Multi Antenna Time of Arrival Estimation

Projecte final de carrera fet en col.laboració amb KTH Royal Institute of TechnologyEnglish: In the communications literature exist many documents that explain how to use spatial diversity to improve the performance of the system. However, the use of spatial diversity has not been studied in depth for GNSS, although in the last years the subject has received some interest. Lately, numerous applications of GNSS for urban indoor applications has emerged. One of the main sources of impairment in the urban and indoor environments is multipath propagation. Spatial diversity is an effective means to resolve the impact of multipath. Therefore, this Master?s Thesis addresses the problem of Time Of Arrival Estimation in DSSS based navigation systems in Non Line Of Sight Signal (NLOSS) environments using antenna array signal processing methods to mitigate the multipath and improve the quality of the signal. The proposed methods are the synchronization of the frequency and delay parameters using the Maximum Likelihood Estimator (MLE), and the use of a Minimum Mean Square Error (MMSE) spatial filtering or beamforming to remove the multipath from the input signal for a correct estimation of the frequency shift and the code delay. The thesis starts by describing the GPS signal composition and the basic theory behind the Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) based methods. The performance of the two methods is assessed through simulations and application on real measurement data. We find that ML provides the best performance while MMSE provides a better trade-off between performance and complexity.Castellano: En la literatura sobre telecomunicaciones existen muchos documentos que explican cómo utilizar la diversidad espacial para mejorar el funcionamiento del sistema. No obstante, el uso de diversidad espacial no ha sido estudiada con profundidad para sistemas de navegación global con satélite (GNSS), aunque haya despertado cierta atención en los últimos años. Últimamente han salido varias aplicaciones de posicionamiento por satélite en entornos urbanos y interiores. Uno de los principales impedimentos de estos sistemas es la propagación multi camino. Pero, la diversidad espacial es una eficaz herramienta para combatir este fenómeno. Por lo tanto, este Proyecto Fin de Carrera es dirigido a solucionar el problema de la propagación multi camino en sistema con arquitectura Direct Spread Spectrum Sequence (DSSS) con el uso de técnicas de procesado de señal con agrupaciones de antenas. Los métodos propuestos son la sincronización de la frecuencia y el retardo utilizando el estimador de máxima verosimilitud (MLE) y el filtrado espacial a partir del mínimo error cuadrático medio (MMSE), o conformador de haz, para eliminar las réplicas de la propagación multi camino para una correcta estimación del retardo y la frecuencia. Este proyecto empieza describiendo la señal GPS y la teoría que hay detrás de los métodos basados en MLE y MMSE. El funcionamiento de los métodos es verificado a partir de simulaciones y su aplicación con datos obtenidos de medidas reales. Al final hemos encontrado que los métodos MLE presentan unos mejores resultados mientras los métodos MMSE presentan un mejor compromiso entre complejidad y resultados.Català: A la literatura sobre telecomunicacions existeixen molts documents que expliquen com utilitzar la diversitat espacial per millorar el funcionament del sistema. No obstant, l'ús de la diversitat espacial no ha estat estudiada a fons per a sistemes de navegació global per satèl·lit (GNSS), encara que en els últims anys, ha rebut cert interès. Últimament han sorgit vàries aplicacions de posicionament per satèl·lit en entorns urbans o interiors. Un dels principals impediments o deficiències d'aquests sistemes en aquests entorns és la propagació multi camí. Però, la diversitat espacial és una eina molt efectiva per combatre l'efecte d'aquest fenomen. Per tant, aquest Projecte de Final de Carrera es dirigeix a solucionar el problema de l'estimació del temps d'arribada en sistemes amb arquitectura Direct Spread Spectrum Sequence (DSSS) amb l'ús de tècniques de processament de senyal amb agrupacions d'antenes. Els mètodes proposats són la sincronització de la freqüència i els retard utilitzant l'estimador de més versemblança (MLE), i l'ús del filtratge espacial a partir del mínim error quadràtic mig (MMSE), o també anomenat conformador de feix, per eliminar les rèpliques degudes a la propagació multi camí per a una correcta estimació de la freqüència i el retard. Aquest projecte comença descrivint el senyal GPS i la teoria darrera els mètodes basats en MLE o MMSE. El funcionament dels dos mètodes és comprovat a partir de simulacions i a partir de la seva aplicació en dades obtingudes a partir de mesures reals. Hem trobat que mentre els mètodes MLE tenen unes millors resultats, els mètodes MMSE presenten un millor compromís entre complexitat i resultats

Towards Efficient Incident Detection in Real-time Traffic Management

Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data

Personalised Clinical Decision Support For Diabetes Management Using Real-time Data

PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a predictive computer model that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection

Optimisation methods meet the smart grid. New methods for solving location and allocation problems under the smart grid paradigm

The smart grid offers a new infrastructure for the management of energy demand and generation towards a sustainable future. Accordingly, there is the objective to provide consumers with a response capacity to stimuli of the electricity market, and at the same time, to efficiently manage the generation system which tends to a diversification of the generators and the energy sources. For that purpose, this thesis is first focused on providing to consumers methods for managing their energy consumption and then reducing costs according to their production activities. Next, this thesis focuses on electricity generation, tackling the problem of how to share out energy production among a set of distributed generators using self-organisation. Finally, it tackles the problem of planning the placement of new generators suing meta-heuristics.La xarxa elèctrica intel·ligent ofereix una nova infraestructura per a la gestió de la demanda i generació d'electricitat cap a un futur més sostenible. En aquest sentit, hi ha l'objectiu de proveir els consumidors de capacitat de reacció davant d'estímuls del mercat elèctric i, al mateix temps, gestionar de forma eficient un sistema de generació que tendeix cap a una diversificació. Amb aquest objectiu, aquesta tesi primer es centra a desenvolupar mètodes perquè els consumidors puguin gestionar els seus consums i així també reduir-ne els costos d'acord amb les seves activitats de producció. Posteriorment, la tesi es centra en la generació elèctrica abordant el problema de com repartir la producció d'energia d'entre un conjunt de generadors distribuïts utilitzant mètodes auto-organitzatius. Finalment, s'aborda la planificació de nous generadors utilitzant mètodes metaheurístics

Multiantenna Time Of Arrival Estimation

In the communications literature exist many documents that explain how to use spatial diversity to improve the performance of the system. However, the use of spatial diversity has not been studied in depth for GNSS, although in the last years the subject has received some interest, [6] and [20]. Lately, numerous applications of GNSS for urban indoor applications has emerged. One of the main sources of impairment in urban and indoor environments is multipath propagation. Spatial diversity is an effective means to resolve the impact of multipath. Therefore, this Master’s Thesis addresses the problem of the Time Of Arrival Estimation in DSSS based navigation systems in Non Line Of Sight (NLOSS) environments using antenna array signal processing methods to mitigate the multipath and improve the quality of the signal. The proposed methods are the synchronization of the frequency and delay parameters using the Maximum Likelihood Estimator (MLE), and the use of a Minimum Mean Square Error (MMSE) spatial filtering or beamforming to remove the multipath from the input signal for a correct estimation of the frequency shift and the code delay. The thesis starts by describing the GPS signal composition and the basic theory behind the Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) based methods. The performance of the two methods are assessed through simulations and application on real measurement data. We find that ML provides the best performance while MMSE provides a better trade-off between performance and complexity

Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes

Type 1 diabetes mellitus is a chronic disease that requires those affected to self-administer insulin to control their blood glucose level. However, the estimation of the correct insulin dosage is not easy due to the complexity of glucose metabolism, which usually leads to blood glucose levels far from the optimal. This paper presents an adaptive and personalised basal insulin recommender system based on Kalman filter theory that can be used with or without continuous glucose monitoring systems. The proposed approach is tested with the UVa/PADOVA simulator with eleven virtual adult subjects. It has been tested in combination with two different bolus calculators, and the performance achieved has been compared with that obtained with the default basal doses of the simulator, which can be assumed as optimal. The achieved results demonstrate that the proposed system rapidly converges to the optimal basal dose, and it can be used with adaptive bolus calculators without the risk of instabilityThis project has received funding from the grant of the University of Girona 2016-2018 (MPCUdG2016) and the European Union Horizon 2020 research and innovation programme under grant agreement No. 689810, www.pepper.eu.com/, PEPPE

