Karim Benzarti
email: benzarti@lcpc.fr

Marc Quiertant

Céline Marty

Sylvain Chataigner

C Aubagnac

Effects of accelerated ageing on the adhesive bond between concrete specimens and external CFRP reinforcements

come

Effects of accelerated ageing on the adhesive bond between concrete specimens and external CFRP reinforcements

INTRODUCTION

Applications of Fiber Reinforced Polymer (FRP) composites to the strengthening of civil infrastructures have met a growing popularity in the last decade. If the effectiveness of the technique is now widely recognized [START_REF] Pendari | Application of polymer composites in civil construction: A general review[END_REF][START_REF] Hamelin | Renforcement des ouvrages d'art par matériaux composites[END_REF], many questions remain regarding durability issues [START_REF] Karbhari | Durability gap analysis for fiber-reinforced polymer composites in civil infrastructure[END_REF]. In a more general way, durability aspects are still to be addressed for structural assemblies involving adhesively bonded joints [START_REF] Cognard | Some recent progress in adhesion technology and science[END_REF].

Design guidelines for FRP strengthened concrete structures usually introduce safety coefficients on the FRP properties, to account for possible deteriorations of both the composite reinforcement itself and the adhesive bond under service conditions [START_REF] Gangarao | Reinforced concrete design with FRP composites[END_REF][START_REF] Karbhari | Design factors, reliability, and durability prediction of wet layup carbon/epoxy in external strengthening[END_REF]). In recent years, extensive researches were undertaken to investigate the environmental ageing behavior of FRP/concrete bonded interfaces [START_REF] Grace | Durability Evaluation of Carbon Fiber-Reinforced Polymer Strengthened Concrete Beams: Experimental Study and Design[END_REF][START_REF] Yang | Hygrothermal ageing of an epoxy adhesive used in FRP strengthening of concrete[END_REF][START_REF] Silva | Degradation of bond between FRP and RC beams[END_REF][START_REF] Marouani | Composites realized by hand lay-up process in civil engineering environment: initial properties and durability[END_REF]. Most of these studies showed significant effects of wet environments (direct immersion or exposure to water saturated air) on the long-term performances.

The present study focuses on two FRP reinforcement systems commercially available on the French market, both based on a unidirectional pultruded carbon plate associated to a bi-component/room temperature curing epoxy adhesive.

Concrete specimens were strengthened using these two systems, and the ageing behavior of both the constitutive materials (concrete, epoxies, CFRP plates) and the bonded interfaces were investigated. In this regard, specific ageing conditions were cho-sen, i.e., a temperature of 40°C and a relative humidity of at least 95%. Such conditions were intended to accelerate the moisture sorption phenomenon, while keeping the temperature value just below the usual glass transition temperature (Tg) range of ambient cured epoxies used in construction.

The present paper is structured as follows: -sample preparation and storage conditions are first briefly recalled, -ageing behaviors of the constitutive materials are then presented, with a special emphasis on epoxy adhesives, -another section is devoted to the propertyevolutions of the ageing FRP/concrete interfaces, which were assessed using two different charac- and Epo2) are respectively used for bonding of systems S1 and S2, according to the product datasheets. FRP strengthened concrete blocks were specifically designed to enable multiple mechanical characterizations, as shown in Figure 1: concrete compressive strength can be measured on samples cored from the previous specimens, according to EN 12 390-3 standard (2003); adhesive bond characterizations of the concrete/FRP interfaces can be performed either by the pull-off test according to EN 1542EN standard (1999)), or by a single lap shear test previously detailed in [START_REF] Chataigner | Characterization of composite to concrete bonded interface: Description of the single lap shear test[END_REF].

Figure 1. Detail of the CFRP strengthened specimens Concrete blocks were prepared from CEM II 32.5 cement and silico-calcareous aggregates with a w/c ratio of 0.55. A maturing period of 28 days was observed before the application of FRP reinforcements.

For each FRP system, a series of 39 reinforced concrete specimens was prepared; this number was intended for 13 test sessions corresponding to different periods of ageing (3 repeated tests per session). Bonding of the FRP systems was achieved by a professional staff according to the guidelines. The bonded length was 200mm on each specimen.

Besides, samples of the bulk epoxies were also fabricated, either parallelepiped or dumbbell shaped, in order to study the moisture sorption and perform thermal analyses and tensile tests. Figure 2 shows some pictures of the various samples.

Ageing conditions

A curing period of 15 days at room temperature was observed before the first test session. Then, samples were placed in a climatic room under ageing conditions (temperature of 40°C and relative humidity higher than 95%), for a total duration up to 20 months (628 days). Series of 3 specimens were peri-odically removed from the chamber in order to be tested. Results of the various characterizations will be presented in the next sections. In all cases, the first test session corresponds to the initial state of the specimens, just before starting the ageing treatment.

AGEING BEHAVIOURS OF CONSTITUTIVE MATERIALS

Evolution of the concrete compressive strength

Compressive strength of the concrete material was measured on cylindrical samples cored from the CFRP strengthened specimens, at each test session.

With ageing time, a slight and progressive increase was observed in the compressive strength values (from 35 MPa in the initial state to about 43 MPa after 20 months). Such a trend was expected due to the continuation of the hydration process in the warm and wet ageing environment.

Evolution of the CFRP plate properties

Tensile tests performed on CFRP plates from systems S1 and S2 did not reveal any significant evolution of the material stiffness over ageing time.

Young's moduli remained unchanged with values around 160GPa.

Ageing behaviors of the bulk epoxies

Mass uptakes of the two ambient temperature cured epoxies Epo1 and Epo2 were first monitored as a function of ageing time (Figure 3 a). Fickian behaviors were observed for the two systems, but with a much higher saturation level for Epo1 (~2-2.5%) than for system Epo2 (~0.8%). Epo1 (down to 30°C), which indicates an extensive plasticization due to moisture sorption; differently, Epo2 showed much more limited plasticization and kept its Tg value higher than 60°C.

Tensile tests were also performed and the stressstrain curves obtained after various periods of ageing are plotted in Figure 4. Tensile characteristics of the two ageing epoxies both evolved from an elastic behavior toward an elasto-plastic behavior, with a decrease in strength and an improved ductility. However, ductilization was much more pronounced for Epo1 than for Epo2 in line with the extensive plasticization revealed by sorption and DSC experiments.

AGEING BEHAVIOURS OF CONCRETE / FRP INTERFACES

This part investigates the property evolutions of the adhesive bond during ageing as determined using the pull-off and single-lap shear tests.

Pull-off characterizations

Figures 5 and 6 depict respectively the evolutions of the pull-off strengths and failure modes for ageing CFRP strengthened specimens of type S1 and S2. Systems S1 and S2 exhibited very different ageing behaviors: a large decrease in the pull-off strength and an evolution of the failure mode from a substrate failure towards a cohesive failure within the glue were observed for system S1, whereas no significant evolution was observed for system S2. This point will be discussed further in the next section. Failure mode of the shear tested interfaces evolved for both systems S1 and S2, from a concrete failure toward a failure within the glue layer. This is consistent with a deterioration of the glue properties after ageing, as shown previously on samples of the bulk epoxies. Surprisingly, no significant evolution of the maximum shear load was obtained for the two systems, despite the change in the fracture mode. Complementary data provided by a strain gage instrumentation revealed that the shear load transfer along the joint is affected by ageing (increase of the transfer length due to the plasticization of the glue), which might explain the unchanged shear capacity. Another point is the divergence between the pulloff and shear test results. The pull-off method clearly showed a deterioration of the adhesive bond characteristics for system S1 (based on glue EPO1, which undergoes a large plasticization during ageing), but not for system S2. Differently, the shear test revealed a marked evolution of the failure mode for the 2 systems, suggesting bond deterioration in both cases.

It is believed that the pull-off test is less sensitive than the shear test to ageing induced changes in the adhesive bond properties.

FINITE ELEMENT MODELING

Finite element approaches were then implemented using CESAR-LCPC software (CESAR 2010), in order to confirm some of the experimental trends.

Modeling of the pull-off test

In a first step, a modeling of the pull-off configuration (not detailed here) showed that the test is not based on a pure tensile loading of the bonded joint, and revealed local stress concentrations within the substrate due to the partial coring of concrete around the test zone. This finding supports that the pull-off test might exhibit a low sensitivity to actual changes in the bond properties.

Modeling of the single lap shear configuration

A modeling was finally proposed in order to simulate the single lap shear test in the case of a non aged CFRP reinforced specimen (considering a glue layer with a pure elastic behavior) or an aged specimen (with an elasto-plastic behavior of the glue similar to that of EPO1 after ageing).

Simulations confirmed the increase in the load transfer length for the aged specimen due to the plastic deformation of the polymer adhesive, and pointed out a better diffusion of the shear stress within the aged specimen, as shown in Figure 9. These findings are consistent with experimental observations.

CONCLUSION

In this work, ageing behaviors of two FRP strengthening systems based on pultruded carbon plates have been investigated. Ageing conditions consisted in exposing specimens at 40°C and 95% RH.

