2,820 research outputs found
Design of a portable observatory control system
In this thesis, we synthesize the development of a new concept of operation of small robotic telescopes operated over the Internet. Our design includes a set of improvements in control algorithmic and hardware of several critical points of the list of subsystems necessary to obtain suitable data from a telescope.
We can synthesize the principal contributions of this thesis into five independent innovations:
- An advanced drive closed-loop control: We designed an innovative hardware and software solution for controlling a telescope position at high precision and high robustness.
- A complete Telescope Control System (TCS): We implemented a light and portable software using advanced astronomical algorithms libraries for optimally compute in real-time the telescope positioning. This software also provides a new multiple simultaneous pointing models system using state machines which allows reaching higher pointing precision and longer exposure times with external guiding telescopes.
- A distributed software architecture (CoolObs): CoolObs is the implementation of a ZeroC-ICE framework allowing the control, interaction, and communication of all the peripherals present in an astronomical observatory.
- A patented system for dynamic collimation of optics: SAPACAN is a mechanical parallel arrangement and its associated software used for active compensation of low-frequency aberration variations in small telescopes.
- Collimation estimation algorithms: A sensor-less AO algorithm have been applied by the analysis of images obtained with the field camera. This algorithm can detect effects of lousy collimation. The measured misalignments can later feed corrections to a device like SAPACAN.
Due to the constant presence of new technologies in the field of astronomy, it had been one of the first fields to introduce material which was not democratized at this time such as Coupled Charged Devices, internet, adaptive optics, remote and robotic control of devices. However, every time one of these new technologies was included in the field it was necessary to design software protocol according to the epoch’s state of the art software. Then with the democratization of the same devices, years after the definition of their protocols, the same communication rules tend to be used to keep backward compatibility with old - and progressively unused- devices. When using lots of cumulated software knowledge such as with robotic observing, we can dig in several nonsenses in the commonly used architectures due to the previously explained reasons.
The described situation is the reason why we will propose as follows a new concept of considering an observatory as an entity and not a separated list of independent peripherals. We will describe the application of this concept in the field or robotic telescopes and implement it in various completely different examples to show its versatility and robustness.
First of all, we will give a short introduction of the astronomical concepts which will be used all along the document, in a second part, we will expose a state of the art of the current solutions used in the different subsystems of an observing facility and explain why they fail in being used in small telescopes. The principal section will be dedicated to detail and explain each of the five
innovations enumerated previously, and finally, we will present the fabrication and integration of these solutions. We will show here how the joint use of all of them allowed obtaining satisfactory outstanding results in the robotic use of a new prototype and on the adaptation on several existing refurbished telescopes. Finally, we dedicate the last chapter of this thesis to resuming the conclusions of our work.En esta tesis, presentamos el desarrollo de un nuevo concepto de operación de telescopio robótica operados a través de Internet. Nuestro diseño incluye un conjunto de mejoras de los algoritmos de control y hardware de varios puntos crÃticos de la lista de subsistemas necesarios para obtener datos de calidad cientÃfica con un telescopio. Podemos sintetizar las principales contribuciones de esta tesis en cinco innovaciones independientes: - Un control de motor avanzado en bucle cerrado: Diseñamos un hardware y software innovadores para controlar la posición y movimiento fino de un telescopio con alta precisión y alta robustez. - Un software de control de telescopio (TCS) integrado: Implementamos un software ligero y portátil que ocupa bibliotecas de algoritmos astronómicos avanzados para calcular de manera óptima y en tiempo real la posición teórica del telescopio. Este software también proporciona un software innovador de modelo de pointing múltiples simultáneos. Esto permite alcanzar una mayor precisión de seguimiento y asà ocupar tiempos de integración más importante ocupando un telescopio de guÃa mecánicamente apartado al telescopio principal. - Una arquitectura de software distribuido (CoolObs): CoolObs es una implementación de software ocupando la plataforma de desarrollo ZeroC-ICE la cual permite el control, la interacción y la comunicación de todos los periféricos presentes en un observatorio astronómico. - Un sistema patentado para la colimación dinámica de la óptica: SAPACAN es un sistema mecánico de movimiento