A fast Fourier transform device comprises: a first transform means including a first butterfly computation processing means that performs butterfly computation processing and outputs a plurality of sets of first output data in a first order; and a first data rearrangement processing means. The first butterfly computation processing means includes a plurality of radix-n butterfly computation processing means (where n is a multiple of 2), the number of the plurality of radix-n butterfly computation processing means being more than or equal to the number of the plurality of sets, and the plurality of sets of the first output data are output in the first order from the plurality of radix-n butterfly computation processing means.

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.

A radar system configured to determine radar-ground distance measurements. The radar system includes transmission and reception means configured to transmit two radiofrequency signals towards the ground and to receive the signals obtained by the reflection of the two transmitted signals by the ground and computation means configured to determine the frequential representations of the transmitted signals and of the received signals and determine a frequential quantity as a function of the frequential representations. The radar system is wherein the computation means are configured to sample the frequential quantity over a determined number of samples, which provides a sampled signal; determine a number of frequency measurements as a function of a constant distance measurement accuracy value; determine frequency measurements by applying to the sampled signal a spectral decomposition by fast Fourier transform using a decimation of the sampled signal in a ratio dependent on the distance measurement accuracy value, and determine a distance measurement corresponding to each frequency measurement.

A signal processing apparatus comprises an operation processing part that performs operation processing on data represented in the two's complement representation and a storage processing part that performs storage processing on data represented in a second representation format as a data representation format, and in the second representation format, a data value is identical to one in the two's complement representation when the value is positive or zero, and all the bits lower than the most significant bit that indicates the sign in the two's complement representation are inverted when a data value is negative.

Aspects of the subject disclosure may include, for example, selecting a set of traffic counters, wherein the traffic counters provide a profile of viewing habits of a user, and wherein the traffic counters are extracted from video streaming by the user; predicting a size of a video buffer based on the traffic counters selected; and building the video buffer based on the predicted size. Other embodiments are disclosed.

A seizure detection system comprises a sensor and a control system. The sensor is configured to detect acceleration data associated with a patient’s movements. The control system is programmed to monitor, via the sensor, the acceleration data to detect a seizure precursor event. The control system is further programmed to, upon detection of the seizure precursor event, perform a frequency analysis of the acceleration data to identify a frequency of the patient’s movements. The control system is further programmed to determine that the patient is experiencing a seizure event based on the frequency of the patient’s movements.

A method for downhole measurement operations. The method may include taking one or more measurements with a sensor disposed in a bottom hole assembly, converting the one or more measurements into one or more revolutions-per-minute (RPM) measurements, identifying one or more frequency components of the one or more RPM measurements using a Fast Fourier Transform, identifying one or more peaks of the one or more frequency components, and identifying torsional oscillation based at least in part on the one or more peaks. The method may be performed on a non-transitory computer-readable tangible medium comprising executable instructions that cause a computer device to take one or more signal measurements, identify one or more frequency components of the one or more signal measurements using a Fast Fourier Transform, and identify one or more peaks of the one or more frequency components.

It is provided a wearable device for determining when a user has fallen down. The wearable device comprises: a first biometric sensor for obtaining first biometric data of the user, wherein the first biometric sensor is a first accelerometer configured to measure acceleration of a part of a first limb of the user; a second biometric sensor for obtaining second biometric data of the user comprising a finger pressure parameter; and a third biometric sensor for obtaining third biometric data, the third biometric sensor being a second accelerometer configured to measure acceleration of a body part of the user being distinct from the first limb. The wearable device is configured to determine an identity of the user is based on the first biometric data, the second biometric data and the third biometric data, the identity being used to control access to a physical space, and to determine when the user has fallen down.

A aircraft health management system for identifying an anomalous signal from one or more air data systems (ADS) includes one or more of a frequency processor, configured to provide a spectral signal that is representative of a frequency content of the first ADS signal, a noise processor, configured to provide a noise signal that is representative of a noise level of the first ADS signal, and a rate processor, configured to provide a rate signal that is representative of a rate of change of the first ADS signal. The aircraft health management system also includes a comparator configured to provide a differential signal between the first ADS signal and the second ADS signal, and a prognostic processor configured to determine if the ADS signal is anomalous by comparing values representative of a flight condition signal, the differential signal, and the spectral, noise, and/or rate signals.

A system for data driven diagnostics of a machine including a machine learning model instantiated in a computer, the machine learning model being configured to: receive operational data of the machine; and process the operational data to determine machine diagnostics information. The machine learning model is trained using simulated defect information received from a simulation environment.