities among the TFs of the DFT matrix to construct a methodology of operation to be able to formulate the NPAs. The use of the NPAs will avoid the need of retrieving that required TF from pre-saved lookup tables of the DFT matrix and also will avoid the need to calculate it with slow complicated algorithms like CORDIC as used by prior arts.

A non-volatile memory device is configured for in-memory computation of discrete Fourier transformations and their inverses. The real and imaginary components of the twiddle factors are stored as conductance values of memory cells in non-volatile memory arrays having a cross-point structure. The real and imaginary components of inputs are encoded as word line voltages applied to the arrays. Positive and negative valued components of the twiddle factors are stored separately and positive and negative of the inputs are separately applied to the arrays. Real and imaginary parts of the outputs for the discrete Fourier transformation are determined from combinations of the output currents from the arrays.

A mechanism for performing a discrete Fourier-related transform using a hardware accelerator that comprises fixed-function circuitry including convolution hardware configured to perform one or more convolution operations. A matrix multiplication operation used in the discrete Fourier-related transform is performed by the convolution hardware using a convolution operation. A convolution kernel for the convolution operation is derived from a weight matrix representing a multiplicand or multiplier of the matrix multiplication operation.