Tamara Tosic

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We investigate the problem of distributed sensors’ failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighboring sensor measurements. Defective sensors are represented by non-zero values in binary sparse signals. We build on the sparse nature of the binary sensor failure signals and propose(More)
We consider the problem of failure detection in sensor networks and we propose a new distributed detection algorithm based on Group Testing. We examine the presence of defective sensors by employing tests over locally gathered sensor measurements. Tests are represented with binary messages that sensors exchange over dissemination rounds using a gossip(More)
This paper addresses the problem of the interpolation of 2-d spherical signals from non-uniformly sampled and noisy data. We propose a graph-based regularization algorithm to improve the signal reconstructed by local interpolation methods such as nearest neighbour or kernel-based interpolation algorithms. We represent the signal as a function on a graph(More)
This paper addresses the problem of interpolating signals defined on a 2-d sphere from non-uniform samples. We present an interpolation method based on locally weighted linear and nonlinear regression, which takes into account the differences in importance of neighboring samples for signal reconstruction. We show that for optimal kernel function variance,(More)
For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental(More)
We address the problem of data gathering in adhoc networks. We propose a novel framework where sensor signals are quantized and mapped to a finite field. The network nodes then combine the data from different sensors to form messages that are transmitted towards a receiver. The receiver gathers different messages and reconstructs the original signal. We(More)
In this paper, we investigate the approach of comparing two different parallel streams of phoneme posterior probability estimates for OOV word detection. The first phoneme posterior probability stream is estimated using only the knowledge of short-term acoustic observation. In our work we refer this stream as “out-of-context posteriors”. The(More)
Transients in non-linear biological signals (e.g., population dynamics or physiological signals) encode an intrinsic behaviour of system dynamics. We study the problem of detecting dynamical transients given a set of signal trials. In general case, different biological signals emerge from different origins and hence exhibit distinct properties that are hard(More)
We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm(More)
In the past decade, technology developments have triggered the emergence of cheap sensing devices that open novel perspective for large-scale data sensing and analysis in different application domains, such environmental monitoring, healthcare or security, or distributed control systems. Moreover, networks of such sensors can advantageously replace complex(More)
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