"Neural networks based approach for data fusion in multi-frequency navigation receivers"

In this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented, The GPS system modernization phase and the Galileo system development will increase signal availability and hence GNSS system-based applications. This variety of uses pervades almost every aspect of GNSS activity and provides the stimulus for its future improvement. For all these causes, in the past few years, there has been a growing interest in the research on the development of techniques and methods for improving the signal reception. Unfortunately, the receiver measurement is usually affected by errors. As already said, in an urban environment the major error source is given by muttipath fading. The proposed method is based on frequency diversity, i.e. the distortions introduced by the channel can be considered different and uncorrelated for sufficiently spaced frequencies. For this reason it is possible to design receivers that, through the usage of multiple frequencies, can improve the reception of SIS, minimizing the distortion effects of the multipath channel. In the proposed system two frequencies in E band, i.e. E5A and E5B, have been considered, and the derived information is fused by using a Neural Network (NN). The NN bases its adaptive fusion on parameters which represent the amount of noise in each of the considered frequencies. Considering the receiver from an higher level, it would be more accurate and efficient if it would be provided by an artificial intelligence that can be developed within the framework of Cognitive Radio devices, the future paradigm for mobile navigation and communication terminals

"Interaction Modeling and Prediction in Smart Spaces: a Bio-Inspired Approach Based on Autobiographical Memory"

In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New research trends for reaching this goal are based on neurophysiological observations of human brain structure and functioning. A learning technique that is used to provide the SS with the so-called Autobiographical Memory is presented here by drawing inspiration from a bio-inspired model of the interactions occurring between the system and the user. Starting from the hypothesis that user's actions have a direct influence on the internal system state variables and vice versa, a statistical voting algorithm is proposed for inferring the cause/effect relationships among users and the system. The main contribution of this paper lies in proposing a general framework that is able to allow the SS to be aware of its present state as well as of the behavior of its users and to be able to predict the expected consequences of user actions. In this paper, these concepts are explored in order to point out the relevant role of the structural coupling between the system and the environment (i.e., interaction with the user) in the development of context-aware SSs. In fact, a model is proposed here for learning in dynamic cognitive systems , which is able to understand the cause/effect relationships between the changes in system state and the environmental perturbations that are due to the presence of the user. The algorithm that is based on neurophysiological studies on how human self-consciousness arises and evolves is proposed to extract contextual information from heterogeneous sensor signals and to learn and predict interactions involving users that are present in an SS. The innovative aspect of this paper is the new way of modeling the interactions between the user and the system and its engineering implication in the development of context-aware learning/predicting strategies. To do so, multiple heterogeneous data coming from a set of sensors are jointly processed with the aim of detecting internal and external contextual events. Then, a nonparametric probabilistic interaction model is learned, which can be used to predict future events to be able to design anticipative decision strategies. The proposed mechanism, applied for processing and passively learning interactions between the system and the users, introduces new functionalities and modeling capabilities which can be exploited in SS design. This paper is organized as follows. In Section II, an approach for learning interactions between SSs and users, which is inspired by neurophysiological studies, is proposed. A procedure is introduced for using these memorized data to predict changes in the system\u2019s internal status, which are caused by user interactions, potentially allowing self-reaction and self-adaptation capabilities. In Section III, the described algorithms are extensively tested in the scenario of an SS, which is installed in a university laboratory and which monitors the internal status of its devices and the external events produced by user actions. Section IV presents the comparisons with other learning approaches for AmI applications. Finally, in Section V, we conclude by commenting on open issues and possible future improvements

"A hierarchical Neural Network-based receiver for GNSS systems"

In this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented. In particular a hierarchical neural network-based solution, able to estimate the channel in which the receiver operates, will be shown. The proposed method is based on two different Neural Networks and it is able to improve the fine tracking performances in urban environment. The solution takes advantage of the Self Organizing Map (SOM) properties, a particular type of Neural Networks useful in unsupervised systems, to improve the performances in presence of multipath

Neural networks mode classification based on frequency distribution features

The growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. A possible solution for increasing the frequency re-usage within the framework of info-mobility cellular systems is the joint exploitation of Smart Antennas and Cognitive Radio. In the paper a Mode Identification algorithm, based on frequency distribution features and multiple neural network classifiers, for a Cognitive Base Transceiver Station is presented. Simulated results, obtained in a simplified framework, will prove the effectiveness of the proposed approach

"A Reconfigurable Multicamera Architecture for High Resolution Objects Analysis"

In this paper, a multi-camera architecture is presented for heterogeneous targets analysis and tracking. The proposed system can be switched in various configurations enabling automatic and human driven scene analysis. To achieve this, different control strategies have been implemented in order to optimize results in terms of perceptual image quality and pointing precisions

Architettura Basata su Cognitivit\ue0 Embodied per Antenne Intelligenti di Prossima Generazione

Sommario\u2014In questo contributo `e descritta un\u2019architettura per la gestione di antenne intelligenti basata sul paradigma dei sistemi cognitivi. Il sistema proposto si basa su un controllore che apprende dall\u2019evoluzione dell\u2019ambiente circostante la strategia di adattamento ottima. L\u2019apprendimento utilizza un approccio ispirato ai processi di sviluppo della coscienza nei sistemi biologici. L\u2019ottimizzazione delle prestazioni dell\u2019antenna intelligente nell\u2019inseguimento di terminali in movimento sfrutta l\u2019esperienza maturata dal sistema durante il proprio funzionamento. Nel contributo `e illustrata l\u2019architettura di un\u2019applicazione basata sulla strategia proposta e sono mostrati alcuni esempi di funzionamento dello stesso. Index Terms\u2014Antenne intelligenti, sistemi cognitivi, intelligenza artificiale, cognitivit `a embodied