Paolo Ciampolini

Bio: Paolo Ciampolini is an academic researcher from University of Parma. The author has contributed to research in topics: Finite-difference time-domain method & Wearable computer. The author has an hindex of 21, co-authored 174 publications receiving 2268 citations. Previous affiliations of Paolo Ciampolini include University of Bologna & University of Perugia.

Radiation hard silicon detectors—developments by the RD48 (ROSE) collaboration

Abstract: The RD48 (ROSE) collaboration has succeeded to develop radiation hard silicon detectors, capable to withstand the harsh hadron fluences in the tracking areas of LHC experiments. In order to reach this objective, a defect engineering technique was employed resulting in the development of Oxygen enriched FZ silicon (DOFZ), ensuring the necessary O-enrichment of about 2×1017 O/cm3 in the normal detector processing. Systematic investigations have been carried out on various standard and oxygenated silicon diodes with neutron, proton and pion irradiation up to a fluence of 5×1014 cm−2 (1 MeV neutron equivalent). Major focus is on the changes of the effective doping concentration (depletion voltage). Other aspects (reverse current, charge collection) are covered too and the appreciable benefits obtained with DOFZ silicon in radiation tolerance for charged hadrons are outlined. The results are reliably described by the “Hamburg model”: its application to LHC experimental conditions is shown, demonstrating the superiority of the defect engineered silicon. Microscopic aspects of damage effects are also discussed, including differences due to charged and neutral hadron irradiation.

RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes

Abstract: Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.

A new discretization strategy of the semiconductor equations comprising momentum and energy balance

Abstract: A discretization scheme is applied to the hydrodynamic model for semiconductor devices that generalizes the Scharfetter-Gummel method to both the momentum-conservation and the energy-conservation equations. The major advantages of the scheme are: (1) the discretization is carried out without neglecting any terms, thus providing a satisfactory description of such effects as velocity overshoot and carrier heating; and (2) the resulting equations lend themselves to a self-consistent solution procedure similar to those currently used to solve the simpler drift-diffusion equations. Two-dimensional steady-state simulations of an n-channel MOSFET and of an n-p-n BJT (bipolar junction transistor) have been carried out by means of an improved version of the program HFIELDS. Carrier-temperature plots have been obtained with a reasonable computational effort, demonstrating the efficiency of this technique. The results have been compared with those obtained with the standard drift-diffusion model and significant differences in the electron concentration have been found, especially at the drain end of the MOSFET channel. >

Food Image Recognition Using Very Deep Convolutional Networks

Abstract: We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.

Accurate and efficient circuit simulation with lumped-element FDTD technique

Abstract: A three-dimensional (3-D) implementation of the lumped-element finite-difference time-domain (FDTD) algorithm has been carried out. To accomplish proper description of device dynamic responses, the code incorporates accurate models of lumped bipolar devices, including nonlinear capacitances associated with pn and Schottky junctions. The nonlinear system arising from discretized lumped-element equations is solved by means of an iterative Newton-Raphson algorithm, the convergence properties of which are sensitive to the value of the simulation time step. The computational efficiency of the algorithm (as well as its robustness) has significantly been enhanced by introducing an adaptive time-step algorithm, which dynamically adjusts the time-step itself to ensure convergence during the simulation. Several simulation examples are compared with conventional analysis techniques and demonstrate the algorithm reliability as well as its increased efficiency.

The CMS experiment at the CERN LHC

Abstract: The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

ATLAS pixel detector electronics and sensors

Abstract: The silicon pixel tracking system for the ATLAS experiment at the Large Hadron Collider is described and the performance requirements are summarized. Detailed descriptions of the pixel detector electronics and the silicon sensors are given. The design, fabrication, assembly and performance of the pixel detector modules are presented. Data obtained from test beams as well as studies using cosmic rays are also discussed.

Review of displacement damage effects in silicon devices

Abstract: This paper provides a historical review of the literature on the effects of radiation-induced displacement damage in semiconductor materials and devices. Emphasis is placed on effects in technologically important bulk silicon and silicon devices. The primary goals are to provide a guide to displacement damage literature, to offer critical comments regarding that literature in an attempt to identify key findings, to describe how the understanding of displacement damage mechanisms and effects has evolved, and to note current trends. Selected tutorial elements are included as an aid to presenting the review information more clearly and to provide a frame of reference for the terminology used. The primary approach employed is to present information qualitatively while leaving quantitative details to the cited references. A bibliography of key displacement-damage information sources is also provided.

A review of hydrodynamic and energy-transport models for semiconductor device simulation

Abstract: Since Stratton published his famous paper four decades ago, various transport models have been proposed which account for the average carrier energy or temperature in one way or another. The need for such transport models arose because the traditionally used drift-diffusion model cannot capture nonlocal effects which gained increasing importance in modern miniaturized semiconductor devices. In the derivation of these models from Boltzmann's transport equation, several assumptions have to be made in order to obtain a tractable equation set. Although these assumptions may differ significantly, the resulting final models show various similarities, which has frequently led to confusion. We give a detailed review on this subject, highlighting the differences and similarities between the models, and we shed some light on the critical issues associated with higher order transport models.

A smart home application to eldercare: Current status and lessons learned

Abstract: To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.