A measurement apparatus includes an x-ray sensor including an x-ray source having a high voltage power supply for emitting an x-ray spectrum and an x-ray detector for providing a measured x-ray signal value responsive to the x-rays received after transmission through a coated substrate including a sheet material having a coating material thereon. A second sensor is a beta gauge or infrared sensor for providing a second sensor signal that includes data for determining a total weight per unit area of the coated substrate or of the sheet material A computing device receives the measured x-ray signal value and the second sensor signal configured to implement an x-ray based calculation that utilizes absorption coefficients for the coating material and sheet material, the measured x-ray signal value, the x-ray spectrum, and the weight measure as a calculation constraint, for computing at least the weight per unit area of the coating material.

A method and a device for determining the amount of fill volume in a container. A mass of polarizable liquid in a container is positioned within the measurement region of a shielding antenna and a measuring pulse antenna. The shielding antenna is connected to both a measuring device that measures a time-dependent voltage value of an external interference signal and to a compensation signal generator that generates a time-dependent compensation signal compensating this interference signal. The measuring pulse antenna is connected to both a measuring pulse generator that generates a polarization signal to transmit to the fluid fill in the container and to a second measuring device that measures a response signal to derive the mass of the fluid from said response signal.

A radio frequency (RF) grain mass and constituent measurement system utilized onboard a combine harvester includes an RF sensor subsystem for capturing RF sensor readings of a harvested grain within an area of the combine harvester. A memory stores an RF characteristic database, which contains RF characteristic testing data collected for tested grain samples over one or more tested frequency ranges. A controller, operably coupled to the RF sensor subsystem and to the memory, is configured to: (i) receive the RF sensor readings from the RF sensor subsystem; (ii) determine grain mass and a first constituent content of the currently-harvested grain based, at least in part, on an analytical comparison between the RF sensor readings and the RF characteristic testing data; and (iii) perform at least one action in response to determining the grain mass and the first constituent content of the harvested grain.

The weighing platform with a lattice load-bearing structure shaped spatially and made of load-bearing elements and connecting elements intersecting with them, affixed to a joint frame, whereas the load-bearing elements, in the place of intersection with the connecting profiles, have cuts in the shape adjusted to the shape of the connecting elements, and additionally the platform contains at least one load-bearing shell, measurement elements, regulated feet and connectors, according to the invention is characterised in that the connecting elements (8) are located strictly in the cuts (7.1) of the load-bearing structure (7) at the depth g, equal to height h of the connecting elements (8) and at the same time less than half of the height H of the load-bearing elements (7), while to the bottom of the weighing platform at least two profile panels (3) are fixed, with openings (10) for the screw connectors (6), to which panels (3) the measurement elements (4) and regulated feet (5) are fixed.

A measurement apparatus includes a first sensor, scanner head, and computing device coupled to the first sensor that stores relative signal level ranges at selected spectral marker region(s) compared to a common region each corresponding to a composite sheet material grade and an associated sensor calibration. The computing device measures a first signal in the spectral marker region and a second signal in the common region of a composite sheet including a sheet material and a high-z material, determining a current relative signal level comparing a current signal level of the first and second signal, and identifying a current composite sheet material grade for the composite sheet from the composite material grades using the current relative signal level. Based on the current composite material grade a current sensor calibration is chosen from the sensor calibrations, and 1 physical parameter for the composite sheet is determined from the current sensor calibration.

A measurement apparatus includes a first sensor, scanner head, and computing device coupled to the first sensor that stores relative signal level ranges at selected spectral marker region(s) compared to a common region each corresponding to a composite sheet material grade and an associated sensor calibration. The computing device measures a first signal in the spectral marker region and a second signal in the common region of a composite sheet including a sheet material and a high-z material, determining a current relative signal level comparing a current signal level of the first and second signal, and identifying a current composite sheet material grade for the composite sheet from the composite material grades using the current relative signal level. Based on the current composite material grade a current sensor calibration is chosen from the sensor calibrations, and 1 physical parameter for the composite sheet is determined from the current sensor calibration.

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.

An interferometric scattering microscope is adapted by performing spatial filtering of output light, which comprises both light scattered from a sample location and illuminating light reflected from the sample location, prior to detection of the output light. The spatial filtering passes the reflected illumination light but with a reduction in intensity that is greater within a predetermined numerical aperture than at larger numerical apertures. This enhances the imaging contrast for coherent illumination, particularly for objects that are weak scatterers.

A method of generating a three dimensional surface profile of a food object is provided wherein a food object is exposed with a conical X-ray beam while the food object is conveyed. The attenuation of the X-rays after penetrating through the food object is detected, and the detection is performed using a plurality of sensors arranged below the food object. The plurality of sensors are positioned at predetermined angular positions in relation to the X-ray source. For each of the plurality of sensors, the detected attenuation is converted into a penetration length of the X-ray beam, and the penetration length indicates the length from where the X-ray beam enters and leaves the food object. Surface coordinates are sequentially determined using the penetration lengths and the angular positions as input data.

In one embodiment, a method executed by a computing system, comprising: receiving pixel samples from three-dimensional (3D) data corresponding to one or more images comprising one or more animals; fitting curves for the received pixel samples; deriving parameters from the curves; determining measurements based on variations in the parameters; and estimating a weight of the one or more animals by applying one or more regression algorithms to the measurements.