Investigations on the bulk materials revealed that both adherents (concrete, carbon plates) did not notably evolve during ageing, whereas epoxy adhesives underwent more or less severe plasticization effects depending on their formulation.

Adhesive bond characterizations relied on pull-off and single lap shear tests. It was found that results of the two methods were not always correlated. The shear test revealed a clear evolution of failure modes for the 2 FRP plate systems. The weakest part of the assembly, initially assigned to the concrete substrate, became progressively the polymer joint. A strain gage equipment made it possible to detect a major change in the load transfer mechanism along the joint (increase in the transfer length). These findings were finally supported by finite element simulations.

Figure 2 .

 2 Figure 2. Test samples for bulk epoxies (a and b) and CFRP strengthened blocks (c)

Figure 3 .

 3 Figure 3. Mass uptakes (a) and evolution of the glass transition temperature (b) versus ageing time for bulk adhesives Epo1 and Epo2.

Figure 4 .

 4 Figure 4. Tensile stress-strain curves for bulk epoxies Epo1 (a) and Epo2 (b), after various ageing periods.

Figure 5 .

 5 Figure 5. Evolution of the pull-off strengths versus ageing time for specimens strengthened by CFRP systems S1 and S2.

Figure 6 .

 6 Figure 6. Evolution of the failure mode for specimens of type S1 (a) and S2 (b) after pull-off tests.

Figure 7 .

 7 Figure 7. Evolution of the maximum shear load versus ageing time for specimens strengthened by CFRP systems S1 and S2.

Figure 8 .

 8 Figure 8. Evolution of the failure mode for specimens of type S1 (a) and S2 (b) after single lap shear tests.

Figure 9 .

 9 Figure 9. Representation of the stress field within a shear tested CFRP reinforced specimen. Results in the initial state (a) and after ageing (b).


Yann Goyat
email: yann.goyat@lcpc.fr

Thierry Chateau

Francois Bardet

Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

This paper presents an algorithm for modeling and tracking vehicles in video sequences within one integrated framework. Most of the solutions are based on sequential methods that make inference according to current information. In contrast, we propose a deferred logical inference method that makes a decision according to a sequence of observations, thus processing a spatio-temporal search on the whole trajectory. One of the drawbacks of deferred logical inference methods is that the solution space of hypotheses grows exponentially related to the depth of observation. Our approach takes into account both the kinematic model of the vehicle and a driver behavior model in order to reduce the space of the solutions. The resulting proposed state model explains the trajectory with only 11 parameters. The solution space is then sampled with a Markov Chain Monte Carlo (MCMC) that uses a model-driven proposal distribution in order to control random walk behavior. We demonstrate our method on real video sequences from which we have ground truth provided by a RTK GPS (Real-Time Kinematic GPS). Experimental results show that the proposed algorithm outperforms a sequential inference solution (particle filter).

Introduction

Efficient target tracking is a critical component in many computer vision applications such as visual surveillance or robotics. The object-tracking procedure is intended to estimate the state (position, velocity, . . .) of an object at each time given an observation sequence.

Tracking methods can be divided into two major categories: The first category relates to sequential inference tracking (also called online or causal tracking), for which the state of the object at a given time step has been estimated as a function of the record of past and current observations and the record of past states. The second concerns deferred logical inference (also called offline or noncausal tracking), for which the state estimation at a given point in time uses the entire observation sequence.

Sequential tracking is needed when the tracker's output controls real-time processes, which cannot be delayed (such as robotic applications). Sequential tracking is also needed when it is not possible to record the observation data, due to its size, or due to regulations. Much work has been done on sequential visual tracking (model-based approaches [START_REF] Comaniciu | Real-time tracking of non-rigid objects using mean shift[END_REF][START_REF] Isard | Condensation-conditional density propagation for visual tracking[END_REF][START_REF] Isard | BraMBLe: a Bayesian multipleblob tracker[END_REF] or learning-based approaches [START_REF] Williams | A sparse probabilistic learning algorithm for real-time tracking[END_REF][START_REF] Avidan | Support vector tracking[END_REF][START_REF] Avidan | Ensemble tracking[END_REF]). Therefore, most of the applications use sequential methods even though it is not necessary.

For other situations, deferred tracking is much more appealing, as it is not causal. This allows the optimisation process to operate over a larger data set (the whole observation sequence), thus allowing to hope for better results. Deferred visual multiobject tracking have already been successfully experienced on pedestrian tracking in [START_REF] Fleuret | Multicamera people tracking with a probabilistic occupancy map[END_REF] and with a MCMC search in [START_REF] Yu | Multiple target tracking using spatio-temporal Markov chain Monte Carlo data association[END_REF].

The solution presented in this paper is a spatio-temporal deferred logical inference approach. One of the main challenges of such methods is that the solution space of hypotheses grows exponentially related to the (duration) depth of observation. In the specific case of vehicle tracking, priors on both driver behavior and the road geometry can be used. Moreover, the trajectory of the object to be tracked is driven by a kinematic model. Therefore, we propose an 11dimensional reduced state vector of the vehicle trajectory.

Since we use a probabilistic framework, the tracking problem can be seen as the estimation of the distribution of the state vector posterior distribution, given a video sequence. We propose a Markov Chain Monte Carlo (MCMC) method to sample the posterior distribution.

MCMC have been already used in visual tracking. In [START_REF] Khan | MCMC-based particle filtering for tracking a variable number of interacting targets[END_REF][START_REF] Smith | Using particles to track varying numbers of interacting people[END_REF], a MCMC based particle filter is presented for multiobject tracking and an extension is proposed to handle a varying number of objects (Reversible Jump Markov Chain Monte Carlo, RJMCMC). In [START_REF] Yu | Multiple target tracking using spatio-temporal Markov chain Monte Carlo data association[END_REF], the RJMCMC algorithm is used in a deferred logical inference framework to track several vehicles offline from a video sequence.

In MCMC methods, the random walk behavior is driven by proposal distributions. We use priors on driver behavior and road geometry to define efficiently the proposal. Exploration is achieved with the Metropolis-Hasting rule according to a global likelihood function.

We use a likelihood function based on a background subtraction algorithm. A discrete set of positions of the vehicle into the video sequence is generated from the trajectory state. A generic 3D model of a vehicle is then projected into each image and then compared to a background/foreground map of the video sequence. We propose an efficient implementation of the likelihood function using a line integral image to decrease computation time.

Experiments have been done to compare, on real video sequences, the deferred logical inference approach with a classic sequential particle filter.

The remaining of the paper is organized as follows. Section 2 presents the probabilistic framework proposed to solve the tracking problem. Section 3 provides a detailed description of the vision likelihood function. A set of experimental results along with both qualitative and quantitative analysis is presented in Section 4, before we conclude in Section 5.

Proposed Method

This section describes the core of the method, based on a probabilistic framework. Figure 1 represents an illustration of the algorithm. Given a video sequence and an initial state, the method samples the posterior distribution of the trajectory using a random walk method (MCMC). In the following, we begin by presenting the state vector associated to the trajectory model. Then, we give an overview of the Monte Carlo Markov Chain algorithm used to sample the posterior distribution. Finally, we show how to generate new proposals by sampling from an object-specific proposal distribution.

State Vector Reduction.

In a spatio-temporal deferred logical inference approach, the solution space of hypotheses grows exponentially related to the number of observation frames. Estimating a single vehicle planar trajectory along a 100 frame sequence, involves a state space of dimension 300 (planar position and orientation are estimated for each frame). Conducting a Monte-Carlo search in such a space is computationally intractable! To avoid this problem, we do not consider the vehicle position sequence as the state vector, but we implement a trajectory generator, lying on driver behavior priors, road geometry priors, and vehicle kinematic priors. The vehicle trajectory generator detailed in the following, generates trajectory samples, defined in

X (0) X (1) X (2) X (N) p(X | Z) ≈ {X (i) } N i=1 X * X * I 0 Video sequence Acceptance rule Acceptance rule Proposal Proposal Priors { I k } K k = 1
Figure 1: Given a video sequence and an initial state, the method samples the posterior distribution of the trajectory using a random step method (MCMC).

a 11-dimensional state space by driver temporal command parameters. This method drastically reduces the dimension of the state space, thus improving computational efficiency.

Driver Command and Vehicle

Priors. The driver commands are the steering wheel angle, and the vehicle longitudinal acceleration, from which we deduce the vehicle speed through integration. The experiments presented below have been conducted on a mid-velocity curve. While traveling such a curve, a light vehicle driver's command law is commonly modelled by a trapezoid, with steering wheel angle velocities lying between 1.5 and 4 degrees per second, and with absolute longitudinal accelerations lying between 1 m•s -2 and 3 m•s -2 . In order to take into account a wider range of driver commands, and the steering system nonlinearities due to frictions and mechanical compliances, we use a more compliant model: we model the steering command with a double sigmoid (one for entering the curve, and one for releasing from it).

