Search

Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. In this context, this paper conducts a review of popular SLAM approaches with a focus on vSLAM/viSLAM, both at fundamental and experimental levels. It starts with a structured overview of existing vSLAM and viSLAM designs and continues with a new classification of a dozen main state-of-the-art methods. A chronological survey of viSLAM’s development highlights the historical milestones and presents more recent methods into a classification. Finally, the performance of vSLAM is experimentally assessed for the use case of pedestrian pose estimation with a handheld device in urban environments. The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.

Solar roads are transportation infrastructures able both to generate electricity thanks to solar cells placed under a semi-transparent layer and to ensure heavy traffic circulation. In this paper, a novel transparent top layer made of glass aggregates bonded together using a polyurethane glue is presented. The goal is to design a composite material able to support traffic load, guarantee vehicle skid-resistance, allow the passage of sunlight, and protect the solar cells. For this purpose, the authors investigated the effect of different variables (thickness, glue content, and glass aggregate distribution) on the mechanical and optical performances of the material applying the factorial design method. The semi-transparent layer was characterized by performing the three-point bending test and measuring the power loss. Regarding the vehicle friction, experimental tests with the British Pendulum were conducted in order to measure the skid resistance of the surface and compare it with the specifications of a typical road infrastructure. According to the fraction factorial design and the British Pendulum test, the following mixture was developed: 42.8% of 4/6 mm, 42.8% of 2/4 mm, 14.4% of glue in volume, and a thickness of 0.6 cm. The first results are encouraging, and they demonstrate the feasibility of a semi-transparent layer for future applications in full scale.

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo-ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 degrees in texting mode and 12 degrees in pocket mode.

Total knee arthroplasty (TKA) is an effective treatment for severe osteoarthritis. Despite good survival rates, up to 20% of TKA patients remain dissatisfied. Recently, promising new technologies have been developed in knee arthroplasty, and could improve the functional outcomes. The aim of this paper was to present some new technologies in TKA, their current concepts, their advantages, and limitations. The patient-specific instrumentations can allow an improvement of implant positioning and limb alignment, but no difference is found for functional outcomes. The customized implants are conceived to reproduce the native knee anatomy and to reproduce its biomechanics. The sensors have to aim to give objective data on ligaments balancing during TKA. Few studies are published on the results at mid-term of these two devices currently. The accelerometers are smart tools developed to improve the TKA alignment. Their benefits remain yet controversial. The robotic-assisted systems allow an accurate and reproducible bone preparation due to a robotic interface, with a 3D surgical planning, based on preoperative 3D imaging or not. This promising system, nevertheless, has some limits. The new technologies in TKA are very attractive and have constantly evolved. Nevertheless, some limitations persist and could be improved by artificial intelligence and predictive modeling.

With the improvement in sensor technologies, air quality is increasingly being monitored. Two major factors in obtaining relevant information are the optimal placement and the number of air quality sensors. Moreover, in cases of poor air quality, the information of the pollution level given by the deployed sensors is not sufficient. An advanced understanding of the data is required to precisely identify the source pollution and thus propose effective solutions. In this article, a virtual testing strategy based on computational fluid dynamics (CFD) is presented for the optimal placement of indoor air quality sensors. We determine the placement of sensors in view of localizing the maximum of sources emitting on the indoor environment surfaces. Therefore, an adjoint framework is used to obtain the observable region associated with a given sensor position. The proposed method takes into account technical sensor features, such as the limit of detection (LOD). Two applications are studied: a simple 2D case and a real 3D room. In these examples, we first show that reducing the LOD of the sensors by one order of magnitude can increase the observable area by more than 50 % . Then, we note that one-fourth of the potential sensor placements observe almost nothing and that 80 % of the potential sensor placements have an observable area two times smaller than the optimal sensor position determined by the proposed CFD-based strategy.

One of the components of the fifth generation road is its capacity to communicate with its network manager in order to improve maintenance. The aim of the experimental instrumentation presented in this study is to monitor the evolution of characteristic damage in mixed structures: nonvisible cracks rising from the hydraulic base towards the coated pavement surface. The objective of the sensors is to be able to anticipate damage before it reaches the surface. As temperature is also being measured, it is possible to use this data to communicate with users concerning the potential risk of ice forming on the road. The first measurements have been compared with a theoretical model of the structure, and the continuous acquisition of signals will enable the network manager to monitor the behaviour of the road.

This work proposes a fault detection architecture for vehicle embedded sensors, allowing to deal with both system nonlinearity and environmental disturbances and degradations. The proposed method uses analytical redundancy and a nonlinear transformation to generate the residual value allowing the fault detection. A strategy dedicated to the optimization of the detection parameters choice is also developed.

Pavement structures are designed to withstand continuous damage during their design life. Damage starts as soon as the pavement is open to traffic and increases with time. If maintenance activities are not considered in the initial design or considered but not applied during the service life, damage will grow to a point where rehabilitation may be the only and most expensive option left. In order to monitor the evolution of damage and its severity in pavement structures, a novel data compression approach based on cumulative measurements from a piezoelectric sensor is presented in this paper. Specifically, the piezoelectric sensor uses a thin film of polyvinylidene fluoride to sense the energy produced by the micro deformation generated due to the application of traffic loads. Epoxy solution has been used to encapsulate the membrane providing hardness and flexibility to withstand the high-loads and the high-temperatures during construction of the asphalt layer. The piezoelectric sensors have been exposed to three months of loading (approximately 1.0 million loads of 65 kN) at the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) fatigue carrousel. Notably, the sensors survived the construction and testing. Reference measurements were made with a commercial conventional strain gauge specifically designed for measurements in hot mix asphalt layers. Results from the carrousel successfully demonstrate that the novel approach can be considered as a good indicator of damage progression, thus alleviating the need to measure strains in pavement for the purpose of damage tracking.

