Human exposure to electromagnetic fields from WLANs and WBANs in the 2.4 GHz band

226 p.En los últimos años, el masivo crecimiento de las comunicaciones inalámbricas ha incrementado la preocupación acerca de la exposición humana a los campos electromagnéticos debido a los posibles efectos sobre la salud. Esta tesis surge de la necesidad de proporcionar información acerca de este tipo de exposición desde un punto de vista técnico. Por una parte, se han estudiado los niveles de exposición causados por señales WiFi, para lo cual ha sido necesario establecer un procedimiento de medida adecuado para tomar muestras de estas emisiones. Además, se han llevado a cabo campañas de medida para evaluar la exposición a señales WiFi y su variabilidad en el interior de un entorno público. Por otra parte, se ha analizado la potencia absorbida por el cuerpo humano a causa de los novedosos dispositivos wearables. Se han implementado dos antenas de este tipo, apropiadas para dispositivos wearables, se ha analizado detalladamente la exposición debida a estos aparatos y finalmente se han comparado los niveles de exposición producidos por estas antenas y por las señales WiFi

Human exposure to electromagnetic fields from WLANs and WBANs in the 2.4 GHz band

226 p.En los últimos años, el masivo crecimiento de las comunicaciones inalámbricas ha incrementado la preocupación acerca de la exposición humana a los campos electromagnéticos debido a los posibles efectos sobre la salud. Esta tesis surge de la necesidad de proporcionar información acerca de este tipo de exposición desde un punto de vista técnico. Por una parte, se han estudiado los niveles de exposición causados por señales WiFi, para lo cual ha sido necesario establecer un procedimiento de medida adecuado para tomar muestras de estas emisiones. Además, se han llevado a cabo campañas de medida para evaluar la exposición a señales WiFi y su variabilidad en el interior de un entorno público. Por otra parte, se ha analizado la potencia absorbida por el cuerpo humano a causa de los novedosos dispositivos wearables. Se han implementado dos antenas de este tipo, apropiadas para dispositivos wearables, se ha analizado detalladamente la exposición debida a estos aparatos y finalmente se han comparado los niveles de exposición producidos por estas antenas y por las señales WiFi

Sonic Booms in Atmospheric Turbulence (SonicBAT): The Influence of Turbulence on Shaped Sonic Booms

The objectives of the Sonic Booms in Atmospheric Turbulence (SonicBAT) Program were to develop and validate, via research flight experiments under a range of realistic atmospheric conditions, one numeric turbulence model research code and one classic turbulence model research code using traditional N-wave booms in the presence of atmospheric turbulence, and to apply these models to assess the effects of turbulence on the levels of shaped sonic booms predicted from low boom aircraft designs. The SonicBAT program has successfully investigated sonic boom turbulence effects through the execution of flight experiments at two NASA centers, Armstrong Flight Research Center (AFRC) and Kennedy Space Center (KSC), collecting a comprehensive set of acoustic and atmospheric turbulence data that were used to validate the numeric and classic turbulence models developed. The validated codes were incorporated into the PCBoom sonic boom prediction software and used to estimate the effect of turbulence on the levels of shaped sonic booms associated with several low boom aircraft designs. The SonicBAT program was a four year effort that consisted of turbulence model development and refinement throughout the entire period as well as extensive flight test planning that culminated with the two research flight tests being conducted in the second and third years of the program. The SonicBAT team, led by Wyle, includes partners from the Pennsylvania State University, Lockheed Martin, Gulfstream Aerospace, Boeing, Eagle Aeronautics, Technical & Business Systems, and the Laboratory of Fluid Mechanics and Acoustics (France). A number of collaborators, including the Japan Aerospace Exploration Agency, also participated by supporting the experiments with human and equipment resources at their own expense. Three NASA centers, AFRC, Langley Research Center (LaRC), and KSC were essential to the planning and conduct of the experiments. The experiments involved precision flight of either an F-18A or F-18B executing steady, level passes at supersonic airspeeds in a turbulent atmosphere to create sonic boom signatures that had been distorted by turbulence. The flights spanned a range of atmospheric turbulence conditions at NASA Armstrong and Kennedy in order to provide a variety of conditions for code validations. The SonicBAT experiments at both sites were designed to capture simultaneous F-18A or F-18B onboard flight instrumentation data, high fidelity ground based and airborne acoustic data, surface and upper air meteorological data, and additional meteorological data from ultrasonic anemometers and SODARs to determine the local atmospheric turbulence and boundary layer height