As the experiments presented below have been conducted on the second half of the curve, a single sigmoid is used to define the steering angle generator, from parameters defined in Section 2.2 (cf. Figure 2)

f δ (θ δ , k) . = θ δ,2 1 + exp θ δ,3 θ δ,4 -k / θ δ,2 + θ δ,1 , k = time. (1)
The same reasoning applies to the vehicle speed generator To model the vehicle, we use a plain kinematic model, as described in Section 3.1. This model allows us to iteratively generate x k , y k , and α k , for every time step k.

f v (θ v , k), calculated as f δ (θ δ , k), swapping index δ into index v. θ δ,3 θ δ,4 k θ δ,1 θ δ,1 + θ δ,2
The vehicle trajectory generator is represented by a random state vector X . = (l, θ δ , θ v ) T with (i) l . = (x 0 , y 0 , α 0 ) represents the initial position and orientation of the vehicle (into a world reference frame), (ii

) θ δ . = (θ δ,1 , . . . , θ δ,4 ) are the parameters of a sigmoid function δ k = f δ (θ δ , k) representing the discrete temporal evolution of the steering angle, (iii) θ v . = (θ v,1 , . . . , θ v,4
) are the parameters of a sigmoid function v k = f v (θ v , k) representing the discrete temporal evolution of the vehicle velocity.

MCMC.

We want to estimate p(X | Z), the posterior probability density for a model's parameters X, given some observed data Z. Monte-Carlo methods assume that the posterior distribution can be approximated by a set of N samples:

p(X | Z) ≈ X (n) N n=1 . (2)
Sampling from p(X | Z) is a hard problem and many methods have been proposed. Metropolis-Hasting is a random walk algorithm designed to approximate a stationary distribution. At each step, a state X * is proposed according to a proposal density q(X * | X). The proposal state is then accepted or rejected according to an acceptance ratio defined by the Metropolis-Hasting rule

α = min 1, p(Z | X * ) p(Z | X) × q(X | X * ) q(X * | X) . ( 3
)
Metropolis-Hasting rule can be used to build a Markov Chain which approximates the posterior distribution p(X | Z). The resulting method is called Markov Chain Monte Carlo. Moreover, the N b first elements of the chain are removed (burn-in) in the final sampling set. An estimate of the state is given by a maximum likelihood rule applied to the particle set

X . = arg max X N n=1 δ X -X (n) , ( 4
)
where δ is the Dirac function.

Input:

The first element of the chain X 0 and its weight proportional to its likelihood: π(X 0 ) ∝ P(Z | X 0 ) for n = 1 to N + N B do -Choose a move m ∈ {1, . . . , M} among all the parameter of the state X according to prior q (m).

-Draw a proposal X * from the distribution q(X * | X) with X = X n-1 -Evaluate its joint likelihood: p(Z | X * ) -Compute the acceptance ratio using Metropolis-Hasting rule:

α = min 1, p(Z | X * ) p(Z | X) × q(X | X * ) q(X * | X) -Add a nth element to the chain X n = X * with probability α, (otherwise X n = X n-1

Proposals. At iteration n, the MCMC generates a new

proposal by sampling from a proposal distribution q(X * | X (n-1) ) defined by q X * | X (n-1) = m∈{1;...;M} q (m)q X * | X (n-1) , m , [START_REF] Avidan | Support vector tracking[END_REF] where q (m) is a prior distribution used to select the parameter index of X to be modified (M denotes the size of X). A parameter-specific proposal distribution is then defined by

q(X * | X, m) . = p X * m | X (n-1) m j / = m δ X * j -X (n-1) j . (6)
Here, only the mth component (m is selecting with the prior distribution q (m)) of the state vector is moved at iteration n; the other parameters remain unchanged. The MCMC is summarized in Algorithm 1.

Observation

This section presents the observation function defined to compute the likelihood p(Z | X = X (n) ) probability to observe the video sequence, given a sample X (n) . Figure 3 illustrates the observation process. A discrete set of positions of the vehicle into the video sequence is generated from the trajectory sample X. A generic 3D model of a vehicle is then projected into each image and compared to a background/foreground map of the video sequence.

We propose an efficient implementation of the likelihood function using a line integral image to decrease computation time.

State vector X

Vedio Z

Kinematic model δ

Camera parameters

{x k } K k=1 k k {I k } K k=1 {p(z k | x k )} K k=1 p(Z k | X k ) = K k=1 p(z k | x k )
X . = {x k } K k=1 , (7)
with x k . = (x k , y k , α k ) T is a vector which gives the position and orientation of the vehicle at time k into a world reference frame R w associated to a planar ground. x k can be computed in a recursive way using a simple kinematic model of the vehicle. Here, we used a bicycle model (cf. Figure 4)

x k = x k-1 + T • v k-1 • cos(α k-1 ), y k = y k-1 + T • v k-1 • sin(α k-1 ), α k = α k-1 + T • v k-1 L • tan(δ k-1 ), ( 8
)
where T is the sample time used for the video acquisition and L denotes the wheelbase (distance between front and rear wheels). δ k and v k are given by the steering angle and velocity parametric functions presented into Section 2.2. The likelihood function p(Z | X) can be written by p(Z | X) = p(z 1 ; z 2 ; . . . ; z K | x 1 ; x 2 ; . . .

; x K ) (9)
and assuming independence of random variables

p(Z | X) = K k=1 p(z k | x k ). (10)

Computing p(z k | x k

). Since the video sequence comes from a static camera, vehicle extraction is achieved using a background/foreground extraction approach. We use a nonparametric method [START_REF] Goyat | Vehicle trajectories evaluation by static video sensors[END_REF], based on discrete modelization of the background probability density of the pixel color (RGB). The algorithm provides a set of binary images I .

= {I k } K k=1 , where I k (u) = 1 if the pixel u . = (u x , u y ) T is associated to foreground and I k (u) = -1 if the pixel is associated to background.
A simplified three-dimensional geometric model of the vehicle is used, as depicted in Figure 5. This model is composed of two nested parallelepipeds. In a general case, the model may be more complex and contain P M parallelepipeds. Let M (R0) = {M (R0) i } i=1,...,NM represent the model's set of cube vertices (N M = 8 × P M ), expressed within a coordinate system associated with model R 0 . This coordinate system is selected such that the 3 axes all lie in the same direction as that of the world coordinate system R w .

Each point of the vehicle model is projected onto the image via the following equation

m i ∝ C c • (Rw) T (R0) (x k ) • M (R0) i , ( 11
)
with M homogeneous coordinates associated with point M; C c is the camera projection matrix, and (Rw) T (R0) (x k ) the homogeneous transformation matrix between the world coordinate system and the system associated with the 3D model (cf. Figure 5).

The set M (Ri) = {m i } i=1,...,NM is thus built based on the projection of 3D model points within the image.

For a given position x k , the likelihood is linked to the difference between the number of foreground and background pixels inside the vehicle model projection in the image. This computation performed for each particle spends SIGNIFICANT processing time, and we are proposing herein a fast likelihood calculation method based on an approximation of the 3D model projection in the image through its convex hull.

Let E (M (R0) ; x k ) . = {e i } i=1,...,Ne (e i = (x e i , y e i ) as coordinates of e i in the image plane) be the list of convex hull points. (Calculation of the convex hull is not developed in this article; the calculation procedure is conducted using a classical algorithm with a complexity expressed in O(N • log N).) We will now define E k .

= E (M (R0) ; x k ) in order to streamline notations. The likelihood calculation may be performed efficiently by use of a line-by-line integral image defined by

I Σ k x, y T = x i=1 I k i, y T . ( 12
)
Points e i are categorized by pairs featuring the same ycoordinate values, such that (

E k = x e 1 ,
) 13
Convex hull coding within the set E k necessitates a shape discretization along the image lines. Moreover, special attention needs to be paid to coding the upper and lower extremities. On the other hand, it is not at all necessary to sort points positioned on the same line. A compliance measurement relative to a convex hull is computing from the integral image by application of the following relation:

a(E k ) = Ne/2 j=1 2 • I Σ k e 2 j -I Σ k e 2 j-1 -x e 2 j -x e 2 j-1 . ( 14
)
Figure 6 describes the principle behind the likelihood calculation method using the integral image. A line-by-line scanning is performed as part of this method.

Finally, the likelihood expression is written by

p(z k | x k ) ∝ C -1 Ek max(0, a(E k )) (15)
with the normalization constant C Ek .

= Ne/2 j=1 |x e 2 j -x e 2 j-1 |
defining the surface of the convex hull.

Experimental Validation

In this section, experimental results are presented to highlight the relevance of our tracker. We compare the Offline proposed approach to a sequential stochastic filter (particle filter). Initialisation. The first sample of the MCMC must be initialized using priors. We use a data driven method to compute the initial position of the vehicle on the road (x 0 , y 0 ). A nonparametric blob detector [START_REF] Goyat | Vehicle trajectories evaluation by static video sensors[END_REF] is applied to the background/foreground image I 0 . The initial velocity is provided by a specific sensor. Other parameters are initialized using priors given by vehicle or driver behavior. The dimensions of the geometric model are defined for each vehicle with a stochastic process on width and length parameters and using the likelihood computation (cf. Section 3.2). If dimensions seem to be incoherent, a standard vehicle is chosen.

Proposals.

