Territory and Infrastructure : Strategic Identification and Project Pondering

El Territorio hoy es visto como una totalidad organizada que no puede ser pensada separando cada uno de los elementos que la componen; cada uno de ellos es definido por su relación con los otros elementos. Así, un pensamiento que integra diferentes disciplinas y saberes comienza a manejar una realidad que lejos está de definir certezas inamovibles, y comienza a vislumbrar horizontes estratégicos. La adaptación a la no linealidad de las relaciones que se dan sobre el territorio, y la diferencia de velocidades en las que actúan los distintos actores, nos exige hacer de la flexibilidad una característica esencial de la metodología de planificación estratégica. La multi-causalidad de los fenómenos que estructuran el territorio nos obliga a construir criterios cualitativos, entendiendo que nos es imposible la medición de estas cadenas causales y su reconstrucción completa en el tiempo; sin dejar por ello de edificar un marco profundo de acción y transformación que responda a una realidad cierta y veraz. Los fenómenos producidos sobre el territorio nunca actúan de manera aislada, lo que implica una responsabilidad a la hora de comprender las sinergias y la restricción que afectan los resultados de los procesos desatados. La presente ponencia corresponde a la Segunda Fase del proceso de identificación estratégica de los proyectos Plan Estratégico Territorial (PET) que se inició en el año 2005; dicho Plan es llevada a cabo por la Subsecretaría de Planificación Territorial del Ministerio de Planificación Federal y fue abordado sobre la base de tres pretensiones: institucionalizar el ejercicio del pensamiento estratégico, fortalecer la metodología de trabajo transdisciplinaria y multisectorial, y diseñar un sistema de ponderación de proyectos estratégicos de infraestructura, tanto a nivel provincial como nacional, con una fuerte base cualitativa. Este proceso dio como resultado una cartera ponderada de proyectos de infraestructura conjuntamente con una metodología que permitió consolidar los equipos provinciales de planificación, tanto en su relación con los decisores políticos como con los actores de los múltiples sectores del gobierno, y en estos resultados consolidar y reforzar una cultura del pensamiento estratégico sobre el territorio

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Longitudinal Changes in Response to a Cycle-Run Field Test of Young Male National "Talent identification" and Senior Elite Triathlon Squads.

This study investigated the changes in cardiorespiratory response and running performance of 9 male ?Talent Identification? (TID) and 6 male Senior Elite (SE) Spanish National Squad triathletes during a specific cycle-run test. The TID and SE triathletes (initial age 15.2±0.7 vs. 23.8±5.6 years, p=0.03; tests through the competitive period and the preparatory period, respectively, of two consecutive seasons: Test 1 was an incremental cycle test to determine the ventilatory threshold (Thvent); Test 2 (C-R) was 30 min constant load cycling at the Thvent power output followed by a 3-km time trial run; and Test 3 (R) was an isolated 3-km time trial control run, in randomized counterbalanced order. In both seasons the time required to complete the C-R 3-km run was greater than for R in TID (11:09±00:24 vs. 10:45±00:16 min:ss, pmenor que 0.01; and 10:24±00:22 vs. 10:04±00:14, p=0.006, for season 2005/06 and 2006/07, respectively) and SE (10:15±00:19 vs. 09:45±00:30, pmenor que 0.001 and 09:51±00:26 vs. 09:46±00:06, p= 0.02 for season 2005/06 and 2006/07, respectively). Compared to the first season, completion of the time trial run was faster in the second season (6.6%, pmenor que 0.01 and 6.4%, pmenor que 0.01, for C-R and R test, respectively) only in TID. Changes in post-cycling run performance were accompanied by changes in pacing strategy but only slight or non-significant changes in the cardiorespiratory response. Thus, the negative effect of cycling on performance may persist, independently of the period, over two consecutive seasons in TID and SE triathletes; however A improvements over time suggests that monitoring running pacing strategy after cycling may be a useful tool to control performance and training adaptations in TID. O2max 77.0±5.6 vs. 77.8±3.6 mL·kg-1·min-1, NS) underwent three TE D EP C C