paralelo y su software asociado. Se puede ocupar para compensar activamente las aberraciones ópticas de bajo orden en pequeños telescopios. - Algoritmos de estimación de colimación: Se desarrolló un algoritmo de óptica adaptiva sin sensor en base al análisis de imágenes obtenidas con una cámara cerca del plano focal del telescopio. Este algoritmo puede detectar efectos de mala colimación de las ópticas. Los desajustes, una vez medidos, pueden posteriormente ser aplicados como correcciones a un dispositivo como SAPACAN. AstronomÃa es un terreno propicio al desarrollo de nuevas tecnologÃas y, debido a esto, los protocolos de comunicación entre periféricos pueden ser obsoletos porque se han escritos en etapas tempranas de existencia de estas nuevas tecnologÃas. Las mejoras se han hecho de a poco para mantener la compatibilidad de los sistemas ya existentes, ocupando un planteamiento general de la problemática de control de telescopios robóticos, proponemos un nuevo concepto de observatorio robótico visto como una entidad y no una lista de periféricos independientes. A lo largo de esta tesis, describiremos la aplicación de este concepto en el campo de telescopios robóticos e implementarlo en varios sistemas independientes y variados para mostrar la versatilidad y robustez de la propuesta.Postprint (published version
Astrophysically robust systematics removal using variational inference: application to the first month of Kepler data
Space-based transit search missions such as Kepler are collecting large
numbers of stellar light curves of unprecedented photometric precision and time
coverage. However, before this scientific goldmine can be exploited fully, the
data must be cleaned of instrumental artefacts. We present a new method to
correct common-mode systematics in large ensembles of very high precision light
curves. It is based on a Bayesian linear basis model and uses shrinkage priors
for robustness, variational inference for speed, and a de-noising step based on
empirical mode decomposition to prevent the introduction of spurious noise into
the corrected light curves. After demonstrating the performance of our method
on a synthetic dataset, we apply it to the first month of Kepler data. We
compare the results, which are publicly available, to the output of the Kepler
pipeline's pre-search data conditioning, and show that the two generally give
similar results, but the light curves corrected using our approach have lower
scatter, on average, on both long and short timescales. We finish by discussing
some limitations of our method and outlining some avenues for further
development. The trend-corrected data produced by our approach are publicly
available.Comment: 15 pages, 13 figures, accepted for publication in MNRA
Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems
Globally, the buildings sector accounts for 30% of the energy consumption and
more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and
Air Conditioning (HVAC) system is the most extensively operated component and it is
responsible alone for 40% of the final building energy usage. HVAC systems are used
to provide healthy and comfortable indoor conditions, and their main objective is to
maintain the thermal comfort of occupants with minimum energy usage.
HVAC systems include a considerable number of sensors, controlled actuators, and
other components. They are at risk of malfunctioning or failure resulting in reduced efficiency,
potential interference with the execution of supervision schemes, and equipment
deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve
their reliability, efficiency, and performance, and to provide preventive maintenance.
In this thesis work, two neural network-based methods are proposed for sensor and
actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised
sensor data validation and fault diagnosis method using an Auto-Associative Neural
Network (AANN) is developed. The method is based on the implementation of Nonlinear
Principal Component Analysis (NPCA) using a Back-Propagation Neural Network
(BPNN) and it demonstrates notable capability in sensor fault and inaccuracy
correction, measurement noise reduction, missing sensor data replacement, and in both
single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks
(CNNs) is developed for single actuator faults. It is based a data transformation in
which the 1-dimensional data are configured into a 2-dimensional representation without
the use of advanced signal processing techniques. The CNN-based actuator fault
diagnosis approach demonstrates improved performance capability compared with the
commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and
standard Neural Networks).
The presented schemes are compared with other commonly used HVAC fault diagnosis
methods for benchmarking and they are proven to be superior, effective, accurate,
and reliable. The proposed approaches can be applied to large-scale buildings with
additional zones
Markov modelling on human activity recognition
Human Activity Recognition (HAR) is a research topic with a relevant interest
in the machine learning community. Understanding the activities that a person
is performing and the context where they perform them has a huge importance
in multiple applications, including medical research, security or patient monitoring.