A key point of the method concerns the control of the random walk behavior using proposal distributions. Parameter-specific proposals are defined. Since both lower and upper bounds can be defined for all parameters, we choose proposals according to a Beta distribution

P X * m | X (n) m ∝ X * m ξ1-1 • 1 -X * m ξ2-1 (16)
parameters ξ 1 and ξ 2 are computed such as the maximum of the distribution is obtained for X * m = X (n) m .

Details about the Sequential Method.

Behavior of the proposed method is compared with a sequential particle filter. The state vector is defined as X k . = (x y , y x , α x , v x , δ x ) T . Dynamics are controlled by the kinematic bicycle model with a zero centered normal law applied to both the steering angle and velocity variation. Moreover, the likelihood function is slightly modified by removing the normalizing constant. The particle set is resampled at each iteration using a SIR algorithm.

Results

. In order to compare the two methods, a vehicle, equipped with a RTK GPS accurate to within one centimeter, has been used. (A calibration between the GPS reference frame and the camera has been achieved but details are not presented in this paper.) This vehicle traveled through the test section 20 times along various trajectories at speeds ranging from 40 to 80 km/hr. The error was quantified as the average distance between each estimated vehicle position and the straight line passing through the two closest GPS points. For each test, at least five vehicle runs were carried out, which enabled deriving a very rough statistic on the recorded measurements. For the tests actually conducted, the vehicle has been tracked in a curve over a distance of approximately 100 m (minimum radius = 130 m).

All the presented here have been done using 200 particles for the two methods.

Table 1 presents the average error and related standard deviations for the two tested methods. The proposed deferred logical inference provides a lower global error than the sequential particle filter.

Figure 7 plots the estimation accuracy as a percentage of correct positions (vertical axis) versus an error tolerance (horizontal axis) for both methods. On the left graph the error tolerance is the position absolute error, ranging from 0 to 50 cm, while on the right graph the error tolerance is the vehicle orientation (yaw angle) absolute error, ranging from 0 to 5 degrees. The curve associated to the proposed method outperforms the sequential particle filter both for the position and the orientation estimation. Moreover, the right graph emphasizes the benefit of the deferred method, which integrates the vehicle and driver priors in every generated trajectory, thus bringing more time consistency than the sequential method.

Figure 8 illustrates the two methods on a real sequence. Curves on the right column show zooms on local trajectories. The middle column illustrates the image projection of the vehicle position for the sequential method. The right column illustrates the image projection of the vehicle position for the deferred method. It is of high interest to notice the noisy estimation provided by the sequential method, where the estimated trajectory does not seem to match the vehicle kinematic model. The reason for this weak consistency is that the maximum a posteriori estimate may be found on different particles at every time step. In contrast, the spatio-temporal deferred approach ensures faithfulness to the model, thus explaining the observed improvement.

Conclusion

We have presented a solution for estimating vehicle trajectories using a single static color camera. A spatiotemporal deferred logical inference solution which takes into account both vehicle kinematics and driver behavior has been proposed, using a stochastic approach to estimate the posterior distribution of the trajectory. By choosing a MCMC, the random walk evolution is controlled by injecting priors on both driver and vehicle behavior and on geometric knowledge about the road. Moreover, a global likelihood Experiments have been achieved to demonstrate that the proposed method outperforms a classic sequential particle filter solution using statistics performed on real video sequences. Two points explain this improvement. First, the spatio-temporal deferred approach processes over the whole data set, thus ensuring time consistency. Second, the spatiotemporal deferred approach, unlike the sequential approach, ensures total faithfulness to the model at any time step, because the maximum a posteriori estimate may be found on different particles at every time step.

The method discussed in this paper is currently operating 24 hours a day with various weather conditions to provide statistics on curve trajectories. For the purpose of covering the entire curve, the system is composed of three color cameras with very little overlap. The system has successfully analyzed observations recorded under actual traffic conditions over several-day periods.

Figure 2 :

 2 Figure 2: Graphic representation of the sigmoid parameters.

Figure 3 :

 3 Figure 3: Illustration of the likelihood function. A discrete set of positions of the vehicle into the video sequence is generated from the trajectory sample. A generic 3D model of a vehicle is then projected into each image and compared to a background/foreground map of the video sequence.

3. 1 .

 1 Building a Discrete Set of Vehicle Positions. Let X define a discrete set of temporal positions and orientations of the vehicle, associated to a sample X of the posterior distribution

Figure 4 :Figure 5 :

 45 Figure 4: The bicycle model synthesizes the displacement of a four-wheel vehicle, through the displacement of two wheels whose centers are connected by a rigid axis of length L. Ackerman's theory serves to estimate the steering angle of the front axis of a vehicle traveling at low speed.

Figure 6 :

 6 Figure 6: Illustration of the vision likelihood computation. The 3D model of the vehicle (shown in green/clear) is reprojected onto the image generated from the background-shape extraction. This projection is approximated by its convex hull (shown in red/dark on the right image). The likelihood calculation proceeds in a lineby-line integral image of the log-likelihood ratio.

Figure 7 :

 7 Figure 7: Percentage of correct position/orientation related to the tolerance. (a) position absolute tolerance. (b) orientation (yaw angle) absolute tolerance.

Figure 8 :

 8 Figure 8: Snapshots illustrating the two methods. Left column: zoom on local trajectories. Middle column: the bounding box illustrates the position of the vehicle estimated with the sequential method. Right column: the bounding box illustrates the position of the vehicle estimated with the sequential method.

  ). end for Burn-in: delete the N B first elements of the chain. Output: N-element Markov Chain of state hypothesis: {X n } n=NB +1,...,NB +N Algorithm 1: MCMC algorithm.

Table 1 :

 1 Position error (the true position is given by a RTK GPS) for the proposed deferred logical inference method and a sequential particle filter.

	Method	Position error (m)	Position std. (m)	Orientation error (degrees)	Orientation std. (degrees)
	Sequential filter	0.27	0.26	3.67	3.36
	Deferred logical inference	0.20	0.22	1.12	0.97

Preliminary call for papers

The 2011 European Signal Processing Conference (EUSIPCO 2011) is the nineteenth in a series of conferences promoted by the European Association for Signal Processing (EURASIP, www.eurasip.org). This year edition will take place in Barcelona, capital city of Catalonia (Spain), and will be jointly organized by the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) and the Universitat Politècnica de Catalunya (UPC). applications as listed below. Acceptance of submissions will be based on quality, relevance and originality. Accepted papers will be published in the EUSIPCO proceedings and presented during the conference. Paper submissions, proposals for tutorials and proposals for special sessions are invited in, but not limited to, the following areas of interest. • Multimedia signal processing and coding.

Areas of Interest

• Image and multidimensional signal processing.

• Signal detection and estimation.

• Sensor array and multi channel signal processing.

• Sensor fusion in networked systems.

• Signal processing for communications.

• Medical imaging and image analysis.

• Non stationary, non linear and non Gaussian signal processing.

Submissions

Montserrat Nájar (UPC)

Tutorials

Daniel P. Palomar (Hong Kong UST) Beatrice Pesquet Popescu (ENST)

Publicity

Stephan Pfletschinger (CTTC) Mònica Navarro (CTTC)

Publications

Antonio Pascual (UPC) Carles Fernández (CTTC)

I d i l Li i & E hibi

Submissions

Procedures to submit a paper and proposals for special sessions and tutorials will be detailed at www.eusipco2011.org. Submitted papers must be camera ready, no more than 5 pages long, and conforming to the standard specified on the EUSIPCO 2011 web site. First authors who are registered students can participate in the best student paper competition.

Important Deadlines:


Christine Piqueras

C Diaz-Guerra

J Piqueras

C Opagiste

Cathodoluminescence microscopy of superconducting and non-superconducting Tl 2 Ba 2 CuO 6+x polycrystals

HAL is a

Introduction

The luminescence of different hightemperature superconductors (HTSC's) has been studied by a number of authors in the past years. Investigations of ceramic or crystalline samples of YBa 2 Cu 3 0 7 -x (YBCO), Bi 2 Sr 2 CaCu 2 0 x (BSCCO) and Tl 2 Ba 2 CuO 6+x (TBCO) have revealed similar luminescence spectral features suggesting that the emission bands are related to structural properties, as the existence of Cu-O planes, common to the mentioned materials. Visible luminescence has been mainly observed in the blue-green spectral range [1][2][3][4][5][6][7][8][9][10][11][12] often showing well resolved bands peaked at about 430 nm (2.9 eV) and 540 nm (2.3 eV). The luminescence emission of samples without postpreparation treatment is in general weak and the 430 nm bands dominate the spectra. This band is considered to be intrinsic to the abovementioned HTSC systems and has been attributed in the case of YBCO and BSCCO to an oxygen quasimolecule [8] or to a complex involving F center and negative molecular oxygen ion in YBCO [10]. The band has been also observed in TBCO samples prepared under high pressure at high temperature [11]. The 540 nm band has a rather complex behavior. It is absent in untreated YBCO and BSCCO but appears after different thermal or irradiation treatments. In particular, an increase of cathodoluminescence (CL) emission under highexcitation conditions in the scanning electron microscope has been reported in YBCO and BSCCO studies [1][2][3]7,12,13]. Spectral analysis shows that this effect is mainly due to the appearance or drastic increase of the 540 nm band although the emission at 430 nm also increases [7,12]. The increase of the 540 nm emission has been also observed after laser irradiation or vacuum annealing. Since the mentioned treatments cause a variation in the oxygen stoichiometry or distribution, the possibility that the 540 nm luminescence is related to mechanisms in the oxygen sub-lattice has been investigated. Miller et al. [14] reported an increase of CL signal in oxygen-depleted zones of YBCO produced by electron irradiation. Similar effects were reported in Refs. [3] and [7]. In Ref. [7] a correlation between irradiated oxygen-depleted zones -as detected by micro Raman spectroscopy -and enhanced 540 nm CL emission, was observed. A further study of YBCO sintered samples prepared with different oxygen contents [12] revealed that oxygen depletion alone does not cause the appearance of strong 540 nm emission. In fact the band appears during controlled electron irradiation in the scanning microscope in oxygen-poor samples but not in samples with full or high oxygen content. This observation leads to the conclusion that the centers causing the 540 nm emission in YBCO involve oxygen vacancies but are formed only after irradiation or annealing treatments similar for instance to the F centers in ionic crystals.

