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Furthermore, the multiple detection algorithm is based on a threshold which decides the minimum number of detections in each cluster to be considered as a detection.
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Implementation of proposed k-best multiple input multiple output detection algorithm on Field Programmable Gate Array is a challenging task by considering area and power.
Under such circumstances, we propose a novel multiple-target detection algorithm, named regularized non-Gaussianity based multiple-target detector (RNGMD), which uses the non-Gaussianity statistics to characterize the statistical characteristics of targets' spectra.
Using the simplified model in (15), a joint interference reconstruction and multiple-user detection algorithm is proposed in this section.
Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.
We report more than 25 novel families of complex tandem repeat elements in the cat genome uncovered by multiple repeat detection algorithms.
Sweeney describes Scrub, a de-identification system that uses multiple PHI detection algorithms competing in parallel to categorize and label PHI in text reports [ 20].
Enterprise ESSENCE is capable of collecting and analyzing a variety of data types and sources, and uses multiple anomaly detection algorithms to flag unusually high counts of disease indicators or alerts that are difficult for a human observer to see due to the volume and rate of change.
Besides, the existing multiple-target detection algorithms use second-order statistics, which could characterize Gaussian data well.
A low-complexity multiple-input multiple-output (MIMO) subspace detection algorithm is proposed.
Then, a multiple-symbol combined differential detection algorithm base on the theories of multiple-symbol differential detection and maximum-likelihood detection is proposed.
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