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The joint dependency within the dependent sources is modeled by a multidimensional pdf, and hence, correct permutation is achieved.
The sample coverage approach indicated a slight dependence between the PDR and DISS (CCV assuming independent sources 0.10; CCV assuming dependent sources 0.20) and no dependence between the NDR and PDR (CCV assuming independent sources 0.00; CCV assuming dependent sources 0.10).
As a main conclusion, we have found that the separation of dependent sources is possible but additional constraints, or assumptions, on the type of dependence among sources must be taken into account.
The dependence was stronger between the NDR and DISS (CCV assuming independent sources 0.33; CCV assuming dependent sources 0.45).
We consider in this article the case of dependent sources without assuming nonstationarity nor color.
As a result, weather dependent sources cannot be used alone to efficiently supply a load.
We are interested now to look at the problem of separation of potentially dependent sources.
In this section, we introduce particular dependent sources based on products of independent signals.
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This is prohibited in experiment, however, by the presence of time-dependent sources of decoherence.
A large class of time-dependent sources such as vehicle, submarines, aircraft, and explosion-induced waves belongs to moving sources.
We study the problem of approximate decoding for inter-dependent sources where the difference between source vectors is characterized by a unimodal distribution.
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