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In this paper we address the problem of decentralized and robust linear filtering for target tracking using networks of (radar) sensors taking nonlinear range and bearing measurements.
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The overall correlation between the ConQuass score and GDT-TS is good (0.678), especially when filtering for targets with a high-quality alignment (0.843 if using the 20% filtering, Fig. 5A; red).
For large, AA-biased genes, the proteome data set of normally transcribed genes was filtered for targets that consisted of ≥ 1,755 total codons with an AA AG ratio > 1.3 (Fig. 3b).
The miRNAs were filtered for targets with a minimum of 5 counts in at least 25% of the samples.
We predicted siRNA off-targets for all the 8 + 19 = 27 siRNA sequences in the dataset with/without filtering for conserved target sites.
After filtering for conservation, 36 putative target sites were identified and are listed in Table S1.
Filtering for highly connected SL targets of these cancer genes highlights DNA replication and repair genes, including the replication fork protection complex, the replication initiation complex, the RFC Ctf18, the RNaseH complex, and two proteasome subunits.
We have proposed an innovative method for combining extended likelihood data association with evolving population particle filtering for robust and accurate multiple target tracking.
Correlation filters for target detection are usually designed by analytical optimization of performance criteria.
The author proposed a modified Kalman filter for target tracking combined with the send-on-delta sampling [36].
To address this issue, we present a distributed computation particle probability hypothesis density(PHD) filter for target tracking.
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