The present disclosure concerns a method of processing measurement data. The method includes gathering measurement data by a measurement component, processing the measurement data by the measurement component, thereby producing at least one two-dimensional histogram of a measurement quantity depending on a variable for at least one period of time, forwarding, by the measurement component, the at least one two-dimensional histogram to a processing component, and processing, by the processing component, the at least one two-dimensional histogram received from the measurement component, thereby generating data associated with at least one histogram and data associated with a waterfall diagram having several waterfall lines, wherein each of the several waterfall lines is associated with an individual histogram. Further, the present disclosure concerns a system for processing measurement data.

A detection data storage device includes a detection probe, an inspection instrument, a processor and a storage controller. The processor includes a software component. The storage controller includes a key and a prompt lamp. When a circuit is detected by the detection probe, a detection signal is generated. After the detection signal is received by the inspection instrument, a detection data is generated and transmitted to the software component of the processor. If the software component judges that the detection data matches a standard value, the software components issues a prompt signal to the prompt lamp of the storage controller. In response to the prompt signal, the prompt lamp emits a light beam. After the light beam from the prompt lamp is received by the user and the key is pressed, the detection data is stored in a storage unit.

A detection data storage device includes a detection probe, an inspection instrument, a processor and a storage controller. The processor includes a software component. The storage controller includes a key and a prompt lamp. When a circuit is detected by the detection probe, a detection signal is generated. After the detection signal is received by the inspection instrument, a detection data is generated and transmitted to the software component of the processor. If the software component judges that the detection data matches a standard value, the software components issues a prompt signal to the prompt lamp of the storage controller. In response to the prompt signal, the prompt lamp emits a light beam. After the light beam from the prompt lamp is received by the user and the key is pressed, the detection data is stored in a storage unit.

The system includes a positioning system for mounting the circuit board to be tested and for mounting a sensor assembly. A control system registers the position of the sensor assembly relative to the circuit board to be tested and for moving the sensor assembly about the circuit board. The sensor assembly detects noise or other emissions generated by the circuit elements on the board. The noise emissions are separate from the operating signals of the circuit. The spectrum analyzer receives the emissions from the sensor assembly and produces frequency spectrum data over a selected frequency range with amplitude information. A processing system then compares the frequency spectrum information with frequency spectrum information from boards known to be good and provides information as to any differences and whether they are in an acceptable tolerance range.

Accordingly embodiments herein disclose a method for estimating frequencies of multiple sinusoids by an electronic device (100). The method includes receiving a signal, where the signal comprises the multiple sinusoids. Further, the method includes estimating an initial frequency of each of the multiple sinusoids present in the received signal, determining that a first candidate parameter is less than zero, where the candidate parameter is a function of an estimated Signal-to-noise ratio (SNR) and an estimated threshold. Further, the method includes performing zero-padding on the received signal. Further, the method includes re-estimating frequencies obtained from zero-padded version of the received signal. Further, the method includes validating the re-estimated frequencies obtained from zero-padded version of the received signal based on validation criteria. Further, the method includes predicting the re-estimated frequencies or the initial frequencies as optimal frequencies based on the validation. Further, the method includes refining re-estimated frequencies using iterative filtering.

Accordingly embodiments herein disclose a method for estimating frequencies of multiple sinusoids by an electronic device (100). The method includes receiving a signal, where the signal comprises the multiple sinusoids. Further, the method includes estimating an initial frequency of each of the multiple sinusoids present in the received signal, determining that a first candidate parameter is less than zero, where the candidate parameter is a function of an estimated Signal-to-noise ratio (SNR) and an estimated threshold. Further, the method includes performing zero-padding on the received signal. Further, the method includes re-estimating frequencies obtained from zero-padded version of the received signal. Further, the method includes validating the re-estimated frequencies obtained from zero-padded version of the received signal based on validation criteria. Further, the method includes predicting the re-estimated frequencies or the initial frequencies as optimal frequencies based on the validation. Further, the method includes refining re-estimated frequencies using iterative filtering.

A failure sign determining device includes an acquirer to acquire pieces of sensor data based on respective values measured by multiple sensors, an FFT processor to execute fast Fourier transform on each of the pieces of sensor data and thereby generate a piece of frequency spectrum data, and a determiner to determine the existence of a failure sign on the basis of comparison between the piece of frequency spectrum data and a spectrum range defined for the sensor. The determiner, only when determining that a failure sign exists, transmits at least either of the piece of frequency spectrum data and the piece of sensor data to an analysis apparatus.

A failure sign determining device includes an acquirer to acquire pieces of sensor data based on respective values measured by multiple sensors, an FFT processor to execute fast Fourier transform on each of the pieces of sensor data and thereby generate a piece of frequency spectrum data, and a determiner to determine the existence of a failure sign on the basis of comparison between the piece of frequency spectrum data and a spectrum range defined for the sensor. The determiner, only when determining that a failure sign exists, transmits at least either of the piece of frequency spectrum data and the piece of sensor data to an analysis apparatus.

Various embodiments disclosed herein provide for a glitch detection and level detection method that use information contained in the signal itself to determine at which resolution or granularity the glitch detection and level detection operates. In particular, the glitch detection method comprises defining a glitch in terms of a change in the area under the waveform which can serve to disambiguate glitches from noises and other transient side effects of level transmissions. Likewise, the level detection method uses an entropy-based metric to identify levels that are significant in context of the entire signal and not in absolute terms.

Various embodiments disclosed herein provide for a glitch detection and level detection method that use information contained in the signal itself to determine at which resolution or granularity the glitch detection and level detection operates. In particular, the glitch detection method comprises defining a glitch in terms of a change in the area under the waveform which can serve to disambiguate glitches from noises and other transient side effects of level transmissions. Likewise, the level detection method uses an entropy-based metric to identify levels that are significant in context of the entire signal and not in absolute terms.