Suchergebnisse

Squared-loss mutual information (SMI) is a surro- gate of Shannon mutual information that is more advantageous for estimation. On the other hand, the coherence matrix of a pair of random vectors, a power-normalized version of the sample cross-covariance matrix, is a well-known second-order statistic found in the core of fundamental signal processing problems, such as canonical correlation analysis (CCA). This paper shows that SMI can be estimated from a pair of independent and identically distributed (i.i.d.) samples as a squared Frobenius norm of a coherence matrix estimated after mapping the data onto some fixed feature space. Moreover, low computation complexity is achieved through the fast Fourier transform (FFT) by exploiting the Toeplitz structure of the involved autocorrelation matrices in that space. The performance of the method is analyzed via computer simulations using Gaussian mixture models. ; This work is supported by projects TEC2016-76409-C2-1-R (WINTER), Ministerio de Economia y Competividad, Spanish National Research Plan, and 2017 SGR 578 - AGAUR, Catalan Government. ; Peer Reviewed ; Postprint (published version)

We study the problem of estimating the overall mutual information in M independent parallel discrete-time memory-less Gaussian channels from N independent data sample pairs per channel (inputs and outputs). We focus on the case where the number of active channels L is sparse in comparison with the total number of channels (L ≪ M), for which the direct application of the maximum likelihood principle is problematic due to overfitting, especially for moderate to small N. For this regime, we show that the bias of the mutual information estimate is reduced by resorting to the minimum description length (MDL) principle. As a result, simple pre-processing based on a per-channel threshold on the empirical squared correlation coefficient is required with a fixed threshold that monotonically decreases with N as 1 - N -1/N , for N ≥ 4. The resulting improvement is shown in terms of the estimated information bias. ; This work is supported by projects TEC2016-76409-C2-1-R (WINTER), Ministerio de Economia y Competividad, Spanish National Research Plan, and 2017 SGR 578 - AGAUR, Catalan Government. ; Peer Reviewed ; Postprint (author's final draft)

Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors. ; This work was (partially) funded by the ERDF (a way of making Europe) by the European Union through grant RTI2018-095076-B-C21, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia María de Maeztu) through grant CEX2019-000918-M, the Spanish State Research Agency (PID2020-114322RBI00), the European Union's Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 658358), the Catalan Government Excellence Research Groups Grant to GRC Geociències Marines (ref. 2017 SGR 315), the Spanish ...