An improved method for integrating curve peaks as compared to techniques such as the trapezoidal rule wherein integration parameters are at fixed x-axis positions. Integration parameters are instead specified relative to a peak center, which allows the peak to shift over time due to hardware changes, temperature fluctuation, pressure changes, etc., while maintaining integration parameters at optimal locations for that peak. As such, the present disclosure finds particular utility in spectroscopy wherein, in the case of Raman spectroscopy, for example, specific wavenumber shift locations may drift over time, leading to inaccurate results based upon absolute integration parameters.

Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

A spectrum analyzing method and a gingivitis evaluating device are provided. The spectrum analyzing method includes steps as follows. A diffuse reflection signal of a gingiva is calculated, and a gingiva spectrum is generated. The gingiva spectrum and a plurality of reference gingiva spectra are respectively applied with a time-series similarity calculation, and a plurality of similarity values are generated. The plurality of reference gingiva spectra correspond to various gingival indexes (GI). A minimum similarity value of the plurality of similarity values is obtained. A GI result is output according to the minimum similarity value.

Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.