32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), MIAMI, ETATS-UNIS, 16-/09/2019 - 20/09/2019; Pedestrian location in urban or indoor environments is particularly complex. Indeed, GNSS technology generally used for localization is no longer sufficient in these challenging environments. However, the presence of many sensors in consumer equipment like smartphones allows the implementation of different methods. PDR (Pedestrian Dead Reckoning) is a position estimation method using inertial and magnetic sensor data. It is based on the determination of two elements: the step length and the walking direction. This direction is difficult to estimate for handheld sensors because the orientation of the sensor is not always aligned with the walking direction. Methods based on the study of horizontal hand accelerations can overcome this difficulty, but performance on real scenarios is not sufficient. This article proposes a new method for estimating the walking direction and position based on an extended Kalman filter. For this purpose, the angular estimates from the WAISS and MAGYQ algorithms are merged to update the estimate of the walking direction. Phase measurements are used with TDCP updates to correct the velocity and correct the walking direction. 6 experiments carried out with three subjects over distances between 650 and 1300m in texting mode, in real and challenging conditions are conducted. The mean angular error obtained is 4.6° and the mean position error is 0.5% of the travelled distance

EMC Europe 2019, International Symposia and Workshops on Electromagnetic Compatibility, Barcelone, ESPAGNE, 02-/09/2019 - 06/09/2019; Mapping or scanning the electromagnetic emissions of electronic cards or any environment requires the use of relatively complex mechanical or electronic devices. These systems may be difficult to transport and not well suited to perform measurements in confined environments. Moreover, they can require long acquisition times. Being autonomous, compact and fast, the scanning system proposed in this article aims to overcome these constraints. It makes it possible to map the electromagnetic environment directly on site or in locations difficult to access, such as inside a vehicle for example. This scanner is composed of a smartphone coupled to removable EM field sensors to allow covering different configurations. Using augmented reality technology, it has an interface that allows communication via an USB port between the smartphone and the various sensors. In this paper, the device is described and measurement results illustrating its potential are presented.

International audience; Experimental acoustic sensor networks are currently tested in large cities, and appear more and more as a useful tool to enrich modeled road traffic noise maps through data assimilation techniques. One challenge is to be able to isolate from the measured sound mixtures acoustic quantities of interest such as the sound level of road traffic. This task is anything but trivial because of the multiple sound sources that overlap within urban sound mixtures. In this paper, the Non-negative Matrix Factorization (NMF) framework is developed to estimate road traffic noise levels within urban sound scenes. To evaluate the performances of the proposed approach, a synthetic corpus of sound scenes is designed, to cover most common soundscape settings, and whom realism is validated through a perceptual test. The simulated scenes reproduce then the sensor network outputs, in which the actual occurrence and sound level of each source are known. Several variants of NMF are tested. The proposed approach, named threshold initialized NMF, appears to be the most reliable approach, allowing road traffic noise level estimation with average errors of less than 1.3 dB over the tested corpus of sound scenes.

Ballast deterioration, under dynamic loads, remains an important issue on high-speed tracks that can lead to high maintenance costs. This ballast deterioration leads to settlements. Several studies have shown that these settlements were linked to high accelerations produced in the ballast by high-speed train (HST) passages. The solution with bituminous underlayment was used since 1980s in several countries like United States, Italy, Spain, especially on high-traffic and high-speed lines (HSL). In France, the interest in this technique is recent. Following the East European HSL satisfactory behavior, a layer of asphalt concrete was used under the ballast layer of the Bretagne-Pays de la Loire (BPL) HSL. It is intended, in addition to the schedule savings and the protection of the subgrade during the construction phase, to reduce acceleration amplitudes produced at the passage of HST, to ensure moisture stability in the subgrade and thereby to decrease the maintenance costs of the tracks. BPL HSL includes 105 km of innovative track with an asphalt concrete (GB) ballast sublayer, and 77 km with a granular layer under the ballast (UGM). Out of the instrumented sections of the BPL track, 3 sections are constructed with GB subballast layer and one with a layer of UGM as a subballast layer. A total of 127 sensors that includes accelerometers, anchored displacement sensors, temperature and humidity probes, and extensometers are used. Sensors are placed at various positions and depths in the track structures. Data were first acquired during a speed up test phase, under controlled conditions, with the same train passing at speeds ranging from 160 to 352 km/h. This paper presents the different sensors used for the instrumentation as well as the acquisition system installed to collect all measurements. Data treatment and processing is explained in details. Finally, results obtained for different speeds are presented, with a focus on accelerometer and anchored displacement sensor measurements, on two sections, allowing, among others, comparisons between the response of structures with and without asphalt concrete. The role of the GB, as a subballast layer, in damping the vertical displacement of the sub ballast structure and reducing the accelerations peaks in the ballast layer for ballasted tracks is demonstrated.

