A remote photoplethysmography (RPPG) system includes an input interface to receive a sequence of measurements of intensities of different regions of a skin of a person indicative of vital signs of the person; a solver to solve an optimization problem to determine frequency coefficients of photoplethysmographic waveforms corresponding to the measured intensities at the different regions, wherein the solver determines the frequency coefficients to reduce a distance between intensities of the skin reconstructed from the frequency coefficients and the corresponding measured intensities of the skin while enforcing joint sparsity on the frequency coefficients; and an estimator to estimate the vital signs of the person from the determined frequency coefficients of photoplethysmographic waveforms.

A method for electronic circuit spectral analysis includes receiving a list of parts for an electronic circuit, determining a part admittance matrix with algebraic nodes for each part, and determining a circuit admittance matrix for exterior nodes, interior nodes, and algebraic nodes based on the part admittance matrices. The method also includes reducing interior nodes of the circuit admittance matrix for exterior nodes, interior nodes, and algebraic nodes to determine a circuit admittance matrix for exterior nodes and algebraic nodes, reducing algebraic nodes to transform the circuit admittance matrix for exterior nodes and algebraic nodes into a Green's Function, evaluating the Green's Function to determine a circuit exterior node admittance matrix over a frequency spectrum, and transforming the circuit exterior node admittance matrix to a circuit scattering matrix over the frequency spectrum.

A method for electronic circuit spectral analysis includes receiving a list of parts for an electronic circuit, determining a part admittance matrix with algebraic nodes for each part, and determining a circuit admittance matrix for exterior nodes, interior nodes, and algebraic nodes based on the part admittance matrices. The method also includes reducing interior nodes of the circuit admittance matrix for exterior nodes, interior nodes, and algebraic nodes to determine a circuit admittance matrix for exterior nodes and algebraic nodes, reducing algebraic nodes to transform the circuit admittance matrix for exterior nodes and algebraic nodes into a Green's Function, evaluating the Green's Function to determine a circuit exterior node admittance matrix over a frequency spectrum, and transforming the circuit exterior node admittance matrix to a circuit scattering matrix over the frequency spectrum.

A frequency mask trigger capable of triggering based on a logical combination of two or more areas of a frequency mask transforms a frame of digital data representing an input signal into a frequency spectrum having a plurality of frequency bins, with each frequency bin having a power amplitude value. A frequency mask is defined having a plurality of reference power levels, one reference power level being associated with each frequency bin. Two or more areas of the frequency mask are defined, with each mask area corresponding to one or more of the frequency bins. A violation status is determined for each mask area by comparing all of the power amplitude values within each mask area to the associated reference power level. If any of the power amplitude values within the mask area violates the associated reference power level, then the entire mask area is deemed to be violated. A trigger signal is generated when a logical combination of the violation statuses of the mask areas is satisfied.

According to one embodiment, a time-series data waveform analysis device implemented by a computer including at least one hardware processor is provided. The hardware processor configured to: add a shapelet being a part of a partial time series included in labeled time-series data to a shapelet set; randomly extract one or more labeled time-series data and calculate a feature value of the shapelet for the extracted labeled time-series data according to a TSS method; update a parameter, which includes the shapelet and a weight coefficient for the shapelet, based on the feature value according to a stochastic gradient descent method; remove the shapelet, the corresponding weight coefficient of which is 0, from the shapelet set; and create an evaluation function based on the shapelet in the shapelet set and the weight coefficient.

According to one embodiment, a time-series data waveform analysis device implemented by a computer including at least one hardware processor is provided. The hardware processor configured to: add a shapelet being a part of a partial time series included in labeled time-series data to a shapelet set; randomly extract one or more labeled time-series data and calculate a feature value of the shapelet for the extracted labeled time-series data according to a TSS method; update a parameter, which includes the shapelet and a weight coefficient for the shapelet, based on the feature value according to a stochastic gradient descent method; remove the shapelet, the corresponding weight coefficient of which is 0, from the shapelet set; and create an evaluation function based on the shapelet in the shapelet set and the weight coefficient.

A method and system for extracting values representative of modulation signal components from a modulated signal, the modulated signal containing a modulation signal, including developing a local clock signal which correlates in time to the modulated signal and includes a number of phase sectors per cycle and converting the modulated signal into a current that is representative of the signal and routing the current to the inverting input of an amplifier and charging one of a plurality of capacitive devices during each phase sector and sequentially connecting the capacitive devices between the output of the amplifier and the inverting input of the amplifier in non-overlapping sequences, the total of sequences being equal to one full cycle of the clock.

An apparatus for detecting object features can include: a probe signal transmitter configured to load a digital intermediate frequency signal onto a carrier signal, and to transmit a loaded signal outwards; an echo signal receiver configured to receive an echo signal, and to extract an object feature signal by performing respective down conversions on a quadrature signal of the carrier signal and a quadrature signal of the digital intermediate frequency signal; and a signal processor configured to identify object features according to the object feature signal.

An apparatus for detecting object features can include: a probe signal transmitter configured to load a digital intermediate frequency signal onto a carrier signal, and to transmit a loaded signal outwards; an echo signal receiver configured to receive an echo signal, and to extract an object feature signal by performing respective down conversions on a quadrature signal of the carrier signal and a quadrature signal of the digital intermediate frequency signal; and a signal processor configured to identify object features according to the object feature signal.

A spectrum analyzer having a memory function to adopt a digital-data-based frequency sweep scheme while achieving performance comparable to performance of a high-speed FFT spectrum analyzer, and a method of controlling the spectrum analyzer, in which the spectrum analyzer includes: an ADC for converting a BWP signal, which is at least one analog unit frequency band signal, into a digital data sample at a predetermined sample rate according to a span set by a user; a digital sweep part for sweeping the data sample passed through the ADC while digitally decimating the data sample through a decimation processing block having a two-stage cascaded structure, and processing the swept data sample to increase a frequency sweep speed; and a control unit for controlling the digital sweep part according to various items input, set, and selected by the user to perform spectrum analysis and output a spectrum analysis result.