Active Optical Sensors for Tree Stem Detection and Classification in Nurseries.

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops.

Localización personal en entornos interiores con tecnología RFID

Este trabajo presenta un sistema de posicionamiento local (LPS) para personas en entornos interiores basado en la combinación de tecnología RFID activa y una metodología bayesiana de estimación de la posición a partir de la fuerza de las señales de RF recibidas. La complejidad inherente a la propagación de las ondas de RF en entornos interiores causa grandes fluctuaciones en el nivel de la fuerza de la señal, por lo que las técnicas bayesianas, de naturaleza estadística, tienen ventajas significativas frente a métodos de posicionamiento más comunes, como multilateración, minimización cuadrática o localización por fingerprinting. En la validación experimental del sistema RFID-LPS se consigue un error de posicionamiento medio de 2.10 m (mediana de 1.84 m y 3.89 m en el 90% de los casos), en un área abarcada de 475 m2 con 29 tags RFID, y con velocidades de desplazamiento de hasta 0.5 m/s, prestaciones iguales o superiores a otros sistemas del estado del arte. Aunque existen precedentes en Robótica móvil, la combinación de métodos bayesianos y tecnología RFID activa usada en este trabajo es original en el marco de los sistemas de localización de personas, cuyos desplazamientos son generalmente más impredecibles que los de los robots. Otros aspectos novedosos investigados son la posibilidad de alcanzar una estimación conjunta de posición y orientación de un usuario con dos métodos distintos (uso de antenas directivas y aprovechamiento de la atenuación de la señal de RF por el cuerpo humano), la escalabilidad del sistema RFID-LPS, y la estimación de la posición por técnicas bayesianas en sistemas simples que pueden detectar los marcadores RFID, pero no medir su fuerza de señal.

Uncertainty in optical bio-sensors due to the spectral displacement of the interference modes of the transduction signal. Incertidumbre en bio-sensores ópticos asociada al desplazamiento espectral de los modos de interferencia de la señal de transducción

The analysis of the interference modes has an increasing application, especially in the field of optical biosensors. In this type of sensors, the displacement Δν of the interference modes of the transduction signal is observed when a particular biological agent is placed over the biosensor. In order to measure this displacement, the position of a maximum (or a minimum) of the signal must be detected before and after placing the agent over the sensor. A parameter of great importance for this kind of sensors is the period Pν of the signal, which is inversely proportional to the optical thickness h0 of the sensor in the absence of the biological agent. The increase of this period improves the sensitivity of the sensor but it worsens the detection of the maximum. In this paper, authors analyze the propagation of uncertainties in these sensors when using least squares techniques for the detection of the maxima (or minima) of the signal. Techniques described in supplement 2 of the ISO-GUM Guide are used. The result of the analysis allows a metrological educated answer to the question of which is the optimal period Pν of the signal. El análisis del comportamiento de los modos de interferencia tiene una aplicación cada vez más amplia, especialmente en el campo de los biosensores ópticos. En este tipo de sensores se observa el desplazamiento Δν de los modos de interferencia de la señal de transducción al reconocer un de-terminado agente biológico. Para medir ese desplazamiento se debe detectar la posición de un máximo o mínimo de la señal antes y después de dicho desplazamiento. En este tipo de biosensores un parámetro de gran importancia es el periodo Pν de la señal el cual es inversamente proporcional al espesor óptico h0 del sensor en ausencia de agente biológico. El aumento de dicho periodo mejora la sensibilidad del sensor pero parece dificultar la detección del mínimo o máximo. Por tanto, su efecto sobre la incertidumbre del resultado de la medida presenta dos efectos contrapuestos: la mejora de la sensibilidad frente a la dificultad creciente en la detección del mínimo ó máximo. En este trabajo, los autores analizan la propagación de incertidumbres en estos sensores utilizando herramientas de ajuste por MM.CC. para la detección de los mínimos o máximos de la señal y técnicas de propagación de incertidumbres descritas en el suplemento 2 de la Guía ISO-GUM. El resultado del análisis permite dar una respuesta, justificada desde el punto de vista metrológico, de en que condiciones es conveniente o no aumentar el periodo Pν de la señal.

