Golden Space-Time Trellis Coded Modulation

In this paper, we present a concatenated coding scheme for a high rate $2\times 2$ multiple-input multiple-output (MIMO) system over slow fading channels. The inner code is the Golden code \cite{Golden05} and the outer code is a trellis code. Set partitioning of the Golden code is designed specifically to increase the minimum determinant. The branches of the outer trellis code are labeled with these partitions. Viterbi algorithm is applied for trellis decoding. In order to compute the branch metrics a lattice sphere decoder is used. The general framework for code optimization is given. The performance of the proposed concatenated scheme is evaluated by simulation. It is shown that the proposed scheme achieves significant performance gains over uncoded Golden code.Comment: 33 pages, 13 figure

Acoustic coupling between two air bubbles in water

The acoustic coupling between two air bubbles immersed in water is clearly demonstrated. The system is acoustically forced, and its response is detected. The experimental results confirm that both theoretically predicted eigenmodes, respectively symmetrical and antisymmetrical, do exist. Their frequencies, measured as a function of the bubbles spacing, follow theoretical estimations within a 10% accuracy.Comment: 14 pages, 6 figures, submitted to European Physical Journal E (2nd version

Phase Precoded Compute-and-Forward with Partial Feedback

In this work, we propose phase precoding for the compute-and-forward (CoF) protocol. We derive the phase precoded computation rate and show that it is greater than the original computation rate of CoF protocol without precoder. To maximize the phase precoded computation rate, we need to 'jointly' find the optimum phase precoding matrix and the corresponding network equation coefficients. This is a mixed integer programming problem where the optimum precoders should be obtained at the transmitters and the network equation coefficients have to be computed at the relays. To solve this problem, we introduce phase precoded CoF with partial feedback. It is a quantized precoding system where the relay jointly computes both a quasi-optimal precoder from a finite codebook and the corresponding network equations. The index of the obtained phase precoder within the codebook will then be fedback to the transmitters. A "deep hole phase precoder" is presented as an example of such a scheme. We further simulate our scheme with a lattice code carved out of the Gosset lattice and show that significant coding gains can be obtained in terms of equation error performance.Comment: 5 Pages, 4 figures, submitted to ISIT 201

A Ranking Distance Based Diversity Measure for Multiple Classifier Systems

International audienceMultiple classifier fusion belongs to the decision-level information fusion, which has been widely used in many pattern classification applications, especially when the single classifier is not competent. However, multiple classifier fusion can not assure the improvement of the classification accuracy. The diversity among those classifiers in the multiple classifier system (MCS) is crucial for improving the fused classification accuracy. Various diversity measures for MCS have been proposed, which are mainly based on the average sample-wise classification consistency between different member classifiers. In this paper, we propose to define the diversity between member classifiers from a different standpoint. If different member classifiers in an MCS are good at classifying different classes, i.e., there exist expert-classifiers for each concerned class, the improvement of the accuracy of classifier fusion can be expected. Each classifier has a ranking of classes in term of the classification accuracies, based on which, a new diversity measure is implemented using the ranking distance. A larger average ranking distance represents a higher diversity. The new proposed diversity measure is used together with each single classifier's performance on training samples to design and optimize the MCS. Experiments, simulations , and related analyses are provided to illustrate and validate our new proposed diversity measure

Sur la décombinaison de fonctions de croyance

International audienceThe evidence combination is a kind of decision-level information fusion in the theory of belief functions. Given two basic belief assignments (BBAs) originated from different sources, one can combine them using some combination rule, e.g., Dempster's rule to expect a better decision result. If one only has a combined BBA, how to determine the original two BBAs to combine? This can be considered as a defusion of information. This is useful, e.g., one can analyze the difference or dissimilarity between two different information sources based on the BBAs obtained using evidence decombination. Therefore, in this paper, we research on such a defusion in the theory of belief functions. We find that it is a well-posed problem if one original BBA and the combined BBA are both available, and it is an under-determined problem if both BBAs to combine are unknown. We propose an optimization-based approach for the evidence decombination according to the criteria of divergence maximization. Numerical examples are provided illustrate and verify our proposed decombination approach, which is expected to be used in applications such the difference analysis between information sources in information fusion systems when the original BBAs are discarded, and performance evaluation of combination rules