Browsing by Author "Palopoli, Luigi"

Now showing 1 - 20 of 24

Algorithms and techniques towards the Self-Organization of Mobile Wireless Sensor, Robot and UAV Networks
(2011-11-23) Costanzo, Carmelo; Viterbo, Emanuele; Palopoli, Luigi
Bioinformatic methods for gene discovery and protein prediction
(2011-11-23) Leone, Ofelia; Palopoli, Luigi
Data mining techniques for fraud detection
(2014-03-07) Guarascio, Massimo; Saccà, Domenico; Manco, Giuseppe; Palopoli, Luigi
Designing Cloud services for data processing and knowledge discovery
(2012-10-24) Marozzo, Fabrizio; Palopoli, Luigi; Talia, Domenico; Trunfio, Paolo
Discovering Exceptional Individuals and Properties in Data
(2014-03-07) Fassetti, Fabio; Angiulli, Fabrizio; Palopoli, Luigi
Distributed command governor strategies for multi-agent dynamical systems
(2011-11-23) Francesco Tedesco, Francesco Tedesco; Palopoli, Luigi; Casavola, Alessandro
This dissertation presents a class of novel distributed supervision strategies for multi-agent linear systems connected via data networks and subject to coordination constraints. Such a coordination-by-constraint paradigm is characterized by a set of spatially distributed dynamic systems, connected via communication channels, with possibly dynamical coupling amongst them which need to be supervised and coordinated in order to accomplish their overall objective. The basic design philosophy of the Command Governor (CG) set-point management is used here in order to maintain a pre-stabilized system within prescribed constraints. While in traditional CG schemes the set-point manipulation is undertaken on the basis of the actual measure of the state, in this dissertation it is shown that the CG design problem can be solved also in the case that such an explicit measure is not available by forcing the state evolutions to stay ”not too far” from the manifold of feasible steady-states. This approach, referred to as Feed-Forward CG (FF-CG), is a convenient solution to be used in distributed applications where the cost of measuring the overall state and distributing it amongst the agents may be a severe limitation. Several distributed strategies, based both on CG and FF-CG ideas, will be fully described and analyzed. First, we propose some “non-iterative” schemes in which the agents acting as supervisors communicate once during the decision process. In this respect, a “sequential” distributed strategy in which only one agent at the time is allowed to manipulate its own reference signal is proposed. Such a strategy, although interesting by itself in some applications, will be instrumental to introduce a more effective “parallel” distributed strategy, in which all agents are allowed, under certain conditions, to modify their own reference signals simultaneously. Then an “iterative” procedure, borrowed from the literature, has been here adapted in order to build more efficient distributed schemes which however require larger amount of data exchanges for their implementation. With the aim of evaluating the distributed methods here proposed, several cases of study involving the coordination autonomous vehicles, power networks and water networks management are illustrated.
A Domain-Specific approach for Programming Wireless Body Sensor Network Systems
(2011-11-23) Gravina, Raffaele; Palopoli, Luigi; Fortino, Giancarlo
The progress of science and medicine during the last years has contributed to signi cantly increase the average life expectancy. The increase of elderly population will have a large impact especially on the health care system. Furthermore, especially in more developed countries, there is an always growing interest in maintaining, and improving the quality of life. Wireless Body Sensor Networks (BSNs) can contribute to improve the quality of health care services. BSNs involve wireless wearable physiological sensors applied to the human body for strictly medical and non medical purposes. They can enhance many human-centered application domains such as e-Health, sport and wellness, and even social applications such as physical/ virtual social interactions. However, there are still open issues that limit their wide di usion in real life; primarily, the programming complexity of these systems, due to lack of high-level software abstractions, and to hardware constraints of wearable devices. In contrast to low-level programming and general-purpose middleware, domain-speci c frameworks are an emerging programming paradigm designed to ful ll the lack of suitable BSN programming support. With this aim, this thesis proposes a novel domain-speci c approach for programming signal-processing intensive BSN applications. The de nition of this approach resulted in a domain-speci c programming framework named SPINE (Signal Processing in Node Environment) which is one important contribution of this thesis, along with other interesting contributions derived from enhancements and variants to the main proposal. Additionally, to provide validation and performance evaluation of the proposed approach, a number of BSN applications (including human activity monitoring, physical energy expenditure estimation, emotional stress detection, and step-counting) have been developed atop SPINE. These research prototypes showed the e ectiveness and e ciency of the proposed approach and improved their respective state-of-the-art. Finally, a Platform-Based Design (PBD) methodology, which is widely adopted for the design of traditional embedded systems, is proposed for the design of BSN systems.
