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The views expressed in this report are those of the author and do not reflect the official policy or position of the Department of Defense or the United States Government. ; The localization of mobile wireless communication units using time difference of arrival (TDOA) is studied. The wavelet transform is used to increase the accuracy of TDOA estimation. Several denoising techniques based on the wavelet transform are presented. These techniques are applied to different types of test signals and to a simulated baseband GSM signal. The results of the denoising techniques are compared to the ones employing no denoising in terms of the mean square error. The denoising techniques allow a 28 to 81 percent improvement in the TDOA estimation. ; TENCAP Office, Department of the Navy ; N4175698WR87397 ; Approved for public release; distribution is unlimited.

In order to ensure secure communication in digital military radio systems, multiple methods are used to protect the transmission from being intercepted by enemy electronic warfare systems. An intercepted transmission can be used to estimate several parameters of the transmitted signal such as its origin (position or direction) and of course the transmitted message itself. The methods used in traditional electronic warfare direction-finding systems have in general poor performance against wideband low power signals while the considered correlation-based time-difference of arrival (TDOA) methods show promising results. The output from a TDOA-based direction-finding system using two spatially separated receivers is the TDOA for the signal between the receiving sensors which uniquely describes a hyperbolic curve and the emitter is located somewhere along this curve. In order to measure a TDOA between two digital radio receivers both receiver systems must have the same time and frequency references to avoid degradation due to reference imperfections. However, in some cases, the receivers are separated up to 1000 km and can not share a common reference. This is solved by using a reference module at each of the receiver sites and high accuracy is achieved using the NAVSTAR-GPS system but, still, small differences between the outputs of the different reference modules occurs which degrades the performance of the system. In a practical electronic warfare system there is a number of factors that degrade the performance of the system, such as non-ideal antennas, analog receiver filter differences, and the analog to digital converter errors. In this thesis we concentrate on the problems which arises from imperfections in the reference modules, such as time and frequency errors.

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error.

En esta Tesis se presenta un procedimiento para localizar radiotransmisores pasivos situados en el entorno de la Tierra mediante satélites artificiales y mediciones TDOA. Las localizaciones de los radiotransmisores se determinan en forma cerrada mediante ecuaciones algebraicas que corresponden a los modelos newtoniano y postnewtoniano del campo gravitatorio terrestre. La Geolocalización, es decir, la localización de un radiotransmisor cuyas señales son captadas por un conjunto de receptores en posiciones conocidas, es una técnica muy importante que tiene numerosas aplicaciones en diversos escenarios, tanto civiles como militares. Entre estas aplicaciones cabe destacar las de la vigilancia aérea, terrestre y marítima; la búsqueda y rescate de personas y buques en alta mar y la aproximación controlada de los aviones a los aeropuertos. También se utiliza en el diseño de sistemas inteligentes de transporte, así como para el seguimiento de animales salvajes, teléfonos móviles y de vehículos. El problema de la Geolocalización es, pues, en cierto sentido, el problema inverso del de Navegación. Dependiendo de la naturaleza de los radiotransmisores, los receptores están situados en tierra o, bien, en el espacio exterior, aunque también existen configuraciones mixtas. Los métodos más usuales para determinar la posición de un radiotransmisor son: el método AOA (Angle Of Arrival), con el que se trata de determinar ángulos de llegada de la señal emitida; el método TOA (Time Of Arrival), con el que se utilizan los instantes de llegada de la señal a los receptores y elmétodo TDOA (Time Di¤erence Of Arrival), en el que los datos son las diferencias de los tiempos de llegada de la señal a los receptores. En este caso, los receptores más comúnmente utilizados son los satélites artificiales. De estos tres métodos, el más adecuado para localizar un radiotransmisor pasivo, es decir, que no coopera en su localización, es el método TDOA. Las razones de su idoneidad son las siguientes: primera, las antenas que se utilizan con el método AOA se han de calibrar con mucha frecuencia para alcanzar suficiente precisión, y esto implica un mantenimiento muy costoso; segunda, para utilizar el método TOA se necesita que el radiotransmisor coopere (al menos proporcionando el instante de emisión de la señal) y, finalmente, con el método TDOA, igual que con el método TOA, no se necesitan receptores dedicados exclusivamente al fin que se persigue y, por lo tanto, el coste de la localización es relativamente bajo. El problema de la Geolocalización de un radiotransmisor que está situado en el entorno de la Tierra mediante mediciones TDOA consiste, pues, en la utilización como sistema de receptores de un conjunto apropiado de satélites arti ciales para la determinación computacional de las coordenadas espaciales del radiotransmisor en el instante en que éste emite una señal, así como de ese instante, utilizando mediciones TDOA. Con el procedimiento que se presenta en esta Tesis para la resolución de este problema se alcanzan los dos siguientes objetivos: primero, se obtienen localizaciones, en muchos casos únicas, mediante ecuaciones algebraicas cuya resolución numérica resulta relativamente sencilla (excepto en algunos casos) y, segundo, se incrementa la precisión nominal newtoniana de cada localización utilizando el modelo postnewtoniano del campo gravitatorio terrestre. Para alcanzar estos objetivos hemos utilizado, por un lado, la función de universo de Synge, tanto para el modelo newtoniano como para el modelo postnewtoniano del campo gravitatorio terrestre y, por otro, hemos introducido un conjunto apropiado de modificaciones en los dos métodos de Geolocalización por TDOA propuestos por Ho y Chan para satélites geoestacionarios, de manera que estas modificaciones permiten utilizar, además de este tipo de satélites, cualquier otro.

