When Pervasive Apps operate over longer period of time, transfer learning can be exploited to attribute meaning to signals originating from unknown new sensors.
Thus, one important function of a cognitive radio is dynamically to avoid transmitting in occupied spectrum by detecting signals received from unknown competing systems.
This tagging approach is able to detect signals from hitherto unknown regulatory or functional elements in a given genetic region [ 28, 36, 37].
We consider the problem of detecting an unknown signal from an unknown noise type.
Another difficulty arises in TOA-based geolocation techniques, where the TOA parameters cannot be measured from an unknown radio wave emitter because the time stamp information (time of departure (TOD) of the signal) of the transmitted signal from an unknown radio emitter is not available.
The aim is to design residual generators, which deliver a residual signal fully decoupled from unknown disturbances.
Some association signals from loci of unknown function are likely to be markers of rarer causative variants which contribute significantly to the familial aggregation of the particular disease.
In fact, B16 tumor cells are known targets for NK activity [ 40], despite some level of MHC-I expression, suggesting that the signals provided by ligation of the inhibitory receptors are overridden by signals from as yet unknown ligands for NK activating receptors, as B16 cells do not express ligands for the NKG2D receptor on NK cells [ 55].
Every training set has residuals which account for currents from unknown signals, such as noise.
By assuming that the unknown p emitted signals, (i.e. sources), are statistically independent from each other, ICA consists on retrieving a set of independent signals, (output signals), from the observation of unknown mixtures of the p sources.