Clones Identification and Genetic Characterization of Garnacha Grapevine by Means of Different PCR-Derived Marker Systems

This study uses PCR-derived marker systems to investigate the extent and distribution of genetic variability of 53 Garnacha accessions coming from Italy, France and Spain. The samples studied include 28 Italian accessions (named Tocai rosso in Vicenza area; Alicante in Sicily and Elba island; Gamay perugino in Perugia province; Cannonau in Sardinia), 19 Spanish accessions of different types (named Garnacha tinta, Garnacha blanca, Garnacha peluda, Garnacha roja, Garnacha erguida, Garnacha roya) and 6 French accessions (named Grenache and Grenache noir). In order to verify the varietal identity of the samples, analyses based on 14 simple sequence repeat (SSR) loci were performed. The presence of an additional allele at ISV3 locus (151 bp) was found in four Tocai rosso accessions and in a Sardinian Cannonau clone, that are, incidentally, chimeras. In addition to microsatellite analysis, intravarietal variability study was performed using AFLP, SAMPL and M-AFLP molecular markers. AFLPs could discriminate among several Garnacha samples; SAMPLs allowed distinguishing few genotypes on the basis of their geographic origin, whereas M-AFLPs revealed plant-specific markers, differentiating all accessions. Italian samples showed the greatest variability among themselves, especially on the basis of their different provenance, while Spanish samples were the most similar, in spite of their morphological diversity.

Icosahedral Ni nanowires formed from nanocontacts breaking: Identification and characterization by Molecular Dynamics

We present and discuss an algorithm to identify and characterize the long icosahedral structures (staggered pentagonal nanowires with 1-5-1-5 atomic structure) that appear in Molecular Dynamics simulations of metallic nanowires of different species subjected to stretching. The use of this algorithm allows the identification of pentagonal rings forming the icosahedral structure as well as the determination of its number np , and the maximum length of the pentagonal nanowire Lpm. The algorithm is tested with some ideal structures to show its ability to discriminate between pentagonal rings and other ring structures. We applied the algorithm to Ni nanowires with temperatures ranging between 4K and 865K, stretched along the [111], [100] and [110] directions. We studied statistically the formation of pentagonal nanowires obtaining the distributions of length Lpm and number of rings np as function of the temperature. The Lpm distribution presents a peaked shape, with peaks located at fixed distances whose separation corresponds to the distance between two consecutive pentagonal rings.

Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets.

AR drone identification and navigation control at CVG-UPM

This article presents the proposal of the Computer Vision Group to the first phase of the international competition “Concurso de Ingeniería de Control 2012, Control Aut ́onomo del seguimiento de trayectorias de un vehículo cuatrirrotor”. This phase consists mainly of two parts: identifying a model and designing a trajectory controller for the AR Drone quadrotor. For the identification task, two models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. The trajectory controller design is based on the simplified model, whereas the physical model is used to tune the controller to attain a certain level of robust stability to model uncertainties. The controller design is simplified by the hypothesis that accurate positions sensors will be available to implement a feedback controller.

Localization and Tracking in Energy-Constrained Networks with Dynamic Connectivity

