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Accurate indoor localization is a challenging problem in a multipath environment. In order to tackle this problem, several methods have been proposed. Direct localization is one of these methods that makes use of a two-dimensional search in a planar geometry. In this paper, we use a compressed sensing framework in the direct localization technique to estimate the location of a user in an indoor multipath environment. We form a penalized `0-norm structure for this problem, and then convert this structure to an Ising energy problem.

A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region configurations is proposed. Most spatial ATF interpolation methods are limited to estimation in the region of receivers. A kernel method for region-to-region ATF interpolation makes it possible to estimate the ATFs for both source and receiver regions from a discrete set of ATF measurements.

This is the poster for the SPL paper: SAM-7.5: Robust TDOA Source Localization Based on Lagrange Programming Neural Network to be presented in the upcoming ICASSP 2022 conference in Singapore.
More details can be found at 10.1109/LSP.2021.3082035.
Thank you for your time.

Bridge weigh-in-motion (BWIM) is a technique of estimating vehicle loads on bridges and can be used to assess a bridge's structural fatigue and therefore its life.
BWIM can be realized by analyzing the bridge deflection in terms of its response to moving axle loads.
To obtain accurate load estimates, current BWIM systems require strain sensors, whose (re-) installation costs have limited their application.

In this paper, a new robust principal component analysis (RPCA) method is proposed which enables us to exploit the main components of a given corrupted data with non-Gaussian outliers. The proposed method is based on the alpha-divergence which is a parametric measure from information geometry. The proposed method which is adjustable by the hyperparameter alpha, reduces to the classical PCA under certain parameters.

The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years.

Creating sound zones has been an active area of research since it was first introduced. Generally, this can be done either by maximizing an acoustic contrast that represents the acoustic potential energy ratio between the bright and dark zones or by minimizing a reproduction error between the desired and reproduced sound fields. However, the former suffers from severe distortion in the reproduced sound field, whereas the latter suffers from poor acoustic contrast.

In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported end-to-end deep learning based approaches work well in localizing a single source directly from multi-channel raw-audio, but are not easily extendable to localize multiple sources due to the well known permutation problem.