Embodiments of a water detection apparatus are disclosed that can detect characteristics of food products. The apparatus may include a conveying mechanism configured to move a food product through an aperture in the apparatus, a transmitter coil configured to transmit a signal within the aperture, and a receiver coil configured to receive the signal altered by the food product, and a signal processing unit configured to determine an output value associated with the food product moving through the aperture. The output value can be based, at least in part, on the signal and the altered signal. The output value can corresponds to a characteristic of the food product.

Embodiments of a water detection apparatus are disclosed that can detect characteristics of food products. The apparatus may include a conveying mechanism configured to move a food product through an aperture in the apparatus, a transmitter coil configured to transmit a signal within the aperture, and a receiver coil configured to receive the signal altered by the food product, and a signal processing unit configured to determine an output value associated with the food product moving through the aperture. The output value can be based, at least in part, on the signal and the altered signal. The output value can corresponds to a characteristic of the food product.

Disclosed herein is a system to detect and characterize individual particles and cells using at least either optic or electric detection as the particle or cell flows through a microfluidic channel. The system also provides for sorting particles and cells or isolating individual particles and cells.

Disclosed herein is a system to detect and characterize individual particles and cells using at least either optic or electric detection as the particle or cell flows through a microfluidic channel. The system also provides for sorting particles and cells or isolating individual particles and cells.

A banknote recognition and classification method and system are provided. In the method and system, a large number of reliable samples which are easy to acquire currently are used to establish a sample signal degradation model which meets application requirements by means of a statistical method, sample information is input into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned; the various banknote sample information is input to perform classifier learning, and a banknote classification model is output; then a classifier learns to perform classification and recognition on a sample to be recognized.

A banknote recognition and classification method and system are provided. In the method and system, a large number of reliable samples which are easy to acquire currently are used to establish a sample signal degradation model which meets application requirements by means of a statistical method, sample information is input into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned; the various banknote sample information is input to perform classifier learning, and a banknote classification model is output; then a classifier learns to perform classification and recognition on a sample to be recognized.

A support for transporting containers in a microwave inspection device, including a supporting body which can be moved along a feed path and at least two receiving seats connected to the top of the supporting body to be moved along said feed path, wherein said supporting body has two or more supporting portions (S1, S2) which are at least partly separated from each other by one or more openings and each carrying at least one respective receiving seat, said supporting portions (S1, S2) being connected or integral with each other in a lower region of the supporting body.

A support for transporting containers in a microwave inspection device, including a supporting body which can be moved along a feed path and at least two receiving seats connected to the top of the supporting body to be moved along said feed path, wherein said supporting body has two or more supporting portions (S1, S2) which are at least partly separated from each other by one or more openings and each carrying at least one respective receiving seat, said supporting portions (S1, S2) being connected or integral with each other in a lower region of the supporting body.

A method for analyzing device test data includes accessing a core analytics rule that is based on manufacturing data of a plurality of devices. Each of the plurality of devices are produced in one of a plurality of manufacturing facilities and are of a same type as a first device being tested on a tester. The method also includes receiving initial test results of a plurality of other devices of a same type tested at a testing facility, generating, based on the initial test results, an edge analytics rule, modifying the core analytics rule based on the edge analytics rule, wherein the modified core analytics rule including modified binning limits, applying the modified core analytics rule to testing data obtained by testing the first device, and determining, based on applying the modified core analytics rule, that the first device is an outlier with respect to the modified binning limits.

A method for analyzing device test data includes accessing a core analytics rule that is based on manufacturing data of a plurality of devices. Each of the plurality of devices are produced in one of a plurality of manufacturing facilities and are of a same type as a first device being tested on a tester. The method also includes receiving initial test results of a plurality of other devices of a same type tested at a testing facility, generating, based on the initial test results, an edge analytics rule, modifying the core analytics rule based on the edge analytics rule, wherein the modified core analytics rule including modified binning limits, applying the modified core analytics rule to testing data obtained by testing the first device, and determining, based on applying the modified core analytics rule, that the first device is an outlier with respect to the modified binning limits.