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We have developed and evaluated a tree-based sequential detection method for detecting data transmitted over a sparse ISI channel.
Interestingly, detection of underdispersion has been important in detecting data falsification [ 16]; faithful randomness is difficult to fabricate.
The other one is detecting data reported by sensors.
Detecting data flow anomalies should be the first priority, followed by services exposed to untrusted entities and inward from there.
The proposed technique helps in detecting data that has not been reported or has been compromised.
We describe a general approach for detecting data alignments in large unordered noisy multidimensional datasets.
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After the detection of BS (-m), it sends the detected data to BS (-m + 1).
If drift is detected, data from the accelerometer can help to reconstruct the gyroscope orientation on two axes; the compass can help on the final axis by indicating where the magnetic north is.
However, no enzymatic activity could be detected (data not shown).
To detect the third data stream, the effect of the first detected data stream is subtracted out, that is, ( denotes the first detected data symbol).
Palo Alto Networks has a proprietary hardware and software technology that detects data threats as they come into an enterprise environment.
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