The 540 nm band has been also reported in TBCO [11] but contrary to that observed in YBCO and BSCCO this luminescence is already present in as prepared sintered samples. A complete characterization of the TBCO samples used in Ref. [11] has shown that 540 nm luminescence is inhomogeneous in the sample and is not related to impurity phases but to regions with variations in the oxygen content. The fact that no post-preparation treatment is necessary to detect the 540 nm band in TBCO makes this material very suitable to study the origin of this band and in particular the influence of the oxygen stoichiometry on this and other luminescence bands. To this purpose the luminescence properties of Tl 2 Ba 2 CuO 6+x samples with different superconducting transition temperatures have been investigated in this work. Since the crystallographic structure of Tl 2 Ba 2 CuO 6 is either orthorhombic (Ccc2) [15] or tetragonal I4/mmm [16] and both phases are superconducting with Tc between 0 and 92 K [18,19], we have studied samples of both structures.

Experimental method

The synthesis procedure of the samples has been described in detail in Refs. [17] and [20]. Tl 2 Ba 2 0 5 and CuO x powders were used as starting components. Two different batches were prepared from a stoichiometric amount of powders. For batch A the powder were first kept for three days at 750°C under 1 bar oxygen whereas batch B was not pre-treated. The powder mixture was pressed under 5 kbar into pellets, then placed in a furnace under 100 bar of argon or of oxygen and the appropriate thermal treatment was given. In the first case tetragonal superconducting (TS) samples are obtained while the treatment under oxygen yields orthorhombic non-superconducting (ONS) samples. A series of annealing treatments at low oxygen pressure have shown that starting samples with different symmetry i.e. ONS and TS transform into orthorhombic superconducting, indicating that the final state of the specimens does not depend on the initial symmetry. In this work the three samples whose codes and relevant data are listed in Table 1 have been studied. One sample (TM-850) is tetragonal superconducting, one (TKO-930) is orthorhombic nonsuperconducting and the last (TH2-850b) is orthorhombic and superconducting.

Samples from batch A are X-ray pure. Some traces of CuO and Ba 2 Cu 3 0 x impurities are detected; both are estimated to be < 0.1% by volume. Optical micrographs show only T1-2201 grains. The samples from batch B contain, besides the 2201 stoichiometric grains, less than 5% by volume of starting Tl 2 Ba 2 0 5 impurity phase and very small CuO grains (< 0.1% by volume). Further details on these samples are described in Refs. [17] and [20].

Samples were polished with diamond paste but, for comparison, also non-polished surfaces were investigated. The samples were observed in the emissive and CL modes in a Hitachi S-2500 scanning electron microscope at accelerating voltages of 20-30 kV and temperatures between 80 and 300 K. For CL measurements an optical lens was used to concentrate the light on a photomultiplier attached to a window of the microscope. To record spectra a light guide feeding the light to an Oriel 78215 computer controlled monochromator was used. It has been observed in different luminescent materials that CL spectra recorded with focused and defocused electron beam are different as a consequence of the presence of radiative centers with a low concentration. For this reason CL spectra were recorded under different focusing conditions of the electron beam on the samples.

Results

Microscopy of Tl 2 Ba 2 CuO 6

The general appearance of emissive mode and CL images is similar in the three samples studied. Fig. 1 shows a typical emissive image of an unpolished surface and the panchromatic CL image. An inhomogeneous distribution of luminescence, not related to specific topographic features is observed. The polished surfaces, Fig. 2, show many pores in the topographic image which correspond to regions of enhanced CL intensity. In order to check if this effect is due to particles related to the mechanical polishing treatment which remain in the pores after cleaning the sample, the inner surfaces of a number of pores were observed at higher magnification. Inside the pores well defined grain surfaces and no foreign particles are observed. These microscopy results indicate that the enhanced CL in the pores is related to grain surfaces, freshly exposed during the polishing treatment.

CL spectroscopy of Tl 2 Ba 2 CuO 6

Luminescence spectra have been found to depend on the sample considered, on the state of the surface (polished or unpolished), on temperature and on the excitation conditions (focused or unfocused electron beam). In the following, representative results are described with some emphasis on the behavior of the 430 nm (2.9 eV) and 540 nm (2.3 eV) luminescence bands and on the comparison between the different samples.

Spectra of unpolished surfaces

Cross-sections of the sintered samples have been used as unpolished surfaces. In the same samples the upper polished surfaces were studied as described in the next section.

Fig. 3 shows the spectra recorded in unpolished surfaces of the three samples at 80 K. Two bands centered, respectively, at about 430 nm and 540 nm are observed. In the nonsuperconducting sample the 540 nm emission is not well resolved as a separate band while the 430 nm one dominates. In the TH2-850b sample (T c = 52.4 K) both bands are well resolved and the intensity of the 430 nm band is still higher. In the TM-850 sample (T~ = 91.1 K) both bands are of comparable intensity. These results indicates a relative increase of the 540 nm band by increasing the critical temperature Tc of the sample.

Fig. 4 shows the temperature dependence of the spectra of sample TM-850. The total intensity increases with decreasing temperature and the relative weight of the bands changes. A temperature decrease favors the appearance of a well resolved 430 nm band. From room temperature to approximately 200 K the 540 nm emission is stronger than the 430 nm blue one. A similar behavior of the CL spectra with temperature was also found in the other samples investigated. Spectra of Figs. 3 and4 were recorded under excitation with a focused electron beam and correspond to an area of the sample including bright and dark regions of the corresponding CL image.

Some differences in the spectra are observed when a defocused electron beam is used or when specific areas of the sample are chosen. In particular, defocusing the electron beam (i.e. reducing the excitation density) causes a relative increase of the green (540 nm) band. On the other side, spectra recorded in highly luminescent areas of the superconducting sample TH2-850b (Fig. 5) show at low temperature only the 420 nm band extending in the green region, and at room temperature a broad band centered between 580 and 600 nm. The low-temperature spectra of the highly luminescent areas of the non-superconducting sample show in addition a well resolved band peaked at 720 nm (Fig. 6).

Spectra of polished surfaces

The highly luminescent regions in polished surfaces correspond to pores, as observed in the CL images (Fig. 2). Spectral analysis shows that emission from the pores is mainly due to the 540 nm band. Fig. 7 shows the spectrum recorded during excitation with focused electron beam in a pore of sample TH2-850b. In this figure the 540 nm band is dominant and the higher-energy emission appears as a shoulder. As in the case of unpolished surfaces, defocusing causes an increase of the green band in the pores of the superconducting samples. Spectra recorded in large areas, including pores and polished material, generally show a dominant 540 nm green band as well as emission in the blue region which appears as a shoulder of the main band and whose relative intensity increases by decreasing temperature. As in the spectra of the unpolished surfaces the relative contribution of this green emission increases with the Tc value of the sample. In the non-superconducting sample the shape of the spectra shows a strong dependence on the porosity of the considered area. In porous regions the contribution of the 430 nm band is often higher than in both superconducting samples. Fig. 8 shows spectra of different areas in the TKO-930 sample. It is observed that the relative intensity of the 430 nm blue band is high as compared with the spectrum of the superconducting sample shown in Fig. 7.