We present an original method using a low cost accelerometer and a Kalman-filter based algorithm to monitor cardiopulmonary resuscitation chest compressions (CC) depth. A three-axis accelerometer connected to a computer was used during CC. A Kalman filter was used to retrieve speed and position from acceleration data. We first tested the algorithm for its accuracy and stability on surrogate data. The device was implemented for CC performed on a manikin. Different accelerometer locations were tested. We used a classical inertial navigation algorithm to reconstruct CPR depth and frequency. The device was found accurate enough to monitor CPR depth and its stability was checked for half an hour without any drift. Average error on displacement was ±0.5mm. We showed that depth measurement was dependent on the device location on the patient or the rescuer. The accuracy and stability of this small low-cost accelerometer coupled to a Kalman-filter based algorithm to reconstruct CC depth and frequency, was found well adapted and could be easily implemented.

TRA 2016, European Research and Technology Conference on Transport and Mobility, VARSOVIE, POLOGNE, 18-/04/2016 - 21/04/2016; Recently, IFSTTAR has taken an interest in the use of probe vehicles for the road monitoring of some characteristics such as the pavement longitudinal profile. After studying the feasibility of the method on a metrological angle, a measurement chain and an automated data processing tool have been developed. The process begins with the data collection using low-cost sensors embedded in each probe vehicle and ends with the presentation to the road managers of the profile quality indicator matched on a map. Thereafter, this innovative pavement monitoring method has been tested with the deployment of a dozen of probe vehicles on a road network of about one thousand kilometers long. This article aims to present, in a first step, the different tools developed by IFSTTAR (data collection systems, map-matching algorithms, database format, etc.) and then, in a second step, to comment the results coming from the application of the pavement monitoring method on the road test network (level of accuracy, technological and scientific limits, opinion of the road manager, etc.).

2018 International Conference on Indoor Positioning and Indoor Navigation, NANTES, FRANCE, 24-/09/2018 - 27/09/2018; Inertial and magnetic sensors based PDR approaches are particularly interesting for pedestrian location since they don't require any specific infrastructure. Estimating the walking direction, which is essential for PDR strategy, remains difficult with handheld sensors. WAISS is a new method that integrates hand movement and is independent of the misalignment between the walking direction and the pointing direction that estimates the walking direction. It uses statistical models of the hand accelerations in the horizontal plane. The paper studies how to create the best possible models. Among the features under study are the number of strides used to learn the models, different acquisition contexts and walking directions. Finally, the complexity of models needed for a given person is discussed. 100 strides over curved and straight line walks combined with a bi-modal Gaussian Mixture Model gives the best walking direction estimate with a 15° mean error over a 325 m indoor/outdoor walk performed by four subjects.

AMAA 2018, Advanced Microsystems for Automotive Applications, Berlin, ALLEMAGNE, 11-/09/2018 - 12/09/2018; The AWARE (All Weather All Roads Enhanced vision) French public funded project is aiming at the development of a low cost sensor fitting to automotive and aviation requirements, and enabling a vision in all poor visibility conditions, suchas night, fog, rain and snow. In order to identify the technologies providing the best all-weather vision, we evaluated the relevance of four different spectral bands: Visible RGB, Near-Infrared (NIR), Short-Wave Infrared (SWIR) and Long-Wave Infrared (LWIR). Two test campaigns have been realized in outdoor natural conditions and in artificial fog tunnel, with four cameras recording simultaneously.This paper presents the detailed results of this comparative study, focusing on pedestrians, vehicles, traffic signs and lanes detection.; Le projet financé par les fonds publics français AWARE (All Weather All Roads Enhanced) vise à mettre au point un capteur à faible coût qui réponde aux exigences de l'industrie automobile et de l'aviation fonctionnant de nuit, avec brouillard, pluie et neige. Afin d'identifier les technologies offrant la meilleure vision possible par tous les temps, nous avons évalué la pertinence de quatre bandes spectrales possibles : RVB visible, infrarouge proche (NIR), infrarouge à ondes courtes (SWIR) et infrarouge à ondes longues (LWIR). Deux campagnes de test ont été réalisées dans des conditions naturelles en extérieures et dans un tunnel à brouillard, avec quatre caméras enregistrant simultanément. Cet article présente les résultats détaillés de cette étude comparative qui a focalisée sur la détection des piétons, des véhicules, des panneaux de signalisation et des voies.