On the Analytical Approach to Present Engineering Problems: Photovoltaic Systems Behavior, Wind Speed Sensors Performance, and High-Speed Train Pressure Wave Effects in Tunnels

At present, engineering problems required quite a sophisticated calculation means. However, analytical models still can prove to be a useful tool for engineers and scientists when dealing with complex physical phenomena. The mathematical models developed to analyze three different engineering problems: photovoltaic devices analysis; cup anemometer performance; and high-speed train pressure wave effects in tunnels are described. In all cases, the results are quite accurate when compared to testing measurements.

On the harmonic analysis of cup anemometer rotation speed: A principle to monitor performance and maintenance status of rotating meteorological sensors

The calibration results of one anemometer equipped with several rotors, varying their size, were analyzed. In each case, the 30-pulses pert turn output signal of the anemometer was studied using Fourier series decomposition and correlated with the anemometer factor (i.e., the anemometer transfer function). Also, a 3-cup analytical model was correlated to the data resulting from the wind tunnel measurements. Results indicate good correlation between the post-processed output signal and the working condition of the cup anemometer. This correlation was also reflected in the results from the proposed analytical model. With the present work the possibility of remotely checking cup anemometer status, indicating the presence of anomalies and, therefore, a decrease on the wind sensor reliability is revealed.

Using aircraft as wind sensors for estimating accurate wind fields for air traffic management applications

A study which examines the use of aircraft as wind sensors in a terminal area for real-time wind estimation in order to improve aircraft trajectory prediction is presented in this paper. We describe not only different sources in the aircraft systems that provide the variables needed to derivate the wind velocity but the capabilities which allow us to present this information for ATM Applications. Based on wind speed samples from aircraft landing at Madrid-Barajas airport, a real-time wind field will be estimated using a data processing approach through a minimum variance method. Finally the accuracy of this procedure will be evaluated for this information to be useful to Air Traffic Control.

Debonding identification in FRP-plated RC structures using PZT sensors

The application of the Electro-Mechanical Impedance (EMI) method for damage detection in Structural Health Monitoring has noticeable increased in recent years. EMI method utilizes piezoelectric transducers for directly measuring the mechanical properties of the host structure, obtaining the so called impedance measurement, highly influenced by the variations of dynamic parameters of the structure. These measurements usually contain a large number of frequency points, as well as a high number of dimensions, since each frequency range swept can be considered as an independent variable. That makes this kind of data hard to handle, increasing the computational costs and being substantially time-consuming. In that sense, the Principal Component Analysis (PCA)-based data compression has been employed in this work, in order to enhance the analysis capability of the raw data. Furthermore, a Support Vector Machine (SVM), which has been widespread used in machine learning and pattern recognition fields, has been applied in this study in order to model any possible existing pattern in the PCAcompress data, using for that just the first two Principal Components. Different known non-damaged and damaged measurements of an experimental tested beam were used as training input data for the SVM algorithm, using as test input data the same amount of cases measured in beams with unknown structural health conditions. Thus, the purpose of this work is to demonstrate how, with a few impedance measurements of a beam as raw data, its healthy status can be determined based on pattern recognition procedures.

Methodology based on FBG sensors for monitoring of FRP-strengthened beams

Advanced composite materials are increasingly used in the strengthening of reinforced concrete (RC) structures. The use of externally bonded strips made of fibre-reinforced plastics (FRP) as strengthening method has gained widespread acceptance in recent years since it has many advantages over the traditional techniques. However, unfortunately, this strengthening method is often associated with a brittle and sudden failure caused by some form of FRP bond failure, originated at the termination of the FRP material or at intermediate areas in the vicinity of flexural cracks in the RC beam. Up to date, little effort in the early prediction of the debonding in its initial instants even though this effect is not noticeable by simple visual observation. An early detection of this phenomenon might help in taking actions to prevent future catastrophes. Fibre-optic Bragg grating (FBG) sensors are able to measure strains locally with high resolution and accuracy. Furthermore, as their physical size is extremely small compared with other strain measuring components, it enables to be embedded at the concrete-FRP interface for determining the strain distribution without influencing the mechanical properties of the host materials. This paper shows the development of a debonding identification methodology based on strains experimentally measured. For, it a simplified model is implemented to simulate the behaviour of FRP-strengthened reinforced concrete beams. This model is taken as a basis to. develop an model updating procedure able to detect minor debonding at the concrete-FRP interface from experimental strains obtained by using FBG sensors embedded at the interface

Ground and airborne level optical sensors: is it possible to estimate maize crop N status?

