Cooperative Positioning using Massive Differentiation of GNSS Pseudorange Measurements

With Differential GNSS (DGNSS), Single Differentiation (SD) of GNSS pseudorange mea- surements is computed with the aim of correcting harmful errors such as ionospheric and tropospheric delays. These errors can be mitigated to up to very few centimeters, which denotes a performance improvement with respect to the Standard Point Positioning (SPP) solution, widely used in GNSS receivers. However, with DGNSS it is necessary to have a very precise knowledge of the coordinates of a reference station in order to experience this performance improvement. We propose the Massive User-Centric Single Differentiation (MUCSD) algorithm, which is proven to have a comparable performance to DGNSS with- out the need of a reference station. Instead, N cooperative receivers which provide noisy observations of their position and clock bias are introduced in the model. The MUCSD algorithm is mathematically derived with an Iterative Weighted Least Squares (WLS) Estimator. The estimator lower bound is calculated with the Cramér-Rao Bound (CRB). Several scenarios are simulated to test the MUCSD algorithm with the MassiveCoop-Sim simulator. Results show that if the observations provided by the cooperative users have a noise of up to 10 meters, DGNSS performance can be obtained with N = 10. When observations are very noisy, the MUCSD performance still approaches DGNSS for high values of N

Massive Differencing of GNSS Pseudorange Measurements

Global Navigation Satellite Systems (GNSS) is a popular positioning solution able to provide high accuracy, integrity, reliability and high coverage. GNSS performance may be enhanced through aiding systems such as Differential GNSS (DGNSS), which aims to mitigate disruptive sources of error by using corrections sent from a reference station. In this paper, we investigate a method that provides performance results comparable to those by DGNSS without the need for a reference station. We propose the Massive User-Centric Single Difference (MUCSD) algorithm, which leverages a set of collaborative receivers exchanging observables and, potentially, their noisy estimates of position and clock bias. MUCSD is implemented as an iterative weighted least squares (WLS) estimator and its lower accuracy bound, as given by the Cramér-Rao Bound (CRB), is derived as a performance benchmark for the WLS solution. Simulation results are provided as a function of the number of collaborative users and the exchanged information uncertainty. Results show that, without having to access costly-to-maintain reference stations, MUCSD asymptotically outperforms DGNSS as the number of collaborative receivers grows

Recursive classification of satellite imaging time-series: An application to water mapping, land cover classification and deforestation detection

A wide variety of applications of fundamental importance for security, environmental protection and urban development need access to accurate land cover monitoring and water mapping, for which the analysis of optical remote sensing imagery is key. Classification of time-series images, particularly with recursive methods, is of increasing interest in the current literature. Nevertheless, existing recursive approaches typically require large amounts of training data. This paper introduces a recursive classification framework that improves the decision-making process in multitemporal and multispectral land cover classification algorithms while requiring low computational cost and minimal supervision. The proposed approach allows the conversion of an instantaneous classifier into a recursive Bayesian classifier by using a probabilistic framework that is robust to non-informative image variations. Three experiments are conducted using Sentinel-2 data. The first one consists in the water mapping of an embankment dam in California (United States), the second one is a land cover classification experiment of the Charles river area in Boston (United States) and the last experiment addresses deforestation detection in the Amazon rainforest (Brazil). A classifier based on the Gaussian mixture model (GMM), a logistic regression (LR) classifier, and a spectral index classifier (SIC) are compared to their recursive counterparts. SICs are introduced to convert the NDWI, MNDWI and NDVI spectral indices into predictive probabilities. Two state-of-the-art deep learning-based models are also used as a benchmark for the water mapping experiment. Results show that the proposed method significantly increases the robustness of existing instantaneous classifiers in multitemporal settings. Our method also improves the performance of deep learning-based classifiers without the need for additional training data.Comment: Without supplementary results: 30 pages, 11 figures. With supplementary results: 40 pages, 21 figure

A Collaborative RTK Approach to Precise Positioning for Vehicle Swarms in Urban Scenarios

