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The selective reporting cooperative sensing algorithm explores the information present in silence periods to reduce the sensing report signalling overhead and associated energy and resource usage.
Using the principles of information theory, the algorithm explores the information present in the silence periods to maintain the same performance of standard reporting schemes.
This systematic review explores the information that is known about social capital and social support available to incarcerated adults and identifies gaps in the literature relating to the social capital of this population.
To tackle the issue of reducing the sensing report overhead, this paper introduces a new cooperative sensing scheme that introduces silence periods in the reporting and, relying on information theory principles, explores the information present in these periods to reduce by 50% the sensing reporting overhead while maintaining the same performance of standard reporting schemes.
The proposed method systematically explores the information encoded in nucleotide sequences by learning sequence dynamics and matching latent distributions on each position, and it can be easily extended to visualize the importance of subsequences and positions, thus providing a general method for sequence-based classification problems in bioinformatics.
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The study was designed to explore the information and support needs of family carers, with a view to informing potential future interventions.
Interdisciplinary Connections: Economics- Explore the information about each of the new Iraqi ministries at the New York Times interactive feature entitled "The New Iraqi Leadership" (available in the Multimedia Box at //www.nytimes.com/pages/world/worldspecial/index.html).html
To this end, Schnek et al. [25] explore the information flow among continents (Asia, Europe, North America etc).
By exploring the information entropy on image patches, the number of sets on each of them is learned.
By using periods of different lengths for model calibration, we explore the information content of these three datasets for runoff predictions.
DFT methods explore the information concerning the scattering region, transmission coefficient, electron current and non-equilibrium forces in the system [7, 8, 9, 10, 11].
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