Adjusting N fertilizer application to crop requirements is a key issue to improve fertilizer efficiency, reducing unnecessary input costs to farmers and N environmental impact. Among the multiple soil and crop tests developed, optical sensors that detect crop N nutritional status may have a large potential to adjust N fertilizer recommendation (Samborski et al. 2009). Optical readings are rapid to take and non-destructive, they can be efficiently processed and combined to obtain indexes or indicators of crop status. However, other physiological stress conditions may interfere with the readings and detection of the best crop nutritional status indicators is not always and easy task. Comparison of different equipments and technologies might help to identify strengths and weakness of the application of optical sensors for N fertilizer recommendation. The aim of this study was to evaluate the potential of various ground-level optical sensors and narrow-band indices obtained from airborne hyperspectral images as tools for maize N fertilizer recommendations. Specific objectives were i) to determine which indices could detect differences in maize plants treated with different N fertilizer rates, and ii) to evaluate its ability to identify N-responsive from non-responsive sites.

Caracterización Avanzada de Tanques de Fermentación de Café mediante una Red Multidistribuida de Sensores RFID

La fermentación de las bayas de café se considera una et apa crítica en el procesado del café debido a su impacto en la calidad final del producto. La temperatura es una de las principales variables de control que puede ser utilizada para predecir el final del proceso, teniendo en cuenta que varios autores indican que el control de esta etapa es fundamental para evitar la mala calidad de la be bida final. En la práctica, la fermentación es el paso menos controlado del proceso, haciendo que los beneficiaderos operen lejos de sus condiciones óptimas en términos de costes de operación (es decir, elevados consumos de energía y agua) y de calidad del producto final. El objetivo de este trabajo es caracterizar los gradientes de temperatura que se dan en los tanques de fermentación mediante una red multi-distribuida de sensores autónomos, inalámbricos y de bajo coste (registradores de temperatura del tipo RFID, identificadores de radiofrecuencia semipasivos modelo TurboTag®).Para ello se utilizan dos metodologías: la interpolación espacial en coordenadas polares y los diagramas de espacio de fase. Se supervisaron dos fermentaciones reales de café, en El Cauca (Colombia), mediante sensores sumergidos directamente en la masa en fermentación. Los fermentadores eran tanques de plástico cubiertos, uno de ellos colocado en el interior de un almacén, permaneciendo el otro a la intemperie. El rango de variación máximo de temperatura en los tanques fue de 4,5ºC. La interpolación espacial mostró, incluso en el fermentador bajo las condiciones menos desfavorables en el interior del almacén, un gradiente radial de temperatura instantáneo de 0,1 °C/cm desde el centro hasta el perímetro del tanque y un gradiente vertical de temperatura de 0,25 °C/cm para sensores con coordenadas polares iguales. La combinación de ambas metodologías permitió la identificación consistente de los puntos calientes y fríos de ambas fermentaciones.

Caracterización Avanzada de Tanques de Fermentación de Café medinate una Red Multidistribuida de Sensores RFID

Las hortalizas mínimamente procesadas (MP) son productos frescos, higienizados, que sufren alteraciones físicas durante el proceso de elaboración que afectan a su metabolismo, determinando incrementos en la tasa respiratoria y producción de etileno. Los daños que se originan por las operaciones físicas, vuelven a estos productos más susceptibles a la colonización de microorganismos, inducen procesos de cicatrización de heridas y afectan su calidad organoléptica y funcional. Por ello en estos productos es especialmente crítico el aseguramiento de las condiciones de refrigeración desde el productor hasta el consumidor. En este estudio se presenta el análisis de las temperaturas registradas en Santiago (Chile) durante la cadena de producción y distribución de lechugas baby leaf MP tipo Salanova®, monitorizadas mediante sensores de temperatura y humedad relativa (I-Buttons®) y tarjetas RFID con sensor de temperatura (TurboTag®), colocados en el interior de las bolsas de lechuga y en el exterior de las cajas de agrupación. El objetivo del presente trabajo es generar información sobre el historial térmico de estos productos desde la huerta a la nevera del consumidor, así como optimizar los protocolos de colocación de dispositivos y sistematizar procedimientos de análisis de datos espacio-temporales. Con los datos registrados, se simuló la cadena de producción, distribución y venta, realizando un seguimiento de la calidad comercial del producto, evaluándose la tasa respiratoria y las características sensoriales sin degustación. Se observó que no solo se producen saltos térmicos discretos, sino que las lechugas estuvieron durante gran parte de su vida útil en condiciones sub-óptimas de temperatura, comprometiéndose su calidad sensorial.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

