Polytechnic University of Milan

About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Finite element method & Population. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.

Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation

Abstract: In this paper an automated methodology is presented (i) to orient a set of close-range images captured with a calibrated camera, and (ii) to extract dense and accurate point clouds starting from the estimated orientation parameters. The whole procedure combines different algorithms and techniques in order to obtain accurate 3D reconstructions in an automatic way. The exterior orientation parameters are estimated using a photogrammetric bundle adjustment with the image correspondences detected using area- and feature-based matching algorithms. Surface measurements are then performed using advanced multi-image matching techniques based on multiple image primitives. To demonstrate the reliability, precision and robustness of the procedure, several tests on different kinds of free-form objects are illustrated and discussed in the paper. Three-dimensional comparisons with range-based data are also carried out. Resume Dans cet article une methodologie automatique est presentee (i) pour orienter un ensemble d’images acquises en plan rapproche avec une camera etalonnee, et (ii) pour extraire des nuages de points denses et precis en utilisant les parametres d’orientation estimes. L’ensemble de la procedure combine plusieurs algorithmes et techniques destines a fournir une reconstruction 3D precise de maniere automatique. Les parametres d’orientation externe sont estimes au moyen d’une compensation par faisceaux, les correspondances entre images etant detectees avec des algorithmes d’appariement de voisinages ou de primitives. Les surfaces sont alors mesurees avec des techniques avancees d’appariement multi-images basees sur des primitives multi-images. Afin de demontrer la fiabilite, la precision et la robustesse de la procedure, plusieurs tests sur des objets de differentes formes libres sont presentes et discutes dans l’article. Des comparaisons en 3D avec des donnees issues de mesures laser sont egalement effectuees. Zusammenfassung Dieser Beitrag stellt eine automatische Methode vor, um (i) einen Satz von Nahbereichsaufnahmen einer kalibrierten Kamera zu orientieren, und (ii) dichte und genaue Punktwolken auf der Basis der geschatzten Orientierungsparameter zu extrahieren. Die gesamte Prozedur kombiniert verschiedene Algorithmen und Techniken, um eine genaue 3D Rekonstruktion auf automatische Weise zu erhalten. Die Parameter der auseren Orientierung werden mit einer photogrammetrischen Bundelausgleichung bestimmt, wobei die Bildverknupfungen mit intensitatsbasierten und merkmalsgestutzten Bildzuordnungsverfahren erzeugt werden. Die Messung von Oberflachenpunkten nutzt neuartige Mehrbildzuordnungstechniken, die sich auf Mehrfachprimitive stutzen. Die Zuverlassigkeit, Genauigkeit und Robustheit der Prozeduren wird durch mehrere Tests mit unterschiedlichen Freiformoberflachen illustriert und diskutiert. Weiterhin werden 3D Vergleiche zur Erfassung mit aktiven Systemen durchgefuhrt. Resumen En este articulo se describe una metodologia automatica para (i) orientar un conjunto de imagenes de objeto cercano obtenidas con una camara calibrada, y (ii) extraer nubes de puntos densas y exactas partiendo de parametros de orientacion estimados. Todo el procedimiento combina diferentes algoritmos y tecnicas para conseguir reconstrucciones tridimensionales de forma automatica. Los parametros de orientacion exterior se estiman con un ajuste fotogrametrico por haces y las correspondencias de imagen determinadas con algoritmos de correspondencia de objetos caracteristicos y correspondencia de areas. A continuacion se toman medidas de superficie utilizando tecnicas avanzadas de correspondencia multiimagen basadas en primitivas de multiples imagenes. Para demostrar la fiabilidad, precision y robustez del procedimiento se han realizado varias pruebas con distintas clases de objetos con formas libres cuyos resultados se presentan y discuten en el articulo. Finalmente, tambien se hacen comparaciones tridimensionales con medidas de rango.

Statistical Fluctuations in HfO x Resistive-Switching Memory: Part I - Set/Reset Variability

Abstract: Resistive switching memory (RRAM) relies on the voltage-driven formation/disruption of a conductive filament (CF) across a thin insulating layer. Due to the 1-D structure of the CF and discrete nature of defects, the set and reset states of the memory device generally display statistical variability from cycle to cycle. For projecting cell downscaling and designing improved programming operations, the variability as a function of the operation parameters, such as the maximum current in the set process and maximum voltage in the reset process, need to be evaluated and understood. This paper addresses set/reset variability, presenting statistical data for HfOx-based RRAM and introducing a physics-based Monte Carlo model for switching statistics. The model can predict the distribution of the set state as a function of the compliance (maximum) current during set and distribution of the reset state as a function of the stop (maximum) voltage during reset. Numerical modeling results are finally presented to provide additional insight into discrete fluctuation events.

Comprehensive Analysis of Random Telegraph Noise Instability and Its Scaling in Deca–Nanometer Flash Memories

Abstract: This paper presents a comprehensive investigation of random telegraph noise (RTN) in deca-nanometer Flash memories, considering both the nor and the nand architecture. The statistical distribution of the threshold voltage instability is analyzed in detail, evidencing that the slope of its exponential tails is the critical parameter determining the scaling trend for RTN. By means of 3-D TCAD simulations, the slope is shown to be the result of cell geometry, atomistic substrate doping, and random placement of traps over the cell active area. Finally, the slope dependence on cell geometry (width, length, and oxide thickness), doping, and bias conditions is summarized in a powerful formula that is able to predict the RTN instabilities in deca-nanometer Flash memories.