Practical aspects of designing for and evaluating structural integrity

The application of procedures for designing and evaluating structural integrity of various construction techniques is discussed. The fatigue performance improvement of the following conditions is described: (1) conical fasteners, (2) hole preparation, (3) interference fit, and (4) interference protection. The application of fatigue tests to determine fail-safe conditions is analyzed

Approximate maximum likelihood estimation of two closely spaced sources

The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

Computable lower bounds for deterministic parameter estimation

This paper is primarily tutorial in nature and presents a simple approach(norm minimization under linear constraints) for deriving computable lower bounds on the MSE of deterministic parameter estimators with a clear interpretation of the bounds. We also address the issue of lower bounds tightness in comparison with the MSE of ML estimators and their ability to predict the SNR threshold region. Last, as many practical estimation problems must be regarded as joint detection-estimation problems, we remind that the estimation performance must be conditional on detection performance, leading to the open problem of the fundamental limits of the joint detectionestimation performance

On the influence of detection tests on deterministic parameters estimation

In non-linear estimation problems three distinct regions of operation can be observed. In the asymptotic region, the Mean Square Error (MSE) of Maximum Likelihood Estimators (MLE) is small and, in many cases,close to the Cramer-Rao bound (CRB). In the a priory performance region where the number of independent snapshots and/or the SNR are very low, the MSE is close to that obtained from the prior knowledge about the problem. Between these two extremes, there is an additional transition region where MSE of estimators deteriorates with respect to CRB. The present paper provides exemples of improvement of MSE prediction by CRB, not only in the transition region but also in the a priori region, resulting from introduction of a detection step, which proves that this renement in MSE lower bounds derivation is worth investigating

Avancées récentes en asservissement visuel

National audienceLa communauté française est très active dans le domaine de l'asservissement visuel. Cet article se propose d'en présenter les avancées ressentes, aussi bien sur les aspects théoriques (modélisation d'informations visuelles et élaboration de lois de commande assurant diverses propriétés de robustesse, d'invariance, de stabilité, de découplage, etc.) que sur les nouvelles applications traitées (en robotique médicale, sur des engins volants,etc.)

Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.Comment: fixed authors lis

Synthetic aperture radar demonstration kit for signal processing education

A Synthetic Aperture Radar scale model has been developed to improve signal processing teaching. Based on low frequency ultrasound transmission, it is a low cost demonstration kit. The overall software is directly running on Matlab® and allows easy and realtime modifications. This educational tool can be used to illuminate a scene using different waveforms, and then see the effects on the formed image. It can also be used in a more advanced way to test different signal processing in order to improve image focusing or to reduce computation burden

Generic decoupled image-based visual servoing for cameras obeying the unified projection model

In this paper a generic decoupled imaged-based control scheme for calibrated cameras obeying the unified projection model is proposed. The proposed decoupled scheme is based on the surface of object projections onto the unit sphere. Such features are invariant to rotational motions. This allows the control of translational motion independently from the rotational motion. Finally, the proposed results are validated with experiments using a classical perspective camera as well as a fisheye camera mounted on a 6 dofs robot platform