1,721,187 research outputs found
Analysis and Development of Consensus-based Estimation Schemes
Full text linkIn the last few decades we assisted to an extraordinary expansion of the Internet and of wireless technologies. These interconnection technologies allow a continuously increasing number of devices to exchange information. This fact, together with the parallel increase in the availability of inexpensive nodes carrying a wide range of sensing capabilities, attracts the interest in developing
large-scale sensing platforms, which could be used to measure a variety of physical phenomena.
However, these huge networks of simple devices are subject to tight energy and bandwidth constraints, making efficient distributed estimation and data fusion algorithms a strong need, to avoid unmanageable computational and communicational burden on network bottleneck nodes.
%In this thesis we address some issues in this research avenue
In this thesis we address some issues in this field, presenting and analyzing distributed algorithms to solve specific distributed estimation problems and carrying out the analysis of some other recently-proposed algorithms.
To perform data fusion in a distributed fashion, we relay on consensus algorithms, namely algorithms that achieve agreement on a common value in the network.
Using consensus as a basic brick to build estimation algorithms we can take advantage of the solid understanding on this problem that many recent contributions deepened and sharpened, and we can leverage for our analysis on powerful and effective tools.
In the thesis we propose a distributed algorithm for offset removal and an algorithm for least-square identification of the wireless-channel parameters, motivated by the application of localization and tracking of a moving object. We present moreover a novel linear algebra inequality, useful in the analysis of randomized algorithms. This result comes into play when we carry out an analysis of a recently-proposed distributed Kalman filtering algorithm. Finally, we look at the intriguing set up of a network cooperation to estimate different but correlated quantities, proposing and analyzing a distributed algorithm that performs inference over a simple Gauss-Markov random field.Gli ultimi decenni sono stati segnati dallo straordinario sviluppo di Internet e dalla pervasiva diffusione della tecnologia wireless, consentendo ad un numero sempre maggiore di dispositivi di scambiare tra loro informazioni.
Questo fatto, assieme alla crescente disponibilità, a prezzi modici, di nodi equipaggiati con un'ampia varietà di dispositivi di misura, rende tecnologicamente concretizzabile l'idea di sviluppare grandi piattaforme di sensing, incaricate di monitorare qualsivoglia grandezza fisica.
Tuttavia, queste grandi reti di dispositivi estremamente semplici hanno stringenti vincoli sul consumo energetico e sulla banda di comunicazione, che rendono criticamente necessario lo sviluppo di tecniche efficienti per la stima e la data-fusion, così da evitare carichi computazionali e di comunicazione insostenibili ai colli di bottiglia della rete.
Questa tesi si propone di contribuire proprio in questo settore, presentando alcuni algoritmi per la soluzione distribuita di specifici problemi di stima ed analizzando le prestazioni di algoritmi recentemente proposti.
Strumento chiave nella decentralizzazione della stima è la teoria del consensus, che propone algoritmi in grado di portare l'intera rete a concordare su una specifica quantità. L'utilizzo di algoritmi di consensus come elemento base nella costruzione di algoritmi di stima ci consente di sfruttare la solida comprensione di questo problema, affinata dai molti risultati recentemente proposti in letteratura, e di sfruttare degli strumenti di analisi ben consolidati.
