An infant sleep device may include a platform for supporting an infant, a base upon which the platform is supported, and one or more weight sensors positioned to measure weight of an infant positioned on the platform.

An infant sleep device may include a platform for supporting an infant, a base upon which the platform is supported, and one or more weight sensors positioned to measure weight of an infant positioned on the platform.

A measurement apparatus includes a beta gauge for generating a first sensor response signal from a composite sheet including a sheet material having a coating thereon including a high-z material or the sheet material has particles including the high-z material embedded in the sheet material. A second sensor being an x-ray or an infrared (IR) sensor provides a second sensor response signal from the composite sheet. A computing device is coupled to receive the first and the second sensor response signal that includes a processor having an associated memory for implementing an algorithm that uses the first and the second sensor response signal to simultaneously compute two or more weight measures selected from (i) a weight per unit area of the high-z material, (ii) a weight per unit area of the sheet material, and (iii) a total weight per unit area of the composite sheet.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors

Vehicle center of gravity (CoG) height and mass estimation techniques utilize a light detection and ranging (LIDAR) sensor configured to emit light pulses and capture reflected light pulses that collectively form LIDAR point cloud data and a controller configured to estimate the CoG height and the mass of the vehicle during a steady-state operating condition of the vehicle by processing the LIDAR point cloud data to identify a ground plane, identifying a height difference between (i) a nominal distance from the LIDAR sensor to the ground plane and (ii) an estimated distance from the LIDAR sensor to the ground plane using the processed LIDAR point cloud data, estimating the vehicle CoG height as a difference between (i) a nominal vehicle CoG height and the height difference, and estimating the vehicle mass based on one of (i) vehicle CoG metrics and (ii) dampening metrics of a suspension of the vehicle.

Vehicle center of gravity (CoG) height and mass estimation techniques utilize a light detection and ranging (LIDAR) sensor configured to emit light pulses and capture reflected light pulses that collectively form LIDAR point cloud data and a controller configured to estimate the CoG height and the mass of the vehicle during a steady-state operating condition of the vehicle by processing the LIDAR point cloud data to identify a ground plane, identifying a height difference between (i) a nominal distance from the LIDAR sensor to the ground plane and (ii) an estimated distance from the LIDAR sensor to the ground plane using the processed LIDAR point cloud data, estimating the vehicle CoG height as a difference between (i) a nominal vehicle CoG height and the height difference, and estimating the vehicle mass based on one of (i) vehicle CoG metrics and (ii) dampening metrics of a suspension of the vehicle.

In one example in accordance with the present disclosure, an electronic device is described. The example electronic device includes an input device with a force sensor to measure a force applied to the input device. The example electronic device also includes a controller to receive a user selection of an object type for an object to be placed on a location of the input device. The controller is to determine a weight of the object in response to receiving the user selection. The controller is to also receive a force measurement from the force sensor in response to placement of the object on the input device. The controller is to calibrate the force sensor based on the object weight and the force measurement.

In one example in accordance with the present disclosure, an electronic device is described. The example electronic device includes an input device with a force sensor to measure a force applied to the input device. The example electronic device also includes a controller to receive a user selection of an object type for an object to be placed on a location of the input device. The controller is to determine a weight of the object in response to receiving the user selection. The controller is to also receive a force measurement from the force sensor in response to placement of the object on the input device. The controller is to calibrate the force sensor based on the object weight and the force measurement.

The present disclosure relates to a smart bottle holder and a daily water consumption monitoring method and system thereof. The smart bottle holder includes a bottle holder main body for accommodating a bottle, and a control unit provided in a bottom part of the bottle holder main body. The control unit includes a weighing module, for obtaining a total weight of the bottle and water therein; a controller; a communication module, for sending water consumption data to an external terminal; and a power module, for supplying power to the weighing module, controller, and communication module. Compared with the prior art, the present disclosure has stronger structural mobility and wider range of applications. Also, the present disclosure is more power-saving and environmentally friendly and can provide more accurate water consumption statistics.

Generating consensus biomass estimates include providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model and providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model. The first biomass estimation model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second biomass estimation model. An aggregated biomass estimate is determined based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.