A method may include capturing, by a camera at an infusion pump, one or more images of the pump loaded with an infusion set. A machine learning model may be applied to the images to detect nonconformities that may be present in the images of the pump loaded with the infusion set. Examples of nonconformities include a misload of the intravenous set in which an upper fitment, a lower fitment, and/or a tubing of the intravenous set is misplaced within the pump. In response to an output of the machine learning model indicating a presence of a nonconformity in the one or more images of the pump loaded with the infusion set, a corrective action may be performed. For example, the pump may be prevented from performing an infusion and a message identifying the nonconformities may be generated. Related methods and articles of manufacture are also disclosed.

A fingerprint sensor includes a sensor pixel arranged in a sensing area, including a pixel electrode coupled to a first node; a first transistor coupled between the first node and a first or second power line, the first transistor including a first gate electrode coupled to a first scan line and a second gate electrode opposite to the first gate electrode; a first capacitor coupled between the first node and a second scan line; a second transistor coupled between a readout line and the first power line, the second transistor including a first gate electrode coupled to the first node and a second gate electrode opposite to the first gate electrode; and a third transistor coupled between the second transistor and the first power line, the third transistor including a first gate electrode coupled to the second scan line and a second gate electrode opposite to the first gate electrode.

A fingerprint sensor includes a sensor pixel arranged in a sensing area, including a pixel electrode coupled to a first node; a first transistor coupled between the first node and a first or second power line, the first transistor including a first gate electrode coupled to a first scan line and a second gate electrode opposite to the first gate electrode; a first capacitor coupled between the first node and a second scan line; a second transistor coupled between a readout line and the first power line, the second transistor including a first gate electrode coupled to the first node and a second gate electrode opposite to the first gate electrode; and a third transistor coupled between the second transistor and the first power line, the third transistor including a first gate electrode coupled to the second scan line and a second gate electrode opposite to the first gate electrode.

Image capture devices for monitoring parking at various geographic locations and systems for monitoring parking are described herein.

Image capture devices for monitoring parking at various geographic locations and systems for monitoring parking are described herein.

System, methods, and other embodiments described herein relate to estimating depth using a machine learning (ML) model. In one embodiment, a method includes acquiring image data according to criteria from a detector that uses a lens to resolve multiple angles of light per section of the detector. The method also includes mapping a kernel to the image data according to a view associated with the section and a size of the kernel. The method also includes processing the image data using the ML model to produce the depth according to the size of the kernel.

A polarized image acquisition section 11a acquires a polarized image of a target object having one or more polarization directions. A polarization parameter acquisition section 12-1 calculates the average brightness α of a polarization model on the basis of a non-polarized image subjected to sensitivity correction. Further, the polarization parameter acquisition section 12-1 calculates the amplitude β of the polarization model on the basis of the calculated average brightness α, pre-stored information regarding the zenith angle θ of the normal line of the target object, a refractive index r, and reflectance property information indicative of whether a subject is diffuse reflection or specular reflection. A polarization model detection section 13-1 is able to detect the polarization properties of the target object through the use of an image polarized in one or more polarization directions, by calculating the phase ϕ of the polarization model on the basis of a polarized image of the target object having one or more polarization directions, the average brightness α, and the amplitude β of the polarization model.

A polarized image acquisition section 11a acquires a polarized image of a target object having one or more polarization directions. A polarization parameter acquisition section 12-1 calculates the average brightness α of a polarization model on the basis of a non-polarized image subjected to sensitivity correction. Further, the polarization parameter acquisition section 12-1 calculates the amplitude β of the polarization model on the basis of the calculated average brightness α, pre-stored information regarding the zenith angle θ of the normal line of the target object, a refractive index r, and reflectance property information indicative of whether a subject is diffuse reflection or specular reflection. A polarization model detection section 13-1 is able to detect the polarization properties of the target object through the use of an image polarized in one or more polarization directions, by calculating the phase ϕ of the polarization model on the basis of a polarized image of the target object having one or more polarization directions, the average brightness α, and the amplitude β of the polarization model.

An inspection apparatus includes a specimen stage, one or more imaging devices and a set of lights, all controllable by a control system. By translating or rotating the one or more imaging devices or specimen stage, the inspection apparatus can capture a first image of the specimen that includes a first imaging artifact to a first side of a reference point and then capture a second image of the specimen that includes a second imaging artifact to a second side of the reference point. The first and second imaging artifacts can be cropped from the first image and the second image respectively, and the first image and the second image can be digitally stitched together to generate a composite image of the specimen that lacks the first and second imaging artifacts.

A texture recognition device and a display device are provided. The texture recognition device includes a backlight element, configured to provide first backlight; a light constraint element, configured to perform a light divergence angle constraint process on the first backlight to obtain second backlight with a divergence angle within a preset angle range, the second backlight being transmitted to a detection object; and a photosensitive element, configured to detect the second backlight reflected by a texture of the detection object to recognize a texture image of the texture of the detection object.