SAUCAN Augustin Alexandru, CHONAVEL Thierry, SINTES Christophe, LE CAILLEC Jean-Marc

**CPHD-DOA Tracking of Multiple Extended Sonar Targets in Impulsive Environments**. IEEE transactions on signal processing, march 2016, vol. 64, n° 5, pp. 1147-1160*In this paper, we propose a novel phased-array track before detect (TBD) filter for tracking multiple distributed (extended) targets from impulsive observations. Since the targets are angularly spread, we track the centroid Direction Of Arrival (DOA) of the target-generated (or backscattered) signal. The main challenge stems from the random target signals that, conditional to their respective states, constitute non-deterministic contributions to the system observation. The novelty of our approach is twofold. First, we develop a Cardinalized Probability Hypothesis Density (CPHD) filter for tracking multiple targets with non-deterministic contributions, more specifically, Spherically Invariant RandomVector (SIRV) processes. This is achieved by analytically integrating the SIRV and angularly distributed target signals in the update step. Thus, ensuring a more efficient implementation than existing solutions, that generally consider augmenting the target state with the target signal. Secondly, we provide an improved auxiliary particle CPHD filter and clustering methodology. The auxiliary step is carried out for persistent particles, while for newly birthed particles an adaptive importance distribution is given. This is in contrast with existing solutions that only consider the auxiliary step for birthed particles. Simulated data results showcase the improved performance of the proposed filter. Results on real sonar phased-array data are presented for underwater 3D image reconstruction applications. *

LIU Mengyuan, KITSCH Averi, MILLER Steven, CHAU Vann, POSKITT Kenneth, ROUSSEAU François, SHAW Denis, STUDHOLME Colin

**Patch-based augmentation of Expectation-Maximization for brain MRI tissue segmentation at arbitrary age after premature birth**. Neuroimage, february 2016, vol. 127, pp. 387-408*Accurate automated tissue segmentation of premature neonatal magnetic resonance images is a crucial task for quantification of brain injury and its impact on early postnatal growth and later cognitive development. In such studies it is common for scans to be acquired shortly after birth or later during the hospital stay and therefore occur at arbitrary gestational ages during a period of rapid developmental change. It is important to be able to segment any of these scans with comparable accuracy. Previous work on brain tissue segmentation in premature neonates has focused on segmentation at specific ages. Here we look at solving the more general problem using adaptations of age specific atlas based methods and evaluate this using a unique manually traced database of high resolution images spanning 20 gestational weeks of development. We examine the complimentary strengths of age specific atlas-based Expectation-Maximization approaches and patch-based methods for this problem and explore the development of two new hybrid techniques, patch-based augmentation of Expectation-Maximization with weighted fusion and a spatial variability constrained patch search. The former approach seeks to combine the advantages of both atlas- and patch-based methods by learning from the performance of the two techniques across the brain anatomy at different developmental ages, while the latter technique aims to use anatomical variability maps learnt from atlas training data to locally constrain the patch-based search range. The proposed approaches were evaluated using leave-one-out cross-validation. Compared with the conventional age specific atlas-based segmentation and direct patch based segmentation, both new approaches demonstrate improved accuracy in the automated labeling of cortical gray matter, white matter, ventricles and sulcal cortical-spinal fluid regions, while maintaining comparable results in deep gray matter. *

GABORIT Philippe, RUATTA Olivier, SCHREK Julien

**On the complexity of the Rank Syndrome Decoding problem**. IEEE transactions on information theory, february 2016, vol. 62, n° 2, pp. 1006-1019*In this paper, we propose two new generic attacks on the rank syndrome decoding (RSD) problem. Let C be a random [n, k] rank code over GF(qm) and let y = x + e be a received word, such that x ∈ C and rank(e) = r. The first attack, the support attack, is combinatorial and permits to recover an error e of rank weight r in min(O((n - k)3m3qr1(km/n)J, O((n - k)3m3q⌈(r-1)I(((k+1)m)/n)J))⌉ operations on GF(q). This new attack improves the exponent for the best generic attack for the RSD problem in the case n > m, by introducing the ratio m/n in the exponential coefficient of the previously best known attacks. The second attack, the annulator polynomial attack, is an algebraic attack based on the theory of q-polynomials introduced by Ore. We propose a new algebraic setting for the RSD problem that permits to consider equations and unknowns in the extension field GF(qm) rather than in GF(q) as it is usually the case. We consider two approaches to solve the problem in this new setting. The linearization technique shows that if n ≥ (k + 1) (r + 1) - 1 the RSD problem can be solved in polynomial time. More generally, we prove that if [(((r + 1)(k + 1)- (n + 1))/r)1 ≤ k, the RSD problem can be solved with an average complexity of O(r3k3qrΓ(((r+1)(k+1)-(n+1))/r)l)⌉ operations in the base field GF(q). We also consider solving with Gröbner bases for which we discuss theoretical complexity, we also consider hybrid solving with Gröbner bases on practical parameters. As an example of application, we use our new attacks on all recent cryptosystems parameters, which repair the GPT cryptosystem, we break all examples of published proposed parameters, and some parameters are broken in less than 1 s in certain cases. *

ROUSSEAU François, STUDHOLME Colin, JARDRI Renaud, THOMASON Moriah

**In Vivo Fetal Brain Analysis Using MR Imaging**. Fetal Development: Research on Brain and Behavior, Environmental Influences, and Emerging Technologies, Springer, 2016, pp. 407-427, ISBN 978-3-319-22022-2*This chapter provides a review of the current research into in utero magnetic resonance (MR) imaging of the human fetal brain. It is divided into three parts: structural imaging, diffusion imaging and functional imaging. In each part, a description of MR sequences is provided, as well as advanced image processing techniques that are used to build 3D images from motion scattered slices. Combination of fast MR techniques and post-processing algorithms leads to key applications in the context of in utero fetal brain studies such as brain folding analysis, connectome analysis and functional network analysis. The recent advances in in utero fetal brain MR imaging described in this chapter open new perspectives in early brain development understanding. *