CL spectroscopy of precursors

In the polished surface of the CuO x sample the spectrum shown in Fig. 9 with two bands centered at about 445 and 530 nm, respectively, is recorded. Unpolished surfaces show a more complicated spectrum (Fig. 10) with a broad band extending from 400 to 670 nm and a well resolved band peaked at about

Discussion

CL images of all samples reveal the existence of luminescence emission with an inhomogeneous spatial distribution. This observation, which agrees with the previous results referred to in Section 1, is independent of the superconducting properties of the samples and on their crystal structure -tetragonal or orthorhombic. The general appearance of CL images, shown in Figs. 1 and2 for polished and unpolished surfaces, respectively, is similar for samples corresponding to batches A and B although the impurity phase content has been found to differ markedly in both batches. According to the available information on the impurity phases in the samples [17,20,21] the inhomogeneous distribution of the CL intensity cannot be attributed to these impurities. The two phases present in batch A samples appear in a very small amount as compared with the extension of bright regions in the CL image. Previous characterization [21] by different techniques has shown that these samples contain regions extending hundreds of microns which contain only 2201 grains without significant variations of the cation stoichiometry. In the batch B sample the volume of the impurity phase is higher [20,21] due to the presence of unreacted Tl 2 Ba 2 0 5 precursor inclusions, but the amount is lower than 5% in volume. The similarity of CL images of the three samples (two of batch B and one of batch A) and the fact that emission is distributed all over the sample enables one to rule out that the localized Tl 2 Ba 2 0 5 inclusions have a significant influence on the CL observed in the batch B samples. This point will be later discussed in view of the spectral response of the precursors. In the following we discuss the possible origin of the emission as an intrinsic effect of the Tl 2 Ba 2 CuO 6 , not related to impurity phases.

To our knowledge, intrinsic luminescence of Tl 2 Ba 2 CuO 6 has been only reported in our previous work [11] on a superconducting sample similar to the TM-850 used in this one. CL inhomogeneity has been attributed to local variations of the oxygen content, influencing the intensity of the 530 nm band. As described above, a similar relationship between 530 nm emission and oxygen content has been reported for YBCO samples. On the other hand, models proposed for the 430 nm emission, e.g. Refs. [8] and [10], also involve processes with oxygen atoms or oxygen vacancies. For these reasons, the possibility that features in the oxygen sublattice determine or influence the spectral response of the different samples used in this work is considered here. In particular, the samples differ in oxygen content as a result of mass losses during synthesis process. From EDAX and plasma emission spectroscopy measurements it has been concluded [17,20,21] that the high-Tc tetragonal sample (TM-850) has a lower oxygen content than the orthorhombic non-superconducting sample (TKO-930). The difference between the samples has been estimated to an amount of 0.4 per formula unit. The other sample investigated (TH2-850b) has an intermediate Tc value (52.4 K) and is partially oxy genated [20]. As Fig. 3 shows a decrease in oxygen content is related to a relative intensity increase of the 540 nm band. Such a correlation has been observed in YBCO samples [7,12] and leads to the conclusion that centers causing a 540 nm emission involve oxygen vacancies. The present observations support this possibility as proposed also in Ref. [11]. A difference between the YBCO samples described in Refs. [7] and [11] and the Tl 2 Ba 2 CuO 6 used here is that in the former the 540 nm emission appears only after irradiation or annealing treatments while in the latter the band is observed without post-synthesis treatments. It is proposed that the 530-540 nm emission is related to a complex center involving oxygen vacancies. The center -as the F type center in ionic crystals -would be formed after specific treatments in YBCO while in the Tl 2 Ba 2 CuO 6 appears during the particular hightemperature synthesis process used. Comparison of results on YBCO and Tl 2 Ba 2 CuO 6 shows that the intensity of the 530-540 nm band is not related to the superconducting behavior (Tc) of the sample but to the oxygen content. In YBCO, the band is more intense in low-oxygen nonsuperconducting samples [7,12] and in Tl 2 Ba 2 CuO 6 the intensity is higher in low-oxygen superconducting samples. The relationship between the 540 nm band and oxygen content also explains the inhomogeneity of the luminescence revealed by CL images, which would be due to an inhomogeneous oxygen concentration in the sample [11]. This effect is used to explain [21] the reduced superconducting volume fraction (f= 45%) of the superconducting samples of this work.

The 540 nm band shows an increase, relative to the 420 nm band when a defocused electron beam (low excitation density) is used. A similar effect has been observed in other HTSC samples as well as in semiconducting materials. In the latter, deep-level emission bands are in many cases enhanced by defocusing the microscope electron beam, see, e.g., Refs. [22] and [23] as a consequence of the presence of radiative centers with a low concentration. In the spectra recorded under conditions of defocused beam the correlation between oxygen content and intensity of the 540 nm band is maintained. Comparison between spectra from polished and unpolished surfaces of the same sample shows a higher relative intensity of the green band after polishing. The above described effect of an enhanced 540 nm band intensity by decreasing oxygen content applies when samples with the same surface state -polished or unpolished -are compared.

The 420 nm band, which is also related to processes in the oxygen sublattice, has a different origin than the 540 nm emission, as revealed by the different temperature dependence of both bands (Fig. 4), the different response to changes in excitation density and by the decrease of the 420 nm band by surface polishing (Fig. 7). The presence of the 420 nm band in all samples studied in this work agrees with similar observations in YBCO and BSCCO and supports the proposal [8,10] of its intrinsic character. In our experiments we could not determine if the relative enhancement of the 540 nm in polished surfaces is due to generation of the corresponding centers or to a quenching effect influencing mainly the 420 nm emission. In any case the surface state has to be considered when comparison among different samples is performed.

In addition to the sample dependence of the relative intensity of the 540 nm band, other minor, mostly space-localized differences, have been detected. For instance in some bright areas of the TH2-850b sample the 420 nm band is clearly dominant at low temperature, as the spectrum of Fig. 5 shows. The same observation applies to the polished non-superconducting sample in which the inner surfaces of the pores show strong 420 nm emission (Fig. 8). While such an effect is probably related to the strong inhomogeneity of the intrinsic luminescent centers, other spectral features would be due to impurity phases. This is the case of the well resolved band at 720 nm (Fig. 6) observed in small, well localized regions of the nonsuperconducting sample (TKO-930). A band at the same wavelength has been reported in Cu 2 O [24,25] and was attributed to oxygen vacancies. As Fig. 10 shows this band is also observed in unpolished surfaces of the CuO x precursors used in the preparation of our Tl 2 Ba 2 CuO 6 samples. This indicates that the regions showing 720 nm emission include CuO x precursor.

In polished CuO x the red emission is quenched and the spectrum of Fig. 9 with bands centered at about 440 and 540 nm is recorded. The spectrum is very similar to the spectra of Tl 2 Ba 2 CuO 6 sample and to previously reported CL spectra of YBCO and BSCCO. Since emission in Tl 2 Ba 2 CuO 6 is not due to CuO x inclusions, as discussed above, the analogy could be related to the common structural features of HTSC's and copper oxides. In particular the basic structural unit of CuO is similar to the CuO 2 layers in HTSC's. On the other hand CuO is a charge-transfer insulator [26] and superconducting cuprates show charge-transfer excitations between O 2p and Cu 3d states, see e.g. Refs. [27][28][29][30].

Tl 2 Ba 2 O 5 impurities have been only detected in the samples of batch B and consequently they do not determine the luminescence features common to the three samples studied in this work. However, since we are not aware of previous luminescence work on Tl 2 Ba 2 O 5 we recorded its CL spectra at different temperatures in view to future works related to this precursor and HTSC materials. As Fig. 11 shows, Tl 2 Ba 2 O 5 has emission bands at 400-420 nm and 540-560 nm. Contrary to that observed in Tl 2 Ba 2 CuO 6 , the relative intensity of the 540-560 nm band increases by decreasing temperature.

Conclusions

The luminescence emission of the Tl 2 Ba 2 CuO 6+X samples investigated shows an inhomogeneous spatial distribution not related to impurity phases. This result applies to superconducting and non-superconducting material and does not depend on the crystal structure, tetragonal or orthorhombic. A band at 540 nm (2.3 eV) is related to the oxygen content. It increases with reducing oxygen content and it is proposed to be due to a complex center involving oxygen vacancies. Another band centered at about 430 nm (2.9 eV), previously attributed to processes in the oxygen sublattice, has a different origin than the 540 nm emission. Comparison with the luminescent behavior of the precursors, CuO x and Tl 2 Ba 2 O 5 , shows common features in the spectra of Tl 2 Ba 2 CuO 6+x and CuO x .

Fig. 1 :

 1 Fig. 1: (a) Panchromatic CL image of an unpolished surface of the superconducting TH2-850b sample and (b) the corresponding secondary image.

Fig. 2 :

 2 Fig. 2 : (a) Panchromatic CL image of the polished surface of the TH2-850b superconducting sample and (b) the corresponding secondary image.

Fig. 3 :

 3 Fig. 3 :. CL spectra recorded under focused electron beam at the unpolished surfaces of the three samples at 80 K. (a) TM-850 superconducting sample, (b) TH2-850b superconducting sample, (c) TKO 930 non-superconducting sample.

Fig. 4 .

 4 Fig. 4. Temperature dependence of the CL spectra of TM-850 superconducting sample.

Fig. 5 .

 5 Fig. 5. CL spectra from a bright area of the TH2-850b superconducting sample at different temperatures.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Low-tempera~re (80 K) eL spectrum from a highly luminescent area of the non-superconducting TKO 930 sample.

Fig. 8 .

 8 Fig. 8. CL spectra from the polished surface of TKO 930 non-superconducting sample, recorded at 80 K in (a) a nonporous area, (b) a porous area.

Fig. 9 .

 9 Fig. 9. CL spectrum from the polished surface of a CuO x precursor sample.

Fig. 10 .