<p class="Pa7"> <strong>Objective: </strong>Conflicting reports exist about hospital arrival time after stroke onset in Hispanics compared with African Americans and Caucasians. Our current study inves­tigates race-ethnic disparities in hospital arrival times after stroke onset.</p><p class="Pa7"><strong>Methods: </strong>We performed a retrospective analysis of hospital arrival times in Hispanic, African American, and Caucasian acute ischemic stroke patients (N=1790) present­ing to a tertiary-care hospital in the Bronx, New York. A multivariable logistic regression model was used to identify the association between race-ethnicity and hospital arrival time adjusting for age, sex, socioeconomic status (SES), NIH stroke scale (NIHSS), history of stroke, preferred language and transportation mode to the hospital.</p><p class="Pa7"><strong>Results: </strong>There were 338 Caucasians, 662 Hispanics, and 790 African Americans in the cohort. Compared with Caucasians, African Americans and Hispanics were younger (P&lt;.0001 respectively), had lower SES (P&lt;.001 respectively) and were less likely to use EMS (P=.003 and P=.001, respec­tively). A greater proportion of Hispanic and African American women had delayed hos­pital arrival times (≥3 hours) after onset of stroke symptoms compared with Caucasian women (74% of Hispanic, 72% of African American, and 59% of Caucasian women), but this difference between race-ethnicities is no longer present after adjusting for socioeconomic status. Compared with Cau­casian men, hospital arrival ≥3 hours after symptom onset was more likely for African American men (OR 1.72, 95% CI:1.05- 2.79) but not Hispanic men (OR .80, 95% CI .49-1.30).</p><p class="Default"><strong>Conclusions: </strong>African American men and socially disadvantaged women delay in presenting to the hospital after stroke onset. Future research should focus on identify­ing the factors contributing to pre-hospital delay among race-ethnic minorities. <em></em></p><p class="Default"><em>Ethn Dis. </em>2017;27(2):125-132; doi:10.18865/ed.27.2.125.</p>

"AEC Category: Health and Safety, Military category: 14." ; "Nevada test site May-October 1957"--Cover. ; "July 1959." ; "WT-1495." ; Mode of access: Internet.

Accurate geolocation and tracking of Radio-Frequency Interference (RFI) sources, which affect wireless and satellite systems such as Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom) systems, are considered to be a significant issue. Several studies connected to civil and military operations on this issue have been investigated recently. The literature review has surveyed many algorithm simulations for optimizing geolocation and target-tracking estimation. Although most of these algorithms have their own advantages, they have weaknesses, such as accuracy, mathematical complexity, difficulties in implementation, and validation in the real environment, etc. This study has been concerned with investigating the accuracy of geolocation and tracking under high speed and powerful rotation using extracted data from the Orolia Skydel simulator, which simulates the space environment involving Low Earth Orbit (LEO) satellites as sensors and Unmanned Aerial Vehicles (UAV) as RFI emitters. Various scenarios modeled using the Orolia Simulator for quasi-real dynamic trajectories of LEO satellites have been created. The assumed approaches have been verified by Cramer–Rao Lower Bound (CRLB) and Posterior CRLB (PCRLB) to determine the increase in Root Mean Square Error (RMSE) value. The simulation scenarios have been performed using the Monte Carlo iteration. Eventually, the overall achieved results of the considered approaches using data acquired from the Orolia Simulator were presented and compared with theoretical simulation.

Operational TEWS play a key role in reducing tsunami impact on populated coastal areas around the world in the event of an earthquake-generated tsunami. Traditionally, these systems in the NEAM region have relied on the implementation of decision matrices. The very short arrival times of the tsunami waves from generation to impact in this region have made it not possible to use real-time on-the-fly simulations to produce more accurate alert levels. In these cases, when time restriction is so demanding, an alternative to the use of decision matrices is the use of datasets of precomputed tsunami scenarios. In this paper we propose the use of neural networks to predict the tsunami maximum height and arrival time in the context of TEWS. Different neural networks were trained to solve these problems. Additionally, ensemble techniques were used to obtain better results. ; This work was funded by "Innovative ecosystem with artificial intelligence for Andalusia 20205" project of CEI Andalucía Tech and University of Málaga, UMA-CEIATECH-05. The numerical results presented in this work were performed with the computational resources provided by the Spanish Network for Supercomputing (RES) grants AECT-2020-1-0009 and AECT-2020-2-0001. Finally, this research has been partially supported by the Spanish Government research project MEGAFLOW (RTI2018-096064-B-C21), ChEESE project (EU Horizon 2020, grant agreement N. 823844), and eFlows4HPC project (funded by the EuroHPC JU under contract 955558 and the Ministerio de Ciencia e Innovación, Spain). Partial funding for open access charge: Universidad de Málaga