Linear Time Periodic Analysis of Dc-Dc converter

Aim of this thesis is to analyze Dc-Dc converters by using the techniques of Linear Periodic Time varying (LTP) systems to estimate the amount of subharmonics injected in the load. Dc-Dc converters are used to transform a Dc input to a Dc output of different voltage. In this thesis we study in particular the so called "switch mode" converters. In this kind of devices the conversion is obtained by using fast commutations of (at least) two switches. Due to the discrete switch-positions these converters are considered a typical example of hybrid systems. Linear models with fixed coefficients (LTI system) give a description of the system inadequate to predict and to analyze harmonic effects, while linear models with coefficients that vary periodically, namely LTP system, can be used effectively to this aim. We use therefore a Linear Time Periodic (LTP) system to describe the converter. This kind of description in much more accurate but the model and the tools used to study it are more complex. In the thesis we first introduce the LTP system theory and its main results. In particular we introduce the concept of Harmonic Transfer Function (HTF). A LTP model for a Dc-Dc converter is then derived and it is shown that this model accurately describes the response of the converter. Furthermore this LTP model is used to analyze the open and closed loop behavior of the system. It is shown that the linear model estimates correctly the amplitude of the subharmonics in the output. The thesis has been developed at the Automatic Control Department, Lund University, Sweden under the supervision of Andreas Wernrud and Anders Rantzer. The Italian supervisor of this thesis is Giorgio Picci, Dipartimento di Ingegneria dell' Informazione, Università degli studi di Padova, Ital

Architectural Comparison Model for Area-Efficient PMAP Turbo-Decoders

In this paper, a methodology to compare highthroughput turbo decoder architectures, is proposed. The model is based on the area-efficiency estimation of different architectures and design choices. Moreover, it is specifically oriented to the exploration of Parallel-MAP (PMAP) architectures, combined with both the Max-Log-MAP algorithm and the recently proposed Local-SOVA. The main objective is the search for optimal radix-orders, capable to maximize the area-efficiency of the decoder. In this scenario, it is proved that i) radix-orders higher than 4 are expected to drastically reduce the area-efficiency; ii) the optimal choice between radix-2 and radix-4 architectures strongly depends on the area distribution between logic and memory

Procedure per il rilevamento delle piante infestanti a partire da immagini acquisite da drone

Nei sistemi agricoli, le piante infestanti sono un fattore limitante per le colture agricole, poiché competono per diverse risorse tra cui radiazione solare, spazio, acqua e sostanza nutritive, causando notevoli perdite economiche tutt’altro che trascurabili. Dunque, una corretta gestione delle specie vegetali infestanti è alla base di un’agricoltura economicamente sostenibile. Negli ultimi anni sono stati sviluppati dei sensori (RGB, multispettrali, iperspettrali, termici) di dimensioni tali da poter essere alloggiati anche sui droni in modo da poter monitorare i campi coltivati da altezze diverse. Questo progresso tecnico costituisce uno dei pilastri indispensabile allo sviluppo di tecniche di agricoltura di precisione, in grado di fornire all’agricoltore informazioni preziose sullo stato del terreno e sullo sviluppo delle colture. La ricerca e la messa a punto di nuovi metodi di elaborazione dei dati hanno consentito inoltre di estrarre il maggior numero possibile di informazioni dai dati telerilevati permettendo all’agricoltore di pianificare interventi specifici al giusto momento e di ridurre significativamente la quantità degli input utilizzati, in particolare modo diserbanti, risorse idriche e fertilizzanti. Nell’ambito del rilevamento e del controllo delle specie vegetali infestanti, i droni sono in grado di produrre immagini digitali degli appezzamenti coltivati che possono essere trasformate, mediante l’applicazione di opportuni algoritmi, in mappe di intensità di infestazione, di prescrizione, di guida all’esecuzione di trattamenti erbicidi sito-specifici. Negli ultimi anni, le tecnologie informatiche abbinate ai sistemi di visione artificiale, ossia dispositivi e tecniche in grado di acquisire e rielaborare immagini per ottenere informazioni, hanno permesso di rilevare in maniera accurata sia le colture di interesse che le specie vegetali infestanti, ricavando informazioni importanti per una gestione sito-specifica delle malerbe. In questo studio, le immagini digitali, ottenute tramite droni, sono state pre-elaborate ed annotate. Partendo da tale strato informativo si è proceduto all’implementazione un’attività di training basata sul ricorso alle reti neurali. La rete neurale è stata addestrata e testata inizialmente su immagini note presenti nel sottoinsieme del data-set appositamente creato con Labelbox per valutarne la precisione. Successivamente la rete è stata testata su immagini non segmentate precedentemente per esaminare l’efficacia delle procedure messe a punto. Nella nostra ricerca, i dati forniti durante l’addestramento non sono risultati sufficienti ad “insegnare” alla rete neurale come discriminare la copertura delle piante infestanti da quella del mais. Ulteriori ricerche saranno necessarie per implementare procedure di successo

