Abstract: Methods and systems for identifying and accurately monitoring evaporation from casks storing high value liquor are presented herein. A Cask Identification and Evaporation Monitoring (CIEM) system includes instrumentation to identify cask location within a high volume storage facility and monitor loss of content from each cask. The resulting data is communicated to a CIEM tracking server for storage and further analysis. The CIEM tracking server identifies excessive short term losses, e.g., leaks, arising anywhere in the storage facility. In addition, the CIEM tracking server identifies long term loss trends. The long term loss trends are enable data-driven management of the cask conditioning process, including tasks such as scheduling of cask rotation within the storage facility, bottling, etc.

Abstract: Methods and systems for accurately and securely dispensing high value beverages are presented herein. A beverage distribution system includes one or more replaceable beverage cartridges mounted to a beverage dispensing device. Each of the beverage cartridges includes a fluid reservoir with an output port, an output control valve, a flowmeter, and a local controller. The local controller receives signals from the flow meter and determines a cumulative amount of beverage fluid dispensed from the beverage cartridge based on the received signals. In one embodiment, the beverage dispensing device includes a fluid pump and a master controller. The master controller also estimates the cumulative amount of beverage fluid dispensed from each beverage cartridge based on control commands communicated to each beverage cartridge. If a difference between the estimated cumulative amounts of fluid dispensed from a beverage cartridge exceeds a predetermined threshold value, an alert is generated.

Abstract: Methods and systems for generating and storing a vehicle based notification record including evidentiary License Plate Recognition (LPR) information are presented herein. A vehicle based notification record includes evidentiary images generated by an LPR imaging system, a portable imaging system, or a combination thereof. The vehicle based notification record includes the collected evidentiary images in their native format including metadata. The collected images and metadata are preserved in their original format during communication, storage, and retrieval operations. In some embodiments, the geographic location of image capture is appended to the image metadata. In some embodiments, the images are digitally signed after capture and the digital signature is appended to the metadata of each respective image. In some embodiments, a clock of the LPR imaging system and a clock of the portable imaging system are synchronized with a network time server and time of image capture is appended to image metadata.

Abstract: Methods and systems for estimating a location of a wireless electronic device within an environment and linking the presence of the wireless electronic device to a specific set of images collected in the vicinity of the estimated location are presented herein. In one aspect, the physical location of a wireless electronic device at a particular time is estimated based on the strength or time of reception of radio frequency signals collected by three or more geo-locator modules. Each of the three or more geo-locator modules identify a media access control (MAC) address associated with a wireless electronic device. In a further aspect, the geolocation of each identified MAC addressed device is tracked and recorded over time. In another further aspect, the wireless electronic device is linked to images collected by an imaging device while the wireless electronic device remains within a monitored area of the imaging device.

Abstract: Methods and systems for estimating the speed of passing vehicles based on License Plate Recognition (LPR) image information are described herein. The distance traversed by a vehicle between image frames is estimated based on a difference between a pixel location of a tracking point on the vehicle in a first image and a second pixel location of the tracking point in second image of the vehicle. In some embodiments, the displacement identified in the images is converted to a projected displacement on the roadway surface based on the mapping between pixel locations within the field of view of the image sensor and locations on the surface of the roadway. The projected displacement is corrected by accounting for the difference in height of the camera module and tracking point over the roadway surface. An estimate of vehicle speed is calculated from the corrected displacement and the elapsed time between image frames.

Abstract: Possible locations of a person of interest are ranked based on LPR instances captured around physical locations and license plate numbers associated with the person of interest. An LPR instance includes an indication of a vehicle license plate number, a physical location, and a time when the LPR instance was captured by a LPR system. A possible location of the person of interest may be a location of an LPR instance that matches the license plate number or an address location associated with the person of interest. The ranking may be based on the number of LPR visits to each location, the number of license plate number matches at each location, or an attribute of a cluster of LPR instances. In some examples, an electronic message is rapidly communicated to an entity if a target license plate number is found at a highly ranked location.

