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The work in [8] summarizes the second-order statistics-based approaches for blind identification.
We discuss approaches for blind source separation where we can use more sensors than sources to obtain a better performance.
Existing approaches for blind spot measurement that follow international standards typically require a time-consuming set-up and are limited by the number of different visibility analyses that can be performed.
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This paper presents a novel approach for blind audio watermarking.
In [17], a Bayesian approach for blind separation of linear mixtures of sources was developed.
In [15], a composite approach for blind grayscale logo watermarking is presented.
In [15], an approach for blind recognition of binary linear block codes in low code-rate situations is presented.
In addition, a new approach for blind separation of nonstationary sources using their TFDs, was proposed in [75].
In this paper, we develop an approach for blind recognition of the coding parameters of a communication system which uses binary cyclic codes.
In this paper we present a new approach for blind identification of non-minimum phase FIR channels for single input single output (SISO) and multiple inputs single output (MISO) scenarios.
The fact that the basis spectra chosen were averages from different samples to those that underwent unmixing further underscores the robustness of the signatures, as well as the promise of this approach for blind tissue delineation and identification.
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