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

Children with SLI can exhibit reduced attention to a talker's mouth

It has been demonstrated that children with specific language impairment (SLI) show difficulties not only with auditory but also with audiovisual speech perception. The goal of this study was to assess whether children with SLI might show reduced attention to the talker's mouth compared to their typically developing (TD) peers. An additional aim was to determine whether the pattern of attention to a talking face would be related to a specific subtype of SLI. We used an eye-tracker methodology and presented a video of a talker speaking the children's native language. Results revealed that children with SLI paid significantly less attention to the mouth than the TD children. More specifically, it was also observed that children with a phonological-syntactic deficit looked less to the mouth as compared to the children with a lexical-syntactic deficit

Mètode D.A.I. Desenvolupament d'ajuda pedagògica a qualsevol infant

El present informe no pretén, altra cosa que, explicar com es fan els aprenentatges, ni com es construeix la intelligència sinó que persegueix d'aprop més aviat com es pot treballar l'ajuda pedagògica a qualsevol infant, des de la tasca tutorial fins la de suport i prioritàriament la de suport a la integració (especialitzada en educació especial) ordinària i sense excloure els centres específics en E.E. No és una nova teoria, ni l'esbós d'un nou mètode de més, sinó que aprofitant l'ocasió dels moments actuals de reforma es tracta d'aportar, i més encara de proposar, un canvi en l'enginy psicodidàctic per millorar l'estimulació de l'activitat escolar i l'organització de recursos i del professorat per obrir una oferta a la llibertat de decisions que l'infant ha de construir per arribar a la seva autonomia. Aquest treball planteja que per integrar necessitats també cal integrar mètodes, tècniques... en la vida escolar, el que manca són esquemes per a programar i coordinar un model de seguiment actiu, pràctic, útil, adaptable i manejable de l'infant, sensible als seus progressos i que obri la il?lusió a noves perspectives de constitució, organització i ampliació inspirats en l'escola de la integració per a qualsevol infant. La demanda pedagògica es centra en una resposta de classificar activitats, potenciar la creativitat i promocionar recursos de cara a un mètode que garanteixi la professionalització i la cientificitat de la tasca diària. L'oferta de noves creacions d'aules d'educació especial en centres ordinaris, la sistematització de la tasca docent i l'especialització del tema invitin a aprendre de la pròpia experiència docent prioritzin una alternativa de recerca activa

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

Serum alkaline phosphatase relates to cardiovascular risk markers in children with high calcium-phosphorus product

Although alkaline phosphatase (ALP) correlates with cardiovascular risk in adults, there are no studies in children. We evaluated the association between serum ALP levels, calcium-phosphorus product (Ca*P) and cardiovascular risk markers in healthy children. Children aged 7.9 ± 1.4 (n = 379) were recruited in this cross-sectional study. The main outcome measures were systolic and diastolic blood pressure (SBP and DBP) and carotid intima-media thickness (cIMT). Additional assessments were body-mass index (BMI), waist circumference, homeostatic model assessment of insulin resistance (HOMA-IR) and fasting lipids, ALP, serum calcium, phosphorus and Ca*P. ALP was directly correlated with BMI (p < 0.0001), waist circumference (p < 0.0001), SBP (p < 0.0001), cIMT (p = 0.005), HOMA-IR (p < 0.0001), and fasting triglycerides (p = 0.0001). Among them, in children with Ca*P values above the median the associations were BMI (r = 0.231; p = 0.001), waist (r = 0.252; p < 0.0001), SBP (r = 0.324; p < 0.0001), cIMT (r = 0.248; p = 0.001) and HOMA-IR (r = 0.291; p < 0.0001)]. ALP independently associated with SBP (β = 0.290, p < 0.001) and cIMT (β = 0.179, p = 0.013) in children with higher Ca*P, after adjusting for confounding variables. Circulating ALP is associated with a more adverse cardiovascular profile in children with higher Ca*P. We suggest that serum ALP and Ca*P levels could contribute to the assessment of risk for cardiovascular disease in children

Personalized Respiratory Medicine: Exploring the Horizon, Addressing the Issues. Summary of a BRN-AJRCCM Workshop Held in Barcelona on June 12, 2014.

This Pulmonary Perspective summarizes the content and main conclusions of an international workshop on personalized respiratory medicine coorganized by the Barcelona Respiratory Network (www.brn.cat)and the AJRCCM in June 2014. It discusses (1) its definition and historical, social, legal, and ethical aspects; (2) the view from different disciplines, including basic science, epidemiology, bioinformatics,and network/systems medicine; (3) the bottlenecks and opportunities identified by some currently ongoing projects; and (4) the implications for the individual, the healthcare system and the pharmaceutical industry. The authors hope that, although it is not a systematic review on the subject,this document can be a useful reference for researchers, clinicians, healthcare managers, policy-makers,and industry parties interested in personalized respiratory medicine

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

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