Abstract: LPR instances around physical locations and license plate numbers associated with a person of interest are analyzed to predict the relative likelihood of locating the person of interest at a particular location. An LPR information query includes an indication of a physical location and a license plate number associated with a person of interest. The relative likelihood of locating a person of interest at a particular location at a future point in time is determined based on the LPR instances received. In one example, the relative likelihood of locating the person of interest is based on the relative value of clusters of LPR instances around physical locations associated with the person of interest. Additional license plate numbers are associated with a person of interest based on their appearance within a search zone and time window of LPR instances of a license plate number already associated with a person of interest.

Abstract: Possible locations of a person of interest are ranked based on LPR instances captured around physical locations and license plate numbers associated with the person of interest. An LPR instance includes an indication of a vehicle license plate number, a physical location, and a time when the LPR instance was captured by a LPR system. A possible location of the person of interest may be a location of an LPR instance that matches the license plate number or an address location associated with the person of interest. The ranking may be based on the number of LPR visits to each location, the number of license plate number matches at each location, or an attribute of a cluster of LPR instances. In some examples, an electronic message is rapidly communicated to an entity if a target license plate number is found at a highly ranked location.

Abstract: Possible locations of a person of interest are ranked based on LPR instances captured around physical locations and license plate numbers associated with the person of interest. An LPR instance includes an indication of a vehicle license plate number, a physical location, and a time when the LPR instance was captured by a LPR system. A possible location of the person of interest may be a location of an LPR instance that matches the license plate number or an address location associated with the person of interest. The ranking may be based on the number of LPR visits to each location, the number of license plate number matches at each location, or an attribute of a cluster of LPR instances. In some examples, an electronic message is rapidly communicated to an entity if a target license plate number is found at a highly ranked location.

Abstract: LPR instances around physical locations and license plate numbers associated with a person of interest are analyzed to predict the relative likelihood of locating the person of interest at a particular location. An LPR information query includes an indication of a physical location and a license plate number associated with a person of interest. The relative likelihood of locating a person of interest at a particular location at a future point in time is determined based on the LPR instances received. In one example, the relative likelihood of locating the person of interest is based on the relative value of clusters of LPR instances around physical locations associated with the person of interest. Additional license plate numbers are associated with a person of interest based on their appearance within a search zone and time window of LPR instances of a license plate number already associated with a person of interest.

Abstract: Methods and systems for Methods and systems for classifying the locations of a vehicle of interest based on License Plate Recognition (LPR) instances are described herein. Locations associated with LPR instances matching a particular license plate number are classified based on LPR information gathered within search zones around each location. Clusters of one or more LPR instances associated with a target license plate number are identified. A search zone is defined around a cluster of one or more LPR instances associated with a target license plate number. LPR instances associated with other license plate numbers within the search zone are received from an LPR server, and a location associated with the search zone is classified based on LPR information gathered within the search zone. In some examples, the location classification is based on LPR activity matching a target license plate number, general LPR activity within the search zone, or both.

Abstract: Possible locations of a person of interest are ranked based on LPR instances captured around physical locations and license plate numbers associated with the person of interest. An LPR instance includes an indication of a vehicle license plate number, a physical location, and a time when the LPR instance was captured by a LPR system. A possible location of the person of interest may be a location of an LPR instance that matches the license plate number or an address location associated with the person of interest. The ranking may be based on the number of LPR visits to each location, the number of license plate number matches at each location, or an attribute of a cluster of LPR instances. In some examples, an electronic message is rapidly communicated to an entity if a target license plate number is found at a highly ranked location.

Abstract: A method for data mining information obtained in an integrated circuit fabrication factory (“fab”) that includes steps of: (a) gathering data from the fab from one or more of systems, tools, and databases that produce data in the fab or collect data from the fab; (b) formatting the data and storing the formatted data in a source database; (c) extracting portions of the data for use in data mining in accordance with a user specified configuration file; (d) data mining the extracted portions of data in response to a user specified analysis configuration file; (e) storing results of data mining in a results database; and (f) providing access to the results.

Abstract: A method for data mining information obtained in an integrated circuit fabrication factory (“fab”) that includes steps of: (a) gathering data from the fab from one or more of systems, tools, and databases that produce data in thefab or collect data from the fab; (b) formatting the data and storing the formatted data in a source database; (c) extracting portions of the data for use in data mining in accordance with a user specified configuration file; (d) data mining the extracted portions of data in response to a user specified analysis configuration file; (e) storing results of data mining in a results database; and (f) providing access to the results.