Wireless Module for Sensing Superficial Vibrations of Soils

En el presente trabajo se evalúa la viabilidad de implementar la tecnología XBee en el desarrollo de sensores acelerométricos inalámbricos (SAI) para el registro en superficie de las vibraciones que generan las ondas sísmicas que se propagan en el suelo. Se verificó experimentalmente la incidencia de la distancia y de la presencia de obstáculos en el radioenlace establecido entre un coordinador y un dispositivo final, mediante la determinación del número de paquetes recibidos exitosamente en diferentes condiciones de operación. Adicionalmente se determinó la influencia de la velocidad de transmisión sobre la frecuencia de muestreo de señales asociadas a vibraciones mecánicas provenientes de un terreno de prueba, a través de la medición de los periodos de muestreos efectivos del proceso “Conversión A/D – Transmisión”. Se concluye que los errores en la recepción de los paquetes de datos introducidos por la atenuación del canal y por la presencia de obstáculos, imponen serias restricciones sobre la distancia máxima permisible entre los módulos de comunicación. Las velocidades de transmisión características de la tecnología XBee en asocio con el tiempo de conversión A/D del microcontrolador, permiten llevar a cabo registros a una frecuencia máxima de muestreo de 1kHz; útil para aplicaciones en tiempo real de prospección sísmica donde las señales típicas están dentro un rango espectral de 0 a 500 Hz. Para incrementar la frecuencia de muestreo del sensor para aplicaciones de prospección con señales de anchos de banda superiores a los 500 Hz, se probó exitosamente un prototipo que emplea una memoria externa de rápida escritura para el almacenamiento de datos, mejorando significativamente el muestreo de la señal y que rescata la tecnología XBee debido a sus excelentes características de bajo consumo.In the present work, the feasibility of implementing the XBee technology in wireless accelerometric sensors (WAS) development for sensing of elastic waves on soils surface is analyzed. The incidence of distance and obstacles between a coordinator and end-device pair in their radio link by examining the number of packets received successfully was verified. Additionally, it was investigated the influence of the transmission rate over the sampling frequency of signals associated to mechanical vibrations from a testing ground by measuring the effective sampling periods of the "A / D Conversion - Transmission" process. The data reception errors introduced by the channel attenuation and the presence of obstacles, impose severe restrictions on the maximum allowable distance between the communication modules. The transmission rate features provided by XBee technology in association with the A / D time sampling of the microcontroller, allow to carry out recordings to a maximum sampling frequency of 1 kHz , useful for real-time applications where seismic signals are into the spectral range 0 to 500 Hz. In order to increase the sampling frequency of the sensor for prospection applications with signals with bandwidths greater than 500 Hz , it was successfully tested a prototype that uses a fast external memory for storing data, which significantly improves the sampling signal allowing to retake XBee technology due to its excellent low consumption features

The disarmament of land mines post conflict with emphasis on Western SAHARA

Abstract: Please refer to full text to view abstract.LL.M. (International Law

Clustering of multiple instance data.

An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is referred to as a target concept (TC). Existing methods either only identify a single target concept, do not provide a mechanism for selecting the appropriate number of target concepts, or do not provide a flexible representation for target concept memberships. Thus, they are not suitable to handle data with large intra-class variation. In this dissertation we propose new algorithms that learn multiple target concepts simultaneously. The proposed algorithms combine concepts from data clustering and multiple instance learning. In particular, we propose crisp, fuzzy, and possibilistic variations of the Multi-target concept Diverse Density (MDD) metric, along with three algorithms to optimize them. Each algorithm relies on an alternating optimization strategy that iteratively refines concept assignments, locations, and scales until it converges to an optimal set of target concepts. We also demonstrate how the possibilistic MDD metric can be used to select the appropriate number of target concepts for a dataset. Lastly, we propose the construction of classifiers based on embedded feature space theory to use our target concepts to predict the label of prospective MIL data. The proposed algorithms are implemented, tested, and validated through the analysis of multiple synthetic and real-world data. We first demonstrate that our algorithms can detect multiple target concepts reliably, and are robust to many generative data parameters. We then demonstrate how our approach can be used in the application of Buried Explosive Object (BEO) detection to locate distinct target concepts corresponding to signatures of varying BEO types. We also demonstrate that our classifier strategies can perform competitively with other well-established embedded space approaches in classification of Benchmark MIL data