Abstract: Disclosed is a computer-implemented method of dynamically compressing and scaling a base model during run-time, wherein the base model is a deep neural network containing a task-specific head and a backbone divided into a plurality of segments. The method includes providing a plurality of candidate early exit modules and corresponding plurality of candidate threshold values for each segment, heuristically searching a plurality of optimal early exit configurations for the base model, based on a plurality of combinations of accuracy and complexity with respect to the base model, wherein an early exit configuration enumerates which early exit module and what threshold value to be used at output of each segment, loading an optimal early exit configuration onto the base model during run-time, and running the backbone part of the base model segment by segment and ending the run-time at a segment when a confidence value of an output of corresponding early exit module exceeds corresponding threshold value.

Abstract: The impedance cardiography device comprises an impedance measuring unit, connected to the human body to be measured, a differentiator, a comparator and a microcontroller integrated with an analog-to-digital converter, characterized by that the device further comprises two peak voltage detection units with different polarities (positive and negative), the strobing outputs of them being connected to the digital (binary) inputs of the microcontroller and the analog hold outputs to the inputs of the analog-to-digital converter.

Abstract: The impedance cardiography device comprises an impedance measuring unit, connected to the human body to be measured, a differentiator, a comparator and a microcontroller integrated with an analog-to-digital converter, characterized by that the device further comprises two peak voltage detection units with different polarities (positive and negative), the strobing outputs of them being connected to the digital (binary) inputs of the microcontroller and the analog hold outputs to the inputs of the analog-to-digital converter.

Abstract: A continuous time digital signal processing (CT DSP) token includes a first signal indicating a change has occurred and a second signal indicating a direction of the change. An amplitude generation circuit operates to generate an amplitude value x in response to the token. A power estimation circuit processes the amplitude value x to generate a digital power signal in accordance with the formula: x2±2x+1.

Abstract: A continuous time digital signal processing (CT DSP) token includes a first signal indicating a change has occurred and a second signal indicating a direction of the change. An amplitude generation circuit operates to generate an amplitude value x in response to the token. A power estimation circuit processes the amplitude value x to generate a digital power signal in accordance with the formula: x2±2x+1.