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The SAM Workshop is an important IEEE Signal Processing Society event dedicated to sensor array and multichannel signal processing. The organizing committee invites the international community to contribute with state-of-the-art developments in the field.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         

Personal audio systems are designed to deliver spatially separated regions of audio to individual listeners. This paper presents a method for improving the privacy of such systems. The level of a synthetic masking signal is optimised to provide specified levels of intelligibility in the bright and dark sound zones and reduce the potential for annoyance of listeners
in the dark zone by responding to changes in ambient noise.


In this paper, we are interested in phased-array
imaging of an unknown area using narrowband RF signals and
arrays synthesized by an unmanned vehicle. Typical phased
array imaging approaches use fixed or pre-determined array
configurations for imaging, which are not usually informative
for the whole area. In this paper, we then propose an iterative
adaptive imaging approach where we identify the uncertain
regions in an initial image that need to be sensed better, find
the optimal array location and orientation for such a sensing


In this paper, we are interested in the high-resolution
imaging of an unknown area based on only power measurements
of a small number of wireless transceivers located on one
side of the unknown area. In order to do so, we propose a
framework that achieves a polynomial order reduction in the
number of antennas required for high-resolution imaging. More
specifically, we show that by spacing the antennas at multiples
of the wavelength and applying subspace-based analysis, we can
image M targets using only 2M+1 transmit/receive antennas (as


Hybrid analog-digital beamforming has been shown to reduce hardware cost and power consumption in massive MIMO systems, at the expense of increased radiated power for given performance targets. To alleviate the above shortfall, in this paper we exploit the concept of constructive interference (CI) that has been shown to offer significant radiated power savings in fully-digital multi-user downlink MIMO systems. We explore analog beamforming design, and develop solutions specifically tailored for CI-based hybrid beamforming.


In this paper, we consider a multi-user massive MIMO network with hybrid beamforming architecture at the base station. The objective is to jointly perform user selection and design analog-digital hybrid beamformers in order to maximize a given utility function while satisfying various pertinent constraints. The problem is combinatorial and impractical to solve


To solve the problem of beam selection or capturing the highest possible signal power, we propose a sequential test that can adapt to the SNR operating point and speed up the selection procedure in terms of the number of required observations in comparison to a perfectly tuned fixed length test assuming genie knowledge.


A gridless sound field decomposition method based on the reciprocity gap functional (RGF) is proposed. An intuitive and powerful way of reconstructing a sound field inside a region including sound sources is to decompose the sound field into Green's functions. Current methods based on sparse representation require discretization of the reconstruction region into grid points to construct the dictionary matrix; however, this procedure causes an off-grid problem and has a high computational cost.


Sparse sensor arrays can achieve significantly more degrees of freedom than the number of elements by leveraging the co-array, a virtual structure that arises from the far field narrowband signal model. Although several sparse array configurations have been developed for passive sensing tasks, less attention has been paid to arrays suitable for active sensing. This paper presents a novel active sparse linear array, called the Interleaved Wichmann Array (IWA). The IWA only has a few closely spaced elements, which may make it more robust to mutual coupling effects.


We study multi-agent task allocation where multiple tasks must be divided among multiple autonomous robots. Algorithms
for solving such problems are typically developed under the assumption of perfect communication, without considering
the lossy nature of the underlying wireless network. In this paper, leveraging a sophisticated unmanned aerial vehicle (UAV)