 10 Fig. 10. CL emission spectra from an unpolished surface of a CuO x sample at several temperatures.

Fig, 11

 11 Fig,11shows spectra at different temperatures of the Tl 2 Ba 2 0 5 precursor. Two well resolved bands at about 420 and 550 nm, respectively, are observed. The total emission is higher at low temperatures, specially the 550 nm band.

Fig. 11 .

 11 Fig. 11. Temperature dependence of the CL spectra of the Tl 2 Ba 2 0 5 precursor. (Spectra have been shifted for convenience.)

Acknowledgements

CDG acknowledges MEC for a FPI research grant. This work has been supported by DGICYT (Project PB93-1256).


Marcel Suri
email: marcel.suri@jrc.it

Jan Remund

Tomas Cebecauer

Dominique Dumortier

Lucien Wald

Thomas Huld

Philippe Blanc

M Šúri

First Steps in the Cross-Comparison of Solar Resource Spatial Products in Europe

Keywords: solar radiation database, maps, benchmarking

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Solar energy technologies and energy simulation of buildings need high quality climatic data in the phase of localisation (siting), design, financing, and system operation and management. The choice of the best technological option depends among other things on the geographic region, as the performance of solar energy systems is influenced by solar resource and other climate parameters.

Several spatial databases of solar resource information are now available as a result of European and national projects. They have been developed from various data inputs, covering different time periods, where diverse approaches have been applied. Although quality assessments of the individual databases have been performed, no inter-comparison of the outputs was performed. When comparing various data sources, differences show up which is confusing, especially to users who are not fully aware of the uncertainties and the limits of data application. Therefore, better understanding of the geographic distribution and variability of solar resource in Europe is needed.

In this contribution we open a complex issue of benchmarking the solar radiation databases and underlying models for deriving information relevant to energy technology. We focus on a comparison of six spatial databases and integrated systems that offer solar resource and climate data and energy-related services for Europe: Meteonorm [START_REF] Remund | Handbook of METEONORM Version 6.0, Part II: theory[END_REF], ESRA [START_REF] Greif | European Solar Radiation Atlas (ESRA)[END_REF], Satel-Light [START_REF] Dumortier | Satel-Light, a www server which provides high quality daylight and solar radiation data for Western and Central Europe[END_REF], NASA SSE/RETScreen [START_REF]Surface meteorology and Solar Energy (SSE) Release 6.0 Methodology[END_REF], HelioClim/SoDa [START_REF] Rigollier | The method Heliosat-2 for deriving shortwave solar radiation from satellite images[END_REF], and PVGIS [START_REF] Šúri | Geographic Aspects of Photovoltaics in Europe: Contribution of the PVGIS Web Site[END_REF]. This list is not exhaustive, and in future also other databases may be considered, including those that cover smaller regions. We compare yearly sum of global irradiation as obtained by querying each database. Map analysis compares horizontal irradiation, while on a set of 37 randomly selected points we compare irradiation received by inclined and 2-axis tracking surfaces.

Specifications of the solar radiation databases and underlying methods

General specifications

Spatially-distributed (map) solar radiation databases are classified according to several factors:

• Input data from which they have been created: (a) observations from the meteorological stations (global, diffuse and direct irradiances, and other relevant climate data), (b) digital satellite images or (c) combination of both; here also ancillary atmospheric data used in the models are considered, such as water vapour, ozone and aerosols;

• Period of time (typically a number of years) which is represented by the input data;

• Spatial resolution, i.e geographical distribution of the meteorological sites, grid resolution of the satellite data and resulting outputs;

• Time resolution, which characterises periodicity of the measurement of the input data and of the resulting parameters. Thus a primary database may include time series with periodicity of a few minutes up to hourly and daily averages (sums), or it may contain only monthly and long-term averages.

• Methodical approach used for computation of the primary database: typically solar radiation models combined with interpolation methods (e.g. geostatistical methods or splines, in case that ground observations are used) or algorithms for satellite data processing (e.g. Heliosat). Primary database typically consists of global, direct normal or diffuse irradiances (irradiation in case of time-integrated products) and also some auxiliary parameters such as clear-sky index.

• Simulation models used for calculation of derived parameters, such as global irradiance for inclined and sun-tracking surface, spectral products (e.g., illuminances, UV and PV-related irradiances), estimation of terrain effects, derived statistical products (e.g. synthetic time series).

Quality of an individual data set is assessed for a set of locations by comparing them to ground measurements, where the first order statistics is calculated (bias, root mean square deviation, standard deviation, the correlation coefficient) and the frequency distribution is analysed. In this work we focus on the relative map-based cross comparison of several solar radiation products. Such comparison provides means for improved understanding of regional distribution of the uncertainty by combining all existing resources (calculating the average of all) and quantifying their mutual agreement by the means of standard deviation.

Analysed databases and integrated systems

Each of the databases analysed here is integrated within a system (software setup) that provides additional tools for search, query, maps display, and calculation of derived parameters. PVGIS (the European section), includes solar radiation database developed by combination of solar radiation model and interpolated ground observations. The datasets Satel-Light and HelioClim-2 (accessible through the SoDa web portal) are built from Meteosat and MSG satellite images, respectively. NASA SSE release 6 (accessible also through RETScreen software) combines results from ISCCP satellite project with NCAR reanalysis products. Primary data incorporated in Meteonorm version 6.1 and ESRA are developed by interpolation of ground observed data with support of satellite images (MSG and SRB, respectively). Table 2. Methods used in calculation of primary and derived parameters.

Database/ system

Global horizontal radiation

Diffuse fraction

Inclined surface (diffuse model)

Simulation of time series

Derived parameters

Satel-Light Heliosat Here we focus on the subsection of the European continent (Fig. 1) where all the data sources overlap.

Method

Map comparison

Map-based comparison as performed here is a type of relative benchmarking of solar databases. It does not point to the "best" database, but it gives an indication of the user's uncertainty at any location within the region. As the existing spatial products cover different periods of time, this comparison introduces also uncertainty resulting from the interannual variability of solar radiation.

Here we perform a cross-comparison of maps of long-term average of yearly sum of global horizontal irradiation.

The maps from all data providers are harmonised and integrated into a geographical information system (GIS) with latitude/longitude spatial reference. The grid resolution of 5 arc minutes is chosen to provide a representative outlook at the regional (rather than local) differences within the continent. Choosing such a resolution, more detailed features that are present in the databases of HelioClim-2, Satel-Light, PVGIS and Meteonorm are suppressed (smoothed out), but the regional features are well pronounced. Integration of 6 data sources of yearly sums of global horizontal irradiation provides three results:

• Map of overall average gives the user an indication of spatial distribution of solar resource estimated by the simple averaging of the 6 datasets;

• Map of standard deviation provides information on magnitude of differences between the combined data sources, i.e. user's uncertainty. If we assume that the estimates are normally distributed, from standard deviation a confidence interval can be calculated in which the value falls corresponding to a given probability. For example, a multiple of 1.95996 would give a range where the value from the average map falls with 95% probability.

• Maps of differences between individual databases and the overall average indicate deviation of the values in the particular dataset from the overall average.

Point analysis

The maps give good insight into regional differences, however due to the coarse grid resolution (5 arc minutes) they smooth out local effects. Some of the integrated systems incorporate specific algorithms, such as an application of high-resolution Digital Elevation Model (DEM), which may increase the difference between results for a given site. Thus, for a set of 37 randomly selected points we analyse the values and resulting differences that a user obtains for a particular site when consulting directly each data source/system. In areas with higher agreement of all 6 databases (standard deviation lower than 4%) we selected 15 points, and in areas with higher disagreement (standard deviation between 5% and 11%) another 22 points.

For each point we calculated the yearly sum of global irradiation for (1) south-oriented surface inclined at 34 degrees (close to the typical optimum angle for PV systems in Europe), and (2) for 2-axis sun-tracking systems.

Results

While the map of yearly sum of global horizontal irradiation (Fig. 1) shows the average of 6 databases, the map of standard deviation (Fig. 2) indicates how much the datasets differ in various regions. Higher disagreement between the databases can be found not only in higher mountains (the Alps, Pyrenees, Carpathians, etc.), but also along some coastal zones and in flat regions, e.g. around the Baltic and North Seas. Higher standard deviation in Bulgaria and Romania, in South Scandinavia, and Po plain relates to uneven distribution of the input ground data and questionable quality of the older ground measurements that are used in ESRA and PVGIS. However, cumulative distribution of the map values (Fig. 4 right) shows that standard deviation does not exceed 6% in 78% of the study region, and only in rare (extreme) cases is it higher than 12%.

The analysis on 37 selected points shows some cases with higher differences (some of them are not present in the coarse-resolution maps) with average standard deviation 5.6%. Mean bias difference (see yellow box in the Fig. 3) indicates that satellite databases (Satel-Light and HelioClim-2) show generally higher values compared to the products relying on ground measurements (ESRA, Meteonorm and PVGIS). This difference may be partially explained by the time period of data covered. While Satel-Light and HelioClim-2 represent last 10 years, ESRA and PVGIS are based on one decade of data (1980s), and Meteonorm represents two decades (1980s and 1990s). The NASA SSE product partially spans both periods and also generally gives results that are intermediate between the two groups. Results for 37 randomly selected sites are shown in Fig. 3. Fig. 3. Yearly sum of global horizontal irradiation -differences of the values from 6 databases relative to the overall average. First 15 points represent areas with higher agreement between databases; the other 22 points are randomly selected in areas where the difference between the databases is higher.