In mammals and birds, the reproductive system is very sensitive to states of energy deficit. For example, chronic food restriction in prepubertal female rats prevents the normal onset of puberty [1]. In chickens, food restriction decreases body weight, the numbers of yellow preovulatory follicles, and the proportion of atretic yellow follicles and increases the age at onset of lay [2]. In pigs, a negative energy balance and a decrease in body fat result in a reduction in litter size and viability of piglets [3]. In sheep, it is well known that an increase in availability of energy substrates is associated with an increase in prolificacy [4]. Furthermore in human, both clinical and experimental studies reveal the negative consequences of obesity on male and female reproductive function [5]. Obese women also have higher rates of many complications in pregnancy, including gestational diabetes and rates of cesarean delivery [6]. Although the clinical impact of obesity on male and female infertility has been well described, the mechanisms involved that could lead to effective treatment are still being elucidated. The hypothalamic pituitary gonadal (HPG) axis is central to the reproductive system. Pulsatile gonadotropin-releasing hormone (GnRH) released from the hypothalamus stimulates the secretion of luteinising hormone (LH) and follicle-stimulating hormone (FSH) from the anterior pituitary. In females, FSH promotes follicle maturation and LH regulates ovulation whereas in males, FSH is mainly involved in spermatogenesis and LH stimulates the synthesis and the secretion of testosterone by the testis. Compelling evidence indicates that common regulatory signals involving energy sensors are implicated in the integrated control of energy balance and reproduction. At the whole animal level, energy sensors play a crucial role to indicate whether energy reserves are abundant (obesity) or poor (in case of food restriction and sports training). These energy sensors can be hormones, kinases, or nutrients. Some fertility disorders related to abnormal metabolism have highlighted the importance of the role of hormones produced by white adipose tissue (adipokines) in the regulation of mechanisms involved in human and animal reproductive functions [7]. It is well known that white adipose tissue provides energy in the form of fatty acids and glycerol to other organs, especially when dietary intake does not meet requirements. However, adipokines produced by white adipose tissue play an important role in the regulation of the reproductive axis [8]. The most studied and described adipokine in the literature is leptin [9]. For example, leptin has been proved to cooperate with other regulatory signals such as ghrelin (the endogenous ligand of the growth hormone (GH)) in the integrated control of energy balance and reproduction [10]. The major involvement of leptin in the regulation of food intake, adiposity, insulin sensitivity, and reproduction has paved the way for the discovery of new adipokines such as adiponectin, visfatin, chemerin, and apelin [11]. Deregulation of plasma levels of these adipokines has been linked to the onset of pathologies such as obesity, insulin resistance, diabetes (type 2 or gestational), and polycystic ovary syndrome (PCOS) [12–15]. Furthermore, the removal of adipose tissue has been demonstrated in mice to inhibit reproductive functions [20]. Thus, an adequate amount and distribution of white adipose tissue may be essential for the completion of normal gametogenesis and fertility. Given the tight link between energy metabolism and reproduction, in this special issue, we propose to discuss the role of some energy sensors such as adipokines on the fertility in different species including mammals and birds. Birds have some metabolic and reproductive peculiarities interesting to study the relationship between adipokines, metabolism, and reproduction. This special issue includes one novel research article and 4 reviews summarized as follows

Geographic routing has gained much attention as a basic routing primitive inwireless sensor networks due to its memory-less, scalability, efficiency and low overheadfeatures. Greedy forwarding is the simplest geographic routing scheme, it uses the distanceas a forwarding criterion. Nevertheless, it may suffer from communication holes, whereno next hop candidate is closer to the destination than the node currently holding thepacket. For this purpose, a void handling technique is needed to recover from the voidproblem and successfully deliver data packets if a path does exist between source anddestination nodes. Many approaches have been reported to solve this issue at the expenseof extra processing and or overhead. This paper proposes GRACO, an efficient geographicrouting protocol with a novel void recovery strategy based on ant colony optimization(ACO). GRACO is able to adaptively adjust the forwarding mechanism to avoid theblocking situation and effectively deliver data packets. Compared to GFG, one of the bestperforming geographic routing protocols, simulation results demonstrate that GRACOcan successfully find shorter routing paths with higher delivery rate, less control packetoverhead and shorter end-to-end delay.

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal&mdash, End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 &plusmn, 1309 ha in 2016 to 7605 &plusmn, 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

IF=2.472; This paper presents a robust observer design methodology to estimate tire forces in heavy-duty vehicles. The proposed methodology uses low-cost sensors and observer-based numerical differentiators. In addition to the already available sensors, low-cost sensors from the CAN bus are employed to get additional measurements. Sliding mode differentiators are used to optimize the sensors' configuration. Validation of the proposed estimation methodology is made by comparing the results with that generated using the PROSPER software simulator. The finite-time convergence with small estimation error suggests that the proposed methodology is valid and can be applied in real time.

This article aims at modeling the WIM sensor electrical behavior to provide a better understanding of the mechanical and electrical proprieties of piezoelectric sensors. The model is considered in the frequency domain, supposing any sinusoidal solicitation with constant frequency or any other type of solicitation. Tests, having real WIM sensors placed on the pavement of a test track at IFSTTAR/Nantes was used to verify the nature of the electro-mechanical (EM) behavior of the real sensors over punching, flexural and extension effects. Also, two different pavement numerical simulations deliver the mechanical response using linear elasticity and viscoelastic proprieties. The output of these two simulations are compared with two WIM signals obtained at same load and speed at two different temperature conditions.

Contrary to Global Navigation Satellite System or Wi-Fi based navigation, pedestrian dead reckoning (PDR) method with handheld inertial and magnetic sensors gives the opportunity to achieve indoor/outdoor ubiqui- tous pedestrian localization. A remaining PDR critical issue is the estimation of the walking direction. Existing methods are principally searching for the energy main axis, but they do not consider the variability of handmovements introduc- ing robustness issues. A new method, based on statistical models and likelihood maximization adjusted to the person and his/her activity, is proposed in this paper. Performance is assessed with experiments in amotion capture room and a shopping mall. The new statistical approach gives glob- ally better results than state of the art methods. A 1.4° to 15.3° error on the walking direction estimates is found over several '1-km walk' tests indoors

IPIN 2016, International conference on Indoor Positioning and Indoor Navigation, MADRID, ESPAGNE, 04-/10/2016 - 07/10/2016; Map aided Pedestrian Dead Reckoning (PDR) methods seek to guarantee an autonomous positioning solution for pedestrians. Inertial sensors, assembled in one measurement unit, are usually fused with map data in order to palliate the well-known drift errors and step detection limitations, especially indoors as no GNSS signal is available. In addition to these remaining limitations, the measurement unit carrying mode has also to be accounted for in order to correct the walking directions, which is still an open issue in the literature. The aim of this study was to handle the above-mentioned difficulties within the framework of handheld devices; a magnetic and inertial measurement unit was used during experiments. First a routing graph was built according to the pedestrian's possible motions, depending on his mobility profile and surroundings, and second, an adaptive decision process was developed to determine which data among GNSS and the graph elements should be used to assist the location process; a particle filter was implemented to achieve these objectives. The main findings of this study can be summarized in three points. First, the step length model can be calibrated using GNSS within the straight phases of the subject's walk, preferably using particle filters because they offer a greater freedom in the positioning configuration; second, the misalignment between the walking direction and the pointing direction of the measurement unit is assessable in the geometrically constrained areas; and finally, the space configuration can be exploited to make a convenient choice of the external measurements to be considered.

