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Data in large-scale genetic studies of complex human diseases, such as substance use disorders, are often incomplete. Despite great progress in genotype imputation, e.g., the IMPUTE2 method, considerably less progress has been made in inferring phenotypes. We designed a novel approach to integrate individuals' comorbid conditions with their genotype data to infer missing (unreported) diagnostic criteria...
Since it was found that real mobility processes exhibit significant degree of correlation (correlated mobility) and nodes are often heterogeneously distributed in clustered networks (cluster scalability), there has been a great interest in studying their impact on network performance, such as throughput and delay. However, limited works have been done to investigate their impact jointly, which may...
Correlation-based algorithms are low-complexity spectrum sensing methods requiring little knowledge on primary signals or noise signals. However, their detection performance severely degrades in the low signal-to-noise ratio (SNR) regime with low signal correlation, which happens to be quite common in practice. In this paper, a weighted correlation- based spectrum sensing scheme and its simplified...
Algorithms based on signal correlation have low computational complexity and require little knowledge on primary signals or noise signals. However, their detection performance becomes relatively terrible in low signal-to-noise ratio (SNR) regime with weak signal correlation, which happens to be quite common in practical systems. In this paper, a weighted blind spectrum sensing algorithm based on signal...
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