Systems used for target localization, such as goods, individuals, or animals, commonly rely on operational means to meet the final application demands. However, what would happen if some means were powered up randomly by harvesting systems? And what if those devices not randomly powered had their duty cycles restricted? Under what conditions would such an operation be tolerable in localization services? What if the references provided by nodes in a tracking problem were distorted? Moreover, there is an underlying topic common to the previous questions regarding the transfer of conceptual models to reality in field tests: what challenges are faced upon deploying a localization network that integrates energy harvesting modules? The application scenario of the system studied is a traditional herding environment of semi domesticated reindeer (Rangifer tarandus tarandus) in northern Scandinavia. In these conditions, information on approximate locations of reindeer is as important as environmental preservation. Herders also need cost-effective devices capable of operating unattended in, sometimes, extreme weather conditions. The analyses developed are worthy not only for the specific application environment presented, but also because they may serve as an approach to performance of navigation systems in absence of reasonably accurate references like the ones of the Global Positioning System (GPS). A number of energy-harvesting solutions, like thermal and radio-frequency harvesting, do not commonly provide power beyond one milliwatt. When they do, battery buffers may be needed (as it happens with solar energy) which may raise costs and make systems more dependent on environmental temperatures. In general, given our problem, a harvesting system is needed that be capable of providing energy bursts of, at least, some milliwatts. Many works on localization problems assume that devices have certain capabilities to determine unknown locations based on range-based techniques or fingerprinting which cannot be assumed in the approach considered herein. The system presented is akin to range-free techniques, but goes to the extent of considering very low node densities: most range-free techniques are, therefore, not applicable. Animal localization, in particular, uses to be supported by accurate devices such as GPS collars which deplete batteries in, maximum, a few days. Such short-life solutions are not particularly desirable in the framework considered. In tracking, the challenge may times addressed aims at attaining high precision levels from complex reliable hardware and thorough processing techniques. One of the challenges in this Thesis is the use of equipment with just part of its facilities in permanent operation, which may yield high input noise levels in the form of distorted reference points. The solution presented integrates a kinetic harvesting module in some nodes which are expected to be a majority in the network. These modules are capable of providing power bursts of some milliwatts which suffice to meet node energy demands. The usage of harvesting modules in the aforementioned conditions makes the system less dependent on environmental temperatures as no batteries are used in nodes with harvesters--it may be also an advantage in economic terms. There is a second kind of nodes. They are battery powered (without kinetic energy harvesters), and are, therefore, dependent on temperature and battery replacements. In addition, their operation is constrained by duty cycles in order to extend node lifetime and, consequently, their autonomy. There is, in turn, a third type of nodes (hotspots) which can be static or mobile. They are also battery-powered, and are used to retrieve information from the network so that it is presented to users. The system operational chain starts at the kinetic-powered nodes broadcasting their own identifier. If an identifier is received at a battery-powered node, the latter stores it for its records. Later, as the recording node meets a hotspot, its full record of detections is transferred to the hotspot. Every detection registry comprises, at least, a node identifier and the position read from its GPS module by the battery-operated node previously to detection. The characteristics of the system presented make the aforementioned operation own certain particularities which are also studied. First, identifier transmissions are random as they depend on movements at kinetic modules--reindeer movements in our application. Not every movement suffices since it must overcome a certain energy threshold. Second, identifier transmissions may not be heard unless there is a battery-powered node in the surroundings. Third, battery-powered nodes do not poll continuously their GPS module, hence localization errors rise even more. Let's recall at this point that such behavior is tight to the aforementioned power saving policies to extend node lifetime. Last, some time is elapsed between the instant an identifier random transmission is detected and the moment the user is aware of such a detection: it takes some time to find a hotspot. Tracking is posed as a problem of a single kinetically-powered target and a population of battery-operated nodes with higher densities than before in localization. Since the latter provide their approximate positions as reference locations, the study is again focused on assessing the impact of such distorted references on performance. Unlike in localization, distance-estimation capabilities based on signal parameters are assumed in this problem. Three variants of the Kalman filter family are applied in this context: the regular Kalman filter, the alpha-beta filter, and the unscented Kalman filter. The study enclosed hereafter comprises both field tests and simulations. Field tests were used mainly to assess the challenges related to power supply and operation in extreme conditions as well as to model nodes and some aspects of their operation in the application scenario. These models are the basics of the simulations developed later. The overall system performance is analyzed according to three metrics: number of detections per kinetic node, accuracy, and latency. The links between these metrics and the operational conditions are also discussed and characterized statistically. Subsequently, such statistical characterization is used to forecast performance figures given specific operational parameters. In tracking, also studied via simulations, nonlinear relationships are found between accuracy and duty cycles and cluster sizes of battery-operated nodes. The solution presented may be more complex in terms of network structure than existing solutions based on GPS collars. However, its main gain lies on taking advantage of users' error tolerance to reduce costs and become more environmentally friendly by diminishing the potential amount of batteries that can be lost. Whether it is applicable or not depends ultimately on the conditions and requirements imposed by users' needs and operational environments, which is, as it has been explained, one of the topics of this Thesis.