Application of PZT and FBG impedance sensors for structural health monitoring of reinforced concrete beams strengthened with FRP composite materials

Esta Tesis tiene como objetivo principal el desarrollo de métodos de identificación del daño que sean robustos y fiables, enfocados a sistemas estructurales experimentales, fundamentalmente a las estructuras de hormigón armado reforzadas externamente con bandas fibras de polímeros reforzados (FRP). El modo de fallo de este tipo de sistema estructural es crítico, pues generalmente es debido a un despegue repentino y frágil de la banda del refuerzo FRP originado en grietas intermedias causadas por la flexión. La detección de este despegue en su fase inicial es fundamental para prevenir fallos futuros, que pueden ser catastróficos. Inicialmente, se lleva a cabo una revisión del método de la Impedancia Electro-Mecánica (EMI), de cara a exponer sus capacidades para la detección de daño. Una vez la tecnología apropiada es seleccionada, lo que incluye un analizador de impedancias así como novedosos sensores PZT para monitorización inteligente, se ha diseñado un procedimiento automático basado en los registros de impedancias de distintas estructuras de laboratorio. Basándonos en el hecho de que las mediciones de impedancias son posibles gracias a una colocación adecuada de una red de sensores PZT, la estimación de la presencia de daño se realiza analizando los resultados de distintos indicadores de daño obtenidos de la literatura. Para que este proceso sea automático y que no sean necesarios conocimientos previos sobre el método EMI para realizar un experimento, se ha diseñado e implementado un Interfaz Gráfico de Usuario, transformando la medición de impedancias en un proceso fácil e intuitivo. Se evalúa entonces el daño a través de los correspondientes índices de daño, intentando estimar no sólo su severidad, sino también su localización aproximada. El desarrollo de estos experimentos en cualquier estructura genera grandes cantidades de datos que han de ser procesados, y algunas veces los índices de daño no son suficientes para una evaluación completa de la integridad de una estructura. En la mayoría de los casos se pueden encontrar patrones de daño en los datos, pero no se tiene información a priori del estado de la estructura. En este punto, se ha hecho una importante investigación en técnicas de reconocimiento de patrones particularmente en aprendizaje no supervisado, encontrando aplicaciones interesantes en el campo de la medicina. De ahí surge una idea creativa e innovadora: detectar y seguir la evolución del daño en distintas estructuras como si se tratase de un cáncer propagándose por el cuerpo humano. En ese sentido, las lecturas de impedancias se emplean como información intrínseca de la salud de la propia estructura, de forma que se pueden aplicar las mismas técnicas que las empleadas en la investigación del cáncer. En este caso, se ha aplicado un algoritmo de clasificación jerárquica dado que ilustra además la clasificación de los datos de forma gráfica, incluyendo información cualitativa y cuantitativa sobre el daño. Se ha investigado la efectividad de este procedimiento a través de tres estructuras de laboratorio, como son una viga de aluminio, una unión atornillada de aluminio y un bloque de hormigón reforzado con FRP. La primera ayuda a mostrar la efectividad del método en sencillos escenarios de daño simple y múltiple, de forma que las conclusiones extraídas se aplican sobre los otros dos, diseñados para simular condiciones de despegue en distintas estructuras. Demostrada la efectividad del método de clasificación jerárquica de lecturas de impedancias, se aplica el procedimiento sobre las estructuras de hormigón armado reforzadas con bandas de FRP objeto de esta tesis, detectando y clasificando cada estado de daño. Finalmente, y como alternativa al anterior procedimiento, se propone un método para la monitorización continua de la interfase FRP-Hormigón, a través de una red de sensores FBG permanentemente instalados en dicha interfase. De esta forma, se obtienen medidas de deformación de la interfase en condiciones de carga continua, para ser implementadas en un modelo de optimización multiobjetivo, cuya solución se haya por medio de una expansión multiobjetivo del método Particle Swarm Optimization (PSO). La fiabilidad de este último método de detección se investiga a través de sendos ejemplos tanto numéricos como experimentales. ABSTRACT This thesis aims to develop robust and reliable damage identification methods focused on experimental structural systems, in particular Reinforced Concrete (RC) structures externally strengthened with Fiber Reinforced Polymers (FRP) strips. The failure mode of this type of structural system is critical, since it is usually due to sudden and brittle debonding of the FRP reinforcement originating from intermediate flexural cracks. Detection of the debonding in its initial stage is essential thus to prevent future failure, which might be catastrophic. Initially, a revision of the Electro-Mechanical Impedance (EMI) method is carried out, in order to expose its capabilities for local damage detection. Once the appropriate technology is selected, which includes impedance analyzer as well as novel PZT sensors for smart monitoring, an automated procedure has been design based on the impedance signatures of several lab-scale structures. On the basis that capturing impedance measurements is possible thanks to an adequately deployed PZT sensor network, the estimation of damage presence is done by analyzing the results of different damage indices obtained from the literature. In order to make this process automatic so that it is not necessary a priori knowledge of the EMI method to carry out an experimental test, a Graphical User Interface has been designed, turning the impedance measurements into an easy and intuitive procedure. Damage is then assessed through the analysis of the corresponding damage indices, trying to estimate not only the damage severity, but also its approximate location. The development of these tests on any kind of structure generates large amounts of data to be processed, and sometimes the information provided by damage indices is not enough to achieve a complete analysis of the structural health condition. In most of the cases, some damage patterns can be found in the data, but none a priori knowledge of the health condition is given for any structure. At this point, an important research on pattern recognition techniques has been carried out, particularly on unsupervised learning techniques, finding interesting applications in the medicine field. From this investigation, a creative and innovative idea arose: to detect and track the evolution of damage in different structures, as if it were a cancer propagating through a human body. In that sense, the impedance signatures are used to give intrinsic information of the health condition of the structure, so that the same clustering algorithms applied in the cancer research can be applied to the problem addressed in this dissertation. Hierarchical clustering is then applied since it also provides a graphical display of the clustered data, including quantitative and qualitative information about damage. The performance of this approach is firstly investigated using three lab-scale structures, such as a simple aluminium beam, a bolt-jointed aluminium beam and an FRP-strengthened concrete specimen. The first one shows the performance of the method on simple single and multiple damage scenarios, so that the first conclusions can be extracted and applied to the other two experimental tests, which are designed to simulate a debonding condition on different structures. Once the performance of the impedance-based hierarchical clustering method is proven to be successful, it is then applied to the structural system studied in this dissertation, the RC structures externally strengthened with FRP strips, where the debonding failure in the interface between the FRP and the concrete is successfully detected and classified, proving thus the feasibility of this method. Finally, as an alternative to the previous approach, a continuous monitoring procedure of the FRP-Concrete interface is proposed, based on an FBGsensors Network permanently deployed within that interface. In this way, strain measurements can be obtained under controlled loading conditions, and then they are used in order to implement a multi-objective model updating method solved by a multi-objective expansion of the Particle Swarm Optimization (PSO) method. The feasibility of this last proposal is investigated and successfully proven on both numerical and experimental RC beams strengthened with FRP.