EuCAP 2016 - 10th European Conference on Antennas and Propagation, Davos, SUISSE, 10-/04/2016 - 15/04/2016; With Advanced Driver Assistance Systems (ADAS) getting always more sophisticated, the related Virtual Prototyping platforms have to propose a very high level of accuracy with improved flexibility regarding vehicles, sensors, environments and scenarios. In this paper a new strategy is introduced for RADAR sensors modeling aimed at allowing high accuracy while limiting the related development efforts.

Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L 10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L den estimation as long as individual measurements dispersions remain too large.

This letter presents a vehicle detection state machine for magnetometer sensors. The state machine designed by Chinrungrueng and Kaewkamnerd for a single lane is extended to $n$ motorway lanes with multiple lane changes. The proposed method has good performance in terms of the detection rate and the false detections, which are mainly due to the interference from a vehicle in an adjacent lane and lane changing. The state machine is evaluated on a real motorway and compared with industrial sensors.

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements, and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE; International audience; In this paper, we introduce a geometric method for 3D reconstruction of the exterior environment using a panoramic microwave radar and a camera. We rely on the complementarity of these two sensors considering the robustness to the environmental conditions and depth detection ability of the radar, on the one hand, and the high spatial resolution of a vision sensor, on the other. Firstly, geometric modeling of each sensor and of the entire system is presented. Secondly, we address the global calibration problem, which consists of finding the exact transformation between the sensors' coordinate systems. Two implementation methods are proposed and compared, based on the optimization of a non-linear criterion obtained from a set of radar-to-image target correspondences. Unlike existing methods, no special configuration of the 3D points is required for calibration. This makes the methods flexible and easy to use by a non-expert operator. Finally, we present a very simple, yet robust 3D reconstruction method based on the sensors' geometry. This method enables one to reconstruct observed features in 3D using one acquisition (static sensor), which is not always met in the state of the art for outdoor scene reconstruction.The proposed methods have been validated with synthetic and real data.

IPIN 2015, International Conference on Indoor Positioning and Indoor Navigation, BANFF, CANADA, 13-/10/2015 - 16/10/2015; Pedestrian navigation systems based on smartphone are experiencing fast progress in indoor environment. Pedestrian dead reckoning approaches combined with improved inertial sensors' quality and the exploitation of magnetic field are used to mitigate the sensor drifts. The last remaining issue is related to the hand dynamic. It consists in estimating the angular misalignment between the smartphone pointing direction and the walking direction. Even though, some methods exist, their performances are lacking accuracy and reliability. A comparison of the three main methods to estimate this angular misalignment is performed. These methods are based on Principal Component Analysis (PCA), Forward and Lateral Accelerations Modeling (FLAM) and Frequency analysis of Inertial Signals (FIS). Despite better results for the FIS method all algorithm suffer from large outliers and a need for improved robustness is identified

International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2015), Lisbonne, Portugal, 20-/05/2015 - 22/05/2015; In this paper a new fault detection architecture will be presented. Inspired by multi-model data fusion algorithms and fuzzy logic decisions, it consists in the comparison between the estimation of a dynamic mode using each sensor independently. This method is used to deal with important non-linearity and strong interaction with the environment usually encountered in the domain of the intelligent vehicles localization. The concept of analytic redundancy is also used to ignore model uncertainties

We address the reconstruction of relevant two-dimensional (2D) flows in drinking water networks, especially in key elements such as pipe junctions, in view of representative water quality simulations. From the optimal control theory, a specific inverse technique using few sensors and computational fluid dynamics (CFD) models has been developed. First, we determine the boundary velocities, i.e., the control parameters, by minimizing a data misfit functional. Then, knowing the boundary velocities, a direct solve of the flow model is performed to get the space–time cartography of the water flow. To reduce the number of control parameters to be determined and thus restrict the number of sensors, the spatial shape of the boundary velocities is considered as an a priori information given by the water pipes engineering literature. Thus, only the time evolution of the boundary velocities has to be determined. The whole numerical procedure proposed in this paper easily fits in a general purpose finite element software, featuring user's friendliness for a wide engineering audience. Two ways are investigated to reduce the computation time associated to the flow reconstruction. The adjoint framework is used in the minimization process. The reconstruction of the flow using coarse discretizations and simple flow models, instead of 2D Navier–Stokes equations, is studied. The influence of the flow modeling and of the dicretization on the quality of the reconstructed velocity is studied on two examples: a water pipe junction and a 200 m subsection from a French water network. In the water pipe junction, we show that at a Reynolds number of 200 a hybrid approach combining an unsteady Stokes reconstruction and a single direct Navier–Stokes simulation outperforms the algorithms based on a single model. In the network subsection, we obtain an L2 error less than 1% between the reference velocity based on Navier–Stokes equations (Reynolds number of 200) and the velocity reconstructed from Stokes equation. In this case, the reconstruction lasts less than 1 min. Stokes based reconstruction of a Navier–Stokes flow in junctions at Reynolds number up to 100 yields the same accuracy and proves fast.

