Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Tarracomap: Development of an archaeological application on Google Maps navigation System

This paper describes the result of a research about diverse areas of the information technology world applied to cartography. Its final result is a complete and custom geographic information web system, designed and implemented to manage archaeological information of the city of Tarragona. The goal of the platform is to show on a web-focused application geographical and alphanumerical data and to provide concrete queries to explorate this. Various tools, between others, have been used: the PostgreSQL database management system in conjunction with its geographical extension PostGIS, the geographic server GeoServer, the GeoWebCache tile caching, the maps viewer and maps and satellite imagery from Google Maps, locations imagery from Google Street View, and other open source libraries. The technology has been chosen from an investigation of the requirements of the project, and has taken great part of its development. Except from the Google Maps tools which are not open source but are free, all design has been implemented with open source and free tools.

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Development of In Situ Detection Methods for Materials-Related Distress (MRD) in Concrete Pavements: Phase 2, HR-1081, 2005

This project utilized information from ground penetrating radar (GPR) and visual inspection via the pavement profile scanner (PPS) in proof-of-concept trials. GPR tests were carried out on a variety of portland cement concrete pavements and laboratory concrete specimens. Results indicated that the higher frequency GPR antennas were capable of detecting subsurface distress in two of the three pavement sites investigated. However, the GPR systems failed to detect distress in one pavement site that exhibited extensive cracking. Laboratory experiments indicated that moisture conditions in the cracked pavement probably explain the failure. Accurate surveys need to account for moisture in the pavement slab. Importantly, however, once the pavement site exhibits severe surface cracking, there is little need for GPR, which is primarily used to detect distress that is not observed visually. Two visual inspections were also conducted for this study by personnel from Mandli Communications, Inc., and the Iowa Department of Transportation (DOT). The surveys were conducted using an Iowa DOT video log van that Mandli had fitted with additional equipment. The first survey was an extended demonstration of the PPS system. The second survey utilized the PPS with a downward imaging system that provided high-resolution pavement images. Experimental difficulties occurred during both studies; however, enough information was extracted to consider both surveys successful in identifying pavement surface distress. The results obtained from both GPR testing and visual inspections were helpful in identifying sites that exhibited materials-related distress, and both were considered to have passed the proof-of-concept trials. However, neither method can currently diagnose materials-related distress. Both techniques only detected the symptoms of materials-related distress; the actual diagnosis still relied on coring and subsequent petrographic examination. Both technologies are currently in rapid development, and the limitations may be overcome as the technologies advance and mature.

Characterization of the neuronal microtubule regulator SCG10 in transfected cells and " in vivo "

SUMMARY The ability of neuronal processes to find their way along complex paths and to establish appropriate connections depends on continual rearrangements of the cytoskeletal components. The regulation of microtubules plays an important role for morphological changes underlying nevrite outgrowth, axonal elongation, and growth cone steering. SCG10 (superior cervical ganglion clone 10) is a neuronal growthassociated protein developmentally regulated and highly enriched in the neuronal growth cones. SCG10 presents a microtubule destabilizing activity that could participate to the regulation of microtubule dynamics and thus explain microtubule behaviors in the growth cone during axonal elongation and turning. It is here suggested that a tight control of the opposite effects on microtubules of SCG10 and the stabilizing microtubule-associated protein MAP1B allows a fine tuning of cytoskeletal rearrangement and may provide the required microtubule dynamic instability to promote axonal growth. Moreover, antibodyblockade of SCG10 function, that leads to growth cone pauses similar as those triggered by the guidance molecule EphB, and the modulation of SCG10 activity by the Rho GTPase Rnd1 suggest a potential role for SCG10 in the signal transduction pathways of extracellular guidance cues. The identification of the active zone protein Bassoon as a potential interaction partner for the SCG10-related protein NPC2, using atomic force microscopy as well as COS-7 and neuronal cell cultures, also gives new insights for a role of this protein family into the processes of synapse genesis or plasticity. Finally, SCG10 mutant mice generated by gene targeting and expressing a soluble form of the protein have been characterized during early postnatal development and in the adulthood. Due to the deletion of its membrane binding domain, SCG10 specific subcellular targeting to growth cones is compromised and results in impairments of motor and coordination development. Further histological analysis in the sciatic nerve reveal that these symptoms are associated with neurodegenerative signs. RESUME Une navigation correcte des prolongements cellulaires neuronaux leur permettant de former des connections appropriées repose sur de continuels réarrangements des constituants de leur cytosquelette. La régulation des microtubules joue notamment un rôle important dans les changements morphologiques qui accompagnent la croissance axonale et les réorientations du cône de croissance. SCG10 (superior cervical ganglion clone 10) est une protéine étroitement associée à la croissance neuronale, hautement régulée durant le développement et abondante au niveau du cône de croissance. SCG10 présente une activité déstabilisatrice sur les microtubules qui pourrait permettre une régulation des paramètres dynamiques propres aux microtubules et ainsi expliquer leur comportement durant la navigation du cône de croissance. Il est ici proposé qu'un contrôle précis des effets opposés de SCG10 et d'une autre protéine stabilisante associée aux microtubules (MAP1 B) permette un réglage fin des réarrangements du cytosquelette et puisse ainsi produire l'instabilité dynamique nécessaire à la croissance anale. Par ailleurs, le blocage de la fonction de SCG10 par un anticorps spécifique, conduisant à des pauses du cônes de croissance similaires à celles provoquées par la molécule de guidage EphB, ainsi que la modulation de l'activité de SCG10 par la Rho GTPase Rnd1 suggèrent une potentielle implication de SCG10 dans les voies de transduction des signaux provenant de molécules de guidage extracellulaires. L'identification d'une interaction de la protéine synaptique Bassoon avec la protéine NPC2 apparentée à SCG10, au moyen de la microscopie à force atomique et dans des cultures de cellules neuronales et COS-7, ouvre des perspectives concernant ces protéines dans la formation et la plasticité synaptiques. Finalement, des souris mutantes pour SCG10 produites par ciblage de gène et exprimant une forme soluble de la protéine ont été caractérisées durant la phase précoce du développement et à l'âge adulte. La délétion du domaine permettant l'ancrage de SCG10 aux membranes compromet sa sub-localisation au niveau du cône de croissance et résulte en l'apparition de troubles moteurs et de la coordination. Des analyses histologiques complémentaires au niveau du nerf sciatique montrent que ces symptômes sont associés avec des signes neurodégénératifs.

