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- Bogdan Dumitrescu, Alexandru Onose, Petri Helin, Ioan Tabus
- IEEE Transactions on Signal Processing
- 2012

Starting from the orthogonal (greedy) least squares method, we build an adaptive algorithm for finding online sparse solutions to linear systems. The algorithm belongs to the exponentially windowed recursive least squares (RLS) family and maintains a partial orthogonal factorization with pivoting of the system matrix. For complexity reasons, the… (More)

- Alexandru Onose, Rafael E. Carrillo, +4 authors Yves Wiaux
- 2016

In the context of next generation radio telescopes, like the Square Kilometre Array, the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We focus… (More)

- Alexandru Onose, Bogdan Dumitrescu
- Signal Processing
- 2013

- Alexandru Onose, Bogdan Dumitrescu, Ioan Tabus
- 2011 IEEE International Conference on Acoustics…
- 2011

We present a sliding window RLS for sparse filters, based on the greedy least squares algorithm. The algorithm adapts a partial QR factorization with pivoting, using a simplified search of the filter support that relies on a neighbor permutation technique. For relatively small window size, the proposed algorithm has a lower complexity than recent… (More)

- Alexandru Onose, Bogdan Dumitrescu
- 2014 22nd European Signal Processing Conference…
- 2014

Randomized coordinate descent (RCD), attractive for its robustness and ability to cope with large scale problems, is here investigated for the first time in an adaptive context. We present an RCD adaptive algorithm for finding sparse least-squares solutions to linear systems, in particular for FIR channel identification. The algorithm has low and tunable… (More)

- Abdullah Abdulaziz, Arwa Dabbech, Alexandru Onose, Yves Wiaux
- 2016 24th European Signal Processing Conference…
- 2016

With the advent of the next-generation radio-interferometric telescopes, like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, we propose a generic non-parametric low-rank and joint-sparsity image model for the… (More)

- Alexandru Onose, Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux
- 2016 24th European Signal Processing Conference…
- 2016

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on… (More)

- Alexandru Onose, Bogdan Dumitrescu
- 2012 IEEE International Conference on Acoustics…
- 2012

We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overdetermined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution is… (More)

- Luke Pratley, Jason D. McEwen, Mayeul D’Avezac, Rafael E. Carrillo, Alexandru Onose, Yves Wiaux
- 2016

Next-generation radio interferometers, such as the Square Kilometre Array (SKA), will revolutionise our understanding of the universe through their unprecedented sensitivity and resolution. However, to realise these goals significant challenges in image and data processing need to be overcome. The standard methods in radio interferometry for reconstructing… (More)

- Alexandru Onose, Bogdan Dumitrescu
- 2013 International Symposium on Intelligent…
- 2013

We propose a distributed adaptive algorithm for finding sparse solutions to systems of linear equations. The algorithm is greedy in nature. At each time moment, it first combines the current nonzero elements of the solution received from neighbor nodes by averaging them and then adapts the solution via a coordinate descent update using the local data. The… (More)