Abstract: A system and method for computer vision assisted parking space monitoring, vehicle identification, transactionable event determination, calculation of parking fees, automated transaction processing, invoice generation, user communication, and automated payment processing from a registered user via a registered user account with prior user authorization and providing traffic and parking data to interested users and third parties.

Abstract: A testing device determines an Effective Isotropic Radiated Power (EIRP) of a wanted signal at a beam-peak direction and a maximum Total Radiated Power (TRP) of the wanted signal. The testing device then determines a power difference (?P) between the EIRP of the wanted signal at the beam-peak direction and the maximum TRP of the wanted signal. The testing device determines EIRP of a spectral emission mask (SEM) at each measurement bandwidth step at the beam-peak direction. The testing device then determines TRP at each measurement bandwidth step by determining a difference between the EIRP of the SEM at that measurement bandwidth step and the power difference (?P). The testing device compares the TRP at each measurement bandwidth step to a SEM specification, and reports whether the SEM specification has been met.

Abstract: Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

Abstract: A system and method for computer vision assisted parking space monitoring, vehicle identification, transactionable event determination, calculation of parking fees, automated transaction processing, invoice generation, user communication, and automated payment processing from a registered user via a registered user account with prior user authorization and providing traffic and parking data to interested users and third parties.

Abstract: A testing device determines an Effective Isotropic Radiated Power (EIRP) of a wanted signal at a beam-peak direction and a maximum Total Radiated Power (TRP) of the wanted signal. The testing device then determines a power difference (?P) between the EIRP of the wanted signal at the beam-peak direction and the maximum TRP of the wanted signal. The testing device determines EIRP of a spectral emission mask (SEM) at each measurement bandwidth step at the beam-peak direction. The testing device then determines TRP at each measurement bandwidth step by determining a difference between the EIRP of the SEM at that measurement bandwidth step and the power difference (?P). The testing device compares the TRP at each measurement bandwidth step to a SEM specification, and reports whether the SEM specification has been met.

Abstract: Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

Abstract: Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

Abstract: Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

Abstract: A computer-implemented method includes receiving an indication that an order scheduled for later pickup is to be picked up earlier than scheduled. A plurality of robots that are holding items that form at least part of the order are then identified and instructed to move immediately to a pickup location and to make the items of the order held by the plurality of robots available for retrieval from the plurality of robots.

Abstract: A computer-implemented method includes receiving an indication that an order scheduled for later pickup is to be picked up earlier than scheduled. A plurality of robots that are holding items that form at least part of the order are then identified and instructed to move immediately to a pickup location and to make the items of the order held by the plurality of robots available for retrieval from the plurality of robots.