Abstract: A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system uses a machine learning tool to select an asset for a current user of a user equipment device, for example, to select an ad for delivery to a current user of a digital set top box in a cable network. The machine learning tool first operates in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the machine learning tool processes current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system uses a machine learning tool to select an asset for a current user of a user equipment device, for example, to select an ad for delivery to a current user of a digital set top box in a cable network. The machine learning tool first operates in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the machine learning tool processes current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system uses a machine learning tool to select an asset for a current user of a user equipment device, for example, to select an ad for delivery to a current user of a digital set top box in a cable network. The machine learning tool first operates in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the machine learning tool processes current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A targeted advertising system uses a machine learning tool to select an asset for a current user of a user equipment device, for example, to select an ad for delivery to a current user of a digital set top box in a cable network. The machine learning tool first operates in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the machine learning tool processes current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: A method and a system for current limiting using PWM control have been provided. The method includes sensing a load current to produce a control voltage. The control voltage is compared with a reference voltage to produce a detected output signal. Thereafter, the detected output signal is delayed and multiplied with a master PWM signal.

Abstract: A method and system managing radio frequency identification (RFID) systems. The system monitors the performance of systems that use RFID tags and readers. It operates at multiple levels to ensure the optimal performance of readers, tags, antennae, and the information processing systems that acquire and convey tag data. The management system may employ artificial intelligence techniques such as genetic algorithms, fuzzy logic, neural networks, Bayesian networks, support vector machines or statistical methods to develop, maintain and exploit models of RFID system behavior. By comparing the actual performance of RFIDs and related components, the management system can detect and report failures and partial failures of components. The management system may also send signals to components to enhance performance of the overall RFID system.

Abstract: Aspects of the invention may be found in a method and system for processing an image by a consumer handheld communication device and may comprise receiving an indication that a capture and send function is activated and capturing at least one image by the consumer handheld communication device. The consumer handheld communication device may wirelessly transfer at least one captured image to a policing authority and/or a service provider providing wireless service for the consumer handheld communication device. An audio and/or a visual signal may be displayed while capturing the image. The capture and send function may be activated by a single button. If the capture and send function is activated for a specified period, then a special emergency number may also be dialed.

Abstract: This disclosure provides a solar rechargeable aircraft that is inexpensive to produce, is steerable, and can remain airborne almost indefinitely. The preferred aircraft is a span-loaded flying wing, having no fuselage or rudder. Traveling at relatively slow speeds, and having a two-hundred foot wingspan that mounts photovoltaic cells on most all of the wing's top surface, the aircraft uses only differential thrust of its eight propellers to turn. Each of five segments of the wing has one or more motors and photovoltaic arrays, and produces its own lift independent of the other segments, to avoid loading them. Five two-sided photovoltaic arrays, in all, are mounted on the wing, and receive photovoltaic energy both incident on top of the wing, and which is incident also from below, through a bottom, transparent surface. The aircraft includes hinges and actuators capable of providing an adjustable dihedral for the wing. The actuators can be motors or control surfaces.