Systems and methods for automatically grading a user device are provided. Such systems and methods can include (1) a lighting element positioned at an angle relative to a platform, (2) an imaging device positioned at the angle relative to the platform such that light emitted from the lighting element and a field of view of the imaging device form a right angle where the light emitted from the lighting element and the field of view meet at a user device when the user device is positioned at a predetermined location on the platform, and (3) control circuitry that can activate the lighting element, instruct the imaging device to capture an image of a screen of the user device while the user device is at the predetermined location and is being illuminated by the first lighting element, and parse the image to determine whether the screen is damaged.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

An apparatus for determining defective hair follicles includes an image acquiring unit for acquiring an image of a follicle and a hair for each follicle separated from a scalp of an alopecic patient in an incisional hair transplant or each follicle directly extracted from an alopecic patient in a non-incisional hair transplant, an image processing unit for extracting an outline pattern of the image of the follicle and the hair by performing a contour detection process or an edge detection process on the image, a follicle shape database for storing hair pixel patterns related to various shapes of hairs and follicle pixel patterns related to various shapes of follicles, and a follicle determining unit for determining whether a follicle is normal follicle or defective follicle by comparing the outline pattern of the image with the hair pixel patterns and follicle pixel patterns stored in the follicle shape database.

Systems and methods for generating assessments based on construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. Further, at least one electronic record associated with the construction site may be obtained. The image data and the at least one electronic record may be analyzed to generate at least one assessment related to the construction site. For example, the image data may be analyzed to identify at least one discrepancy between the at least one electronic record and the construction site, and the identified at least one discrepancy may be used in the generation of the at least one assessment.

A connected ecosystem for a laboratory environment comprises an electronic lab notebook, and instrumented biosafety cabinet, and one or more sensing vessels containing cell cultures. The electronic lab notebook interfaces with the instrumented biosafety cabinet to provide instructions, guidance, and monitoring of a user during the set up of the experimental protocol and to receive commands from the user via one of several input modalities. After the experimental protocol has been set up in the instrumented biosafety cabinet, cell cultures may be moved to an incubator where the connected ecosystem may provide automatic monitoring of the cultures. The automatic monitoring is provided by sensors integrated into cell culture vessels and supplemented by images of the cell cultures captured by a camera. The user may be informed of deviations from expected results detected based on the automatic monitoring.

The present invention provides a food and/or beverage item counting device to be provided in a food and/or beverage item provision system including a transport path that passes along a customer table to transport carriers each configured to allow a food and/or beverage item to be placed thereon, the food and/or beverage item counting device including: a first information acquiring unit disposed upstream of the table and configured to acquire information relating to each of the carriers on the transport path; a second information acquiring unit disposed downstream of the table and configured to acquire information relating to each of the carriers on the transport path; and a first calculating unit configured to calculate the number of the food and/or beverage items taken out from the transport path to the table, wherein each of the carriers is provided with identification information for identifying the carrier.

Recycling systems can include a conveyor system, an optical device, and a processor operable to detect, identify, and count redeemable objects. The conveyor system can include a plurality of bins and be configured to move the bins from a first end of the conveyor system to a second end of the conveyor system. The optical device can be situated and configured to capture an image of a plurality of objects within a bin of the conveyor system. A processor including computer-readable instructions can be configured to detect and identify the objects within an image captured by the optical device as belonging to one or more object classes. Each object class can determine whether an object is redeemable or unredeemable. The processor can be configured to provide a count of only those objects identified as redeemable.

In one embodiment, a method includes determining an operational status of a vehicle including a radar antenna. The operational status is related to autonomous-driving operations of the vehicle in an environment. The method includes determining an expected amount of signaling resources associated with the radar antenna to be utilized by the vehicle while the vehicle performs the autonomous-driving operations, based at least on the operational status of the vehicle and the environment. The method includes determining to switch one or more of the signaling resources associated with the radar antenna from a first mode to a second mode based on the expected amount of signaling resources to be utilized by the radar antenna while the vehicle performs the autonomous-driving operations. The method includes causing the one or more of the signaling resources associated with the radar antenna to switch from the first mode to the second mode.