A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

Various aspects of a generator, ultrasonic device, and method for estimating a state of an end effector of an ultrasonic device are disclsoed. The ultrasonic device includes an electromechanical ultrasonic system defined by a predetermined resonant frequency, including an ultrasonic transducer coupled to an ultrasonic blade. A control circuit measures a complex impedance of an ultrasonic transducer, wherein the complex impedance is defined as

[00001]$Z_g(t)=\frac{V_g\left(t\right)}{I_g\left(t\right)}.$

The control circuit receivs a complex impedance measurement data point and compares the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern. The control circuit then classifies the complex impedance measurement data point based on a result of the comparison analysis and assigns a state or condition of the end effector based on the result of the comparison analysis.

Measurement apparatus and method for digital data acquisition. A first operation mode is provided for real-time processing of digital data having a reduced sample rate or resolution. Furthermore, a second operation mode is provided for processing the measurement signal off-line with a higher accuracy. In particular, the high accuracy data may be temporarily stored and analyzed upon the operation mode is changed from the real-time mode to the off-line mode.

Provided is a method for managing radio frequency (RF) and ultrasonic signals output by a generator that includes a surgical instrument comprising an RF energy output and an ultrasonic energy output and a circuit configured to receive a combined RF and ultrasonic signal from the generator. The method includes receiving a combined radio frequency (RF) and ultrasonic signal from a generator, generating a RF filtered signal by filtering RF frequency content from the combined signal; filtering ultrasonic frequency content from the combined signal; generating an ultrasonic filtered signal; providing the RF filtered signal to the RF energy output; and providing the ultrasonic filtered signal to the ultrasonic energy output.

A frequency characteristic measurement device that measures the frequency characteristic of a measurement target includes: a multi-sine signal generation unit that generates a multi-sine signal; a sweep sinusoidal wave generation unit that generates a plurality of sweep sinusoidal waves; an input signal switching unit that selects any one of the multi-sine signal and the sweep sinusoidal waves so as to input the selected one to the measurement target; a data acquisition unit that acquires, at a predetermined sampling frequency, sampling data of an input signal which is input to the measurement target and sampling data of an output signal which is output from the measurement target; and a characteristic calculation unit that calculates a frequency characteristic including the gain and the phase of the input and output signals in the measurement target from the sampling data of the input and output acquired.

A data generation method by a computer is disclosed. First waveform data including marking information at a first position on a waveform, and acquiring second waveform data are acquired. A transformation function is specified that transforms the first waveform data to reduce the difference between a first value of a time axis for a first characteristic point in the first waveform data and a second value of the time axis for a second characteristic point, in the second waveform data, corresponding to the first characteristic point. Third waveform data are generated, in which the marking information is applied at a second position corresponding to the first position in the first waveform data, the second position being determined by using the transformation function.

The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.

The present invention relates to a generation of a signal by executing a waveform dataset comprising waveform descriptive parameters. The execution of the waveform description parameters is limited by target device information specifying one or more specific target devices and time information specifying an execution period of the waveform descriptive parameters. By providing a waveform dataset comprising not only the waveform descriptive parameters, but also further information, in particular time information for limiting the execution period of the waveform descriptive parameters, the generation of the respective waveform signal is controlled.

Certain aspects of the present disclosure provide a method for processing input data by a configurable nonlinear activation function circuit, including determining a nonlinear activation function for application to input data; determining, based on the determined nonlinear activation function, a set of parameters for a configurable nonlinear activation function circuit; and processing input data with the configurable nonlinear activation function circuit based on the set of parameters to generate output data.

An apparatus includes a digitally controlled oscillator (DCO), which includes an inductor coupled in series with a first capacitor. The DCO further includes a second capacitor coupled in parallel with the series-coupled inductor and first capacitor, a first inverter coupled in parallel with the second capacitor, and a second inverter coupled back-to-back to the first inverter. The DCO further includes a digital-to-analog-converter (DAC) to vary a capacitance of the first capacitor.