A nonparametric approach for model individualization in an artificial pancreas

The identification of patient-tailored linear time invariant glucose-insulin models is investigated for type 1 diabetic patients, that are characterized by a substantial inter-subject variability. The individualized linear models are identified by considering a novel kernel-based nonparametric approach and are compared with a linear time invariant average model in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean squared error. Model identification and validation are based on in-silico data collected from the adult virtual population of the UVA/Padova simulator. The data generation involves a protocol designed to produce a sufficient input excitation without compromising patient safety, compatible also with real life scenarios. The identified models are exploited to synthesize an individualized Model Predictive Controller (MPC) for each patient, which is used in an Artificial Pancreas to maintain the blood glucose concentration within an euglycemic range. The MPC used in several clinical studies, synthesized on the basis of a non-individualized average linear time invariant model, is also considered as reference. The closed-loop control performance is evaluated in an in-silico study on the adult virtual population of the UVA/Padova simulator in a perturbed scenario, in which the MPC is blind to random variations of insulin sensitivity in each virtual patient. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Accuracy of a CGM Sensor in Pediatric Subjects With Type 1 Diabetes. Comparison of Three Insertion Sites: Arm, Abdomen, and Gluteus

Patients with diabetes, especially pediatric ones, sometimes use continuous glucose monitoring (CGM) sensor in different positions from the approved ones. Here we compare the accuracy of Dexcom\uae G5 CGM sensor in three different sites: abdomen, gluteus (both approved) and arm (off-label)

Additive Manufacturing For Thermal Management Applications: From Experimental Results To Numerical Modeling

Additive Manufacturing (AM) of copper and copper alloys has opened new frontiers in heat transfer applications, going beyond the capabilities of conventional technologies. Despite the great design freedom offered by AM, when dealing with metal powders, a few issues should be considered to exploit the great capabilities of this manufacturing technology. In fact, the surface roughness of the components is expected to affect the performance of the devices, which can be remarkably different from the ones simulated with software. This paper presents a critical analysis of the accuracy of the numerical tools to simulate the fluid flow behaviour inside cooling channels obtained via AM. The work shows the major limitations of the standard approaches to accurately predict the pressure drops in straight and complex channels. Three different copper channels of growing complexity were built via LPBF (Laser Powder Bed Fusion) and then they were experimentally tested at different water flow rates to evaluate the predictive abilities of the numerical model. The results revealed that the surface roughness deeply affects the fluid flow behaviour, thus the numerical models need to be calibrated to become a reliable design tool. The proposed procedure can be considered the first attempt in this direction and allows for a proper integration of the AM with the numerical simulation tools, to boost the design capabilities of LPBF technology

Neem oil nanoemulsions: characterisation and antioxidant activity

The aim of the present work is to develop nanoemulsions (NEs), nanosized emulsions, manufactured for improving the delivery of active pharmaceutical ingredients. In particular, nanoemulsions composed of Neem seed oil, contain rich bioactive components, and Tween 20 as nonionic surfactant were prepared. A mean droplet size ranging from 10 to 100nm was obtained by modulating the oil/surfactant ratio. Physicochemical characterisation was carried out evaluating size, f-potential, microviscosity, polarity and turbidity of the external shell and morphology, along with stability in simulated cerebrospinal fluid (CSF), activity of Neem oil alone and in NEs, HEp-2 cell interaction and cytotoxicity studies. This study confirms the formation of NEs by Tween 20 and Neem oil at different weight ratios with small and homogenous dimensions. The antioxidant activity of Neem oil alone and in NEs was comparable, whereas its cytotoxicity was strongly reduced when loaded in NEs after interaction with HEp-2 cells

Clinical evaluation of a decision support system for glucose infusion in hypoglycaemic clamp experiments.

AIM To provide a preliminary evaluation of the accuracy and safety of Gluclas decision support system suggestions in a hypoglycaemic clamp study. METHODS This analysis was performed using data from 32 participants (four groups with different glucose-insulin regulation: post Roux-en-Y gastric bypass with and without postprandial hypoglycaemia syndrome, postsleeve gastrectomy and non-operated controls) undergoing Gluclas-assisted hypoglycaemic clamps (target: 2.5 mmol/L for 20 minutes at 150 minutes after oral glucose ingestion). Gluclas provided glucose infusion rate suggestions upon manual entry of blood glucose values (every 5 minutes), which were either followed or overruled by investigators after critical review. Accuracy and safety were evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), average glucose level, coefficient of variation (CV) and minimal glucose level during the 20-minute hypoglycaemic period. RESULTS Investigators accepted 84% of suggestions, with a mean deviation of 30.33 mg/min. During the hypoglycaemic period, the MAE was 0.16 (0.12-0.24) (median [interquartile range]) mmol/L and the MAPE was 6.12% (4.80%-9.29%). CV was 4.90% (3.58%-7.27%), with 5% considered the threshold for sufficient quality. The minimal glucose level was 2.40 (2.30-2.50) mmol/L. CONCLUSIONS Gluclas achieved sufficiently high accuracy with minimal safety risks in a population with differences in glucose-insulin dynamics, underscoring its applicability to various patient groups

Phenolic derivatives from Baccharis retusa DC. (Asteraceae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo, Inst Ciencias Ambientais Quim & Farmaceut, BR-09972270 São Paulo, BrazilUniv Presbiteriana Mackenzie, Ctr Ciencias & Humanidades, BR-01302907 São Paulo, BrazilUniv Presbiteriana Mackenzie, Ctr Ciencias Biol & Saude, BR-01302907 São Paulo, BrazilUniversidade Federal de São Paulo, Inst Ciencias Ambientais Quim & Farmaceut, BR-09972270 São Paulo, BrazilWeb of Scienc