Dipartimento d'Ingegneria

Engineering design and metallurgy - Progettazione industriale, costruzioni meccaniche e metallurgia

The research interests of the group include all tools and methods related to any stage of product design and manufacturing.
At present the activity is mainly focussed on the following areas: machine design; system dynamics; structural mechanics; computer-aided engineering (including finite elements analysis, computational fluid dynamics and multi-body simulation); fatigue mechanics; random loads fatigue; comfort evaluation; motion sickness analysis; product design; design tools and method in Engineering; engineering drawing; computer-aided design; design for life-cycle; tolerance analysis; machine vision and machine learning for industrial applications.


ELG 10 at 14:00 Tuesday 20th November 2018.
City, University of London.

Abstract:
Colour texture classification plays a fundamental role in a wide range of applications, as for instance surface inspection and grading, content-based image retrieval, computer-assisted diagnosis and remote sensing. Traditionally, the problem has been approached by manually designing suitable functions capable of extracting meaningful visual features from the input images: the so-called ‘hand-crafted’ paradigm. In recent years, however, the arrival on the scene of Convolutional Neural Networks (`Deep Learning') has brought about dramatic advances in many areas of computer vision, and has the potential to significantly change the approach to texture analysis as well. This seminar will comparatively evaluate – both at a theoretical and experimental level – the hand-crafted and Deep Learning paradigms for colour texture classification. There will be specific focus on the use of pre-trained networks as generic feature extractors for colour textures.


Abstract: Colour texture classification plays a fundamental role in a wide range of applications, as for instance surface inspection and grading, content-based image retrieval, computer-assisted diagnosis and remote sensing. Traditionally, the problem has been approached by manually designing suitable functions capable of extracting meaningful visual features from the input images: the so-called ‘hand crafted’ paradigm. In recent years, however, the arrival on the scene of Convolutional Neural Networks (`Deep Learning') has brought about dramatic advances in many areas of computer vision, and has the potential to significantly change the approach to texture analysis as well. This seminar will comparatively evaluate – both at a theoretical and experimental level – the hand-crafted and Deep Learning paradigms for colour texture classification. There will be specific focus on the use of pre-trained networks as generic feature extractors for colour textures.

Intense research is being conducted throughout the world to identify biomarkers seen on computed tomography (CT) imaging that can help predict prognoses in patients with non-small-cell lung cancer (NSCLC). Such biomarkers could help improve and personalize treatment plans for patients. A team of radiologists, engineers, and biomedical specialists at the University of Perugia evaluated 30 three-dimensional shape and textural CT-derived features as potential biomarkers predictive of overall survival of 203 NSCLC patients. Find out more.

SOFTWIND - PRIN 2015

In Research ,
Scritto da Martedì, 23 Maggio 2017 09:09

SOFTWIND - PRIN 2015 

www.softwind.it

E' on line il sito web del PRIN 2015 finanziato dal MIUR, Progetto di Ricerca di Interesse Nazionale dal titolo "SOFTWIND turbines" Il progetto coordinato a livello nazionale dall'Università di Camerino vede Perugia tra le quattro unità componenti il team di ricerca.
Il gruppo del nostro Ateneo è coordinato dal Prof. Filippo Cianetti e composto inoltre dal Prof. Francesco Castellani. Si occuperanno di modellazione multicorpo, di modellazione orientata al controllo dei generatori e di valutazione del comportamento a fatica di queste complesse macchine.
La finalità del progetto è quella appunto di minimizzare la danneggiabilità dei generatori e delle strutture nel loro complesso controllando l'assetto delle pale durante il loro funzionamento e sviluppando materiali innovativi per la loro realizzazione e sensori altrettanto smart da poterne misurare le prestazioni strutturali durante il loro funzionamento.
Secondo il nuovo rapporto di Almalaurea il 93,6% dei laureati magistrali in Ingegneria trova occupazione dopo un anno dalla fine degli studi e con lo stipendio più alto di tutti gli altri laureati (€ 1.717 in media netti al mese). Anche i laureati triennali hanno ottimi risultati con il 76% occupati ed uno stipendio medio di € 1.283.

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