Abstract: Disclosed is a method and apparatus for allocating a set of frequency bands to a plurality of access points. The method includes generating an interference map associated with the set of frequency bands and associated with the plurality of access points. The method includes aggregating a plurality of demands to produce an aggregate demand. Each of the plurality of demands associated with one of the plurality of access points. The method includes dynamically allocating the set of frequency bands to each of the plurality of access points based on the interference map and the aggregate demand.

Abstract: Disclosed is a method and apparatus for allocating a whitespace spectrum associated with a plurality of access points. The method includes reporting a signal strength associated with each of a plurality of other access points to a central controller. The method includes aggregating a plurality of demands to produce an aggregate demand. Each of the plurality of demands is associated with one of a plurality of users. The method includes reporting the aggregated demand to the central controller. The method includes associating one of the plurality of users with the access point based on a user setting or associating one of the plurality of users based on an allocation by the central controller. The allocation from the central controller is based on the signal strength and the aggregated demand. The allocation indicates a frequency band from the set of frequency bands to be allocated by the access point to the user.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and by obtaining transaction data concerning at least one transaction that occurs at the transaction area. The system correlates the video data associated with the transaction area to the transaction data to identify specific video data captured during occurrence of that at least one transaction at the transaction area. Based a transaction classification indicated by the transaction data, the system processes the video data to identify appropriate visual indicators within the video data that correspond to the transaction classification.

Abstract: A system and method for searching physical objects. The system comprises one or more tags, each tag being disposed on one associated object; one or more substations, each substation arranged for communication with tags located within a range around each substation; one or more base stations, each base station arranged for communication with substations located within a range around each base station; an interrogation engine residing in each base station for interrogating, in response to a search query, the substations within the range around each base station, to determine whether a search object of the search query is within the range around the respective substations.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and by obtaining transaction data concerning at least one transaction that occurs at the transaction area. The system correlates the video data associated with the transaction area to the transaction data to identify specific video data captured during occurrence of that at least one transaction at the transaction area. Based a transaction classification indicated by the transaction data, the system processes the video data to identify appropriate visual indicators within the video data that correspond to the transaction classification.

Abstract: Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Abstract: A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.