A Novel Algorithm for the Identification of Dirac Impulses from Filtered Noisy Measurements

International audienceIn this paper we address the recovery of a finite stream of Dirac pulses from noisy lowpass-filtered samples in the discrete-time setting. While this problem has been successfully addressed for the noise-free case using the concept of signals with finite rate of innovation, such techniques are not efficient in the presence of noise. In the FRI framework, the determination of the location of Dirac pulses is based on the singular value decomposition of a matrix whose rank in the noise-free case equals the number of Dirac pulses and the signal can be related to the non zero singular values. However, in noisy situations this matrix becomes full rank and the singular value decomposition is subject to subspace swap, meaning some singular values associated with noise become larger than some values related to the signal. This phenomenon has been recognized as the reason for performance breakdown in the method. The goal of this paper is to propose a novel algorithm that limits the alteration of these singular values in the presence of noise, thus significantly improving the estimation of Dirac pulses

Experimental and numerical investigations of nanoindentation properties at the sub- grain level in Ni-based and Ti-based polycrystalline alloys

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Robust 3D Reconstruction of Dynamic Scenes From Single-Photon Lidar Using Beta-Divergences

In this paper, we present a new algorithm for fast, online 3D reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon lidar in practical applications is the presence of strong ambient illumination which corrupts the data and can jeopardize the detection of peaks/surface in the signals. This background noise not only complicates the observation model classically used for 3D reconstruction but also the estimation procedure which requires iterative methods. In this work, we consider a new similarity measure for robust depth estimation, which allows us to use a simple observation model and a non-iterative estimation procedure while being robust to mis-specification of the background illumination model. This choice leads to a computationally attractive depth estimation procedure without significant degradation of the reconstruction performance. This new depth estimation procedure is coupled with a spatio-temporal model to capture the natural correlation between neighboring pixels and successive frames for dynamic scene analysis. The resulting online inference process is scalable and well suited for parallel implementation. The benefits of the proposed method are demonstrated through a series of experiments conducted with simulated and real single-photon lidar videos, allowing the analysis of dynamic scenes at 325 m observed under extreme ambient illumination conditions.Comment: 12 page

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

Scalable computational methods for 3D reconstruction using single-photon Lidar : towards online approaches

Lidar systems allow the reconstruction of 3D scenes at high resolution. They have recently received considerable interest across a wide domain of applications. The technology consists of illuminating a target with laser pulses and measuring the time of arrival of the pulses that have been reflected on it to estimate both its distance and reflectivity. However, the analysis of Lidar measurements can be considerably challenged by additional detection events that arise from external light sources such as ambient illumination. Algorithms have been developed to ensure efficient 3D reconstructions in the presence of such spurious detection events. The growing development of technological applications based on the Lidar technology requires efficient computational methods. The goal of this thesis is to develop advanced algorithms for single-photon Lidar that can address the problems mentioned above, namely how to ensure robustness and an appropriate computational complexity for adaptive processing. First, a link is established with sampling theory by highlighting that the reconstruction problem can be viewed as restoring a signal having a finite rate of innovation. Since the presence of spurious detection events in Lidar measurements complicates the approach, a denoising algorithm is proposed to reduce the effect of external light sources before addressing reconstruction problems. The 3D reconstruction problem is then addressed from a recent type of multispectral Lidar where the photon returns associated with different wavelengths are concatenated into a single histogram. The proposed Bayesian approaches differ from that considered with the sampling method as they enable the estimation of background illumination levels as well as the modelling of the observations with Poisson processes. Two methods are proposed to ensure satisfying estimation performance in a considerably reduced computational time compared to state-of-the-art methods. Finally, an online algorithm is proposed to address the reconstruction of a dynamic 3D scene. A pseudo-Bayesian framework is adopted where the classical likelihood term is replaced by a maximum divergence estimator. The proposed pseudo-Bayesian online algorithm enables close to real-time estimation of the model parameters and performs efficient 3D video reconstruction.Heriot-Watt University James-Watt Scholarship fundin

Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions

A Novel Pseudo-Bayesian Approach for Robust Multi-Ridge Detection and Mode Retrieval

International audienceThis paper introduces a novel approach for extracting the elementary components present in an observed nonstationary mixture signal. Our technique based on a pseudo-Bayesian approach operates in the time-frequency plane and sequentially estimates the ridge of each component that is required for mode extraction. We compare our results with those obtained with the state-of-the-art Brevdo method which has shown its efficiency for disentangling multicomponent noisy signals. Our results reveal an improvement of the reconstruction performance when compared to the state of the art

EM-based approach to 3D reconstruction from single-waveform multispectral Lidar data

In this paper, we present a novel Bayesian approach for estimating spectral and range profiles from single-photon Lidar waveforms associated with single surfaces in the photon-limited regime. In contrast to classical multispectral Lidar signals, we consider a single Lidar waveform per pixel, whereby a single detector is used to acquire information simultaneously at multiple wavelengths. A new observation model based on a mixture of distributions is developed. It relates the unknown parameters of interest to the observed waveforms containing information from multiple wavelengths. Adopting a Bayesian approach, several prior models are investigated and a stochastic Expectation-Maximization algorithm is proposed to estimate the spectral and depth profiles. The reconstruction performance and computational complexity of our approach are assessed, for different prior models, through a series of experiments using synthetic and real data under different observation scenarios. The results obtained demonstrate a significant speed-up without significant degradation of the reconstruction performance when compared to existing methods in the photon-starved regime

Instantaneous Frequency and Amplitude Estimation in Multi-Component Signals Using an EM-based Algorithm

International audienceThis paper addresses the problem of estimating the instantaneous frequency (IF) and amplitude of the modes composing a non-stationary multi-component signal in the presence of noise. A novel observation model for the signal spectrogram is developed within a Bayesian framework to handle intricate configurations involving noise or overlapping components. The model parameters are estimated using a stochastic variant of the Expectation-Maximization algorithm, bypassing the computationally expensive joint parameter estimation from the posterior distribution. We then design an algorithm for instantaneous amplitude and frequency estimation that accounts for overlap and amplitude variations of the components. To assess the performance of the proposed method, we conduct experiments on both real-world and simulated signals, involving separated or crossing modes. The benefits of our method in terms of efficiency compared with several state-of-the art techniques appear to be significant in that latter case, but also when the amplitude of the components are varying across time