Self-adaptive strategy based on fuzzy control systems for improving performance in wireless sensors networks

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

Lock-in amplifiers for AMR sensors

Anisotropic magnetoresistive (AMR) magnetic sensors are often chosen as the magnetic transducer for magnetic field sensing in applications with low to moderate magnetic field resolution because of the relative low mass of the sensor and their ease of use. They measure magnetic fields in the order of the Earth magnetic field (with typical sensitivities of 1‰/G or 10−2‰/μT), have typical minimum detectable fields in order of nT and even 0.1 nT but they are seriously limited by the thermal drifts due to the variation of the resistivity with temperature (∼2.5‰/°C) and the variation of the magnetoresistive effect with temperature (which affects both the sensitivity of the sensors: ∼2.7‰/°C, and the offset: ±0.5‰/°C). Therefore, for lower magnetic fields, fluxgate vector sensors are generally preferred. In the present work these limitations of AMR sensors are outlined and studied. Three methods based on lock-in amplifiers are proposed as low noise techniques. Their performance has been simulated, experimentally tested and comparatively discussed. The developed model has been also used to derive a technique for temperature compensation of AMR response. The final goal to implement these techniques in a space qualified applied specific integrated circuit (ASIC) for Mars in situ exploration with compact miniaturized magnetometers.