Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute a received instruction; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In another embodiment, the core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, to reserve a predetermined amount of memory space in a thread control memory to store return arguments, and to generate one or more work descriptor data packets to another processor or hybrid threading fabric circuit for execution of a corresponding plurality of execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

System and methods for Light Detecting and Ranging (LIDAR) are disclosed. The LIDAR system includes a light source that is configured project a beam at various wavelengths toward a wavelength dispersive element. The wavelength dispersive element is configured to receive the beam and direct at least a portion of the beam into a field of view (FOV) at an angle dependent on frequency. The system also includes a detector that is positioned to receive portions of the beam reflected from an object within the FOV and a processor that is configured to control the light source and determine a velocity of the object.

A computer-implemented method of processing input data comprising a plurality of samples arranged on a regular grid within a finite sampling window, to train parameters for a kernel of a Gaussian process for modelling the data. The parameters are associated with a mixture of spectral components representing a spectral density of the kernel. The method includes: initialising the parameters; determining a cut-off frequency for delimiting a low-frequency range and a high-frequency range, the cut-off frequency being an integer multiple of a fundamental frequency corresponding to a reciprocal size of the sampling window; performing a discrete Fourier transform on the input data to determine frequency domain data; and processing a portion of the frequency domain data within the low-frequency range to determine smoothed input data. The method further includes iteratively: determining a discretised power spectrum for the kernel; generating a low-frequency covariance matrix from a portion of the discretised power spectrum within the low-frequency range; determining, using the smoothed input data and the low-frequency covariance matrix, a first log-likelihood component for the parameters given the smoothed input data; determining, using a portion of the discretised power spectrum within the high-frequency range, a second log-likelihood component for the parameters given a portion of the frequency domain data within the high-frequency range; and modifying the parameters to increase an objective function comprising the first log-likelihood component and the second log- likelihood component, wherein increasing the objective function increases a probability density associated with the parameters.

Aspects of the disclosure provide a method of a LiDAR system to determine the distance of an object from the LiDAR system. Embodiments of the LiDAR system can use a coarse estimate of the distance of an object from the LiDAR system which is then used by a fast search to determine an estimate of the distance of an object from the LiDAR system within a precision threshold. In some embodiments the LiDAR system can use adaptive precision when determining the distance of an object from the LiDAR system.

A health monitor circuit for an electric machine is described. The health monitor circuit includes at least one sensor configured to measure a parameter of the electric machine, a communication interface, and a microprocessor coupled to the at least one sensor, the communication interface, and a memory. The microprocessor is configured to periodically collect time samples of the parameter measured by the at least one sensor, transmit factors of the time samples to the memory, and perform a high resolution fast-Fourier transform (FFT) on the factors. The microprocessor is also configured to extrapolate results of the high resolution FFT to a produce a high resolution frequency domain waveform, filter the high resolution frequency domain waveform by a parameter, and transmit, via the communication interface, the filtered frequency domain waveform to a remote system for further processing.

An electronic device including an EM sensor module and a method for controlling the electronic device. An electronic device includes an electromagnetic (EM) sensor module, an antenna module electrically connected to the EM sensor module, a memory operationally connected to the EM sensor module, and a processor operationally connected to the EM sensor module, The EM sensor module is configured to detect an electromagnetic signal around the electronic device using the antenna module, determine whether the electromagnetic signal is a valid signal from at least one external electronic device, and send electromagnetic detection data related to all or at least part of the electromagnetic signal to the processor based on the electromagnetic signal being a valid signal.

A device includes a comparison circuit and a calculation circuit coupled to the comparison circuit. The comparison circuit is configured to receive a first digital input value (X) and a second digital input value (Y), and provide a first digital output value that indicates one of a first relationship, a second relationship, and a third relationship between X and Y. The calculation circuit is configured to receive X and Y, receive the first digital output value, and provide a second digital output value. The second digital output value is a first linear combination of X and Y responsive to the first digital output value indicating the first relationship, a second linear combination of X and Y responsive to the first digital output value indicating the second relationship, and a third linear combination of X and Y responsive to the first digital output value indicating the third relationship.

A transmission/reception baseband-processing device includes a calibration-processing unit configured to correct an input signal input to a transmission unit on the basis of a first characteristic according to characteristics of the transmission unit of a transmission/reception front end-processing unit and a calibration reception unit that is a reception unit of a calibration transmission/reception unit and a second characteristic according to a characteristic of the calibration reception unit.

Systems and methods for configuring a reduced instruction set computer processor architecture to execute fully homomorphic encryption (FHE) logic gates as a streaming topology. The method includes parsing sequential FHE logic gate code, transforming the FHE logic gate code into a set of code modules that each have in input and an output that is a function of the input and which do not pass control to other functions, creating a node wrapper around each code module, configuring at least one of the primary processing cores to implement the logic element equivalents of each element in a manner which operates in a streaming mode wherein data streams out of corresponding arithmetic logic units into the main memory and other ones of the plurality arithmetic logic units.

A waveform analysis and vulnerability assessment (WAVE) tool is disclosed that can analyze the characteristics and vulnerabilities of waveforms. The WAVE tool may identify issues in waveforms prior to their implementation in a transmit device or building the back-end processing to receive the waveform at a ground station. The WAVE tool may quantify waveform vulnerabilities, address which vulnerabilities a particular waveform has, and enable the user to modify the waveform design to optimize its performance against threats prior to implementation. Additionally, the WAVE tool may save time and money since new waveforms can be vetted against the tool before implementation. Data from waveforms can be analyzed against a plurality of metrics and scores can be generated providing a quantitative assessment of waveform performance.