A horticultural luminaire includes a first and second horticultural light sources to provide growth lighting to a plant at a first and second wavelengths. A control unit provides first lighting control signals to the first horticultural light source to modulate the first growth lighting and provides second lighting control signals to the second horticultural light source to modulate the second growth lighting. A LiDAR sensor is connected to the lighting control unit to receive the first and second control signals, and having optics to detect reflected first and second growth lighting to determine the distance from plant to sensor and a biometric property of the plant from the received first and second control signals and detected first and second reflected second growth lighting. In some implementations the LiDAR sensor and first and second horticultural light sources are integrated into the horticultural luminaire.

Provided is an optical isolator including a semiconductor substrate, an optical attenuator and an optical amplifier aligned with each other on the semiconductor substrate, an input optical waveguide connected to the optical attenuator, and an output optical waveguide connected to the optical amplifier, wherein a gain of the optical amplifier decreases based on an intensity of light incident on the optical amplifier increasing, wherein a first input light incident on the optical attenuator through the input optical waveguide is output as a first output light through the output optical waveguide, and a second input light incident on the optical amplifier through the output optical waveguide is output as a second output light through the input optical waveguide, and wherein when an intensity of the first input light and an intensity of the second input light are equal, an intensity of the first output light is greater than an intensity of the second output light.

Provided is an optical isolator including a semiconductor substrate, an optical attenuator and an optical amplifier aligned with each other on the semiconductor substrate, an input optical waveguide connected to the optical attenuator, and an output optical waveguide connected to the optical amplifier, wherein a gain of the optical amplifier decreases based on an intensity of light incident on the optical amplifier increasing, wherein a first input light incident on the optical attenuator through the input optical waveguide is output as a first output light through the output optical waveguide, and a second input light incident on the optical amplifier through the output optical waveguide is output as a second output light through the input optical waveguide, and wherein when an intensity of the first input light and an intensity of the second input light are equal, an intensity of the first output light is greater than an intensity of the second output light.

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over- or under-exposure. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on a number of unreliable pixels in measured data and/or based on intensity values of the measured data. For example, unreliable pixels can be determined using phase frame information captured at a receiver of the sensor.

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over- or under-exposure. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on a number of unreliable pixels in measured data and/or based on intensity values of the measured data. For example, unreliable pixels can be determined using phase frame information captured at a receiver of the sensor.

A system and method for robust automatic control of an air-conditioning system in a vehicle includes at least one sensor configured to continuously capture technical driving parameters of the vehicle. The system has a computing unit configured to determine a current air quality from the captured technical driving parameters by way of a suitable algorithm. The system has a control unit configured to control the air-conditioning system in the vehicle, wherein the control of the air-conditioning system includes activating the recirculation circuit of the air-conditioning system and/or activating the fresh-air circuit of the air-conditioning system with reference to the determined air quality.

Surrogates for roadside objects, such as concrete curbs, can be used for vehicle testing. A surrogate for a concrete curb can substantially be similar in size and/or shape as the concrete curb that the surrogate is mimicking. The surrogate can be configured to exhibit substantially the same characteristics as their actual counterpart concrete curb when sensed by one or more vehicle sensors (e.g., cameras, radar sensors, and/or LIDAR sensors). Such surrogates can be used to test automated vehicles, one or more vehicle sensors, a vehicle sensor system, and/or one or more vehicle system (e.g., a road departure mitigation system). The surrogates can be configured to withstand being impacted by a test vehicle without being damaged and without damaging the test vehicle.

Surrogates for roadside objects, such as concrete curbs, can be used for vehicle testing. A surrogate for a concrete curb can substantially be similar in size and/or shape as the concrete curb that the surrogate is mimicking. The surrogate can be configured to exhibit substantially the same characteristics as their actual counterpart concrete curb when sensed by one or more vehicle sensors (e.g., cameras, radar sensors, and/or LIDAR sensors). Such surrogates can be used to test automated vehicles, one or more vehicle sensors, a vehicle sensor system, and/or one or more vehicle system (e.g., a road departure mitigation system). The surrogates can be configured to withstand being impacted by a test vehicle without being damaged and without damaging the test vehicle.

In an embodiment, a method for estimating a composition of food includes: receiving a first three-dimensional (3D) image; identifying food in the first 3D image; determining a volume of the identified food based on the first 3D image; and estimating a composition of the identified food using a millimeter-wave radar.

The disclosure provides a structure diagnosis system and a structure diagnosis method. The structure diagnosis system includes: a lidar scanner scanning a structure to generate a point cloud data; an input interface receiving the point cloud data; and a processor receiving the point cloud data and generating a point cloud data set. The processor executes a surface degradation and geometry abnormal coupling diagnosis module to: marking a first point cloud range of a surface degradation area according to color space value of the point cloud data set; marking a second point cloud range of a geometry abnormal area according to coordinate value of the point cloud data set; when an abnormal area includes the first point cloud range and the second point cloud range at least partially overlapping each other, determining surface degradation or geometry abnormal occurring at the abnormal area and mark the abnormal area with a predetermined mode.