A polarized Raman Spectrometric system for defining parameters of a polycrystaline material, the system comprises a polarized Raman Spectrometric apparatus, a computer-controlled sample stage for positioning a sample at different locations, and a computer comprising a processor and an associated memory. The polarized Raman Spectrometric apparatus generates signal(s) from either small sized spots at multiple locations on a sample or from an elongated line-shaped points on the sample, and the processor analyzes the signal(s) to define the parameters of said polycrystalline material.

A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

An optical wavelength calibrator is configured to be used on premises with instrumentation such as an optical spectrum analyzer. The on-premises calibrator includes both a fixed wavelength source and a tunable wavelength source, with a variable optical attenuator controlling the power level of a calibration beam provided as an output. A controller within the on-premises calibrator is used to generate the control signals for the various components in response to received external commands, typically via from an external GUI of the user's computer system. The controller is used in combination with the tunable wavelength source to provide a series of output calibration signals at different wavelengths, providing the ability to performance calibration across a desired spectral region and not just a single wavelength. The on-premises calibrator maintains real-time wavelength stability of the instrument to minimize down time when compared off-site extensive re-calibration services.

Systems and methods here may be used for automated capturing and analyzing spectrometer data of multiple sample gemstones on a stage, including mapping digital camera image data of samples, applying a Raman Probe to a first sample gemstone under evaluation on the stage, receiving spectrometer data of the sample gemstone from the probe, automatically moving the stage to a second sample, using the image data, and analyzing the other samples.

Systems and methods disclosed herein provide for detecting gas by: illuminating, with a controllable illuminator system, a scene with light including radiation within the infrared (IR) wavelength range; controlling the illuminator system to emit light at a first wavelength corresponding to a first absorption level of a gas and at a second wavelength corresponding to a second absorption level of a gas, such that an equal amount of radiant energy over a time period is emitted onto the scene for each of said first and second wavelengths; and capturing a first IR image of the scene being illuminated with light at said first wavelength and a second IR image of the scene illuminated with light at said second wavelength, and comparing said first and second IR images to determine whether a characteristic for at least one specific gas is represented in said first and/or second IR images.

Systems for and methods for in situ optimization of tunable light emitting diode sources are disclosed herein. During operation, the systems and methods obtain real-time feedback from an image sensor, and that feedback is used to tune the tunable LEDs. By tuning the tunable LEDs, the best values for the LED spectral output can be selected based on the feedback from the image sensor, and an image with improved contrast is obtained. Alternatively, the amount of time to obtain an image with acceptable contrast is reduced.

Aspects relate to a spectral analyzer that can be used for biological sample detection. The spectral analyzer includes an optical window configured to receive a sample and a spectral sensor including a chassis having various component assembled thereon. Examples of components may include a light source, a light modulator, illumination and collection optical elements, a detector, and a processor. The spectral analyzer is configured to obtain spectral data representative of a spectrum of the sample using, for example, an artificial intelligence (AI) engine. The spectral analyzer further includes a thermal separator positioned between the light modulator and the light source.

Systems and methods here may be used for automated capturing and analyzing spectrometer data of multiple sample gemstones on a stage, including mapping digital camera image data of samples, applying a Raman Probe to a first sample gemstone under evaluation on the stage, receiving spectrometer data of the sample gemstone from the probe, automatically moving the stage to a second sample, using the image data, and analyzing the other samples.

A signal processing method is performed by a computer. The signal processing method includes: obtaining first compressed image data including hyperspectral information and indicating a two-dimensional image in which the hyperspectral information is compressed, the hyperspectral information being luminance information on each of at least four wavelength bands included in a target wavelength range; extracting partial image data from the first compressed image data; and generating first two-dimensional image data corresponding to a first wavelength band and second two-dimensional image data corresponding to a second wavelength band from the partial image data.

The present application relates to a spectral restoring method, including: acquiring a light energy response signal matrix output by a photosensitive chip of a spectral imaging device and a standard spectrum; determining a primitive restoring function and a response signal vector of the primitive restoring function based on the light energy response signal matrix, the primitive restoring function restoring a spectral image value of a predetermined channel corresponding thereto using a predetermined pixel value of the photosensitive chip and pixel values in the vicinity thereof; acquiring a restoring tensor, the product of the restoring tensor and the response signal vector being equal to an output of the primitive restoring function based on the response signal vector; and obtaining a restored spectral image based on the product of the restoring tensor and the response signal vector. In this way, by introducing a standard spectrum and a restoring tensor to restore a spectral image, it is possible to achieve spectral restoring with high speed, easy parallel operation and high restoring accuracy.