Nella tesi, motivati dal problema della localizzazione e del tracking di un oggetto, proponiamo un algoritmo per la compensazione degli offset ed un algoritmo per la stima ai minimi quadrati dei parametri caratterizzanti il canale wireless. Inoltre presentiamo un nuovo risultato di algebra lineare, utile nell'analisi di algoritmi randomizzati. Questo risultato giocherà un ruolo centrale nell'analisi qui proposta di un algoritmo distribuito per la stima alla Kalman. Infine, consideriamo l'interessante caso di una rete di sensori incaricata di stimare quantità diverse ma tra loro correlate e proponiamo un algoritmo per l'inferenza di un semplice campo di Gauss-Markov
A Glucose-Specific Metric to Assess Predictors and Identify Models
Full text linkIn diabetes, the mean square error (MSE) metric is extensively used for assessing glucose prediction methods and identifying glucose models. One limitation of this metric is that, by equally treating errors in hypo-, eu-, and hyperglycemia, it is not able to weight the different clinical impact of errors in these three situations. In this paper, we propose a new cost function, which overcomes this limitation and can be used in place of MSE for several scopes, in particular for assessing the quality of glucose predictors and identifying glucose models. The new metric called glucose-specific MSE (gMSE) modifies MSE with a Clark error grid inspired penalty function, which penalizes overestimation in hypoglycemia and underestimation in hyperglycemia, i.e., the most harmful conditions on a clinical perspective. From a mathematical point of view, gMSE retains sensitivity of MSE and inherits some of its important mathematical features, in particular it has no local minima, simplifying the optimization. This makes it suitable for model identification purposes also. First, the goodness of it is demonstrated by means of three experiments, designed ad hoc to evidence its sensitivity to accuracy, precision, and distortion in glucose predictions. Second, a prediction assessment problem is presented, in which two real prediction profiles are compared. Results show that the MSE chooses the worst clinical situation, while gMSE correctly selects the situation with less clinical risk. Finally, we also demonstrate that models identified minimizing gMSE are more accurate in potentially harmful situations (hypo- and hyperglycemia) than those obtained by MS
A majorization inequality and its application to distributed Kalman filtering
No full textIn the analysis of a recently proposed distributed estimation algorithm based on the Kalman filtering and on gossip iterations, we needed to apply a new inequality which is valid for i.i.d. matrix valued random processes. This inequality can be useful in the analysis of the convergence rate of general jump Markov linear systems.
In this paper, we present this inequality. This is based on the theory of majorization and on its use in the analysis of the singular values. Finally we will show the impact of this inequality on the performance analysis of gossip based distributed Kalman filters
IMPROVING PATIENT OVERNIGHT SAFETY: GLUCOSE-SENSOR AND INSULIN-PUMPS FAILURES DETECTED EXPLOITING AN AVERAGE MODEL
No full textModeling transient disconnections and compression artifacts of continuous glucose sensors
Full text linkModel of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices.
No full textt is clinically well-established that minimally invasive subcutaneous continuous glucose monitoring (CGM) sensors can significantly improve diabetes treatment. However, CGM readings are still not as reliable as those provided by standard fingerprick blood glucose (BG) meters. In addition to unavoidable random measurement noise, other components of sensor error are distortions due to the blood-to-interstitial glucose kinetics and systematic under-/overestimations associated with the sensor calibration process. A quantitative assessment of these components, and the ability to simulate them with precision, is of paramount importance in the design of CGM-based applications, e.g., the artificial pancreas (AP), and in their in silico testing. In the present paper, we identify and assess a model of sensor error of for two sensors, i.e., the G4 Platinum (G4P) and the advanced G4 for artificial pancreas studies (G4AP), both belonging to the recently presented “fourth” generation of Dexcom CGM sensors but different in their data processing. Results are also compared with those obtained by a sensor belonging to the previous, “third,” generation by the same manufacturer, the SEVEN Plus (7P). For each sensor, the error model is derived from 12-h CGM recordings of two sensors used simultaneously and BG samples collected in parallel every 15 ± 5 min. Thanks to technological innovations, G4P outperforms 7P, with average mean absolute relative difference (MARD) of 11.1 versus 14.2 %, respectively, and lowering of about 30 % the error of each component. Thanks to the more sophisticated data processing algorithms, G4AP resulted more reliable than G4P, with a MARD of 10.0 %, and a further decrease to 20 % of the error due to blood-to-interstitial glucose kinetics
Retrospective retrofitting method to generate a continuous glucose concentration profile by exploiting continuous glucose monitoring sensor data and blood glucose
No full textContinuous Glucose Monitoring (CGM) devices provide glucose concentration
measurements in the subcutaneous tissue with limited accuracy and precision. Therefore,
CGM readings cannot be incorporated in a straightforward manner in outcome metrics of
clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those
outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with
consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting”
algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the
high accuracy of available BG references (possibly very sparsely collected) and the high
temporal resolution of CGM data (usually noisy and affected by significant bias). The inputs
of the algorithm are: a CGM time series; some reference BG measurements; a model of blood
to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy. The
algorithm first checks for the presence of possible artifacts or outliers on both CGM
datastream and BG references, and then rescales the CGM time series by exploiting a
retrospective calibration approach based on a regularized deconvolution method subject to the
constraint of returning a profile laying within the confidence interval of the reference BG
measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasicontinuous
glucose concentration signal that is better (in terms of both accuracy and precision)
than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained
retrofitted traces can be used to calculate solid outcome measures, avoiding the need of
increasing the data collection burden at the patient level
Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors
No full textContinuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data
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