A high-speed signal subsystem testing system includes a processing system having a transmitter and a receiver, a loop back subsystem coupled to the transmitter and receiver to provide a testing communication path between the transmitter and the receiver, and a communication path testing engine coupled to the transmitter and the receiver. The communication path testing engine generates test signal(s) and transmits the test signal(s) via the transmitter and through the testing communication path provided by the loop back subsystem and, in response, receives test signal result(s) via the receiver and through the testing communication path provided by the loop back subsystem, The communication path testing engine processes the test signal result(s) to generate a testing impedance profile for the testing communication path, and compares the testing impedance profile to an expected impedance profile to determine whether a testing communication path issue exists in the testing communication path.

Various embodiments of the present disclosure provide systems and methods for generally generating and transforming physical quantum representations of multi-dimensional tensor data objects. Specifically, various embodiments enable the rapid and efficient generation of a physical quantum representation representing the Fourier transform of a multi-dimensional tensor data object based at least in part on manipulating another physical quantum representation of the multi-dimensional tensor data object itself via quantum manipulation operations. Information may be extracted from the generated physical quantum representation to determine the Fourier transform of the multi-dimensional tensor data object. Accordingly, various embodiments may comprise quantum manipulation operations for a tensor-form quantum Fourier transform (TQFT) for a multi-dimensional tensor data object. Various embodiments for the TQFT are advantageously comprehensive, versatile, and applicable to any quantum representation form for a multi-dimensional tensor data object. The TQFT may be performed in any quantum computing system and/or simulated quantum computing system.

A for providing control input adjustment for an aircraft, including one or more mode sensors disposed on an aircraft, a mode analysis system, the mode analysis system operable to receive mode sensor data from the one or more mode sensors, and operable to decompose the mode sensor data into decomposed mode data associated with fundamental modes of structural elements of the aircraft associated with the one or more mode sensors, and a flight control computer (FCC) disposed on the aircraft and connected to one or more actuators, the FCC operable to provide a control signal to the one or more actuators according to an association between the decomposed mode data and one or more rotorcraft parameters associated with the one or more actuators.

Utilizing an adaptive multichannel technique to mitigate reverberation present in received audio signals, prior to providing corresponding audio data to one or more additional component(s), such as automatic speech recognition (ASR) components. Implementations disclosed herein are “adaptive”, in that they utilize a filter, in the reverberation mitigation, that is online, causal and varies depending on characteristics of the input. Implementations disclosed herein are “multichannel”, in that a corresponding audio signal is received from each of multiple audio transducers (also referred to herein as “microphones”) of a client device, and the multiple audio signals (e.g., frequency domain representations thereof) are utilized in updating of the filter—and dereverberation occurs for audio data corresponding to each of the audio signals (e.g., frequency domain representations thereof) prior to the audio data being provided to ASR component(s) and/or other component(s).

A method for predicting variables of interest related to a system includes collecting one or more sensor streams over a time period from sensors in the system and generating one or more anomaly streams for the time period based on the sensor streams. Values for variables of interest for the time period are determined based on the sensor streams and the anomaly streams. Next, a time-series predictive algorithm is applied to the (i) the sensor streams, (ii) the anomaly streams, and (iii) the values for the variables of interest to generate a model for predicting new values for the variables of interest. The model may then be used to predict values for the variables of interest at a time within a new time period based on one or more new sensor streams.

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 computer processor comprising a vector unit is disclosed. The vector unit may comprise a vector register file comprising at least one register to hold a varying number of elements. The vector unit may further comprise a vector length register file comprising at least one register to specify the number of operations of a vector instruction to be performed on the varying number of elements in the at least one register of the vector register file. The computer processor may be implemented as a monolithic integrated circuit.

A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.

A hardware accelerator for accelerating the zero knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocol by reducing the computation time of the cryptographic verification is disclosed. The accelerator includes a zk-SNARK engine having one or more processing units running in parallel. The processing unit can include one or more multiply-accumulate operation (MAC) units, one or more fast Fourier transform (FFT) units; and one or more elliptic curve processor (ECP) units. The one or more ECP units are configured to reduce a bit-length of a scalar d.sub.i in an ECP algorithm used for generating a proof, thereby the cryptographic verification requires less computation power.

Implementations described herein disclose an artificial intelligence (AI) based method for generating an oxygen saturation level output signal using the trained neural network. In one implementation, the method includes receiving a photoplethysmographic (PPG) signal, the PPG signal including a red PPG signal and an infrared PPG signal, generating an input feature matrix by performing time-frequency transform of the PPG signal, training a neural network using the input feature matrix and an oxygen saturation level input signal, and generating an oxygen saturation level output signal using the trained neural network.