The improvement of the smart-phones and inertial sensors technologies has
lead to the implementation of activity recognition systems based on these devices,
either by themselves or combining their information with other sensors. Since
humans perform their daily activities sequentially in a specific order, there exist
some temporal information in the physical activities that characterize the different
human behaviour patterns. However, the most popular approach in HAR is to assume
that the data is conditionally independent, segmenting the data in different
windows and extracting the most relevant features from each segment.
In this thesis we employ the temporal information explicitly, where the raw data
provided by the wearable sensors is fed to the training models. Thus, we study
how to perform a Markov modelling implementation of a long-term monitoring
HAR system with wearable sensors, and we address the existing open problems
arising while processing and training the data, combining different sensors and
performing the long-term monitoring with battery powered devices.
Employing directly the signals from the sensors to perform the recognition can
lead to problems due to misplacements of the sensors on the body. We propose an
orientation correction algorithm based on quaternions to process the signals and
find a common frame reference for all of them independently on the position of the
sensors or their orientation. This algorithm allows for a better activity recognition
when feed to the classification algorithm when compared with similar approaches,
and the quaternion transformations allow for a faster implementation.
One of the most popular algorithms to model time series data are Hidden
Markov Models (HMMs) and the training of the parameters of the model is performed
using the Baum-Welch algorithm. However, this algorithm converges to
local maxima and the multiple initializations needed to avoid them makes it computationally expensive for large datasets. We propose employing the theory of
spectral learning to develop a discriminative HMM that avoids the problems of
the Baum-Welch algorithm, outperforming it in both complexity and computational
cost.
When we implement a HAR system with several sensors, we need to consider
how to perform the combination of the information provided by them. Data fusion
can be performed either at signal level or at classification level. When performed
at classification level, the usual approach is to combine the decisions of multiple
classifiers on the body to obtain the performed activities. However, in the simple
case with two classifiers, which can be a practical implementation of a HAR
system, the combination reduces to selecting the most discriminative sensor, and
no performance improvement is obtained against the single sensor implementation.
In this thesis, we propose to employ the soft-outputs of the classifiers in
the combination and we develop a method that considers the Markovian structure
of the ground truth to capture the dynamics of the activities. We will show
that this method improves the recognition of the activities with respect to other
combination methods and with respect to the signal fusion case.
Finally, in long-term monitoring HAR systems with wearable sensors we need
to address the energy efficiency problem that is inherent to battery powered devices.
The most common approach to improve the energy efficiency of such devices
is to reduce the amount of data acquired by the wearable sensors. In that sense,
we introduce a general framework for the energy efficiency of a system with multiple
sensors under several energy restrictions. We propose a sensing strategy to
optimize the temporal data acquisition based on computing the uncertainty of
the activities given the data and adapt the acquisition actively. Furthermore, we
develop a sensor selection algorithm based on Bayesian Experimental Design to
obtain the best configuration of sensors that performs the activity recognition accurately, allowing for a further improvement on the energy efficiency by limiting
the number of sensors employed in the acquisition.El reconocimiento de actividades humanas (HAR) es un tema de investigación
con una gran relevancia para la comunidad de aprendizaje máquina. Comprender
las actividades que una persona está realizando y el contexto en el que las
realiza es de gran importancia en multitud de aplicaciones, entre las que se incluyen
investigación médica, seguridad o monitorización de pacientes. La mejora
en los smart-phones y en las tecnologÃas de sensores inerciales han dado lugar a
la implementación de sistemas de reconocimiento de actividades basado en dichos
dispositivos, ya sea por si mismos o combinándolos con otro tipo de sensores. Ya
que los seres humanos realizan sus actividades diarias de manera secuencial en un
orden especÃfico, existe una cierta información temporal en las actividades fÃsicas
que caracterizan los diferentes patrones de comportamiento, Sin embargo, los algoritmos
más comunes asumen que los datos son condicionalmente independientes,
segmentándolos en diferentes ventanas y extrayendo las caracterÃsticas más relevantes
de cada segmento.
En esta tesis utilizamos la información temporal de manera explÃcita, usando
los datos crudos de los sensores como entrada de los modelos de entrenamiento. Por
ello, analizamos como implementar modelos Markovianos para el reconocimiento
de actividades en monitorizaciones de larga duración con sensores wearable, y
tratamos los problemas existentes al procesar y entrenar los datos, al combinar
diferentes sensores y al realizar adquisiciones de larga duración con dispositivos
alimentados por baterÃas.
