Sentence examples for strength of the error from inspiring English sources

The work shows how a system can be tuned to operate at different degrees of this trade-off, and also the performance which can be achieved in terms of BER/PER, spectral efficiency, and interarrival, duration, strength of the error bursts.

Is error awareness an all-or-nothing prerequisite for some adjustments, or is there a gradual correlation of the adjustments with the strength of the error signal resulting in awareness?

Under some restrictions on the strength of the nonlinearity, error estimates are also established.

As Sara told the journalism students, once an error gets into print it "will live on and on in libraries carefully catalogued, scrupulously indexed... silicon-chipped, deceiving researcher after researcher down through the ages, all of whom will make new errors on the strength of the original errors, and so on and on into an exponential explosion of errata".

Spearman's rank correlation coefficients were used to assess the strength of the relations between the error rate and anaesthetist's self reported workload, and the error rate and compliance scores for use of the new system, per case.

The strength of error computed between the given image and the original (reference) image is weighted based on known features of the HVS.

For example, t-norms can be used to model the strength of soft error propagation and copulas give some insight into Gaussian error propagation.

One of the drawbacks in [14] is strength of the algorithms by handling errors in dynamic scenario of cloud environment, another drawback is that their algorithms used a static sequence of strategies for each request in each resource provider to check whether new request can be accepted or not.

During the learning process, the plasticity of the synaptic strength varies with the magnitude of the error signal, in accordance with the Hebbian and anti-Hebbian rules.

Since, transcriptional suppression can only influence p yz, a transcriptional negative feedback is limited by the trade-off between steady-state error and transient overshoot – increasing the strength of the negative feedback reduces steady-state error at the expense of increasing transient overshoot.

The model is capable of predicting the tensile strength of the bio-composite within 10% error.