The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

Provided herein is a method for measuring the size distribution and/or hardness of free falling rock pieces. The method comprises projecting at least one laser line on the falling rock pieces by a laser device; capturing images of the falling rock pieces at an angle from the at least one laser line by at least one camera; and obtaining size distribution data of the falling rock pieces based on data obtained from a topographical map generated from the captured images. Certain embodiments further comprise: obtaining at least one of the volume and area of individual rock pieces from the topographical map; conducting a data analysis on at least one of the volume and area measurements of the rock pieces to reduce at least one of sampling and measurement errors; determining the size distribution of the falling rock pieces based on the data analysis and, optionally, evaluating a rock hardness index for the rock. Further provided is a method comprising: producing two topographical maps of the pieces from captured images; and obtaining the volume of pieces from the topographical map by adding half-volumes from each of the topographical maps.

An optical method of characterizing an object comprises providing an object to be characterized, the object having at least one nanoscale feature; illuminating the object with coherent plane wave optical radiation having a wavelength larger than the nanoscale feature; capturing a diffraction intensity pattern of the radiation which is scattered by the object; supplying the diffraction intensity pattern to a neural network trained with a training set of diffraction intensity patterns corresponding to other objects with a same nanoscale feature as the object to be characterized, the neural network configured to recover information about the object from the diffraction intensity pattern; and making a characterization of the object based on the recovered information.

An improved additive manufacturing system for manufacturing metal parts by magnetohydrodynamic printing liquid metal. A monitoring system including at least one camera capturing light reflected from a strobe light source. Images of the droplets are captured during their jetting and analyzed to determine whether the jetting performance is meeting specifications. A nozzle of the system has a nozzle bottom and a nozzle stem extending outward therefrom on which a meniscus of liquid metal can form. The nozzle is cleaned by bringing a ceramic rod in the vicinity of the nozzle and jetting a bead of metal which is rotated against the nozzle to remove an amount of dross.

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

A method for characterizing particle objects comprises generating a radiation beam, illuminating with the radiation beam an observation region transited by a particle object, collecting an interference image determined by an interference between a transmitted fraction and a part of the scattered fraction of the radiation beam that propagates around the direction of the optical axis, collecting a part of the scattered fraction that propagates at the scattering angle, and measuring at least one scattered radiation intensity value determined by the part of the scattered fraction, calculating, from the interference image, a pair of independent quantities that define the complex field of the first part of the scattered fraction, calculating, starting from the pair of independent quantities, a theoretical value of scattered radiation intensity, and comparing the measured value with the theoretical scattered radiation intensity value.

An apparatus for sorting cells includes a measurement volume that contains a cell to be measured, a light source that provides light to cause an emission by a fluorescent label attached to the cell, and an optic device that directs the light through the measurement volume. The apparatus flows the cells through the measurement volume such that as the cell flows through the measurement volume, it interacts with the light, resulting in a change in light originating from the measurement volume, the change in light is a fluorescence emission. Another optic device directs a portion of the light originating from the measurement volume to a detector, which detects the portion of the light. A processor operably coupled to the detector generates an estimate of DNA quantity in the cell based on the change in light originating from the measurement volume, and determines a characteristic of the cell from the estimate.

The present invention discloses a three-dimensional diffraction tomography microscopy imaging method based on LED array coded illumination. Firstly, acquiring the raw intensity images, three sets of intensity image stacks are acquired at different out-of-focus positions by moving the stage or using electrically tunable lens. And then, after acquiring the intensity image stacks of the object to be measured at different out-of-focus positions, the three-dimensional phase transfer function of the microscopy imaging system with arbitrary shape illumination is derived. Further, the three-dimensional phase transfer function of the microscopic system under circular and annular illumination with different coherence coefficients is obtained as well, and the three-dimensional quantitative refractive index is reconstructed by inverse Fourier transform of the three-dimensional scattering potential function. The scattering potential function is converted into the refractive index distribution. Thus, the quantitative three-dimensional refractive index distribution of the test object is obtained. The invention realizes high-resolution and high signal-to-noise ratio 3D diffraction tomography microscopic imaging of cells, tiny biological tissues and other samples.

Systems and methods are provided for provided for automatic evaluation of sperm morphology. An image of a semen sample is obtained, and at least a portion of the image is provided to a convolutional neural network classifier. The convolutional neural network classifier evaluates the portion of the image to assign to the portion of the image a set of likelihoods that the portion of the image belongs to a plurality of output classes representing the morphology of sperm within the portion of the image. A metric is assigned to the semen sample based on the likelihoods assigned by the convolutional neural network.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.