For the same 37 points, global irradiation has been calculated for a south-oriented surface inclined at an angle of 34 degrees. Results from five systems are available (all except HelioClim-2). From linear fit of the values on the scattergram (Fig. 4 left) one can see that the standard deviation of estimations for an inclined surface increases in average by 21% compared to the horizontal irradiation (correlation coefficient 0.98). The differences between values for 2-axis tracking surface are calculated only for 3 systems: Meteonorm, NASA SSE/RETScreen and PVGIS. Here the standard deviation increases by 36% compared to the estimates for horizontal surface (correlation coefficient 0.97). Both simulations show a good agreement in the implementation of the underlying algorithms in the compared software. By integration of findings from the map and point analyses (Figs. 2 and4 left) we can estimate uncertainty for any point on the map for yearly values for horizontal, inclined and 2-axis tracking surfaces. Thus the cumulative distribution of user's uncertainty can be calculated from the map data for the whole study region. Fig. 4 (right) shows that for 90% of the study region the uncertainty of estimates of yearly global irradiation (expressed by standard deviation) is lower than 7% for a horizontal surface, 8.3% for a south-facing surface inclined at 34°, and 10% for a 2-axis tracking surface.

Discussion

Differences in the estimates using several solar radiation databases relate to a number of factors.

Analysis of interannual variability for 70 meteorological stations over Europe having data for at least 10 years shows that the standard deviation from the long-term average ranges typically between 3% and 6%. To capture this variability, a database should include data for at least 5 to 10 years depending on the climate region.

There is an inherent difference between in situ (ground) and satellite observations, and the methods how these data are processed. Databases relying on the interpolation of ground observations (Meteonorm, ESRA, and PVGIS Europe) are sensitive to the quality of measurements and density of the measuring stations (which is not satisfactory in many regions), and they typically represent only statistical values. The satellite-derived databases (e.g. HelioClim, NASA SSE, and Satellight) are more affected by higher uncertainty of the cloud cover assessment when the ground is covered by snow and ice and for low sun angles, but they offer time series with high time resolution (e.g. hourly data) and provide spatially-continuous coverage.

Quality and the spatial detail of spatial database is determined by input data used in the models, mainly parameters describing the optical state of the atmosphere (such as Linke atmospheric turbidity, ozone, water vapour, aerosol optical depth), and Digital Elevation Models. The community of data developers lacks high quality aerosol data. High-resolution DEM is presently considered only in Meteonorm and PVGIS. Databases with coarser spatial resolution (e.g. NASA SSE) provide good regional estimates, however for studies at local level they may show deviations as they ignore local climate and terrain features.

The cross-comparison approach presented in this study assumes that each database has equal weight. Uncertainty can be reduced by weighting each contribution that is based on indicators of quality, number of data-years included, spatial resolution and incorporation of terrain effects. Including another two European satellite-derived databases, those owned by German Aerospace Center (DLR) and University of Oldenburg (both in Germany), and application of weighting will provide more complete picture of the uncertainty in the solar resource assessment.

Conclusion

This study provides a first insight into spatial distribution of uncertainty of solar radiation estimates by relative cross comparison of six data sources. In this stage only the yearly sum of global irradiation is considered, and all databases are assumed to give an equal contribution to the overall average. The map of standard deviation from the average indicates combined effect of differences between the databases, and in this study it is used as an indicator of the user's uncertainty.

Differences at the regional level indicate that within 90% of the study region the uncertainty of yearly global irradiation estimates expressed by standard deviation does not exceed 7% for horizontal surface, 8.3% for surface inclined at 34°, and 10% for 2-axis tracking surface. A user has to expect higher differences in the outputs from the studied databases in complex climate conditions of mountains, some coastal zones and in areas where solar radiation modelling cannot rely on sufficient density and quality of input data.

Fig. 1 .

 1 Fig. 1. Yearly sum of global irradiation on horizontal surface -average of 6 databases: Meteonorm v.6, ESRA, PVGIS, NASA SSE v.6, Satel-Light and HelioClim-2 [kWh/m 2 ].Results for 37 randomly selected sites are shown in Fig.3.

Fig. 2 .

 2 Fig. 2. Yearly sum of global horizontal irradiation -standard deviation of the values from 6 databases relative to the overall average shown in Fig. 1 [%]. Results for 37 randomly selected sites are shown in Fig. 3. Part of the contribution from higher values obtained from HelioClim-2 database can be probably explained by Mean Bias Deviation calculated by comparison with data from 11 high-quality BSRN stations ([9] see Tab. 1).

Fig. 4 .

 4 Fig. 4. Estimates of yearly sum of global irradiation. Left: Scattergram of 37 points showing relation between relative standard deviation calculated for horizontal surface and those calculated for 34° inclined and 2-axis tracking system [%]; Right: Cumulative distribution of relative standard deviation [%] for global horizontal irradiation summarised from the map and the standard deviation estimated for inclined, and 2-axis tracking surface by linear fit of points from the left.

Table 1 .

 1 Technical parameters of the solar radiation databases.

	Database &	Data inputs	Period	Time	Spatial resolution	RMSD/
	availability			resolution	(in study region)	MBD (%)
	PVGIS Europe	~560 meteo stations	1981-1990	Monthly	1 km x 1 km + on-	4.7/-0.5
	(internet)			averages	fly disaggreg. by	[6]
					100 m DEM
	Meteonorm 6.1	Meteo stations +	1981-2000	Monthly	Interpol. (on-fly)+	6.2/0
	(CDROM and	satellite data		averages	satellite; disaggreg.	[7]
	internet)				by 100 m DEM
	ESRA	~560 meteo stations +	1981-1990	Monthly	5 arc-minute	~7.5/-
	(CDROM)	SRB satel. data		averages	x 5 arc-minute	[8]
	Satel-Light	Meteosat 5, 6, 7	1996-2000	30-minute 4.6-6.2 km x	21.0/-0.6
	(internet)				6.1-14.2 km	[9]
	HelioClim-2	Meteosat 8 and 9	2004 -2007	15-min	3.1-4.2 km x	25.3/2.2
	(internet)	(MSG)			4.1-9.6 km	[9]
	NASA SSE 6	GEWEX/SRB 3 +	1983-2005	3-hourly	1 arc-degree	8.7/0.3
	(internet)	ISCCP satel. clouds +			x 1 arc-degree	[4]
		NCAR reanalysis

  In Tabs. 1 and 2 we summarise the main characteristics of the databases/systems including the quality indicators Root Mean Square Difference (RMSD) and Mean Bias Deviation (MBD). While NASA, Satel-Light, PVGIS, and partially also Meteonorm systems are available for free, the ESRA, HelioClim-2 and full version of Meteonorm are to be purchased. Geographical extension of the spatial products differs: from global (NASA and Meteonorm) to cross-continental (HelioClim-2 covering Europe, Africa and Southwest Asia) and European (ESRA, PVGIS and Satel-Light).

		1	Skartveit et al.	Skartveit	Real 30-minute data G, B, D,
		(Dumortier diffuse	1998	& Olseth		illuminances,
		clear sky model)		1986		ext. statistics
	Meteonorm	3D inverse distance	Perez et al.	Perez et	Synthetic time series	G, D, B,
	ver. 6.1	interpol. by Zelenka	1991	al. 1987	from monthly	terrain
		et al 1992 and Wald			averages by Aguiar	shadowing
		& Lefevre 2001;			et al 1988, and	(beam and
		Heliosat 1 for sat.			Aguiar & Collares-	diffuse)
		data			Pereira 1992
	PVGIS	3D spline Interpol.	Measured at	Muneer	Simulation of daily	G, D, terrain
	Europe	of ground data +	63 stations, the	1990	profile from monthly	shadowing
		model r.sun: Suri &	rest estim. by		averages by Suri &	(beam only)
		Hofierka 2004	Czeplak 1996		Hofierka 2004
	ESRA	Interpol. of ground	Measured at	Muneer	Simulation of daily	G, D,B,
		data by co-krigging:	63 stations, the	1990	average profile by	clearness,
		Beyer et al. 1997	rest estimated		Collares-Pereira &	zones
			by Czeplak		Rabl 1979, and Liu
			1996		& Jordan 1960
	NASA SSE	Satellite model by	Erbs et al.	Retscreen	Simulation of daily	G, B, D,
	rel. 6	Pinker & Laszlo	1982	method by	average profile by	extended
		1992		Duffie &	Collares-Pereira &	number of
				Beckman	Rabl 1979, and Liu	parameters &
				1991	& Jordan 1960	statistics
	HelioClim	Heliosat-2 (Rigollier	N/A	N/A	Real 15-minute data G
	ver. 2	et al. 2004)