Abstract:
A signal convertor includes a first sensor configured to generate a first signal and a second signal and first and second multiplexers configured receive the first and second signals, respectively, and generate samples. The signal convertor also includes an analog-to-digital (A/D) convertor configured to convert the samples and a processor configured to multiply the samples by a sine vector and by a cosine vector and determine a magnitude of the first and second signals based upon the product of the samples and the sine vector and the product of the samples and the cosine vector. A method for converting a signal is also disclosed.
Abstract:
A fault detection circuit is utilized to automatically detect faults in hold-up power storage devices. The fault detection circuit includes a hold-up monitoring circuit and a memory device. The hold-up monitoring circuit is connected to monitor output of the hold-up power storage device, wherein the hold-up monitoring circuit measures a duration of time that the hold-up power storage device provides sufficient power following a loss of normal power and detects faults based on the measured duration of time. The memory device is connected to store the duration of time measured by the hold-up power storage device following a loss of normal power.
Abstract:
A machine learning system that includes one or more machine learning models implemented in one or more hardware processors, a first-level feature creation module, and a combination module provides an output based on one or more channel inputs. Each of the one or more machine learning models receives the channel inputs and additional feature inputs based on the channel inputs to produce the output. The first-level feature creation module receives the channel inputs, performs a feature creation operation, creates the additional feature inputs, and provides the additional feature inputs to at least one of the machine learning models. The first-level feature creation operation performs a calculation on one or more aspects of the channel inputs, and the combination module receives the one or more machine learning model outputs and produce a machine learning channel output.
Abstract:
Apparatus and associated methods relate to mitigating data-stream dropout in a serial data-stream. A time-sequence of messages of the serial data-stream is received, each containing a data packet communicating an action. Validity of each of the time-sequence of messages received is determined. After receiving each valid message, a plurality of future actions is created based at least in part on the valid message received. The plurality of future actions corresponds to a plurality of future data packets of the time-sequence of messages. After receiving each valid message, the action communicated in the valid message received is performed. After receiving each invalid messages, a next one of the set of sequential future actions created is instead used in place of any action communicated in the data packet of the invalid message received.
Abstract:
A signal convertor includes a first sensor configured to generate a first signal and a second signal and first and second multiplexers configured receive the first and second signals, respectively, and generate samples. The signal convertor also includes an analog-to-digital (A/D) convertor configured to convert the samples and a processor configured to multiply the samples by a sine vector and by a cosine vector and determine a magnitude of the first and second signals based upon the product of the samples and the sine vector and the product of the samples and the cosine vector. A method for converting a signal is also disclosed.
Abstract:
Apparatus and associated methods relate to mitigating data-stream dropout in a serial data-stream. A time-sequence of messages of the serial data-stream is received, each containing a data packet communicating an action. Validity of each of the time-sequence of messages received is determined. After receiving each valid message, a plurality of future actions is created based at least in part on the valid message received. The plurality of future actions corresponds to a plurality of future data packets of the time-sequence of messages. After receiving each valid message, the action communicated in the valid message received is performed. After receiving each invalid messages, a next one of the set of sequential future actions created is instead used in place of any action communicated in the data packet of the invalid message received.
Abstract:
A resolver system includes a rotatable primary winding, a secondary winding fixed relative to the primary winding, and an analog-to-digital converter electrically connected to the secondary winding. A control module is operatively connected to analog-to-digital converter and is responsive to instructions to apply an excitation voltage with an oscillating waveform to the primary winding, induce a secondary voltage using the secondary winding using the excitation voltage, and acquire a plurality of voltage measurements from the secondary winding separated by a time interval corresponding to π/3 of the excitation voltage oscillating waveform.
Abstract:
A fault detection circuit is utilized to automatically detect faults in hold-up power storage devices. The fault detection circuit includes a hold-up monitoring circuit and a memory device. The hold-up monitoring circuit is connected to monitor output of the hold-up power storage device, wherein the hold-up monitoring circuit measures a duration of time that the hold-up power storage device provides sufficient power following a loss of normal power and detects faults based on the measured duration of time. The memory device is connected to store the duration of time measured by the hold-up power storage device following a loss of normal power
Abstract:
A machine learning system that includes one or more machine learning models implemented in one or more hardware processors, a first-level feature creation module, and a combination module provides an output based on one or more channel inputs. Each of the one or more machine learning models receives the channel inputs and additional feature inputs based on the channel inputs to produce the output. The first-level feature creation module receives the channel inputs, performs a feature creation operation, creates the additional feature inputs, and provides the additional feature inputs to at least one of the machine learning models. The first-level feature creation operation performs a calculation on one or more aspects of the channel inputs, and the combination module receives the one or more machine learning model outputs and produce a machine learning channel output.
Abstract:
A Joint Test Action Group (JTAG) communication lockout processor is disclosed. The processor is configured to generate a unlock sequence based on an operational mode change of an operably connected programmable device, and save the unlock sequence to one or more memory registers. The processor can also receive an execution of the unlock sequence via a dual function JTAG communication bus, determine, via an unlock logic, whether the execution of the unlock sequence is valid, and responsive to determining that the execution of the unlock sequence is valid, allow or disallow the JTAG communication with an embedded processor.