0000000000522692
AUTHOR
Pietro Storniolo
showing 6 related works from this author
A Feed-Forward Neural Network for Robust Segmentation of Color Images
1999
A novel approach for segmentation of color images is proposed. The approach is based on a feed-forward neural network that learns to recognize the hue range of meaningful objects. Experimental results showed that the proposed method is effective and robust even in presence of changing environmental conditions. The described technique has been tested in the framework of the Robot Soccer World Cup Initiative (RoboCup). The approach is fully general and it may be successfully employed in any intermediate level image-processing task, where the color is a meaningful descriptor.
Introducing automated reasoning in network management
2004
This paper proposes the adoption of Artificial Intelligence techniques in the field of network management and moni toring. In order to allow automated reasoning on network ing topics, we constructed an accurate ontological model capable of fitting as more as possible networking concepts. The thoroughly representation of the domain knowledge is used by a Logical Reasoner, which is an expert system ca pable of performing management tasks typically executed by human experts. The Logical Reasoner is integrated in a distributed multi-agent architecture for network manage ment, which exploits the dynamic reasoning capabilities of the Situation Calculus formalism to provide a powerful sys tem capa…
A Grid Enabled Parallel Hybrid Genetic Algorithm for SPN
2004
This paper presents a combination of a parallel Genetic Algorithm (GA) and a local search methodology for the Steiner Problem in Networks (SPN). Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the features of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to assess deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. The large dimen…
Rule based reasoning for network management
2006
This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity whe…
A Segmentation System for Soccer Robot Based on Neural Networks
2000
An innovative technique for segmentation of color images is proposed. The technique implements an approach based on thresholding of the hue histogram and a feed-forward neural network that learns to recognize the hue ranges of meaningful objects. A new function for detecting valleys of the histogram has been devised and tested. A novel blurring algorithm for noise reduction that works effectively when used over hue image has been employed. The reported experimental results show that the technique is reliable and robust even in presence of changing environmental conditions. Extended experimentation has been carried on the framework of the Robot Soccer World Cup Initiative (RoboCup).
The Random Neural Network Model for the On-line Multicast Problem
2005
In this paper we propose the adoption of the Random Neural Network Model for the solution of the dynamic version of the Steiner Tree Problem in Networks (SPN). The Random Neural Network (RNN) is adopted as a heuristic capable of improving solutions achieved by previously proposed dynamic algorithms. We adapt the RNN model in order to map the network characteristics during a multicast transmission. The proposed methodology is validated by means of extensive experiments.