Environmental and physiological parameters measurement in images and video
(2012-10-24) Kurylyak, Yuriy; Palopoli, Luigi; Grimaldi, Domenico
Measurement in images and video is a new challenging research direction. Up to now, cameras are mostly used as interaction devices. Computer vision technologies, however, can turn an ordinary video camera to a powerful tool for counting, measuring and inspecting. Using the camera as a measuring sensor is very interesting as allows creating a ”universal” measurement instrument, where new type of measurements can be added just by changing the software. Appearance of smartphones brings measurements in image and video to the new level, introducing a small, portable, autonomous measurement device. A lot of efforts have been made to convert smartphones to mobile tools for measuring the object length, width, size, angles, area, dimensions etc. This Ph.D. thesis investigates novel image and video processing techniques and shows how they can be used for non-invasive measurement of various environmental and physiological parameters. The three logical steps describe the possible types of measurements: in static image, in video and using smartphones. First, the case with a single image affected by a motion blur is considered and appropriate techniques for locating the regions with motion blur and parameters extraction are presented. A new method to detect the locally motion blurred regions from the image with complex still background is introduced. Analysis in the frequency domain, statistical analysis and windowing techniques are used to find blurred object, and the Fourier and Radon transformations are used to compute its motion characteristics. Analysis of video allows measuring additional characteristics of the objects that change over time. Monitoring of the human fatigue level is done by eyelid blinks detection and analysis. Two solutions are proposed: the non-invasive blink detection system based on infrared camera and webcam. The usage of infrared camera with switching light is used for fast and easy pupil detection in each frame, while the webcam is used to create a very cheap but still effective system. The problem of eyes detection is solved by using a cascade of boosted classifiers based on Haar-like features. The algorithm is proposed to detect closure and opening of the eyes and to distinguish voluntary blinks from the involuntary ones. Finally, the smartphone is used for photoplethysmogram acquisition and measurement of vital parameters. The proposed approach utilizes a concept of image acquisition similar to the one of a pulse oximeter. The problem of finger detection in video as well as verification of the proper usage of the system is solved by using colour segmentation in each colour channel. Then, the pulse rate is evaluated based on adaptive and statistical analysis. Moreover, the blood pressure is estimated by means of artificial neural network. A set of parameters are proposed to be extracted from the photoplethysmographic signal and used as the input of the neural network. For wide representation of training data the Multiparameter Intelligent Monitoring in Intensive Care waveform dataset is used.
Evaluation and Optimization of the Energy Behavior of Routing Protocols for Mobile Ad-hoc Networks
(2014-02-28) Fotino, Marco; Palopoli, Luigi; Marano, Salvatore
MANETs (Mobile Ad-hoc NETworks) are networks made up entirely of wireless devices, without the support of any fixed infrastructure. Because of their auto-configuring and self-managing features, and of their many application fields, MANETs have been one of the most investigated research fields in recent years. In particular, the interest of the scientific community is aimed at routing protocols for such networks, which should solve the problem of efficient multihop routing in a distributed environment. My research work is focused on an aspect of MANETs that in recent years has become increasingly important: the evaluation of routing algorithms in terms of energy consumption. My work for the Systems Engineering and Computer Science Ph.D. program is focused on the study of routing algorithms for mobile ad-hoc networks and performance evaluation of such networks, especially trying to highlight and resolve issues related to energy consumption of mobile devices. The study of mobile ad-hoc networks and of the routing algorithms has been carried out mainly through simulations. The algorithms and metrics for routing protocols, obtained by the adaptation, the improvement and the joint application of the solutions known in literature, have been implemented in the ns-2 network simulator software. The methodology used in the context of the Ph.D. program consists in the implementation of the algorithms and in the execution of a large number of simulations, in order to validate the effectiveness of the adopted solutions in the widest possible number of scenarios. Keywords – MANET, Routing, Energy, OLSR, GPSR
The Generative Aspects of Count Constraints: Complexity, Languages and Algorithms
(2011-11-23) Serra, Edoardo; Palopoli, Luigi; Saccà, Domenico
High-level frameworks for the development of wireless sensor network applications
(2011-11-23) Guerrieri, Antonio; Palopoli, Luigi; Fortino, Giancarlo
