Abstract: Disclosed is a time-resolved spectrum rapid measurement system and method, the system including a pulse laser module, a sample stage module, a control computer module, a detector module, a spectrometer module, and a multi-path delay module. The multi-path delay module is configured to split the signal light produced by the samples in the sample stage module to form a plurality of optical paths; add a different optical distance in each optical path to extend the time for the signal light produced by the samples to reach a detector; and make the time for the signal light in each optical path to reach the detector different, to realize multi-path delay. By providing a multi-path delay module in the system, the present disclosure can measure the spectra of a plurality of delay windows in parallel, improving the measurement speed of time-resolved spectra while reducing the measurement error.

Abstract: Disclosed is a time-resolved spectrum rapid measurement system and method, the system including a pulse laser module, a sample stage module, a control computer module, a detector module, a spectrometer module, and a multi-path delay module. The multi-path delay module is configured to split the signal light produced by the samples in the sample stage module to form a plurality of optical paths; add a different optical distance in each optical path to extend the time for the signal light produced by the samples to reach a detector; and make the time for the signal light in each optical path to reach the detector different, to realize multi-path delay. By providing a multi-path delay module in the system, the present disclosure can measure the spectra of a plurality of delay windows in parallel, improving the measurement speed of time-resolved spectra while reducing the measurement error.

Abstract: A method for non-destructive ripeness identification of kiwifruit based on machine vision learning may include: collecting kiwifruit data to obtain an original data set by collecting images of 40-80 kiwifruits in the same period of time over 3-6 days, recording a label, which comprises ripeness information for each of the images, and saving each of the images with the label; extracting the color and the texture of a kiwifruit skin from each of the images in the original data set; and training a deep learning model to learn a connection between the color and the texture of the kiwifruit skin and the ripeness information of the corresponding kiwifruit using the color and the texture of the kiwifruit skin extracted from each of the images and the label.

Abstract: A non-intrusive load monitoring system, including a device classification and prediction sub-process, a new device identification sub-process, and a classifier self-training sub-process. By means of the configuration of these processes, the defect in accuracy of identification of a new device in the prior art is overcome; and when a device other than the devices in a device database is detected, data of the device can be intercepted and stored in the device database, so that the function of accurately identifying existing devices in a device database can be achieved, and the device database can be automatically updated when a new device other than the devices in the device database is discovered.

Abstract: A method for non-destructive ripeness identification of kiwifruit based on machine vision learning may include: collecting kiwifruit data to obtain an original data set by collecting images of 40-80 kiwifruits in the same period of time over 3-6 days, recording a label, which comprises ripeness information for each of the images, and saving each of the images with the label; extracting the color and the texture of a kiwifruit skin from each of the images in the original data set; and training a deep learning model to learn a connection between the color and the texture of the kiwifruit skin and the ripeness information of the corresponding kiwifruit using the color and the texture of the kiwifruit skin extracted from each of the images and the label.

Abstract: Systems and methods for reducing fluorescence and systematic noise in time-gated spectroscopy are disclosed. Exemplary methods include: a method for reducing fluorescence and systematic noise in time-gated spectroscopy may comprise: providing first light using an excitation light source; receiving, by a detector, first scattered light from a material responsive to the first light during a first time window; detecting a peak intensity of the first scattered light; receiving, by the detector, second scattered light from the material responsive to the first light during a second time window; detecting a peak intensity of the second scattered light; recovering a spectrum of the material by taking a ratio of the peak intensity of the first scattered light and the peak intensity of the second scattered light; and identifying at least one molecule of the material using the recovered spectrum and a database of identified spectra.

Abstract: Systems and methods for reducing fluorescence and systematic noise in time-gated spectroscopy are disclosed. Exemplary methods include: a method for reducing fluorescence and systematic noise in time-gated spectroscopy may comprise: providing first light using an excitation light source; receiving, by a detector, first scattered light from a material responsive to the first light during a first time window; detecting a peak intensity of the first scattered light; receiving, by the detector, second scattered light from the material responsive to the first light during a second time window; detecting a peak intensity of the second scattered light; recovering a spectrum of the material by taking a ratio of the peak intensity of the first scattered light and the peak intensity of the second scattered light; and identifying at least one molecule of the material using the recovered spectrum and a database of identified spectra.

Abstract: Systems and methods for reducing fluorescence and systematic noise in time-gated spectroscopy are disclosed. Exemplary methods include: a method for reducing fluorescence and systematic noise in time-gated spectroscopy may comprise: providing first light using an excitation light source; receiving, by a detector, first scattered light from a material responsive to the first light during a first time window; detecting a peak intensity of the first scattered light; receiving, by the detector, second scattered light from the material responsive to the first light during a second time window; detecting a peak intensity of the second scattered light; recovering a spectrum of the material by taking a ratio of the peak intensity of the first scattered light and the peak intensity of the second scattered light; and identifying at least one molecule of the material using the recovered spectrum and a database of identified spectra.

Abstract: Systems and methods for reducing fluorescence and systematic noise in time-gated spectroscopy are disclosed. Exemplary methods include: a method for reducing fluorescence and systematic noise in time-gated spectroscopy may comprise: providing first light using an excitation light source; receiving, by a detector, first scattered light from a material responsive to the first light during a first time window; detecting a peak intensity of the first scattered light; receiving, by the detector, second scattered light from the material responsive to the first light during a second time window; detecting a peak intensity of the second scattered light; recovering a spectrum of the material by taking a ratio of the peak intensity of the first scattered light and the peak intensity of the second scattered light; and identifying at least one molecule of the material using the recovered spectrum and a database of identified spectra.

Abstract: Systems and methods for reducing fluorescence and systematic noise in time-gated spectroscopy are disclosed. Exemplary methods include: a method for reducing fluorescence and systematic noise in time-gated spectroscopy may comprise: providing first light using an excitation light source; receiving, by a detector, first scattered light from a material responsive to the first light during a first time window; detecting a peak intensity of the first scattered light; receiving, by the detector, second scattered light from the material responsive to the first light during a second time window; detecting a peak intensity of the second scattered light; recovering a spectrum of the material by taking a ratio of the peak intensity of the first scattered light and the peak intensity of the second scattered light; and identifying at least one molecule of the material using the recovered spectrum and a database of identified spectra.