Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

HIGH PERFORMANCE, LOW COST SUBSPACE DECOMPOSITION AND POLYNOMIAL ROOTING FOR REAL TIME DIRECTION OF ARRIVAL ESTIMATION: ANALYSIS AND IMPLEMENTATION

This thesis develops high performance real-time signal processing modules for direction of arrival (DOA) estimation for localization systems. It proposes highly parallel algorithms for performing subspace decomposition and polynomial rooting, which are otherwise traditionally implemented using sequential algorithms. The proposed algorithms address the emerging need for real-time localization for a wide range of applications. As the antenna array size increases, the complexity of signal processing algorithms increases, making it increasingly difficult to satisfy the real-time constraints. This thesis addresses real-time implementation by proposing parallel algorithms, that maintain considerable improvement over traditional algorithms, especially for systems with larger number of antenna array elements. Singular value decomposition (SVD) and polynomial rooting are two computationally complex steps and act as the bottleneck to achieving real-time performance. The proposed algorithms are suitable for implementation on field programmable gated arrays (FPGAs), single instruction multiple data (SIMD) hardware or application specific integrated chips (ASICs), which offer large number of processing elements that can be exploited for parallel processing. The designs proposed in this thesis are modular, easily expandable and easy to implement. Firstly, this thesis proposes a fast converging SVD algorithm. The proposed method reduces the number of iterations it takes to converge to correct singular values, thus achieving closer to real-time performance. A general algorithm and a modular system design are provided making it easy for designers to replicate and extend the design to larger matrix sizes. Moreover, the method is highly parallel, which can be exploited in various hardware platforms mentioned earlier. A fixed point implementation of proposed SVD algorithm is presented. The FPGA design is pipelined to the maximum extent to increase the maximum achievable frequency of operation. The system was developed with the objective of achieving high throughput. Various modern cores available in FPGAs were used to maximize the performance and details of these modules are presented in detail. Finally, a parallel polynomial rooting technique based on Newton’s method applicable exclusively to root-MUSIC polynomials is proposed. Unique characteristics of root-MUSIC polynomial’s complex dynamics were exploited to derive this polynomial rooting method. The technique exhibits parallelism and converges to the desired root within fixed number of iterations, making this suitable for polynomial rooting of large degree polynomials. We believe this is the first time that complex dynamics of root-MUSIC polynomial were analyzed to propose an algorithm. In all, the thesis addresses two major bottlenecks in a direction of arrival estimation system, by providing simple, high throughput, parallel algorithms.

Real-Time Passive Capturing of the GSM Radio

This paper addresses the problem of service development based on GSM handset signaling. The aim is to achieve this goal without the participation of the users, which requires the use of a passive GSM receiver on the uplink. Since no tool for GSM uplink capturing was available, we developed a new method that can synchronize to multiple mobile devices by simply overhearing traffic between them and the network. Our work includes the implementation of modules for signal recovery, message reconstruction and parsing. The method has been validated against a benchmark solution on GSM downlink and independently evaluated on uplink channels. Initial evaluations show up to 99% success rate in message decoding, which is a very promising result. Moreover, we conducted measurements that reveal insights on the impact of signal power on the capturing performance and investigate possible reactive measures.

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Real Time FPGA Implementation of an Modulation Classifier for Electronic Warfare Applications

This paper presents an automatic modulation classifier for electronic warfare applications. It is a pattern recognition modulation classifier based on statistical features of the phase and instantaneous frequency. This classifier runs in a real time operation mode with sampling rates in excess of 1 Gsample/s. The hardware platform for this application is a Field Programmable Gate Array (FPGA). This AMC is subsidiary of a digital channelised receiver also implemented in the same platform.

Validation of real time dispersion of Tritium clouds over the Western Mediterranean Basin in different assesments

Real time Tritium concentrations in air coming from an ITER-like reactor as source were coupled the European Centre Medium Range Weather Forecast (ECMWF) numerical model with the lagrangian atmospheric dispersion model FLEXPART. This tool ECMWF/FLEXPART was analyzed in normal operating conditions in the Western Mediterranean Basin during 45 days at summer 2010. From comparison with NORMTRI plumes over Western Mediterranean Basin the real time results have demonstrated an overestimation of the corresponding climatologically sequence Tritium concentrations in air outputs, at several distances from the reactor. For these purpose two clouds development patterns were established. The first one was following a cyclonic circulation over the Mediterranean Sea and the second one was based in the cloud delivered over the Interior of the Iberian Peninsula by another stabilized circulation corresponding to a High. One of the important remaining activities defined then, was the tool qualification. The aim of this paper is to present the ECMWF/FLEXPART products confronted with Tritium concentration in air data. For this purpose a database to develop and validate ECMWF/FLEXPART tritium in both assessments has been selected from a NORMTRI run. Similarities and differences, underestimation and overestimation with NORMTRI will allowfor refinement in some features of ECMWF/FLEXPART