Wireless Sensor Networks (WSNs) are emerging as powerful platforms for distributed embedded computing supporting a variety of high-impact appli- cations. A WSN is a group of small devices (nodes) capable to sample the real world through sensors, actuate commands through actuators, elaborate data on the node, and send messages to other nodes through radio communi- cation. However, programming WSN applications is a complex task that re- quires suitable paradigms and technologies capable of supporting the speci c characteristics of such networks which uniquely integrate distributed sensing, computation and communication. This thesis aims at providing new paradigms to support the development of WSN applications through both a domain-speci c and a general-purpose approach. In particular, this thesis provides three main contributions. The rst is related to the analysis, design and realization of a domain-speci c frame- work for heterogeneous WSNs for exible and e cient distributed sensing and actuation in buildings called Building Management Framework (BMF). BMF provides fast WSN recon guration, in-node processing algorithms, multi-hop networks, and multi-platform support, a programming abstraction to dynami- cally catch the morphology of buildings, actuators support, and an extensible human computer interface. The second contribution refers to the analysis, design and realization of a general-purpose mobile agent system for WSN, namely MAPS (Multi Agent Platform for SunSPOT). MAPS allows an e ec- tive Java-based development of agents and agent-based applications for WSNs by integrating agent oriented, event-driven and state-based programming pa- radigms. Finally, the third contribution regards the analysis, design and re- alization of a domain-speci c framework for rapid prototyping of platform independent Wireless Body Sensor Network (WBSN) applications, namely SPINE2 (signal processing in-node environment version 2). SPINE2 aims at supporting the development of WSN applications raising the level of the used programming abstractions by providing a task-oriented programming model.
Innovative control architectures of power interfacing systems for distributed generation applications
(2014-05-12) Sgrò, Domenico; Picardi, Ciro; Palopoli, Luigi
Knowledge discovery in bioinformatics: from simple to complex structure
(2014-04-01) Fionda, Valeria; Leone, Nicola; Palopoli, Luigi
Metodi di fusione dei dati per sistemi di assistenza alla guida
(2012-10-24) Lupia, Marco; Palopoli, Luigi; Casavola, Alessandro
Questo documento riporta tutta l'attivit a svolta durante il corso di dottora- to riguardo lo studio, l'applicazione e la sperimentazione di metodi di Data Fusion nell'ambito dei sistemi avanzati di assistenza alla guida per il miglio- ramento delle prestazioni e dell'a dabilit a. In linea di principio, sembra ra- gionevole a ermare che combinando in modo ottimale le informazioni proveni- enti da pi u sensori e possibile progettare e realizzare un sistema meno sensibile alle variazioni ambientali e a possibili errori di misura dovuti alla presenza di outliers. Tuttavia, dal punto di vista pratico i costi aggiuntivi che si devono sostenere, sia in termini di hardware necessario alla raccolta dei dati dai sen- sori addizionali che per la necessit a di disporre di sistemi di calcolo pi u potenti, potrebbero non essere giusti cati se i miglioramenti ottenuti nelle situazioni pi u probabili e pi u realistiche di utilizzo sono modesti. Il presente documento o re, innanzitutto, una panoramica su vari algoritmi di visione arti ciale utilizzati per il riconoscimento della segnaletica orizzon- tale e per la stima del tempo di invasione (tTLC). Quest'ultimo parametro gioca un ruolo determinante nell'avvertimento tempestivo del conducente in caso di superamento dei limiti della carreggiata. I vari algoritmi sono stati testati in varie condizioni di guida, valutandone le prestazioni conseguibili e il carico computazionale richiesto. Segue un'analisi dello stato dell'arte dei metodi e delle tecniche di Data Fu- sion pi u promettenti e che che meglio si prestano a migliorare l'accuratezza del calcolo della stima del tempo di invasione ttlc grazie alla disponibilit a di altri sensori oltre alla telecamera. Speci catamente, si sono confrontati i vari meto- di e algoritmi di Data Fusion, particolarizzati rispetto a vari modelli matem- atici della vettura e ai sensori disponibili, valutando le loro prestazioni in situ- azioni tipiche di guida e soprattutto rispetto all'errore percentuale di stima del tempo di invasione ttlc ottenuto, valutando anche il carico computazionale corrispondente. Gli algoritmi pi u promettenti sono stati implementati su piattaforma embedded.Questo documento riporta tutta l'attivit a svolta durante il corso di dottora- to riguardo lo studio, l'applicazione e la sperimentazione di metodi di Data Fusion nell'ambito dei sistemi avanzati di assistenza alla guida per il miglio- ramento delle prestazioni e dell'a dabilit a. In linea di principio, sembra ra- gionevole a ermare che combinando in modo ottimale le informazioni proveni- enti da pi u sensori e possibile progettare e realizzare un sistema meno sensibile alle variazioni ambientali e a possibili errori di misura dovuti alla presenza di outliers. Tuttavia, dal punto di vista pratico i costi aggiuntivi che si devono sostenere, sia in termini di hardware necessario alla raccolta dei dati dai sen- sori addizionali che per la necessit a di disporre di sistemi di calcolo pi u potenti, potrebbero non essere giusti cati se i miglioramenti ottenuti nelle situazioni pi u probabili e pi u realistiche di utilizzo sono modesti. Il presente documento o re, innanzitutto, una panoramica su vari algoritmi di visione arti ciale utilizzati per il riconoscimento della segnaletica orizzon- tale