Multi Antenna Time of Arrival Estimation

Projecte final de carrera fet en col.laboració amb KTH Royal Institute of TechnologyEnglish: In the communications literature exist many documents that explain how to use spatial diversity to improve the performance of the system. However, the use of spatial diversity has not been studied in depth for GNSS, although in the last years the subject has received some interest. Lately, numerous applications of GNSS for urban indoor applications has emerged. One of the main sources of impairment in the urban and indoor environments is multipath propagation. Spatial diversity is an effective means to resolve the impact of multipath. Therefore, this Master?s Thesis addresses the problem of Time Of Arrival Estimation in DSSS based navigation systems in Non Line Of Sight Signal (NLOSS) environments using antenna array signal processing methods to mitigate the multipath and improve the quality of the signal. The proposed methods are the synchronization of the frequency and delay parameters using the Maximum Likelihood Estimator (MLE), and the use of a Minimum Mean Square Error (MMSE) spatial filtering or beamforming to remove the multipath from the input signal for a correct estimation of the frequency shift and the code delay. The thesis starts by describing the GPS signal composition and the basic theory behind the Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) based methods. The performance of the two methods is assessed through simulations and application on real measurement data. We find that ML provides the best performance while MMSE provides a better trade-off between performance and complexity.Castellano: En la literatura sobre telecomunicaciones existen muchos documentos que explican cómo utilizar la diversidad espacial para mejorar el funcionamiento del sistema. No obstante, el uso de diversidad espacial no ha sido estudiada con profundidad para sistemas de navegación global con satélite (GNSS), aunque haya despertado cierta atención en los últimos años. Últimamente han salido varias aplicaciones de posicionamiento por satélite en entornos urbanos y interiores. Uno de los principales impedimentos de estos sistemas es la propagación multi camino. Pero, la diversidad espacial es una eficaz herramienta para combatir este fenómeno. Por lo tanto, este Proyecto Fin de Carrera es dirigido a solucionar el problema de la propagación multi camino en sistema con arquitectura Direct Spread Spectrum Sequence (DSSS) con el uso de técnicas de procesado de señal con agrupaciones de antenas. Los métodos propuestos son la sincronización de la frecuencia y el retardo utilizando el estimador de máxima verosimilitud (MLE) y el filtrado espacial a partir del mínimo error cuadrático medio (MMSE), o conformador de haz, para eliminar las réplicas de la propagación multi camino para una correcta estimación del retardo y la frecuencia. Este proyecto empieza describiendo la señal GPS y la teoría que hay detrás de los métodos basados en MLE y MMSE. El funcionamiento de los métodos es verificado a partir de simulaciones y su aplicación con datos obtenidos de medidas reales. Al final hemos encontrado que los métodos MLE presentan unos mejores resultados mientras los métodos MMSE presentan un mejor compromiso entre complejidad y resultados.Català: A la literatura sobre telecomunicacions existeixen molts documents que expliquen com utilitzar la diversitat espacial per millorar el funcionament del sistema. No obstant, l'ús de la diversitat espacial no ha estat estudiada a fons per a sistemes de navegació global per satèl·lit (GNSS), encara que en els últims anys, ha rebut cert interès. Últimament han sorgit vàries aplicacions de posicionament per satèl·lit en entorns urbans o interiors. Un dels principals impediments o deficiències d'aquests sistemes en aquests entorns és la propagació multi camí. Però, la diversitat espacial és una eina molt efectiva per combatre l'efecte d'aquest fenomen. Per tant, aquest Projecte de Final de Carrera es dirigeix a solucionar el problema de l'estimació del temps d'arribada en sistemes amb arquitectura Direct Spread Spectrum Sequence (DSSS) amb l'ús de tècniques de processament de senyal amb agrupacions d'antenes. Els mètodes proposats són la sincronització de la freqüència i els retard utilitzant l'estimador de més versemblança (MLE), i l'ús del filtratge espacial a partir del mínim error quadràtic mig (MMSE), o també anomenat conformador de feix, per eliminar les rèpliques degudes a la propagació multi camí per a una correcta estimació de la freqüència i el retard. Aquest projecte comença descrivint el senyal GPS i la teoria darrera els mètodes basats en MLE o MMSE. El funcionament dels dos mètodes és comprovat a partir de simulacions i a partir de la seva aplicació en dades obtingudes a partir de mesures reals. Hem trobat que mentre els mètodes MLE tenen unes millors resultats, els mètodes MMSE presenten un millor compromís entre complexitat i resultats

Cloud Based Acquisition System for Diabetic Data

IMEKO TC4 (22è : 2017 : Iasi, Romania). 22nd IMEKO TC4 Symposium and 20th International Workshop on ADC Modelling and Testing: supporting worl development through electrical & electronic mesuraments: September 14-15, 2017, Iasi, RomaniaThe paper presents a system which collects medical data from people with type 1 diabetes mellitus, stores the data into a cloud database and implements web pages for data analysis. This system architecture gives possibility to clinicians and patients to share information about patient evolution without the need for the patient to physically visit the hospital. The system includes a clinical decision support system which helps the patient on insulin doses calibrationThis material is based upon the work which is supported by the European Union through the H2020 “PEPPER” project: Patient Empowerment through Predictive Personalized decision support, http://www.pepper.eu.co

Comparison of Work Scheduling Using Constraint Programming or Auctions

Business processes designers take into account the resources that the processes would need, but, due to the variable cost of certain parameters (like energy) or other circumstances, this scheduling must be done when business process enactment. In this report we formalize the energy aware resource cost, including time and usage dependent rates. We also present a constraint programming approach and an auction-based approach to solve the mentioned problem including a comparison of them and a comparison of the proposed algorithms for solving the

Decision support for grid-connected renewable energy generators planning

Recent technological advances and the incremental demand for electrical energy are leading a growth in the prevalence of distributed generation. There are some off-the-shelf tools to support grid planners in locating and sizing a given number of Distributed Generators (DGs), but they approach the problem using a single set of the variables (either location, size or number of DGs). This paper reviews the problem and provides a new pathway for supporting grid planning with an integrated view; hence, a new planning problem is formulated to jointly determine how many new DGs are needed, of which type, their location and size, while attempting to maximise the profit of the generators, minimise the system losses and improve the voltage profile. Accompanying the new grid planning problem, solution approaches based on meta-heuristic methods are provided. A detailed performance analysis of the proposed approaches is carried out on 14- and 57-bus systems to illustrate what could be the outcomes of the new problem. In so doing, particle swarm optimisation-based approaches are able to find the best optimised solutionsWork developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014e2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R