International audience; Water utilities have started to install water quality sensors to protect their network against intentional and accidental contamination events. In this context, for taking appropriate action, not only are early-warning detection systems important, but so is the identification of the contamination source, and knowledge of the present and future contamination extent. For the latter there is a need for reliable and updated network models. The main objective of this paper is to specify which performance criteria should be considered to place water quality and water quantity sensors for both early detection and model calibration. Firstly, a brief bibliographical review is given for optimal sensor location design. Next, formulations and objectives for early-warning detection are proposed. Problem formulations that aim to minimize the estimator variance for calibration are then specified. Finally, the method is applied on the real WDN system of the CUS water service (1,000 km, 25,000 nodes and 45,000 customers).

11th IEEE Vehicle Power and Propulsion Conference (IEEE VPPC 2015), Montréal, Canada, 19-/10/2015 - 22/10/2015; A fault detection and identification architecture is presented using analytical redundancy in order to detect faulty comportment from proprioceptive sensors. The three dimensional comportment analysis is then studied in order to create a road signature. This signature is then used to discriminate sensors faults from environmental perturbation. Finally, some strategies are developed to verify the faulty comportment origin, and so ensure the decision process

18th IEEE International Conference on Intelligent Transportation Systems (ITSC 2015), Las Palmas, Espagne, 15-/09/2015 - 18/09/2015; A fault detection and identification architecture is presented using analytical redundancy in order to detect faulty comportment from proprioceptive sensors. The three dimensional comportment analysis is then studied in order to create a road signature. This signature is then used to discriminate sensors faults from environmental perturbation. Finally, some strategies are developed to verify the faulty comportment origin, and so ensure the decision process

We are witnessing the clash of two industries and the remaking of in-car market order, as the world of digital knowledge recently made a significant move toward the automotive industry. Mobile operating system providers are battling between each other to take over the in-vehicle entertainment and information systems, while car makers either line up behind their technology or try to keep control over the in-car experience. What is at stake is the map content and location-based services, two key enabling technologies of self-driving cars and future automotive safety systems. These content-based augmented geographic information systems (GIS) as well as Advanced Driver Assistance Systems (ADAS) require an accurate, robust, and reliable estimation of road scene attributes. Accurate localization of the vehicle is a challenging and critical task that natural GPS or classical filter (EKF) cannot reach. This paper proposes a new approach allowing us to give a first answer to the issue of accurate lateral positioning. The proposed approach is based on the fusion of 4 types of data: a GPS, a set of INS/odometer sensors, a road marking detection, and an accurate road marking map. The lateral road markings detection is done with the processing of two lateral cameras and provides an assessment of the lateral distance between the vehicle and the road borders. These information coupled with an accurate digital map of the road markings provide an efficient and reliable way to dramatically improve the localization obtained from only classical way (GPS/INS/Odometer). Moreover, the use of the road marking detection can be done only when the confidence is sufficiently high (punctual use). In fact, the vision processing and the map data can be used punctually only in order to update the classical localization algorithm. The temporary lack of vision data does not affect the quality of lateral positioning. In order to evaluate and validate this approach, a real test scenario was performed on Satory’s test track with real embedded sensors. It shows that the lateral estimation of the ego-vehicle positioning is performed with a sub-decimeter accuracy, high enough to be used in autonomous lane keeping, and land-based mobile mapping.

Collaborative autonomous vehicles will appear in the near future and will transform deeply road transportation systems, addressing in part many issues such as safety, traffic efficiency, etc. Validation and testing of complex scenarios involving sets of autonomous collaborative vehicles are becoming an important challenge. Each vehicle in the set is autonomous and acts asynchronously, receiving and processing huge amount of data in real time, coming from the environment and other vehicles. Simulation of such scenarios in real time require huge computing resources. This paper presents a simulation platform combining the real-time OPAL-RT Technologies for processing and parallel computing, and the Pro-SiVIC vehicular simulator from Civitec for realistic simulation of vehicles dynamic, road/environment, and sensors behaviors. The two platforms are complementary and their combining allow us to propose a real time simulator of collaborative autonomous systems.

International audience; Real-time obstacle detection is an essential function for the future of Advanced Driver Assistance Systems (ADAS), but its applications to the driving safety require a very high reliability: the detection rate must be high, while the false detection rate must remain extremely low. Such features seem antinomic for obstacle detection systems, especially when using a single sensor. Multi-sensor fusion is often considered as a mean to reduce this limitation. In this paper, we propose to use stereo-vision as a post-process to improve the reliability of any obstacle detection system, by reducing the number of false positives. Our algorithm, which is both generic and real-time confirms detections by locally using the stereoscopic data. We evaluated and validated our approach with an initial detection based on a vision system and a laser scanner. The evaluation dataset is real on-road data and contains more than 20000 images.

Regularly confronted with the problem of cotton stickiness during spinning, those in the international market wish to see the implementation of a cotton bale classification system to describe the degree to which a given cotton is contaminated. Thermodetection equip ment such as the H2SD high speed stickiness detector may be suitable for such a task. With a working capacity equivalent to that of HVI lines, this instrument evaluates the stickiness of cottons contaminated by insect honeydew. This paper discusses the feasibility of a qualitative classification based on within-bale distribution of the number of sticky points. A detailed example obtained with the H2SD illustrates the qualitative classification proce dure. Sticky bales can be separated from uncontaminated bales for the latter to be marketed with a guaranteed stickiness level below a set threshold and a litigation risk selected in advance.