e per la stima del tempo di invasione (tTLC). Quest'ultimo parametro gioca un ruolo determinante nell'avvertimento tempestivo del conducente in caso di superamento dei limiti della carreggiata. I vari algoritmi sono stati testati in varie condizioni di guida, valutandone le prestazioni conseguibili e il carico computazionale richiesto. Segue un'analisi dello stato dell'arte dei metodi e delle tecniche di Data Fu- sion pi u promettenti e che che meglio si prestano a migliorare l'accuratezza del calcolo della stima del tempo di invasione ttlc grazie alla disponibilit a di altri sensori oltre alla telecamera. Speci catamente, si sono confrontati i vari meto- di e algoritmi di Data Fusion, particolarizzati rispetto a vari modelli matem- atici della vettura e ai sensori disponibili, valutando le loro prestazioni in situ- azioni tipiche di guida e soprattutto rispetto all'errore percentuale di stima del tempo di invasione ttlc ottenuto, valutando anche il carico computazionale corrispondente. Gli algoritmi pi u promettenti sono stati implementati su piattaforma embedded.
Metodi e algoritmi per il riconoscimento del livello di distrazione e affaticamento nella guida di autoveicoli
(2012-10-24) Cario, Gianni; Palopoli, Luigi; Casavola, Alessandro
Modulation techniques and synchronization methods for DC/AC conversion systems
(2011-11-23) Ferrise, Andrea; Palopoli, Luigi; Eisinberg, Alfredo
New Engine control functions for CO2 reduction and performance improvement
(2011-11-23) Montalto, Iolanda; Palopoli, Luigi; Casavola, Alessandro
Today’s automotive market is extremely competitive and quickly changing. The customers demand excellent driving performance, new legislations impose increasingly stricter constraints and competition imposes increasingly shorter development cycles because of reduced times-to-market. Environmental awareness and public concerns about CO2 emissions have been for a long time a substantial factor in promoting technological advancements in the automotive industry. In this scenario, the actual high penetration of electronic devices in cars is and will be a key factor for the fulfillment of all the above requirements. In fact, in a recent study it has been estimated that 90% of automotive innovation includes the electrical and electronics parts. On the other side, next generation of engines will increase in complexity, functionalities and self monitoring capabilities, with true shifts in technology, like e.g. intelligent alternators and variable valve actuation systems. In this respect, the contents of this thesis summarize my last four years of research activity which has been carried out in the field of engine control systems design and validation. Actually, the problem of the production of polluting substances during the combustion phases depends not only by the engine structure but also on the engine management system. Therefore, the control software plays an important role in the achievement of suitable engine and vehicle performance while maintaining low emission levels. To this end, the complexity of the software functions needs to be increased, both in terms of algorithmic complexity and for the need to handle the additional degrees of freedom available (i.e. model based torque management during take off, valve timing or valve actuation management, different values for battery voltage and so on). In turn, the larger control systems complexity imposes the use of more sophisticated tools and methods for the optimization of the engine control system parameters. Most of the work underlying this thesis has been carried out in the automotive company where I actually work for and reports the results of the many efforts accomplished in addressing such kind of problems. In particular, the research activities have been undertaken and experimented on a gasoline engine equipped with a Variable Valve Actuation (VVA) module. The potentialities of VVA systems represent the actual frontier of the engine technology and therefore such a kind of engine represents a relevant baseline for experimenting novel approaches. In particular, four main topics have been investigated: the first one regards the design of smart alternator management algorithms that allow the achievement of lower emission levels than standard alternators and improve the performance during certain manoeuvres. The second topic regards the development of a new method, named drive off algorithm, for handling the take off phases. This algorithm has been proved so effective in test benches that it has been implemented in all commercial vehicles since the beginning of this year. The third and fourth topics have regarded the way to manage the additional algorithmic complexity due to the availability of the further degrees offered by the new VVA technology. For this reason, a new spark advance algorithm has been developed, being the standard one not so good in adequately taking into account the specificities of the VVA technology. A second more complex aspect being addressed it has been the increased number of engine control parameters to be calibrated for this new kind of engines. The old tuning methodology based on a trial-and-error approach resulted not enough accurate for the novel control requirements and too much time-consuming. A novel tuning methodology has been developed which, on the contrary, is based on an optimization approach and allows one to achieve the desired accuracy in short times. For this reason, it has been adopted in my company since last year and it is actually