Tunnel defect detecting method and system using unmanned aerial vehicle (UAV) are provided, and the UAV is equipped with a light-emitting diode (LED) module, a camera, a laser radar, an ultrasonic distance meter and an inertial measurement unit (IMU). The method includes: collecting images in a tunnel based on the LED module and the camera to obtain a training image set; training by using the training image set to obtain a defect detecting model, collecting real-time tunnel images, detecting suspected defects to the real-time tunnel images by the defect detecting model, obtaining pose information of the UAV based on the camera, the laser radar, the ultrasonic distance meter and the IMU to control the UAV to hover. The method can realize accurate pose estimation and defect detection in the tunnel with no GPS signals and highly symmetrical inside.

High-throughput methods and systems for using morphological and/or autofluorescence signatures of cells to characterize unknown cell/tissue types within a forensic sample are provided. Machine learning algorithms are used to correlate morphological and/or autofluorescence signatures to characteristics such as cell type.

An open path gas detection system includes a transmitter and a receiver. The transmitter is configured to generate illumination across an open path. The receiver is positioned to detect the illumination from the transmitter after the illumination has passed through the open path and detect a gas of interest based on the illumination. However, the laser can also be used for gas detection systems in other circumstances. The transmitter and receiver are configured to communicate wirelessly. A method of operating an open path gas detection system is also provided.

A biological tissue analyzing device configured to analyze a biological tissue using hyperspectral data in which spectral information is associated with each of pixels forming a two-dimensional image and comprising the following (i) and (ii), as well as comprising (iii) and/or (iv): (i) a hyperspectral data acquisition unit configured to acquire the hyperspectral data; (ii) an analysis target region extraction unit configured to extract pixels corresponding to an analysis target region from a two-dimensional image of the biological tissue; (iii) an altered state classification unit configured to roughly classify an altered state of the biological tissue with unsupervised learning; and (iv) an altered state identification unit configured to identify the altered state of the biological tissue with supervised learning.

A computer-implemented method includes: accessing a plurality of geo-exploration data from a first drilling site, wherein the plurality of geo-exploration data include spectroscopic infra-red (IR) data and well logs, wherein at least portions of the plurality of geo-exploration data are based on measurements of core samples taken from the first drilling site; based on, at least in part, the plurality of geo-exploration data, training a set of deep learning models, each deep learning model comprising multiple layers and configured to predict one or more geological formation properties; applying the set of deep learning models to newly received geo-exploration data that also includes spectroscopic IR data; and predicting the one or more geological formation properties based on, at least in part, the newly received geo-exploration data.

The invention generally relates to methods, reagents, and substrates for detecting target analytes, especially spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) for use in food authentication and molecular detection (e.g., when combined with later flow immunoassays (LFIA).

An apparatus for generating a two dimensional map representative of a turbid or clear medium (11) includes a system (12) for generating incoherent light within a medium (11), a light collecting system (13) that is movable or stationary relative to the medium (11) being analyzed and that is arranged for collecting light exiting the medium (11), and a spectrum analyzer (14) configured to determine spectrum data of the light exiting the turbid medium (11) and to transmit the spectrum data to a computing unit (15). The computing unit (15) is configured to generate a two dimensional map, in which one dimension of the map is wavelength and a second dimension is a position of the light collecting system (13). The invention is also direct-ed to a method for classifying media using the two dimensional map generated with the apparatus. The method comprises steps of feeding and training neural networks and using the trained neural networks to classify unknown media.

A spectrum analysis apparatus is an apparatus for analyzing an analysis object on the basis of a spectrum of light generated in the analysis object containing any one or two or more of a plurality of reference objects, and includes an array conversion unit, a processing unit, a learning unit, and an analysis unit. The array conversion unit generates two-dimensional array data on the basis of a spectrum of light generated in the reference object or the analysis object. The processing unit includes a deep neural network. The analysis unit causes the array conversion unit to generate the two-dimensional array data on the basis of the spectrum of light generated in the analysis object, inputs the two-dimensional array data to the deep neural network, and analyzes the analysis object on the basis of data output from the deep neural network.

An apparatus for producing an infrared spectrum according to one example of the present disclosure includes: a toxic chemical gas and background infrared spectrum acquisition portion of acquiring a background of a target area and an infrared spectroscopic signal of a gas contaminant plume existing in the background; and a toxic chemical gas infrared spectrum generation portion of training a Generative Adversarial Network (GAN) using acquired background radiation intensity data as learning data, and automatically generating a toxic chemical gas simulation infrared spectrum signal according to an environment setting inputted from a user using a learned GAN. According to the present disclosure, there is an effect that an infrared spectrum of atmosphere contaminated by a toxic chemical gas may be acquired without outdoor experiments using a real toxic chemical gas.

A method and system are provided for determining a threshing loss in a threshing system. The method includes irradiating a crop sample downstream at least a portion of a threshing system with electromagnetic waves having a frequency in a range of 0.1-10 THz, measuring a reflection and/or a transmission of the electromagnetic waves by the crop sample, establishing, based on the measured reflection and/or transmission, an at least two-dimensional terahertz image of the crop sample, identifying at least one ear in the terahertz image, identifying at least one grain kernel in the identified ear, and determining the threshing loss based on the identified grain kernel.