Simulation has been widely used to estimate the benefits of ADAS with embedded sensors or more recently Cooperative Systems based on Inter-Vehicular Communications. This paper presents the proposal of a new architecture built with the both SiVIC and RTMaps platforms in order to prototype, to test and to validate eco-driving applications. In this architecture, the innovation is mainly due to the real-time immersion of a real driver in a 3D virtual environment. In this 'Hardware In the Loop' platform, major contributions have been made about vehicle modeling updating, interconnection between SiVIC and an HMD. Moreover, a first modeling of a consumption sensor is proposed and used in order to achieve some tests of fuel consumption on the virtual Satory’s track. All these contributions have been tuned with on-road measurements to improve reality of the scenarios. We discuss the results of a simple eco-driving scenario implemented to validate our architecture’s capabilities.

This paper presents a new approach to fuse 3D and 2D information in a driver assistance setup, in particular to perform obstacle detection and tracking. We propose a new cooperative fusion method between two exteroceptive sensors: it is able to address highly non linear dynamic configuration without any assumption on the driving maneuver. Information are provided by a mono-layer laser scanner and a monocular camera which are unsynchronized. The initial detection stage is performed using the 1D laser data, which computes clusters of points which might correspond to vehicles present on the road. These clusters are projected to the image to define targets, which will be tracked using image registration techniques. This multi-object association and tracking scheme is implemented using belief theory integrating temporal and spatial information, which allows the estimation of the dynamic state of the tracks and to monitor appearance and disappearance of obstacles. Accuracy of the method is evaluated on a database made publicly available, focus is cast on the relative localization of the vehicle ahead: estimations of its longitudinal and lateral distances are analysed.

International audience; Ballastless track structure are presently seen as an attractive alternative to conventional ballast. A new model was designed and a real-scale mockup was built and tested in fatigue test at IFSTTAR laboratory. To verify numerical simulations, the railway structure was monitored by conventional strain gages. A distributed fiber-optic strain sensing based on Rayleigh backscatter was also tested in this experiment. Fiber-optic cables were used as long-gage sensors and they were embedded in the concrete slabs of the structure. Firstly, we verified that the sensors were enough mechanical and chemical resistant for withstanding the stresses and the caustic environment experienced during concrete pouring process. Secondly, we validated the strain profiles measurements by comparison with the values obtained by strain gages. A good agreement was found throughout the duration of the fatigue test of 10 million cycles. Moreover, some cracks were observed early during the fatigue test. It is important to be able to monitor them during the service of ballastless track line. We showed that cracks can be detected and localized by fiber-optic distributed strain measurements. Finally, this experiment demonstrates that fiber-optic distributed strain sensing technique based on Rayleigh backscatter is a promising sensing technique to monitor ballastless track structures and more generally, civil engineering structures.

LIVIC-IFSTTAR develops driving assistance services in order to improve the driving safety. These systems are tested on several real prototypes equipped with sensors and perception, decision and control modules. But tests on real prototypes are not always available, effectively some hardware architectures could be too expensive to implement, scenario may lead to hazardous situations. Moreover, lots of reasons could lead to the inability to obtain both sensors and ground truth data for ADAS evaluation. However, safety applications must be tested in order to guaranty their reliability. For this task, simulation appears as a good alternative to the real prototyping and testing stages. In this context, the simulation must provide the same opportunities as reality, by providing all the necessary data to develop and to prototype different types of ADAS based on local or extended environment perception. The sensor data provided by simulation must be as noised and imperfect as those obtained with real sensors. To address this issue, the SiVIC platform has been developed; it provides a virtual road environment including realistic dynamic models of mobile entities (vehicles), realistic sensors, and sensors for ground truth. To test real embedded applications, an interconnection has been developed between SiVIC and third party applications (ie. RTMaps). In this way, the prototyped application can be directly embedded in real prototypes in order to test it in real conditions. A Full Speed Range ACC application is presented in this paper to illustrate the capabilities and the functionalities of this virtual platform.

Structural health monitoring is a key factor in life cycle management of infrastructures. Truly distributed fiber optic sensors are able to provide relevant information on large structures, such as nuclear power plants or nuclear waste disposal facilities. The sensing chain includes an optoelectronic unit and a sensing cable made of one or more optical fibers. A new instrument based on Optical Frequency Domain Reflectometry (OFDR), enables to perform temperature and strain measurements with a centimeter scale spatial resolution over hundred of meters and with a level of precision equal to 1 μ strain and 0.1 °C. Several sensing cables are designed with different materials targeting to last for decades, either embedded in the concrete or attached to the surface of the structure. They must ensure an optimal transfer of temperature and strain from the concrete matrix to the optical fiber. Based on the European guide FD CEN/TR 14748 “Non-destructive testing – Methodology for qualification of non-destructive tests”, a qualification method was developed. Tests were carried out using various sensing cables embedded in the volume or fixed to the surface of plain concrete specimens and representative-scale reinforced concrete structural elements. Measurements were performed with an OFDR instrument, while mechanical solicitations were imposed to the concrete element. Preliminary experiments seem very promising since measurements performed with distributed sensing systems are found comparable to values obtained with conventional sensors used in civil engineering and with the Strength of Materials Modelling. Moreover, the distributed sensing system makes it possible to detect and localize cracks appearing in concrete during the mechanical loading.

