Hence, it is utmost important to derive highly informative binary strings in coping with the rising encryption standard in the future.
Biometric discretization aims to facilitating numerous security applications through deriving stable representative binary strings in practice.
For example, the binary Champernowne sequence 0 1 00 01 10 11 000 001 ⋯, formed by concatenating all finite binary strings in lexicographic order, is disjunctive.
Among the calculated descriptors, if we focus on structural features, molecular fingerprints are binary strings in which each bit represents a feature.
Examples of units of selection include: units of life [6] such as organisms and lymphocytes evolving by somatic selection [7], but also purely informational entities such as viruses, machine code programs [8] and binary strings in a genetic algorithm [9].
Finally, each genome is described by a binary string in which the ith character indicates the presence of bop number i by a 1 and its absence by a 0. Note that homologous bops (those that share >80% sequence identity) are considered equivalent.
Successive time bins are aggregated to the extent of a fixed time window (i.e., a binary string) in order to translate the entire spike train into a sequence of binary symbols.
We have adopted the same individual representation in other evolutionary biclustering works [ 52], where each bicluster is represented by a fixed sized binary string in which a bit is set to one if the corresponding gene or sample is present in the bicluster, and set to zero otherwise.
In general, discretization based on static bit allocation assigns n bits equally to each of the D feature dimensions, thereby yielding a Dn-bit binary string in representing every identity upon concatenating short binary outputs from all individual dimensions.
The first one is based on the codification of the possible solution in a binary string and in the application, in that string, of an assembly algorithm, such as one used in DNA construction.
