A hyperspectral sensing device may include an optical collector configured to collect light and to transfer the collected light to a sensor having spectral resolution sufficient for sensing hyperspectral data. In some examples, the sensor comprises a compact spectrometer. The device further comprises a power supply, an electronics module, and an input/output hub enabling the device to transmit acquired data (e.g., to a remote server). In some examples, a plurality of hyperspectral sensing devices are deployed as a network to acquire data over a relatively large area. Methods are disclosed for performing dark-current calibration and/or radiometric calibration on data obtained by the hyperspectral sensing device, and/or another suitable device. Data obtained by the device may be represented in a functional basis space, enabling computations that utilize all of the hyperspectral data without loss of information.

A hyperspectral sensing device may include an optical collector configured to collect light and to transfer the collected light to a sensor having spectral resolution sufficient for sensing hyperspectral data. In some examples, the sensor comprises a compact spectrometer. The device further comprises a power supply, an electronics module, and an input/output hub enabling the device to transmit acquired data (e.g., to a remote server). In some examples, a plurality of hyperspectral sensing devices are deployed as a network to acquire data over a relatively large area. Methods are disclosed for performing dark-current calibration and/or radiometric calibration on data obtained by the hyperspectral sensing device, and/or another suitable device. Data obtained by the device may be represented in a functional basis space, enabling computations that utilize all of the hyperspectral data without loss of information.

A hyperspectral sensing device may include an optical collector configured to collect light and to transfer the collected light to a sensor having spectral resolution sufficient for sensing hyperspectral data. In some examples, the sensor comprises a compact spectrometer. The device further comprises a power supply, an electronics module, and an input/output hub enabling the device to transmit acquired data (e.g., to a remote server). In some examples, a plurality of hyperspectral sensing devices are deployed as a network to acquire data over a relatively large area. Methods are disclosed for performing dark-current calibration and/or radiometric calibration on data obtained by the hyperspectral sensing device, and/or another suitable device. Data obtained by the device may be represented in a functional basis space, enabling computations that utilize all of the hyperspectral data without loss of information.

The present disclosure relates to a method and related system for spectrum optimization of an illumination light source. Spectrum optimization according to the present disclosure can be based on various optimization parameters, including but not limited to luminous efficacy, color rendering effect, luminous efficacy of radiation, mesopic efficacy of radiation, cirtopic efficacy of radiation, etc. The present method and system are capable of optimizing illumination performance of a light source in various aspects in an individual or integrated manner. Further, the present method and system are capable of accommodating different illumination purposes and conditions by combining and prioritizing different optimization parameters.

The present disclosure relates to a method and related system for spectrum optimization of an illumination light source. Spectrum optimization according to the present disclosure can be based on various optimization parameters, including but not limited to luminous efficacy, color rendering effect, luminous efficacy of radiation, mesopic efficacy of radiation, cirtopic efficacy of radiation, etc. The present method and system are capable of optimizing illumination performance of a light source in various aspects in an individual or integrated manner. Further, the present method and system are capable of accommodating different illumination purposes and conditions by combining and prioritizing different optimization parameters.

A subject identification device includes: an illuminator configured to generate illumination light including components at a plurality of wavelength bands, each of the components having a characteristic in accordance with a respective one of settings; an imager configured to generate an image signal by capturing light from a subject under the illumination light having the illumination characteristic; and a processor including hardware. The processor is configured to: define an illumination characteristic of the illumination light; analyze the image signal to acquire spectral information of the subject; and cross check the spectral information of the subject with subject identification information in order to identify the subject. When the subject is not identified, the processor is configured to define another illumination characteristic that causes spectral information of potentials for the subject to be identified, and subsequently each of the imager and the processor performs a process.

A subject identification device includes: an illuminator configured to generate illumination light including components at a plurality of wavelength bands, each of the components having a characteristic in accordance with a respective one of settings; an imager configured to generate an image signal by capturing light from a subject under the illumination light having the illumination characteristic; and a processor including hardware. The processor is configured to: define an illumination characteristic of the illumination light; analyze the image signal to acquire spectral information of the subject; and cross check the spectral information of the subject with subject identification information in order to identify the subject. When the subject is not identified, the processor is configured to define another illumination characteristic that causes spectral information of potentials for the subject to be identified, and subsequently each of the imager and the processor performs a process.

A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes obtaining data of MALDI-TOF MS of microorganisms having same features; using a kernel density estimation to generate characteristic profiles of an m/z of the data; creating a characteristic MS profile based on the m/z; repeating above three step until characteristic MS profiles of features of the microorganisms is obtained; comparing m/z of MALDITOF MS spectrum of known microorganisms with the characteristic profiles to obtain first matched vectors; using a machine learning method to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing the m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with the characteristic MS profiles to obtain second matched vectors; using the feature classification model to analyze the second matched vectors; and identifying the microorganisms having the unknown features.

A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes obtaining data of MALDI-TOF MS of microorganisms having same features; using a kernel density estimation to generate characteristic profiles of an m/z of the data; creating a characteristic MS profile based on the m/z; repeating above three step until characteristic MS profiles of features of the microorganisms is obtained; comparing m/z of MALDITOF MS spectrum of known microorganisms with the characteristic profiles to obtain first matched vectors; using a machine learning method to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing the m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with the characteristic MS profiles to obtain second matched vectors; using the feature classification model to analyze the second matched vectors; and identifying the microorganisms having the unknown features.

Method and devices for calibrating optical density reflective color fluids to be deposited on substrate are disclosed. Some methods comprise depositing a quantity of a keying color fluid on a first region of the substrate; applying a voltage level to a reflective color fluid application device; depositing, in response to the voltage level applied, a quantity of reflective color fluid on the first region of the substrate and on a second region of the substrate; performing reflectance measurements of the first region and of the second region; performing optical density calculations as a function of the reflectance measurements; varying the voltage level applied to the reflective color fluid application device in response to said optical density calculation until the optical density calculation is within a calibrated range of optical densities.