used for steady state calibration of each motorization of MULTIAIR® engines. The thesis is organized in eight chapters The first and second ones describe the scenario in which my work has been developed, showing the restrictive emission levels required in the automobile world and some technological enhancements for fuel economy, emission legislation fulfillment and engine performance improvements. In the third chapter, the control algorithms developed to manage the smart alternator technology are described, showing the achieved benefits. The details of the control algorithms and the obtained results have been described in the paper (SAE2011) [12]. The fourth chapter describes the control functions used to improve the engine performance during the take off maneuver in MULTIAIR® engines. This work has been published in the paper (EAEC2011) [11]. In the fifth chapter a control function that optimizes the performance of a VVA engine has been detailed. This algorithm calculates the spark ignition timing (one of the necessary engine parameters) in order to obtain the optimal behavior in each engine working mode and in each variable valve mode. This work has been presented at the Fisita 2010 Conference (see [10]). The tools and the methodology developed for the optimization of the engine parameters have been described in another paper presented at the same conference (see [9]). In the sixth chapter, several tools for engine control systems design and calibration have been described. This chapter contains material published in the book’s chapter [16]) and in the conference papers [71], [78], [79], [80], [81]. The seventh chapter concludes the thesis, reporting some results and showing the benefits of the proposed methodologies on a real application
New materials and technologies for compact antennas and circuits at millimeter frequencies
(2012-10-24) Borgia, Antonio; Palopoli, Luigi; Costanzo, Sandra
On the problem of checking chase termination
(2011-11-23) Spezzano, Francesca; Palopoli, Luigi; Greco, Sergio
Pattern extraction from data with application to image processing
(2012-10-24) Amelio, Alessia; Palopoli, Luigi; Pizzuti, Clara
The term Information Extraction refers to the automatic extraction of structured information from data. In such a context, the task of pattern extraction plays a key role, as it allows to identify particular trends and recurring structures of interest to a given user. For this reason, pattern extraction techniques are available in a wide range of applications, such as enterprise applications, personal information management, web oriented and scientific applications. In this thesis, analysis is focused on pattern extraction techniques from images and from political data. Patterns in image processing are defined as features derived from the subdivision of the image in regions or objects and several techniques have been introduced in the literature for extracting these kinds of features. Specifically, image segmentation approaches divide an image in ”uniform” region patterns and both boundary detection and region-clustering based algorithms have been adopted to solve this problem. A drawback of these methods is that the number of clusters must be predetermined. Furthermore, evolutionary techniques have been successfully applied to the problem of image segmentation. However, one of the main problems of such approaches is the determination of the number of regions, that cannot be changed during execution. Consequently, we formalize a new genetic graph-based image segmentation algorithm that, thanks to the new fitness function, a new concept of neighborhood of pixels and the genetic representation, is able to partition images without the need to set a priori the number of segments. On the other hand, some image compression algorithms, recently proposed in literature, extract image patterns for performing compression, such as extensions to 2D of the classical Lempel-Ziv parses, where repeated occurrences of a pattern are substituted by a pointer to that pattern. However, they require a preliminary linearization of the image and a consequent extraction of linear patterns. This could miss some 2D recurrent structures which are present inside the image. We propose here a new technique of image compression which extracts 2D motif patterns from the image in which also some pixels are omitted in order to increase the gain in compression and which uses these patterns to perform compression. About pattern extraction in political science, it consists in detecting voter profiles, ideological positions and political interactions from political data. Some proposed pattern extraction techniques analyze the Finnish Parliament and the United States Senate in order to discover political trends. Specifically, hierarchical clustering has been employed to discover meaningful groups of senators inside the United States Senate. Furthermore, different methods of community detection, based on the concept of modularity, have been used to detect the hierarchical and modular design of the networks of U.S. parliamentarians. In addition, SVD has been applied to analyze the votes of the U.S. House of Representatives. In this thesis, we analyze the Italian Parliament by using different tools coming from Data Mining and Network Analysis with the aim of characterizing the changes occurred inside the Parliament, without any prior knowledge about the ideology or political affiliation of its representatives, but considering only the votes cast by each parliamentarian.