A hierarchical strategy for real-time tracking on-board UAVs

In this paper, we present a real-time tracking strategy based on direct methods for tracking tasks on-board UAVs, that is able to overcome problems posed by the challenging conditions of the task: e.g. constant vibrations, fast 3D changes, and limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations where part of the object to track is out the field of view of the camera. The performance of the proposed strategy is evaluated with images from real-flight tests using different evaluation mechanisms (e.g. accurate position estimation using a Vicon sytem). Results show that our tracking strategy performs better than well known feature-based algorithms and well known configurations of direct methods, and that the recovered data is robust enough for vision-in-the-loop tasks.

Ada real-time services and virtualization

Virtualization techniques have received increased attention in the field of embedded real-time systems. Such techniques provide a set of virtual machines that run on a single hardware platform, thus allowing several application programs to be executed as though they were running on separate machines, with isolated memory spaces and a fraction of the real processor time available to each of them.This papers deals with some problems that arise when implementing real-time systems written in Ada on a virtual machine. The effects of virtualization on the performance of the Ada real-time services are analysed, and requirements for the virtualization layer are derived. Virtual-machine time services are also defined in order to properly support Ada real-time applications. The implementation of the ORK+ kernel on the XtratuM supervisor is used as an example.

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance.

Real time power plant simulation platform for training on Electrical Protections and Automatic Voltage Regulators

Electrical Protection systems and Automatic Voltage Regulators (AVR) are essential components of actual power plants. Its installation and setting is performed during the commissioning, and it needs extensive experience since any failure in this process or in the setting, may entails some risk not only for the generator of the power plant, but also for the reliability of the power grid. In this paper, a real time power plant simulation platform is presented as a tool for improving the training and learning process on electrical protections and automatic voltage regulators. The activities of the commissioning procedure which can be practiced are described, and the applicability of this tool for improving the comprehension of this important part of the power plants is discussed. A commercial AVR and a multifunction protective relay have been tested with satisfactory results.

Real-time determination of airspace wind speed field based on FDR = Determinación en tiempo real del campo de velocidad del viento basado en registradores de datos de vuelo

El estudio del comportamiento de la atmósfera ha resultado de especial importancia tanto en el programa SESAR como en NextGen, en los que la gestión actual del tránsito aéreo (ATM) está experimentando una profunda transformación hacia nuevos paradigmas tanto en Europa como en los EE.UU., respectivamente, para el guiado y seguimiento de las aeronaves en la realización de rutas más eficientes y con mayor precisión. La incertidumbre es una característica fundamental de los fenómenos meteorológicos que se transfiere a la separación de las aeronaves, las trayectorias de vuelo libres de conflictos y a la planificación de vuelos. En este sentido, el viento es un factor clave en cuanto a la predicción de la futura posición de la aeronave, por lo que tener un conocimiento más profundo y preciso de campo de viento reducirá las incertidumbres del ATC. El objetivo de esta tesis es el desarrollo de una nueva técnica operativa y útil destinada a proporcionar de forma adecuada y directa el campo de viento atmosférico en tiempo real, basada en datos de a bordo de la aeronave, con el fin de mejorar la predicción de las trayectorias de las aeronaves. Para lograr este objetivo se ha realizado el siguiente trabajo. Se han descrito y analizado los diferentes sistemas de la aeronave que proporcionan las variables necesarias para obtener la velocidad del viento, así como de las capacidades que permiten la presentación de esta información para sus aplicaciones en la gestión del tráfico aéreo. Se ha explorado el uso de aeronaves como los sensores de viento en un área terminal para la estimación del viento en tiempo real con el fin de mejorar la predicción de las trayectorias de aeronaves. Se han desarrollado métodos computacionalmente eficientes para estimar las componentes horizontales de la velocidad del viento a partir de las velocidades de las aeronaves (VGS, VCAS/VTAS), la presión y datos de temperatura. Estos datos de viento se han utilizado para estimar el campo de viento en tiempo real utilizando un sistema de procesamiento de datos a través de un método de mínima varianza. Por último, se ha evaluado la exactitud de este procedimiento para que esta información sea útil para el control del tráfico aéreo. La información inicial proviene de una muestra de datos de Registradores de Datos de Vuelo (FDR) de aviones que aterrizaron en el aeropuerto Madrid-Barajas. Se dispuso de datos de ciertas aeronaves durante un periodo de más de tres meses que se emplearon para calcular el vector viento en cada punto del espacio aéreo. Se utilizó un modelo matemático basado en diferentes métodos de interpolación para obtener los vectores de viento en áreas sin datos disponibles. Se han utilizado tres escenarios concretos para validar dos métodos de interpolación: uno de dos dimensiones que trabaja con ambas componentes horizontales de forma independiente, y otro basado en el uso de una variable compleja que relaciona ambas componentes. Esos métodos se han probado en diferentes escenarios con resultados dispares. Esta metodología se ha aplicado en un prototipo de herramienta en MATLAB © para analizar automáticamente los datos de FDR y determinar el campo vectorial del viento que encuentra la aeronave al volar en el espacio aéreo en estudio. Finalmente se han obtenido las condiciones requeridas y la precisión de los resultados para este modelo. El método desarrollado podría utilizar los datos de los aviones comerciales como inputs utilizando los datos actualmente disponibles y la capacidad computacional, para proporcionárselos a los sistemas ATM donde se podría ejecutar el método propuesto. Estas velocidades del viento calculadas, o bien la velocidad respecto al suelo y la velocidad verdadera, se podrían difundir, por ejemplo, a través del sistema de direccionamiento e informe para comunicaciones de aeronaves (ACARS), mensajes de ADS-B o Modo S. Esta nueva fuente ayudaría a actualizar la información del viento suministrada en los productos aeronáuticos meteorológicos (PAM), informes meteorológicos de aeródromos (AIRMET), e información meteorológica significativa (SIGMET). ABSTRACT The study of the atmosphere behaviour is been of particular importance both in SESAR and NextGen programs, where the current air traffic management (ATM) system is undergoing a profound transformation to the new paradigms both in Europe and the USA, respectively, to guide and track aircraft more precisely on more efficient routes. Uncertainty is a fundamental characteristic of weather phenomena which is transferred to separation assurance, flight path de-confliction and flight planning applications. In this respect, the wind is a key factor regarding the prediction of the future position of the aircraft, so that having a deeper and accurate knowledge of wind field will reduce ATC uncertainties. The purpose of this thesis is to develop a new and operationally useful technique intended to provide adequate and direct real-time atmospheric winds fields based on on-board aircraft data, in order to improve aircraft trajectory prediction. In order to achieve this objective the following work has been accomplished. The different sources in the aircraft systems that provide the variables needed to derivate the wind velocity have been described and analysed, as well as the capabilities which allow presenting this information for air traffic management applications. The use of aircraft as wind sensors in a terminal area for real-time wind estimation in order to improve aircraft trajectory prediction has been explored. Computationally efficient methods have been developed to estimate horizontal wind components from aircraft velocities (VGS, VCAS/VTAS), pressure, and temperature data. These wind data were utilized to estimate a real-time wind field using a data processing approach through a minimum variance method. Finally, the accuracy of this procedure has been evaluated for this information to be useful to air traffic control. The initial information comes from a Flight Data Recorder (FDR) sample of aircraft landing in Madrid-Barajas Airport. Data available for more than three months were exploited in order to derive the wind vector field in each point of the airspace. Mathematical model based on different interpolation methods were used in order to obtain wind vectors in void areas. Three particular scenarios were employed to test two interpolation methods: a two-dimensional one that works with both horizontal components in an independent way, and also a complex variable formulation that links both components. Those methods were tested using various scenarios with dissimilar results. This methodology has been implemented in a prototype tool in MATLAB © in order to automatically analyse FDR and determine the wind vector field that aircraft encounter when flying in the studied airspace. Required conditions and accuracy of the results were derived for this model. The method developed could be fed by commercial aircraft utilizing their currently available data sources and computational capabilities, and providing them to ATM system where the proposed method could be run. Computed wind velocities, or ground and true airspeeds, would then be broadcasted, for example, via the Aircraft Communication Addressing and Reporting System (ACARS), ADS-B out messages, or Mode S. This new source would help updating the wind information furnished in meteorological aeronautical products (PAM), meteorological aerodrome reports (AIRMET), and significant meteorological information (SIGMET).