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In this paper, a novel antenna decoupling approach is investigated. The introduced concept is based on expelling the currents flowing between the closely spaced antennas through a tapered slot etched on the array ground plane. The method is free from extra-reactive components and transmission lines. Although they are closely spaced and strongly coupled, the technique allows a high improvement in port isolation. A practical implementation of this mechanism is applied to a monopole array of two elements working at 2.45 GHz and closely packed ($0.069\lambda _{0}$). The obtained results are very attractive, and a decoupling of about 40 dB is achieved with good impedance matching. Moreover, radiation diversity is obtained with a gain of 1.9 dBi. Furthermore, the decoupled array is integrated in a real receiving system, and the proposed decoupling technique is checked through the array signal processing. Hence, the obtained results confirm the effectiveness of the proposed decoupling mechanism. In addition to its efficient port isolation, the most attractive features of the proposed decoupling technique are simplicity and ease of implementation in compact multiantenna devices. [ABSTRACT FROM AUTHOR]

The Arctangent function is used in several signal processing applications and it is widely needed for hardware implementations. However, it often constitutes the performance bottleneck because of its inaccuracy and/or its poor performances. We present a generic VLSI design to respond to some applications' need of fast Arctangent functions handling high precision data. The method presented here contrasts with the usual Look Up table based solutions which allow the output customization but limit the precision of the corresponding tangent input. Detailed logical design and FPGA implementation results are provided to show our solution as the best candidate to comply with fast execution as well as high precision for both input and output data. [ABSTRACT FROM AUTHOR]

This paper considers the blind separation of nonstationary sources in the underdetermined case, when there are more sources than sensors. A general framework for this problem is to work on sources that are sparse in some signal representation do- main. Recently, two methods have been proposed with respect to the time-frequency (TF) domain. The first uses quadratic time-frequency distributions (TFDs) and a clustering approach, and the second uses a linear TFD. Both of these methods assume that the sources are disjoint in the TF domain; i.e., there is, at most, one source present at a point in the TF domain. In this paper, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present at a point is strictly less than the number of sensors. The separation can still be achieved due to subspace projection that allows us to identify the sources present and to estimate their corresponding TFD values. In particular, we propose two subspace-based algorithms for TF-nondisjoint sources: one uses quadratic TFDs and the other a linear TFD. Another contribution of this paper is a new estimation procedure for the mixing matrix. Finally, then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones. [ABSTRACT FROM AUTHOR]