Emplear directamente las señales de los sensores para realizar el reconocimiento
de actividades puede dar lugar a problemas debido a la incorrecta colocación de
los sensores en el cuerpo. Proponemos un algoritmo de corrección de la orientación
basado en quaterniones para procesar las señales y encontrar un marco de referencia
común independiente de la posición de los sensores y su orientación. Este
algoritmo permite obtener un mejor reconocimiento de actividades al emplearlo
en conjunto con un algoritmo de clasificación, cuando se compara con modelos similares. Además, la transformación de la orientación basada en quaterniones da
lugar a una implementación más rápida.
Uno de los algoritmos más populares para modelar series temporales son los
modelos ocultos de Markov, donde los parámetros del modelo se entrenan usando
el algoritmo de Baum-Welch. Sin embargo, este algoritmo converge en general
a máximos locales, y las múltiples inicializaciones que se necesitan en su implementación lo convierten en un algoritmo de gran carga computacional cuando se
emplea con bases de datos de un volumen considerable. Proponemos emplear la
teorÃa de aprendizaje espectral para desarrollar un HMM discriminativo que evita
los problemas del algoritmo de Baum-Welch, superándolo tanto en complejidad
como en coste computacional. Cuando se implementa un sistema de reconocimiento de actividades con múltiples
sensores, necesitamos considerar cómo realizar la combinación de la información que proporcionan. La fusión de los datos, se puede realizar tanto a nivel
de señal como a nivel de clasificación. Cuando se realiza a nivel de clasificación, lo
normal es combinar las decisiones de múltiples clasificadores colocados en el cuerpo
para obtener las actividades que se están realizando. Sin embargo, en un caso simple
donde únicamente se emplean dos sensores, que podrÃa ser una implantación
habitual de un sistema de reconocimiento de actividades, la combinación se reduce
a seleccionar el sensor más discriminativo, y no se obtiene mejora con respecto a
emplear un único sensor. En esta tesis proponemos emplear salidas blandas de
los clasificadores para la combinación, desarrollando un modelo que considera la
estructura Markoviana de los datos reales para capturar la dinámica de las actividades.
Mostraremos como este método mejora el reconocimiento de actividades
con respecto a otros métodos de combinación de clasificadores y con respecto a la
fusión de los datos a nivel de señal.
Por último, abordamos el problema de la eficiencia energética de dispositivos
alimentados por baterÃas en sistemas de reconocimiento de actividades de larga
duración. La aproximación más habitual para mejorar la eficiencia energética consiste
en reducir el volumen de datos que adquieren los sensores. En ese sentido, introducimos un marco general para tratar el problema de la eficiencia energética
en un sistema con múltiples sensores bajo ciertas restricciones de energética. Proponemos
una estrategia de adquisición activa para optimizar el sistema temporal
de recogida de datos, basándonos en la incertidumbre de las actividades dados los
datos que conocemos. Además, desarrollamos un algoritmo de selección de sensores
basado diseño experimental Bayesiano y asà obtener la mejor configuración
para realizar el reconocimiento de actividades limitando el número de sensores
empleados y al mismo tiempo reduciendo su consumo energético.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Ignacio SantamarÃa Caballero.- Secretario: Pablo MartÃnez Olmos.- Vocal: Alberto Suárez Gonzále
Electronics for Sensors
The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces
A review of advances in pixel detectors for experiments with high rate and radiation
The Large Hadron Collider (LHC) experiments ATLAS and CMS have established
hybrid pixel detectors as the instrument of choice for particle tracking and
vertexing in high rate and radiation environments, as they operate close to the
LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for
which the tracking detectors will be completely replaced, new generations of
pixel detectors are being devised. They have to address enormous challenges in
terms of data throughput and radiation levels, ionizing and non-ionizing, that
harm the sensing and readout parts of pixel detectors alike. Advances in
microelectronics and microprocessing technologies now enable large scale
detector designs with unprecedented performance in measurement precision (space
and time), radiation hard sensors and readout chips, hybridization techniques,
lightweight supports, and fully monolithic approaches to meet these challenges.
This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog.
Phy
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt.
Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit.
Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte
Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen.
Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt.
Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie
Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert.
Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver
(∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare
Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet
- …