Moisture estimation for precision agriculture through RF sensing

Convenient, non-obtrusive, low-cost, and accurate sensing of fruit moisture content is crucial for the scientific studies of Pomology and Viticulture and their associated agriculture. It can provide early indicators of yield estimation and crop health as well as providing data for food production and precision farming systems. With a focus on grapes, we introduce SING, a scheme that senses grape moisture content by utilizing RF signals but without physical contact with the fruit. In this thesis, we extend the investigation of the theoretical relationship between the dielectric properties and the moisture content of agricultural products to establish a sensing model in the 5 GHz band. To make the work practical, we are first to measure the dielectric properties of grape bunches (not individually as that would be destructive), presenting a unique measurement challenge as internal grapes are hidden. In doing so, we demonstrate that our technique precisely estimates moisture content to a high degree of accuracy (90%). Current RF sensing models to estimate moisture are destructive; they require samples to be constrained in containers. Our work is first to dispense with such impracticalities, and, without contact with the object, accurately measures non-uniform grape clusters in open space. We demonstrate that SING is superior to existing work in its ability to accurately measure the dielectric properties of non-uniform fruit objects and test this through both lab-based experimentation and preliminary outdoor vineyard tests. We also examine the transferability of SING’s approach to real-world scenarios.Open Acces

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

A non-device specific framework for the development of forensic locational data analysis procedure for consumer grade small and embedded devices

Portable and wearable computing devices such as smart watches, navigation units, mobile phones, and tablet computers commonly ship with Global Navigation Satellite System (GNSS) supported locational awareness. Locational functionality is no longer limited to navigation specific devices such as satellite navigation devices and location tracking systems. Instead the use of these technologies has extended to become secondary functionality on many devices, including mobile phones, cameras, portable computers, and video game consoles. The increase in use of location aware technology is of use to forensic investigators as it has the potential to provide historic locational information. The evidentiary value of these devices to forensic investigators is currently limited due to the lack of available forensic tools and published methods to properly acquire and analyse these data sources. This research addresses this issue through the synthesis of common processes for the development of forensic procedure to acquire and interpret historic locational data from embedded, locationally aware devices. The research undertaken provides a framework for the generation of forensic procedure to enable the forensic extraction of historical locational data. The framework is device agnostic, relying instead on differential analysis and structured testing to produce a validated method for the extraction of locational history. This framework was evaluated against five devices, selected on a basis of market penetration, availability and a stage of deduplication. The examination of the framework took place in a laboratory developed specifically for the research. This laboratory replicates all identified sources of location data for the devices selected. In this case the laboratory is able to simulate cellular (2G and 3G), GNSS (NAVSTAR and GLONASS), and Wi-Fi locationing services. The laboratory is a closed-sky facility, meaning that the laboratory is contained within a faraday cage and all signals are produced and broadcast internally. Each selected device was run through a series of simulations. These simulations involved the broadcast of signals, replicating the travel of a specific path. Control data was established through the use of appropriate data recording systems, for each of the simulated location signals. On completion of the simulation, each device was forensically acquired and analysed in accordance with the proposed framework. For each experiment carried out against the five devices, the control and experimental data were compared. In this examination any divergence less than those expected for GNSS were ignored. Any divergence greater than this was examined to establish cause. Predictable divergence was accepted and non-predictable divergence would have been noted as a limitation. In all instances where data was recovered, all divergences were found to be predictable. Post analysis, the research found that the proposed framework was successful in producing locational forensic procedure in a non-device specific manner. This success was confirmed for all the devices tested

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment