Dr. Farid Melgani 

Farid Melgani received the State Engineer degree in electronics from the University of Batna, Algeria, in 1994, the M.Sc. degree in electrical engineering from the University of Baghdad, Iraq, in 1999, and the Ph.D. degree in electronic and computer engineering from the University of Genoa, Italy, in 2003. From 1999 to 2002, he cooperated with the Signal Processing and Telecommunications Group, Department of Biophysical and Electronic Engineering, University of Genoa. Since 2002, he has been an Assistant Professor and then an Associate Professor of telecommunications at the University of Trento, Italy, where he has taught pattern recognition, machine learning, radar remote-sensing systems, and digital transmission. He is currently the Head of the Intelligent Information Processing (I2P) Laboratory, Department of Information Engineering and Computer Science, University of Trento. His research interests are in the area of processing, pattern recognition and machine learning techniques applied to remote sensing and biomedical signals/images (classification, regression, multitemporal analysis, and data fusion). He is coauthor of more than 130 scientific publications and is a referee for several international journals.
Dr. Melgani has served on the scientific committees of several international conferences. He is an Associate Editor of the IEEE Geoscience and Remote Sensing Letters and a Senior Member of the IEEE Society.

Remote Sensing

Biosignals

Spectrophotometry

 

Teaching

Pattern Recognition
Laurea Degree in Information and Organization Engineering
3° year/ 2° Semester
6 credits

Topics

1. Introduction to Pattern Recognition
Definitions and applications. An example of pattern recognition application. Structure and design of a recognition system. Concept of learning.

2. Mathematical Basics
Linear algebra. Notions of the probability theory. Deterministic and statistical distances.

3. Preprocessing and Feature Extraction Techniques
Segmentation methods. Region characterization. Edge detection. Texture analysis. Feature reduction techniques.

4. Supervised Classifiers
Minimum distance classifier. Box classifier. Maximum likelihood classifier. K-nearest neighbors classifier. Linear discriminant functions.

5. Unsupervised Classifiers
Similarity measures and clustering criteria. Maximin algorithm. K-means classifier. Minimal Spanning Tree method. Fuzzy C-means algorithm.distance classifier.

6. Artificial Neural Networks
Introduction to biological neural networks. Perceptron. Multilayer Perceptron. Notions about the Backpropagation learning algorithm.

References
·          R. O. Duda, P. E. Hart e D. G. Stork. Pattern Classification. Second Edition, New York: John Wiley & Sons Inc, 2001.
·          R. Rojas. Neural Networks: A Systematic Introduction. Berlin: Sprinter-Verlag, 1996.
·          Slides of the lecture.