Abstract:
A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive spectral data acquired from a plurality of fruits, wherein the spectral data is obtained within a specified range of wavelengths, at a training stage, train a machine learning model on a training set comprising: (i) the spectral data, and (ii) labels indicating, with respect to each of the fruits, a drop status within a specified time period subsequent to the acquiring, and at an inference stage, apply the machine learning model to target spectral data acquired from a target fruit, to predict the drop status of the target fruit within a specified time range subsequent to the acquiring.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
A user device including a camera, a spectrometer module, and a processing unit is disclosed. In one aspect, the camera is adapted to acquire at least one image of a scenery which falls within a field of view of the camera. The spectrometer module is adapted to acquire spectral information from a region within the scenery which region falls within a field of view of the spectrometer module. The processing unit is adapted to determine, based on information relating the field of view of the spectrometer module to the field of view of the camera, a spectrometer module target area, within the at least one image, corresponding to the region. The processing unit is adapted to output display data to a screen of the user device for providing an indication of the target area on the display.
Abstract:
Methods and systems for performing simultaneous spectroscopic measurements of semiconductor structures at ultraviolet, visible, and infrared wavelengths are presented herein. In another aspect, wavelength errors are reduced by orienting the direction of wavelength dispersion on the detector surface perpendicular to the projection of the plane of incidence onto the detector surface. In another aspect, a broad range of infrared wavelengths are detected by a detector that includes multiple photosensitive areas having different sensitivity characteristics. Collected light is linearly dispersed across the surface of the detector according to wavelength. Each different photosensitive area is arranged on the detector to sense a different range of incident wavelengths. In this manner, a broad range of infrared wavelengths are detected with high signal to noise ratio by a single detector. These features enable high throughput measurements of high aspect ratio structures with high throughput, precision, and accuracy.
Abstract:
Systems and methods for measuring spectra and other optical characteristics such as colors, translucence, gloss, and other characteristics of objects and materials such as skin. Instruments and methods for measuring spectra and other optical characteristics of skin or other translucent or opaque objects utilize an abridged spectrophotometer and improved calibration/normalization methods. Improved linearization methods also are provided, as are improved classifier-based algorithms. User control is provided via a graphical user interface. Product or product formulations to match the measured skin or other object or to transform the skin or other object are provided to lighten, darken, make more uniform and the like.
Abstract:
Provided are an apparatus and method for recognizing an object on the basis of property information on an object obtained using a multi-wavelength spectrometer. An apparatus for recognizing an object using a multi-wavelength spectrometer includes an image processing unit configured to extract an region of interest from an input three-dimensional image and output shape information on the region of interest, a light irradiation unit configured to irradiate light of a plurality of wavelengths to an arbitrary position of an object corresponding to the detected region of interest, a light receiving unit configured to measure a spectrophotometric value for each light of the plurality of wavelengths, and a light processing unit configured to generate a differential spectrophotometric map using a differential value between spectrophotometric values of different wavelengths measured at the same light irradiation position, and recognize the object using the differential spectrophotometric map and the shape information.
Abstract:
A spectrometer includes a light source to project a light beam to a target object, an optical element including a plurality of apertures through which the light beam reflected by the target object transmits, a diffraction element to form diffracted images from a plurality of light beams having transmitted through the optical element, and a light receiving element to receive the diffracted images formed by the diffraction element and including an optical shield to block a diffracted image other than a certain-order diffracted image.