The present invention comprises a novel system and method to detect and estimate the time-frequency span of wireless signals present in a wideband RF spectrum. In preferred embodiments, the Faster RCNN deep learning architecture is used to detect the presence of wireless transmitters from the spectrogram images plotted by searching for rectangular shapes of any size, then localize the time and frequency information from the output of the FRCNN deep learning architecture.

[Problem] The object of the present invention is to provide a static electricity distribution-visualizing material, a static electricity-visualizing film, a static electricity distribution-visualizing device, and a static electricity distribution-visualizing method, which can visualize a charged state to be seen with naked eyes so as to intuitively understand a static electricity distribution. [Solution] A static electricity distribution-visualizing material is manufactured so as to contain at least one of a fluorescent substance, a luminescent substance, an electroluminescent substance, a fractoluminescent substance, a photochromic substance, an afterglow substance, a photostimulated luminescent substance and a mechanoluminescent substance.

A spot welding method includes supplying a welding current having a pulse-shaped waveform to a workpiece by alternately executing a step of maintaining the welding current within a set peak current range and a step of decreasing the welding current from the peak current range toward a bottom current and then increasing the welding current toward the peak current range when an effective value of the welding current reaches a set target range for a plurality of cycles. The spatter detection method includes measuring a pulse width IW(1), IW(2), . . . in each cycle of the pulse-shaped waveform and detecting the occurrence of spatter when a pulse width difference D(M)=IW(M)−IW(M−1) between a pulse width IW(M) in a target cycle (M-th cycle) and a pulse width IW(M−1) in a cycle immediately before the target cycle exceeds a width threshold value Dth.

A pulse baseline value calculation method and a particle counting method of a blood cell analyzer. The said pulse baseline value calculation method, within pulse non-duration time, if an absolute value of a difference value between any two adjacent data of n continuous sampled data is less than a baseline threshold, and the n continuous sampled data are closest to a pulse starting point, an average value of the n continuous sampled data is calculated, and the average value is a pulse baseline value. The present invention has the advantages of setting the baseline threshold and performing comparison, avoiding the sampled data of the baseline where the noise is superimposed, selecting the sampled data with noise or interference within an allowable range for calculation, avoiding accumulating the noise on the final baseline value, making the baseline value be closer to the real data, greatly reducing the erroneous judgment of the baseline value, and making the particle count be more accurate. The method of the present invention can be applied to the particle counting of 3-diff hematology analyzers, 5-diff hematology analyzers, flow cytometers and other biochemical instruments.

A pulse baseline value calculation method and a particle counting method of a blood cell analyzer. The said pulse baseline value calculation method, within pulse non-duration time, if an absolute value of a difference value between any two adjacent data of n continuous sampled data is less than a baseline threshold, and the n continuous sampled data are closest to a pulse starting point, an average value of the n continuous sampled data is calculated, and the average value is a pulse baseline value. The present invention has the advantages of setting the baseline threshold and performing comparison, avoiding the sampled data of the baseline where the noise is superimposed, selecting the sampled data with noise or interference within an allowable range for calculation, avoiding accumulating the noise on the final baseline value, making the baseline value be closer to the real data, greatly reducing the erroneous judgment of the baseline value, and making the particle count be more accurate. The method of the present invention can be applied to the particle counting of 3-diff hematology analyzers, 5-diff hematology analyzers, flow cytometers and other biochemical instruments.

A method comprising: receiving a first current measurement that is taken at a first predetermined time instant; receiving a second current measurement that is taken at a second predetermined time instant; classifying the first current measurement as corresponding to one of a plurality of electrical signals, the first current measurement being classified based, at least in part, on a duty cycle pattern of the plurality of electrical signals; classifying the second current measurement as corresponding to another one of the plurality of electrical signals, the second current measurement being classified based, at least in part, on the duty cycle pattern of the plurality of electrical signals; and adjusting a duty cycle of at least one of the electrical signals based on the first current measurement, the classification of the first current measurement, the second current measurement, and the classification of the second current measurement.

A method comprising: receiving a first current measurement that is taken at a first predetermined time instant; receiving a second current measurement that is taken at a second predetermined time instant; classifying the first current measurement as corresponding to one of a plurality of electrical signals, the first current measurement being classified based, at least in part, on a duty cycle pattern of the plurality of electrical signals; classifying the second current measurement as corresponding to another one of the plurality of electrical signals, the second current measurement being classified based, at least in part, on the duty cycle pattern of the plurality of electrical signals; and adjusting a duty cycle of at least one of the electrical signals based on the first current measurement, the classification of the first current measurement, the second current measurement, and the classification of the second current measurement.

The disclosure relates to apparatus and methods for self-testing of a duty cycle detector. Example embodiments include a circuit (201) comprising: a clock signal generator (205) configured to provide an output clock signal (203) having a duty cycle; a duty cycle detector (208) arranged to receive the output clock signal (203) and provide an output flag if the duty cycle of the clock signal (203) is outside a predetermined range; a controller (214) arranged to provide a duty cycle select signal (216) to the clock signal generator (205) to cause the clock signal (203) to have a duty cycle outside the predetermined range and to receive the output flag to confirm operation of the duty cycle detector (208).

A MEMS device may output a signal during operation that may include an in-phase component and a quadrature component. An external signal having a phase that corresponds to the quadrature component may be applied to the MEMS device, such that the MEMS device outputs a signal having a modified in-phase component and a modified quadrature component. A phase error for the MEMS device may be determined based on the modified in-phase component and the modified quadrature component.

A MEMS device may output a signal during operation that may include an in-phase component and a quadrature component. An external signal having a phase that corresponds to the quadrature component may be applied to the MEMS device, such that the MEMS device outputs a signal having a modified in-phase component and a modified quadrature component. A phase error for the MEMS device may be determined based on the modified in-phase component and the modified quadrature component.