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Error detection is an important activity of program development, which is applied to detect incorrect computations or runtime failures of software.

In contrast, error detection is more challenging in graphs.

In such a system, error detection is performed online, while error masking is achieved by a short-duration offline test.

Finally, after the process at the demultiplexing module is complete, error detection is performed on the data.

Gross error detection is made through a Chi-square (χ2) Hypothesis Testing (HT) applied to the phase composed measurement error (CME).

Error detection is generally provided by the lower protocol layers which use checksums (e.g. Cyclic Redundancy Checksums (CRCs)) to discard corrupted packets and trigger retransmission requests.

As a result, automated methods for error detection were less efficient at detecting sequencing errors and many incorrect base calls were detected upon visual inspection of assembled trace data.

Inhibitory control and error detection are among the highest evolved human self-monitoring functions.

Four schemes for concurrent error detection are analyzed: duplication of a combinational logic, Berger codes, Bose-Lin codes, and parity-check codes.

Array normalizations and error detection were carried out using Silicon Genetics GeneSpring GXX Version 7.2 (Agilent), via the Enhanced Agilent Feature Extraction Import Preprocessor.

Similarly, array normalizations and error detection were carried out using procedures described previously (Slotkin et al. 2007c, 2008d; Slotkin and Seidler 2007).