Enhanced maps (Emaps) have proven their ability in aiding vehicle navigation, by applying constraints to lateral position of the vehicle. This is actually being demonstrated for instance in the frame of several lane level CVIS applications (Cooperative Vehicle Infrastructure Systems, a 6th Framework Programme Integrated Project). In some other research activities, the road direction between nodes or shape points of digital maps appeared to be of great interest as a possible way to improve vehicle heading estimation. Under this process, one assumes that both the vehicle and the road have most of time the same direction. In this article, we propose that the road geometry is described in Emaps by clothoids and contributes to vehicle location in a Bayesian framework by means of a particle filter. Actually, the road direction is a parameter very easily obtained when the road is precisely depicted by series of clothoids and acts as a strong constraint in the location process. Furthermore, the very challenging question of whether a GPS position should be accepted or rejected in such a filter has also been investigated. A strategy that aims at rewarding GPS instead of dead reckoning in case of lateral deviation is shown, with interesting results in particular for an on-board low-cost gyroscope.

In this paper, we are interested in developing a robust tire-force estimator for heavy duty vehicles. We use a combined model of the articulated vehicle: a yaw plane model for the chassis motion and a vertical plane model for the axles. In the proposed method, we make use of the on-board available sensors to which are added low-cost sensors. In order to optimize the sensors configuration, a robust differentiator is used in order to obtain accelerations from the measured velocities. Once the differentiation obtained, the model is inverted to determine the unknown input forces. The approach is validated by comparing the estimation results to those given by the software simulator prosper.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE; International audience; The Wireless Sensor Networks (WSN) with their constant evolution, need new management methods to be monitored efficiently by taking into account the context and their constraints such as energy consumption, reliability and remote monitoring. WSN has diverse application domains: smart home, smart care, environmental data collection etc. In order to manage a large scale WSN, several Wireless sensor network Management Tools (WMTs) are developed. Some of them use SNMP protocol like because it is impossible to implement the full compliance classical SNMP standard on each wireless sensor node. Therefore, it is important to develop a new WMT with a restricted SNMP standard dedicated to WSN applications. In this paper, we present a new WMT named LiveNCM: LiveNode Non invasive Context-aware, and modular Management tool. LiveNCM is divided into two main parts: one is centralized on the fixed network structure and another one, distributed on each node. Each part introduces the concept of non-invasive context-aware to reduce data exchanges and diagnoses the wireless sensor node state with few messages. Moreover, nodes are based on a configurable modular architecture enables to adapt to an application and to a local node constraints. LiveNCM is implemented on the LiveNode platform to validate the energy consumption and on a UNIX system to validate the adopted SNMP sub-agent. Ultimately, a decreasing data exchange and an improvement in the energy consumption in the entire WSN were observed. An implementation of the proposed management method is presented.

This critical review spans the past two decades and outlines sensors developed for measuring total organic matter (TOM) and biodegradable organic matter (BOM) in water. We report the state of the art of the most significant technologies. Although the focus is on in situ devices, we also mention on-line techniques able to lead to sensors sensu stricto and discuss them as possible emerging technologies. We divide the review into sections: (1) TOM; and, (2) BOM. We first briefly outline each technique then analyze the published literature. We conclude with recommendations on the technological choices geared to specific research topics.

Visible and near-visible sensors, which have been widely used in the beginning of the period of interest, have been later overshadowed by the availability of sensors using the microwave part of the spectrum. However, the latter years of this period have shown an obvious come-back with numerous experiments flown with new generation equipment. This paper describes briefly the first generation sensors and how they were used in airborne experiments, explains the developments being conducted from mechanical to push-broom scanners and to non-scanning sensors, shows how some sensors were being integrated in comprehensive systems and what is the trend found in the later years of the period of reference. This is illustrated by examples of existing equipment in France and in Europe, and two case studies: one on the Dutch CAESAR push-broom scanner, the other on the Oceanographic Lidar System developed by the University of Oldenburg in Germany.

Fog is a local meteorological phenomena which drastically reduces the visibility range. Fog detection and visibility range estimation are critical tasks for road operators who need to warn the drivers and advise them on speed reductions. To achieve this task, fixed sensors are quite accurate but they have a reduced spatial cover. Mobile sensors are less accurate, but they have a good spatial cover. Based on the combination of roadside sensors and in-vehicle devices (sensors or fog lamps), a data fusion framework is presented aiming at taking the advantages of both fixed and mobile sensors for the extensive detection and estimation of the fog density. The proposed solution is implemented by means of a local dynamic map fed by vehicle to infrastructure (V2I) communication, which gives a coherent view of the road environment.

A soil-embedded optical fiber sensing cable is evaluated for an embedded cavity detection and sinkhole warning system in railway tunnels. Tests were performed on a decametric structure equipped with an embedded 110 m long fiber optic cable. Both Brillouin optical time-domain reflectometry (B-OTDR) and optical frequency-domain reflectometry (OFDR) sensing techniques were used for cable interrogation, yielding results that were in good qualitative agreement with finite-element calculations. Theoretical and experimental comparison enabled physical interpretation of the influence of ground properties, and the analysis of embedded cavity size and position. A 5 mm embedded cavity located 2 m away from the sensing cable was detected. The commercially available sensing cable remained intact after soil collapse. Specificities of each technique are analyzed in view of the application requirements. For tunnel monitoring, the OFDR technique was determined to be more viable than the B-OTDR due to higher spatial resolution, resulting in better detection and size determination of the embedded cavities. Conclusions of this investigation gave outlines for future field use of distributed strain-sensing methods under railways and more precisely enabled designing a warning system suited to the Ebersviller tunnel specificities.