Positive effect of the fluorine moiety on the oxygen storage capacity of UiO-66 metal–organic frameworks

The capacity to store oxygen and nitrogen within UiO-66 fluorine-containing MOFs has been tested through high pressure adsorption isotherms

A Complete Design Flow of a General Purpose Wireless GPS/Inertial Platform for Motion Data Monitoring

This work illustrates a complete design flow of an electronic system developed to support applications in which there are the need to measure motion parameters and transmit them to a remote unit for real-time teleprocessing. In order to be useful in many operative contexts, the system is flexible, compact, and lightweight. It integrates a tri-axial inertial sensor, a GPS module, a wireless transceiver and can drive a pocket camera. Data acquisition and packetization are handled in order to increase data throughput on Radio Bridge and to minimize power consumption. A trajectory reconstruction algorithm, implementing the Kalman-filter technique, allows obtaining real-time body tracking using only inertial sensors. Thanks to a graphical user interface it is possible to remotely control the system operations and to display the motion data

Promoting Laparoscopic Anterior Approach for a Very Low Presacral Primary Neuroendocrine Tumor Arising in a Tailgut Cyst

Tailgut cysts are rare congenital lesions that develop in the presacral space. As they can potentially conceal primary neuroendocrine tumors, surgical excision is suggested as the treatment of choice. However, specific management guidelines have yet to be developed. A posterior approach is usually preferred for cysts extending to the third sacral vertebral body. Conversely, a transabdominal approach is preferred for lesions extending upward to achieve an optimal view of the surgical field and avoid injuries

Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte

Neutron induced single event burnout on power mosfets

Scuola di dottorato"Archimede" Scienza e Tecnologia dei Sistemi Complessi, Ciclo XXVII a.a. 2014Università della Calabri

Classification models and algorithms in application of multi-sensor systems to detection and identification of gases

Dottorato di Ricerca in Ricerca Operativa, Ciclo XX , a.a. 2006-2007The objective of the thesis is to adopt advanced machine learning tech- niques in the analysis of the output of sensor systems. In particular we have focused on the SVM (Support Vector Machine) approach to classi- ¯cation and regression, and we have tailored such approach for the area of sensor systems of the "electronic nose" type. We designed an Electronic Nose (ENose), containing 8 sensors, 5 of them being gas sensors, and the other 3 being a Temperature, a Humidity, and a Pressure sensor, respectively. Our system (Electronic Nose) has the ability to identify the type of gas, and then to estimate its concentration. To identify the type of gas we used as classi¯cation and regression technique the so called Support Vector Machine (SVM) approach, which is based on statistical learning theory and has been proposed in the broad learning ¯eld. The Kernel methods are applied in the context of SVM, to improve the classi¯cation quality. Classi¯cation means ¯nding the best divider (separator) between two or more di®erent classes without or with minimum number of errors. Many methods for pattern recognition or classi¯cation are based on neural network or other complex mathematical models. In this thesis we describe the hardware equipment which has been designed and implemented. We survey the SVM approach for machine learning and report on our experimentation.Università degli Studi della Calabri

The methodology for active testing of electronic devices under the radiations

The methodology, developed for active testing of electronic devices under the radiations, is presented. The test set-up includes a gamma-ray facility, the hardware board/fixtures and the software tools purposely designed and realized. The methodology is so wide-ranging to allow us the verification of different classes of electronic devices, even if only application examples for static random access memory modules are reported