Abstract: Techniques for automatically generating targeting templates are provided. In one technique, multiple sets of targeting criteria provided by different content providers are identified. Based on the multiple sets of targeting criteria, multiple clusters of sets of targeting criteria are created. For each cluster, a targeting template is generated based on a set of targeting criteria that belong to that cluster. In another technique, multiple users are identified and for each user, a set of attribute-value pairs of that user is identified and added to a plurality of attribute-value pairs. Based on the plurality of attribute-value pairs, a targeting template that specifies targeting criteria is generated and presented for selection.

Abstract: Techniques for automatically generating targeting templates are provided. In one technique, multiple sets of targeting criteria provided by different content providers are identified. Based on the multiple sets of targeting criteria, multiple clusters of sets of targeting criteria are created. For each cluster, a targeting template is generated based on a set of targeting criteria that belong to that cluster. In another technique, multiple users are identified and for each user, a set of attribute-value pairs of that user is identified and added to a plurality of attribute-value pairs. Based on the plurality of attribute-value pairs, a targeting template that specifies targeting criteria is generated and presented for selection.

Abstract: Examples of wideband spectrum sensing techniques for cognitive radio are provided. The techniques permit undersampling (e.g., sub-Nyquist sampling) the spectrum band, which permits use of more cost effective electronic components (e.g., lower frequency analog-to-digital converters (ADC)). The techniques permit determination of whether there are any (or sufficient) sub-channels that exist in the band to use for transmission, so that only a portion of less than the entire spectrum band is sampled. Detection decisions can be based at least partly on a pair of performance metrics more appropriate for wideband spectrum sensing. These performance metrics are the probability of insufficient spectrum opportunities (PISO) and the probability of excessive interference opportunities (PEIO).

Abstract: Examples of wideband spectrum sensing techniques for cognitive radio are provided. The techniques permit undersampling (e.g., sub-Nyquist sampling) the spectrum band, which permits use of more cost effective electronic components (e.g., lower frequency analog-to-digital converters (ADC)). The techniques permit determination of whether there are any (or sufficient) sub-channels that exist in the band to use for transmission, so that only a portion of less than the entire spectrum band is sampled. Detection decisions can be based at least partly on a pair of performance metrics more appropriate for wideband spectrum sensing. These performance metrics are the probability of insufficient spectrum opportunities (PISO) and the probability of excessive interference opportunities (PEIO).

Abstract: A system and a method for efficient service-instance oriented energy management in the IoT are provided. The method comprises: a predicting step for predicting target service instances to be serviced in a subsequent time period based on a service instance stance transition model; a selecting step for selecting an ON-sensor set to be turned on to provide services on which said target service instances are based, according to a critical covering set corresponding to the target service instances, the use history data of sensors in the critical covering set and energy parameters of said sensors; a controlling step for performing, when said time period begins, an ON/OFF control on the sensors in the IoT so that the sensors in the ON-sensor set are turned on and the sensors other than those in the ON-sensor set are turned off; and an updating step for updating the use history data of sensors according to the usage of sensors in said time period.

Abstract: A system and a method for efficient service-instance oriented energy management in the IoT are provided. The method comprises: a predicting step for predicting target service instances to be serviced in a subsequent time period based on a service instance stance transition model; a selecting step for selecting an ON-sensor set to be turned on to provide services on which said target service instances are based, according to a critical covering set corresponding to the target service instances, the use history data of sensors in the critical covering set and energy parameters of said sensors; a controlling step for performing, when said time period begins, an ON/OFF control on the sensors in the IoT so that the sensors in the ON-sensor set are turned on and the sensors other than those in the ON-sensor set are turned off; and an updating step for updating the use history data of sensors according to the usage of sensors in said time period.

Abstract: In one embodiment, a cognitive radio dynamic spectrum access sensing system is configured to incorporate a hidden Markov model and a risk function to determine the primary user state sequence with the minimum amount of associated risk. The system comprises one or more weighted cost factors that can be used to handle a missed detection sensing error differently from a false alarm sensing error. The system further comprises a complete forward partial backward computation designed to increase sensing accuracy with limited effect on sensing delay and complexity.

Abstract: In one embodiment, a cognitive radio dynamic spectrum access sensing system is configured to incorporate a hidden Markov model and a risk function to determine the primary user state sequence with the minimum amount of associated risk. The system comprises one or more weighted cost factors that can be used to handle a missed detection sensing error differently from a false alarm sensing error. The system further comprises a complete forward partial backward computation designed to increase sensing accuracy with limited effect on sensing delay and complexity.