Abstract: This disclosure describes a system for automatically identifying an item from among a variation of items of a same type. For example, an image may be processed and resulting item image information compared with stored item image information to determine a type of item represented in the image. If the matching stored item image information is part of a cluster, the item image information may then be compared with distinctive features associated with stored item image information of the cluster to determine the variation of the item represented in the received image.

Abstract: This disclosure describes a system for processing an image of an item and correctly identifying the item from a group of candidate items. In one implementation, as item image information for a new item is added to an item images data store, a determination is made as to the weight of the item represented by the image, and the item may be associated with a weight class. Each weight class represents items within a defined weight range. Item image information for items in the same weight class may then be used when new items are added to inventory and/or when identifying an item represented in an image.

Abstract: Described is a multiple-camera system and process for detecting a user within a materials handling facility and tracking a position of the user as the user moves through the materials handling facility. In one implementation, a plurality of depth sensing cameras are positioned above a surface of the materials handling facility and oriented to obtain an overhead view of the surface of the materials handling facility, along with any objects (e.g., users) on the surface of the materials handling facility. The depth information from the cameras may be utilized to detect objects on the surface of the materials handling facility, track a movement of those objects and determine if those objects are users.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: An ultrasound-assisted simulated digestion method of a milk protein active peptide and an application thereof in health foods, pertaining to the technical field of intensive processing of dairy products and preparation of health foods. The method firstly employs ultrasonic pretreatment of casein and ?-lactoglobulin, followed by enzymatic hydrolysis with a protease to prepare casein and ?-lactoglobulin polypeptide, and traces the activity of the polypeptide by simulating gastrointestinal digestion, and then simulates absorption by intestinal epithelial cells with Caco-2 cells, to characterize a highly active milk protein polypeptide digested by the gastrointestinal tract and absorbed by the Caco-2 cells simulating absorption by the inner wall of the small intestine. The method has identified five such highly active milk protein polypeptides.

Abstract: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.

Abstract: An item stowed at an inventory location may rest on a surface such as a shelf or may be supported above the surface. The item may exhibit a footprint that is representative of a physical perimeter of the item. For example, the footprint may be an edge of a shadow cast by the item. Information about a size and shape of a footprint associated with a particular item may be stored. An optical sensor array may acquire sensor image data indicative of the footprint of one or more of the items stored at an inventory location. A quantity of items at the inventory location may be determined by comparing the sensor image data with the stored data.

Abstract: This disclosure describes a system for tracking removal or placement of items at inventory locations with a materials handling facility. In some instances, a user may remove an item from an inventory location and the inventory management system may detect that removal and update a user item list associated with the user to include an item identifier representative of the removed item. Likewise, if the user places an item at an inventory location, the inventory management system may detect that placement and update the user item list to remove an item identifier representative of the placed item.

Abstract: This disclosure describes a system for automatically identifying an item from among a variation of items of a same type. For example, an image may be processed and resulting item image information compared with stored item image information to determine a type of item represented in the image. If the matching stored item image information is part of a cluster, the item image information may then be compared with distinctive features associated with stored item image information of the cluster to determine the variation of the item represented in the received image.

Abstract: An inventory location such as a shelf may be used to stow different types of items, with each type of item in a different partitioned area or section of the shelf. Weight data from weight sensors coupled to the shelf is used to determine a change in weight of the shelf and a change in the center-of-mass (“COM”) of the items on the shelf. Based on the weight data and item data indicative of what items are stowed in particular partitioned areas, activity such as a pick or place of an item and the partitioned area in which the activity occurred may be determined. Data from other sensors, such as a camera, may be used to confirm the occurrence of the activity, disambiguate the determination of the particular partitioned area, and so forth.

Abstract: This disclosure describes a system for tracking removal or placement of items at inventory locations with a materials handling facility. In some instances, a user may remove an item from an inventory location and the inventory management system may detect that removal and update a user item list associated with the user to include an item identifier representative of the removed item. Likewise, if the user places an item at an inventory location, the inventory management system may detect that placement and update the user item list to remove an item identifier representative of the placed item.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.

Abstract: The invention is about methods for preparing functional polypeptide by multi-mode ultrasound enhancement on enzymolysis, and it relates to the field of functional peptide production technologies. The objective of the invention is to improve the degree of hydrolysis of protein, increase the anti-hypertensive activity of protein hydrolysate, shorten the enzymolysis time and reduce the energy consumption. To achieve this, some methods are developed, such as the method for preparing the anti-hypertensive peptide from wheat gluten by sequential ultrasound enhanced enzymatic hydrolysis, preparation of anti-hypertensive peptide from corn gluten by ultrasonic reverberation field, and the technology for preparing functional polypeptide by countercurrent mode dual frequency ultrasound pretreatment of prion protein. The invention of solid-liquid scanning ultrasonic pretreatment of corn gluten meal suspension leads to an improved efficiency of enzymatic hydrolysis.

Abstract: Embodiments of the present invention disclose a data scheduling method and a data scheduling device, which is configured to: receive channel quality information sent by a first terminal; calculate a current instantaneous scheduling rate of the first terminal on a first component carrier by using the channel quality information; acquire an average scheduling rate of the first terminal on the first component carrier; calculate a scheduling priority of the first terminal on the first component carrier by using the instantaneous scheduling rate and the average scheduling rate; and allocate a physical resource of the first component carrier to the first terminal. In this way, a multi-carrier terminal is enabled to obtain a stable high rate, differentiation between terminals is achieved.

Abstract: The present disclosure provides a spectrum resource sharing method and an base station. The method includes: acquiring, by a first-communications standard base station, time and frequency information and power of each second-communications standard channel interfering with the first-communications standard base station; if in one transmission time interval, no frequency number in a shared spectrum is occupied by the second-communications standard channels, using, by the first-communications standard base station, the shared spectrum for uplink and downlink data channel scheduling in the one transmission time interval.

Abstract: Embodiments of the present invention disclose a data scheduling method and a data scheduling device, which is configured to: receive channel quality information sent by a first terminal; calculate a current instantaneous scheduling rate of the first terminal on a first component carrier by using the channel quality information; acquire an average scheduling rate of the first terminal on the first component carrier; calculate a scheduling priority of the first terminal on the first component carrier by using the instantaneous scheduling rate and the average scheduling rate; and allocate a physical resource of the first component carrier to the first terminal.

Abstract: The present invention provides a spectrum resource sharing method and an base station. The method includes: acquiring, by a first-communications standard base station, time and frequency information and power of each second-communications standard channel interfering with the first-communications standard base station; if in one transmission time interval, no frequency number in a shared spectrum is occupied by the second-communications standard channels, using, by the first-communications standard base station, the shared spectrum for uplink and downlink data channel scheduling in the one transmission time interval.