Identification and dynamics of polyglycine II nanocrystals in Argiope trifasciata flagelliform silk

Spider silks combine a significant number of desirable characteristics in one material, including large tensile strength and strain at breaking, biocompatibility, and the possibility of tailoring their properties. Major ampullate gland silk (MAS) is the most studied silk and their properties are explained by a double lattice of hydrogen bonds and elastomeric protein chains linked to polyalanine β-nanocrystals. However, many basic details regarding the relationship between composition, microstructure and properties in silks are still lacking. Here we show that this relationship can be traced in flagelliform silk (Flag) spun by Argiope trifasciata spiders after identifying a phase consisting of polyglycine II nanocrystals. The presence of this phase is consistent with the dominant presence of the –GGX– and –GPG– motifs in its sequence. In contrast to the passive role assigned to polyalanine nanocrystals in MAS, polyglycine II nanocrystals can undergo growing/collapse processes that contribute to increase toughness and justify the ability of Flag to supercontract.

Characteristic study, its identification and self-tuned approach to control hydro-power plants

The water time constant and mechanical time constant greatly influences the power and speed oscillations of hydro-turbine-generator unit. This paper discusses the turbine power transients in response to different nature and changes in the gate position. The work presented here analyses the characteristics of hydraulic system with an emphasis on changes in the above time constants. The simulation study is based on mathematical first-, second-, third- and fourth-order transfer function models. The study is further extended to identify discrete time-domain models and their characteristic representation without noise and with noise content of 10 & 20 dB signal-to-noise ratio (SNR). The use of self-tuned control approach in minimising the speed deviation under plant parameter changes and disturbances is also discussed.

Identification and functional characterization of Rhizobium leguminosarum bv. viciae genetic systems involved in nickel homeostasis.

A collection of Rhizobium leguminosarum bv. viciae strains isolated from ultramafic and contaminated soils in Italy and Germany, respectively, was analyzed for resistance to nickel and cobalt ions. These assays led to the identification of strain UPM1137, which is able to grow at high concentrations of nickel and cobalt. In order to identify genetic systems involved in the homeostasis to these metals, a random mutagenesis was carried out in UPM1137 by inserting a Tn5-derivative minitransposon. As a result 4313 transconjugants were obtained, being 39 of them (0.90%) unable to grow at 1.5 mM NiCl2. The identification of the transposon insertion site in these mutants showed that the disrupted genes encode proteins belonging to different functional categories, where the secreted and membrane proteins were the most numerous. The analysis of heavy metal resistance and phenotypes in symbiotic and free –living cells will define the contribution of these genes to metal homeostasis.

Identification and analysis of a nickel-inducible cation diffusion facilitator-NiCoT combined system in Rhizobium leguminosarum bv. viciae

Nickel, like other transition metals, can be toxic to cells even at moderate concentration (low microM range) by displacing essential metals from their native binding sites or by generating reactive oxygen species that cause oxidative DNA damage. For this reason, cells have evolved mechanisms to deal with excess nickel. Efflux systems include members of the Resistance-Nodulation-cell Division (RND) protein family, P-type ATPases, cation diffusion facilitators (CDF) and other resistance factors. Nickel-specific exporters have been characterized in Cupravidus metallidurans, Helicobacter pylori, Achromobacter xylosoxidans, Serratia marcenses and Escherichia coli.