Postnatal intracerebroventricular administrations of NGF alter spatial memory in adulthood

The present work assessed the effects of intracerebroventricular injections (2x5 mg/2.5 ml) of recombined human nerve growth factor (rhNGF) at postnatal days 2 and 3 upon the development of spatial learning capacities in rats. The treated rats were trained at the age of 22 days to escape onto an invisible platform at a fixed position in space in a Morris navigation task. For half of the subjects, the training position was also cued, a procedure aimed at facilitating escape and reducing attention to the distant spatial cues. At the age of 2 months all the rats were retrained in the same task. Treatment effects were found in both immature and adult rats. The injection of NGF induced a slight alteration of the immature rats' performance. In contrast, a marked impairment of spatial abilities was shown in the 2-month-old rats. The most consistent effects were a significant increase in the escape latency and a decrease bias towards the training platform area during probe trials. The reduction of spatial memory was particularly marked if the subjects had been trained in a cued condition. Taken together, these experiments reveal that an acute pharmacological treatment that leads to transient modifications during early development might induce a behavioural change long after treatment. Thus, the development and the maintenance of an accurate spatial representation are tightly related to the development of brain structures that could be altered by precocious NGF administrations.

Speed-cohesion trade-offs in collective decision making in ants and the concept of precision in animal behaviour

In decision making, speed-accuracy trade-offs are well known and often inevitable because accuracy depends on being well informed and gathering information takes time. However, trade-offs between speed and cohesion, that is the degree to which a group remains together as a single entity, as a result of their decision making, have been comparatively neglected. We combine theory and experimentation to show that in decision-making systems, speed-cohesion trade-offs are a natural complement to speed-accuracy trade-offs and are therefore of general importance. We then analyse the decision performance of 32 rock ant, Temnothorax albipennis, colonies in experiments in which accuracy of collective decision making was held constant, but time urgency varied. These experiments reveal for the first time an adaptive speed-cohesion trade-off in collective decision making and how this is achieved. In accord with different time constraints, colonies can decide quickly, at the cost of social unity, or they can decide slowly with much greater cohesion. We discuss the similarity between cohesion and the term precision as used in statistics and engineering. This emphasizes the generality of speed versus cohesion/precision trade-offs in decision making and decision implementation in other fields within animal behaviour such as sexually selected motor displays and even certain aspects of birdsong. We also suggest that speed versus precision trade-offs may occur when individuals within a group need to synchronize their activity, and in collective navigation, cooperative hunting and in certain escape behaviours.

Septal lesions impair the acquisition of a cued place navigation task: attentional or memory deficit?

These experiments were designed to analyze how medial septal lesions reducing the cholinergic innervation in the hippocampus might affect place learning. Rats with quisqualic lesions of the medial septal area (MS) were trained in a water maze and on a homing table where the escape position was located at a spatially fixed position and further indicated by a salient cue suspended above it. The lesioned rats were significantly impaired in reaching the cued escape platform during training. In addition rats, did not show any discrimination of the training sector during a probe trial in which no platform or cue was present. This impairment remained significant during further training in the absence of the cue. When the cued escape platform was located at an unpredictable spatial location, the MS-lesioned rats showed no deficit and spent more time under the cue than control rats during the probe trial. On the homing board, with a salient object in close proximity to the escape hole, the MS rats showed no deficit in escape latencies, although a significant reduction in spatial memory was observed. However, this was overcome by additional training in the absence of the cue. Under these conditions, rats with septal lesions were prone to develop a pure guidance strategy, whereas normal rats combined a guidance strategy with a memory of the escape position relative to more distant landmarks. The presence of a salient cue appeared to decrease attention to environmental landmarks, thus reducing spatial memory. These data confirm the general hypothesis that MS lesions reduce the capacity to rely on a representation of the relation between several landmarks with different salience.

Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.