Abstract: A delay buffer includes a delay device having an input, an output, and a current terminal. The delay buffer also includes a current circuit coupled between a rail and the current terminal. The current circuit includes transistors and switches. Each one of the switches is coupled in series with a respective one of the transistors between the rail and the current terminal.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: Systems and methods are provided for monitoring and/or controlling operations of an artificial lift system of a production well, which employ a gateway device disposed at a wellsite corresponding to the well, and at least one remote cloud-computing system operably coupled to the gateway device. The gateway device includes at least one first interface to the artificial lift system, at least one second interface to the at least one cloud-computing system, and a processor configured to execute a first application that acquires data produced by the artificial lift system and communicated to the gateway device via the first interface.

Abstract: A telemetry method transmits data from a downhole location to a surface location. During transmission an event is detected that makes it desirable to change a transmitted data stream. A downhole processor processes the detected event in combination with a predefined event database and downhole measurement data using a frame building algorithm to compute a digital data stream. Synchronization markers are added to the data stream to obtain a synchronized data stream in which the synchronization markers identify the detected event. The synchronized data stream is transmitted to the surface location using a downhole telemetry tool and received at the surface location to obtain a decoded data stream. A surface processor processes the synchronization markers to identify the detected event and further processes the detected event in combination with a predefined event database and the decoded data stream using the frame building algorithm to obtain the downhole measurements.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for equalizing a transmitter circuit for use in high-speed data links, such as in a serializer/deserializer (SerDes) scheme. One example transmitter circuit generally includes at least one driver stage, a first equalization circuit coupled to an output of the transmitter circuit, and a second equalization circuit coupled to an input of the at least one driver stage. One example method of transmitting data generally includes operating a transmit circuit comprising: at least one driver stage, a first equalization circuit coupled to an output of the transmitter circuit, and a second equalization circuit coupled to an input of the at least one driver stage; and selectively enabling at least one of the first equalization circuit or the second equalization circuit.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: A hybrid output driver is disclosed that supports high-voltage signaling and low-voltage signaling. The high-voltage signaling is powered by a high-power supply voltage that is greater than a low-power supply voltage that powers the low-voltage signaling.

Abstract: A hybrid output driver is disclosed that supports high-voltage signaling and low-voltage signaling. The high-voltage signaling is powered by a high-power supply voltage that is greater than a low-power supply voltage that powers the low-voltage signaling.

Abstract: A telemetry method transmits data from a downhole location to a surface location. During transmission an event is detected that makes it desirable to change a transmitted data stream. A downhole processor processes the detected event in combination with a predefined event database and downhole measurement data using a frame building algorithm to compute a digital data stream. Synchronization markers are added to the data stream to obtain a synchronized data stream in which the synchronization markers identify the detected event. The synchronized data stream is transmitted to the surface location using a downhole telemetry tool and received at the surface location to obtain a decoded data stream. A surface processor processes the synchronization markers to identify the detected event and further processes the detected event in combination with a predefined event database and the decoded data stream using the frame building algorithm to obtain the downhole measurements.