Abstract: Disclosed is a method for identifying and monitoring a shopping behavior in a user. The method includes capturing images from a depth camera mounted on a shelf unit, identifying a user from the captured image, identifying joints of the identified user by performing a deep neural network (DNN) body joint detection on the captured images; detecting and tracking actions of the identified user over a first time period; tracking an object from the bins over a second time period by associating the object with one or more joints among the identified joints that have entered the bins within the shelf unit, and determining an action of the identified user based at least in part on the associated object with the one or more joints and results from the deep learning identification on the bounding box.

Abstract: The described method and system allow the simultaneous detection of multicolored samples, e.g. in live cells or tissues, in a simple experimental geometry. It relies on combining ultrashort ultra-broadband laser sources (10) with a fluorescence microscope setup able to collect fluorescence intensities and/or photon arrival times per excitation volume, as well as nonlinear signals, such as second/third-harmonic and sum-frequency generation. In the description, the presented method is referred to as “SyncRGB method”.

Abstract: Disclosed is a method of predicting shopping events. The method includes obtaining a first set of events from a sensor and a second set of events from a camera, wherein the camera captures one or more users in front of a shelf unit; determining whether a first timestamp from the obtained first set of events and a second timestamp from the obtained second set of events are within a same time interval; determining whether a first bin number matches a second bin number based on a determination that the first timestamp and the second timestamp are within the same time interval; and generating a shopping event for a user based on a determination that the first bin number matches the second bin number, wherein the user is associated with at least one of the obtained first set of events and the second set of events.

Abstract: Disclosed is a method for training a device using machine learning. The method includes obtaining a sensor weight data stream and an environment sensor data stream; determining a new weight value by multiplying a weight measurement among the obtained sensor weight data stream with an environment calibrating coefficient, wherein the environment calibrating coefficient is obtained from using an environment measurement and a pre-recorded calibration file; determining whether a detected change in weight from the device exceeds a predetermined value; and based on a determination that the detected change in weight exceeds the predetermined value, determining a difference in value using the determined new weight value, and identify an item based on inputting the determined difference in value into a machine learning model, wherein the machine learning model is trained to learn unit weights of a particular item to determine an identity of the particular item.

Abstract: Disclosed is a method for identifying and monitoring a shopping behavior in a user. The method includes capturing images from a depth camera mounted on a shelf unit, identifying a user from the captured image, identifying joints of the identified user by performing a deep neural network (DNN) body joint detection on the captured images; detecting and tracking actions of the identified user over a first time period; tracking an object from the bins over a second time period by associating the object with one or more joints among the identified joints that have entered the bins within the shelf unit, and determining an action of the identified user based at least in part on the associated object with the one or more joints and results from the deep learning identification on the bounding box.

Abstract: A process for concentrating manganese from the tailing of a manganese-carrying mineral including removing a coarse particle size fraction from the tailing, desliming and conducting an acidic or a basic reverse cationic flotation. The manganese-carrying minerals are typically minerals with low manganese content from the lithologies “Tabular Pelite” (or PETB), Pelite Siltite (or PEST), Detritic (or DETR), Rich Pelite (or PERC) and Metallurgical Bioxide (or BXME). In another aspect, the present invention also relates to a reverse cationic flotation used to concentrate manganese which is carried out using depressor agents and collector agents as flotation reagents.

Abstract: A process for concentrating manganese from the tailing of a manganese-carrying mineral including removing a coarse particle size fraction from the tailing, desliming and conducting an acidic or a basic reverse cationic flotation. The manganese-carrying minerals are typically minerals with low manganese content from the lithologies “Tabular Pelite” (or PETB), Pelite Siltite (or PEST), Detritic (or DETR), Rich Pelite (or PERC) and Metallurgical Bioxide (or BXME). In another aspect, the present invention also relates to a reverse cationic flotation used to concentrate manganese which is carried out using depressor agents and collector agents as flotation reagents.