Dipartimento d'Ingegneria

Perfetti Renzo

Perfetti Renzo

URL del sito web: http://www.researchgate.net/profile/Renzo_Perfetti/ Email: Questo indirizzo email è protetto dagli spambots. E' necessario abilitare JavaScript per vederlo.

Altre informazioni

  • Telefono
    075 585 3631
  • Ruolo
    Professore ordinario - Full Professor
  • Area
    Elettrotecnica - Electrical Engineering
Venerdì, 21 Marzo 2014 08:59

LIBRO

Circuiti Elettrici  Seconda edizione

Renzo Perfetti

Zanichelli, 2013


La seconda edizione di Circuiti elettrici mantiene la collaudata impostazione basata su una graduale esposizione della teoria, con particolare attenzione alle tecniche di risoluzione dei problemi, descritte in forma algoritmica e illustrate attraverso numerosi esempi svolti. Alcuni argomenti sono presentati secondo un nuovo ordine, più funzionale alle esigenze didattiche. Molti esempi sono stati aggiornati.


Sito web del libro:

http://www.zanichelli.it/ricerca/prodotti/9788808178886/renzo-perfetti/circuiti-elettrici/

Venerdì, 21 Marzo 2014 08:52

ARTICOLO

Recurrent neural network for approximate nonnegative matrix factorization

Giovanni Costantini, Renzo Perfetti, Massimiliano Todisco

 

 

A recurrent neural network solving the approximate nonnegative matrix factorization (NMF) problem is presented in this paper. The proposed network is based on the Lagrangian approach, and exploits a partial dual method in order to limit the number of dual variables. Sparsity constraints on basis or activation matrices are included by adding a weighted sum of constraint functions to the least squares reconstruction error. However, the corresponding Lagrange multipliers are computed by the network dynamics itself, avoiding empirical tuning or a validation process. It is proved that local solutions of the NMF optimization problem correspond to as many stable steady-state points of the network dynamics. The validity of the proposed approach is verified through several simulation examples concerning both synthetic and real-world datasets for feature extraction and clustering applications.

To be published in Neurocomputing (2014)

Giovedì, 20 Marzo 2014 09:42

ARTICOLO

Retinal Blood Vessel Segmentation using Line Operators and Support Vector Classification
     
Elisa Ricci            Renzo Perfetti

IEEE Transactions on Medical Imaging vol. 26, N. 10, 2007


Abstract. In the framework of computer-aided diagnosis of eye diseases, retinal vessel segmentation based on line operators is proposed. A line detector, previously used in mammography, is applied to the green channel of the retinal image. It is based on the evaluation of the average grey level along lines of fixed length passing through the target pixel at different orientations. Two segmentation methods are considered. The first uses the basic line detector whose response is thresholded to obtain unsupervised pixel classification. As a further development we employ two orthogonal line detectors along with the grey level of the target pixel to construct a feature vector for supervised classification using a support vector machine (SVM). The effectiveness of both methods is demonstrated through receiver operating characteristic (ROC) analysis on two publicly available databases of color fundus images.

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