Location information is fundamental in nowadays society and key for prospective driverless vehicles and a plethora of safety-critical applications. Global Navigation Satellite Systems (GNSS) constitute the main information supplier for outdoor positioning, with worldwide all-weather availability. While the use of GNSS carrier phase observations leads to precise location estimates, its performance can be easily jeopardized in urban scenarios, where satellite availability may be limited or observations may be corrupted by harsh propagation conditions. The satellite shortage is especially relevant for Real Time Kinematic (RTK), whose capability to estimate a precise positioning solution rapidly decays with weak observation models. To address this limitation, this article introduces the concept of collaborative RTK (C-RTK), an approach to precise positioning using swarms of vehicles, where a set of users participate in the vehicle network. The idea is that users with good satellite visibility assist users that evolve in constrained environments. This work introduces the C-RTK functional model, an estimation solution and associated performance bounds. Illustrative Monte Carlo simulation results are provided, which highlight that, by exploiting the cross-correlation terms present among the users' observations, C-RTK improves their positioning their of accuracy and availability

Canvi climàtic i interacció planta-pol·linitzador: respostes d’Episyrphus balteatus a BVOCs actuals i predits de Sonchus tenerrimus i Globularia alypum

Quart premi del XI Premi PRBB al millor treball de recerca en Ciències de la Salut i de la VidaTutor: Joan Perdigués del Col·legi Claret Barcelona (Barcelona)Degut a l’escalfament global estan augmentant els canvis al nostre planeta. Un d’aquests canvis és la variació dels compostos volàtils florals, els quals atreuen els pol·linitzadors a les plantes. Si els volàtils canvien, es preveu que hi haurà una important alteració de la interacció planta-pol·linitzador a la natura, principalment afectant les espècies de pol·linitzadors especialistes. El principal objectiu d’aquest treball és estudiar si l’espècie de mosca Episyrphus balteatus té una preferència pels compostos emesos actualment (compostos actuals) o pels compostos que apareixeran o s’emetran en més quantitat en un futur com a conseqüència del canvi climàtic (compostos predits). Duem a terme comparacions entre les reaccions de E. balteatus envers els compostos 1R-α-pinè i 3-carè (compost actual i predit, respectivament, de l’espècie de planta Sonchus tenerrimus) i també envers D-limonè i 1R-α-pinè (compost actual i predit, respectivament, de Globularia alypum). També estudiem la influència de les variables de dejuni, llum, concentració de compost, addició d’altres estímuls olfactius i tipus d’estructura de compost dins el comportament de les mosques. Discutim els resultats obtinguts tenint en compte recerca prèvia

Simulation, Analysis and Detection of Indoor Multipath Fading Channels Using an SVM Classifier

Nowadays, identification of fake data is an elaboratechallenge that calls for the use of machine learning techniques.This is due to the huge amount of data and its complexity makesthe differences indistinguishable even for the trained eye. In thisproject we use the MATLAB wlanTGnChannel System objectto simulate multipath fading channels that are comparable toreal impulse response measurements made by Ericsson AB of anindoor8×8MIMO (Multiple Input Multiple Output) system.We use an SVM classifier to compare the eigenvalues of theircorrelation covariance matrices, obtaining an accuracy of 84%.Comparing their power delay profiles (PDPs) happens to bea classification task of low complexity due to time resolutionlimitation in the real measurements. We suggest that the proposedMATLAB model strongly differs from the real data we have beenprovided with.Nu för tiden så är identifiering av fejkad data en svår utmaning som ofta kräver maskininlärningstekniker. Detta beror på den stora mängden data och att komplexiteten i datat gör att skillnaderna kan vara svår att se även för ett tränat öga. I det här projektet använder vi oss av MATLABs systemobjekt wlanTGnChannel för att simulera flervägs fädningskanaler som kan jämföras med riktiga impulssvarsmätningar gjorda av Ericsson AB av ett innomhus 8 X 8 MIMO(Multiple Input Multiple Output) system. Vi använde en SVM (stödvektormaskins) klassificerare för att jämföra egenvärdena av deras korrelationskovariansmatriser, vilket erhåller en noggranhet på 84%. Att jämföra deras power delay profiles (PDP) råkar vara ett klassificeringsproblem av låg svårighetsgrad på grund av tidsupplösningsbegränsningar för de riktiga mätningarna. Vi vill påstå att den tilltänkta MATLAB- modellen aviker mycket från den givna datan.Kandidatexjobb i elektroteknik 2020, KTH, Stockhol

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable