In particular, we will investigate possibilities how to make the key-estimation procedure separable, that is, conduct key estimation for each decomposition level separately.
To determine the unicity distance of the image-hashing algorithm, the observed image-hash pairs are taken as the input to a key estimation algorithm.
Therefore, instead of an iterative key estimation technique based on successive refinement, the only way to obtain the correct key would involve a rather costly random search through a significant share of the keyspace until a configuration with small Hamming distance is found which can be systematically improved.
Note that these observations have been made under the assumption that 9 out of 10 's are already correct without arguing how this could be achieved in an actual key-estimation algorithm.
The key idea in M-estimation is that the cost function, or the influence function, can be chosen in such a way to provide the estimator desirable properties (in terms of bias and efficiency) when the data are truly generated from the assumed model, and reliable albeit not optimal behavior when the data are generated from another model that is, in some sense, close to the assumed model.
Finally, two KPI-related indices are formed respectively, which are both bounded by key density estimation based approach.
A comprehensive analytical model for managing the virtual radio resources has been proposed, with two key parts: estimation of available radio resources and their allocation to different VNOs.
Our key rate estimation based on the signal pulse's (mu_{mathit {DSP}}) is just an approximation without taking the photon statistics of the decoy states into account, which have a small, but non-negligible effect on the key rate.
It also reports the key diversity estimation metrics that are widely used by different biologists.
For the quantitative estimation, it is key to estimate ∆m1 = m1 − − m1+.
A priori signal-to-noise ratio (SNR) estimation and noise power estimation are key parameters in MMSE estimator, but accurate a priori SNR estimation and noise power estimation are not easy to obtain.
