An imaging device including a first filter that passes a first light and a second light; a second filter that blocks the second light; photoelectric converters that have sensitivity to the first light and the second light; and a processor, in which one of the photoelectric converters detects a light passing through the first filter to generate a first signal, one of the photoelectric converters detects a light passing through the first filter and the second filter to generate a second signal, and the processor performs the second light sensing based on the first signal and the second signal.

A telescopic sensor system for an autonomous vehicle enables sensors located on movable telescopic apparatuses to obtain sensor data when an object obstructs an area scanned by fixed sensors. An example method of controlling a movable telescopic apparatus on an autonomous vehicle includes obtaining, from a first sensor located on the autonomous vehicle, a first sensor data of a first area relative to a location of the autonomous vehicle, performing, from the first sensor data, a first determination that a view of the first area is obstructed, causing, in response to the first determination, a second sensor coupled to the movable telescopic apparatus to extend to a pre-determined position, and obtaining, from the second sensor, a second sensor data of a second area relative to the location of the autonomous vehicle, where the second area includes at least some of the first area.

A telescopic sensor system for an autonomous vehicle enables sensors located on movable telescopic apparatuses to obtain sensor data when an object obstructs an area scanned by fixed sensors. An example method of controlling a movable telescopic apparatus on an autonomous vehicle includes obtaining, from a first sensor located on the autonomous vehicle, a first sensor data of a first area relative to a location of the autonomous vehicle, performing, from the first sensor data, a first determination that a view of the first area is obstructed, causing, in response to the first determination, a second sensor coupled to the movable telescopic apparatus to extend to a pre-determined position, and obtaining, from the second sensor, a second sensor data of a second area relative to the location of the autonomous vehicle, where the second area includes at least some of the first area.

A light-receiving cell and an optical biometrics sensor using the same are provided. The light-receiving cell for converting optical energy into electrical energy includes: one or multiple main light-receiving regions; and a connection region directly connected to the one or multiple main light-receiving regions to form an area-reduced light-receiving region, wherein the light-receiving region has one or multiple area reduced parts to decrease junction capacitance and increase a sensing voltage signal.

A light-receiving cell and an optical biometrics sensor using the same are provided. The light-receiving cell for converting optical energy into electrical energy includes: one or multiple main light-receiving regions; and a connection region directly connected to the one or multiple main light-receiving regions to form an area-reduced light-receiving region, wherein the light-receiving region has one or multiple area reduced parts to decrease junction capacitance and increase a sensing voltage signal.

In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

An iris scanning device that includes a single camera sensor utilized for generating iris scan data is described herein. The iris scanning device includes a selector, a lens, and the camera sensor. The selector is configured to receive a first optical signal representative of a first eye and a second optical signal representative of a second eye. The selector selectively allows a passed optical signal to be optically propagated to the lens and inhibits a blocked optical signal from being optically propagated to the lens during a time period. The passed optical signal is one of the first optical signal or the second optical signal during the time period. The blocked optical signal is a differing one of the first optical signal or the second optical signal during the time period. The lens causes the passed optical signal to be incident on the camera sensor during the time period.

An item management apparatus manages the number of items displayed on an item shelf. An acquisition unit acquires an ideal quantity indicating an ideal number of items to be displayed, and a current quantity indicating the number of items being actually displayed on the item shelf. A detection unit detects the number of times of taking out and placing back an item with respect to the item shelf. An ideal quantity correction unit corrects the ideal quantity based on the current quantity and the number of times of taking out and placing back the item.

The present disclosure discloses an electro-hydraulic varifocal lens-based method for tracking a three-dimensional (3D) trajectory of an object by using a mobile robot. By modeling an optical imaging system, a functional relation between a focusing control current of an electro-hydraulic varifocal lens and an optimal imaging object distance is obtained. Based on this functional relation, depth information of the object in focus with respect to a mobile robot camera and an average velocity of the objective within a time interval with respect to a previous moment can be obtained. With this information, 3D coordinates and motion trajectory of the object in a camera coordinate system can be calculated. At the same time, the mobile robot locates positions and attitudes in a world coordinate system in real time, and transforms the 3D coordinates of the tracked object from the camera coordinate system to the world coordinate system.

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.