A review on egomotion by means of differential epipolar geometry applied to the movement of a mobile robot

The estimation of camera egomotion is an old problem in computer vision. Since the 1980s, many approaches based on both the discrete and the differential epipolar constraint have been proposed. The discrete case is used mainly in self-calibrated stereoscopic systems, whereas the differential case deals with a single moving camera. This article surveys several methods for 3D motion estimation unifying the mathematics convention which are then adapted to the common case of a mobile robot moving on a plane. Experimental results are given on synthetic data covering more than 0.5 million estimations. These surveyed algorithms have been programmed and are available on the Internet.     

Uniform Distribution, Distance and Expectation Problems for Geometric Features Processing

Complex geometric features such as oriented points, lines or 3D frames are increasingly used in image processing and computer vision. However, processing these geometric features is far more difficult than processing points, and a number of paradoxes can arise. We establish in this article the basic mathematical framework required to avoid them and analyze more specifically three basic problems: (1) what is a random distribution of features, (2) how to define a distance between features, (3) and what is the “mean feature” of a number of feature measurements? We insist on the importance of an invariance hypothesis for these definitions relative to a group of transformations that models the different possible data acquisitions. We develop general methods to solve these three problems and illustrate them with 3D frame features under rigid transformations. The first problem has a direct application in the computation of the prior probability of a false match in classical model-based object recognition algorithms. We also present experimental results of the two other problems for the statistical analysis of anatomical features automatically extracted from 24 three-dimensional images of a single patient's head. These experiments successfully confirm the importance of the rigorous requirements presented in this article.     

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.     

Discovery of an Allosteric Ligand Binding Site in SMYD3 Lysine Methyltransferase

SMYD3 is a multifunctional epigenetic enzyme with lysine methyltransferase activity and various interaction partners. It is implicated in the pathophysiology of cancers but with an unclear mechanism. To discover tool compounds for clarifying its biochemistry and potential as a therapeutic target, a set of drug-like compounds was screened in a biosensor-based competition assay. Diperodon was identified as an allosteric ligand; its R and S enantiomers were isolated, and their affinities to SMYD3 were determined (K_D=42 and 84 μM, respectively). Co-crystallization revealed that both enantiomers bind to a previously unidentified allosteric site in the C-terminal protein binding domain, consistent with its weak inhibitory effect. No competition between diperodon and HSP90 (a known SMYD3 interaction partner) was observed although SMYD3–HSP90 binding was confirmed (K_D=13 μM). Diperodon clearly represents a novel starting point for the design of tool compounds interacting with a druggable allosteric site, suitable for the exploration of noncatalytic SMYD3 functions and therapeutics with new mechanisms of action.     

Eucalyptol is an Attractant of the Redbay Ambrosia Beetle,Xyleborus Glabratus

The redbay ambrosia beetle, Xyleborus glabratus, is an invasive wood-boring beetle that has become established in the southeastern United States. The beetle transmits the causal pathogen of lethal laurel wilt to susceptible host trees, which include redbay, an important forest community species, and avocado, a valuable food crop. By examining odors of redbay wood, we developed an artificial lure that captured X. glabratus in redbay forests. Eucalyptol was a critical component of the blend for beetle attraction, and eucalyptol alone in large quantities attracted X. glabratus. Furthermore, eucalyptol stimulated boring by X. glabratus into paper arenas. The results suggest that eucalyptol contributes to host selection behavior of X. glabratus and may be useful for management of this pathogen vector.     

Water retention and gas relative permeability of two industrial concretes

This experimental study aims at identifying the water retention properties of two industrial concretes to be used for long term underground nuclear waste storage structures. Together with water retention, gas transfer properties are identified at varying water saturation level, i.e. relative gas permeability is assessed directly as a function of water saturation level S_w. The influence of the initial de-sorption path and of the subsequent re-saturation are analysed both in terms of water retention and gas transfer properties. Also, the influence of concrete microstructure upon water retention and relative gas permeability is assessed, using porosity measurements, analysis of the BET theory from water retention properties, and MIP. Finally, a single relative gas permeability curve is proposed for each concrete, based on Van Genuchten–Mualem's statistical model, to be used for continuous modelling approaches of concrete structures, both during drying and imbibition.     

Thermodiffusion‐Assisted Pyroelectrics—Enabling Rapid and Stable Heat and Radiation Sensing

Sensors for monitoring temperature, heat flux, and thermal radiation are essential for applications such as electronic skin. While pyroelectric and thermoelectric effects are suitable candidates as functional elements in such devices, both concepts show individual drawbacks in terms of zero equilibrium signals for pyroelectric materials and small or slow response of thermoelectric materials. Here, these drawbacks are overcome by introducing the concept of thermodiffusion‐assisted pyroelectrics, which combines and enhances the performance of pyroelectric and ionic thermoelectric materials. The presented integrated concept provides both rapid initial response upon heating and stable synergistically enhanced signals upon prolonged exposure to heat stimuli. Likewise, incorporation of plasmonic metasurfaces enables the concept to provide both rapid and stable signals for radiation‐induced heating. The performance of the concept and its working mechanism can be explained by ion–electron interactions at the interface between the pyroelectric and ionic thermoelectric materials.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

Subspace Procrustes Analysis

Procrustes analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Later, a non-rigid 2-D model is computed by modeling the residual (e.g., PCA). Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can model rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. We illustrate the benefits of SPA in two different applications. First, SPA is used to learn 2-D face and body models from 3-D datasets. Experiments on the FaceWarehouse and CMU motion capture (MoCap) datasets show the benefits of our 2-D models against the state-of-the-art PA approaches and conventional 3-D models. Second, SPA learns an unbiased 2-D model from CMU MoCap dataset and it is used to estimate the human pose on the Leeds Sports dataset.