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Статті в журналах з теми "Elaborazione dei dati"
Presciutti, O. "Aspetti metodologici nell'acquisizione e nell'elaborazione delle immagini funzionali." Rivista di Neuroradiologia 13, no. 1 (February 2000): 95–98. http://dx.doi.org/10.1177/197140090001300117.
Повний текст джерелаGallucci, Mary M., Cecilia Nubola, and Angelo Turchini. "Visite pastorali ed elaborazione dei dati: Esperienze e metodi." Sixteenth Century Journal 26, no. 1 (1995): 188. http://dx.doi.org/10.2307/2541552.
Повний текст джерелаMoro, Alessandro, and Carlo Spediacci. "Elaborazione computerizzata dei dati aerobiologici a costituzione di una banca dati nazionale." Aerobiologia 2, no. 1-2 (December 1986): 14–22. http://dx.doi.org/10.1007/bf02450000.
Повний текст джерелаDe Nisco, Nicola, Sandra Gorla, and Alessia Valenti. "Una banca dati per Petrarca e il suo tempo: criteri, modelli e obiettivi." DigItalia 16, no. 2 (December 2021): 67–90. http://dx.doi.org/10.36181/digitalia-00037.
Повний текст джерелаMozzillo, Giuseppe, and Enrico Todini. "Valutazione del commitment locale di un programma regionale di politiche attive del lavoro. La scala di distanza culturale e valoriale." RIV Rassegna Italiana di Valutazione, no. 45 (October 2010): 27–46. http://dx.doi.org/10.3280/riv2009-045004.
Повний текст джерелаConti Puorger, Adriana, and Pierpaolo Napolitano. "Caratterizzazione socio-economica della regione Marche per sezioni di censimento." RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, no. 2 (September 2011): 30–59. http://dx.doi.org/10.3280/rest2011-002002.
Повний текст джерелаOriggi, D., L. T. Mainardi, A. Falini, G. Calabrese, G. Scotti, S. Cerutti, and G. Tosi. "Quantificazione automatica di spettri 1H ed estrazione di mappe metaboliche da acquisizioni CSI mediante Wavelet Packets." Rivista di Neuroradiologia 13, no. 1 (February 2000): 31–36. http://dx.doi.org/10.1177/197140090001300106.
Повний текст джерелаKnapton, Michael. "Le campagne trevigiane: i frutti di una ricerca." SOCIETÀ E STORIA, no. 130 (February 2011): 771–800. http://dx.doi.org/10.3280/ss2010-130005.
Повний текст джерелаRuggiero, R., E. Covelli, G. Carannante, M. C. Buonocore, and E. Leone. "L'esame Spect nella patologia del SNC in età pediatrica: Esperienze preliminari." Rivista di Neuroradiologia 10, no. 2_suppl (October 1997): 167. http://dx.doi.org/10.1177/19714009970100s268.
Повний текст джерелаGasparotti, R., A. Orlandini, G. F. Gualandi, and A. Chiesa. "Studio preliminare del circolo cerebrale e dei vasi del collo con angiografia tridimensionale a risonanza magnetica." Rivista di Neuroradiologia 2, no. 3 (October 1989): 241–54. http://dx.doi.org/10.1177/197140098900200306.
Повний текст джерелаДисертації з теми "Elaborazione dei dati"
Schipilliti, Luca. "Progetto del software di acquisizione ed elaborazione dei dati di un Sonar multibeam." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21608/.
Повний текст джерелаFaedi, Alberto. "Elaborazione di dati da sensori inerziali per monitoraggio strutturale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14247/.
Повний текст джерелаChiarelli, Rosamaria. "Elaborazione ed analisi di dati geomatici per il monitoraggio del territorio costiero in Emilia-Romagna." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19783/.
Повний текст джерелаMatrone, Erika. "Elaborazione dei dati inerenti alla raccolta dei rifiuti della Regione Emilia Romagna finalizzata all'individuazione dei più performanti sistemi di raccolta." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12934/.
Повний текст джерелаGuidetti, Mattia. "Ricostruzione di flussi veicolari su scala regionale: analisi dei dati disponibili." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5957/.
Повний текст джерелаFioravanti, Matteo. "Sviluppo di tecniche di elaborazione di dati elettroanatomici per l'analisi dei pattern di attivazione elettrica in fibrillazione atriale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Знайти повний текст джерелаLo, Piccolo Salvatore. "La digestione anaerobica dei fanghi prodotti dal depuratore di Savignano sul Rubicone: elaborazione dei dati sperimentali di impianto e simulazione del processo tramite il modello ADM1." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаBaietta, Alessia. "Preparazione dei dati e generazione delle mappe di TC perfusionale nel cancro al polmone." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9279/.
Повний текст джерелаSPALLANZANI, MATTEO. "Un framework per l’analisi dei sistemi di apprendimento automatico." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2020. http://hdl.handle.net/11380/1200571.
Повний текст джерелаMaking predictions is about getting insights into the patterns of our environment. We can access the physical world through media, measuring instruments, which provide us with data in which we hope to find useful patterns. The development of computing machines has allowed storing large data sets and processing them at high speed. Machine learning studies systems which can automate the detection of patterns in large data sets using computers. Machine learning lies at the core of data science and artificial intelligence, two research fields which are changing the economy and the society in which we live. Machine learning systems are usually trained and deployed on powerful computer clusters composed by hundreds or thousands of machines. Nowadays, the miniaturisation of computing devices is allowing deploying them on battery-powered systems embedded into diverse environments. With respect to computer clusters, these devices are far less powerful, but have the advantage of being nearer to the source of the data. On one side, this increases the number of applications of machine learning systems; on the other side, the physical limitations of the computing machines require identifying proper metrics to assess the fitness of different machine learning systems in a given context. In particular, these systems should be evaluated according not only to their modelling and statistical properties, but also to their algorithmic costs and their fitness to different computer architectures. In this thesis, we analyse modelling, algorithmic and architectural properties of different machine learning systems. We present the fingerprint method, a system which was developed to solve a business intelligence problem where statistical accuracy was more important than latency or energy constraints. Then, we analyse artificial neural networks and discuss their appealing computational properties; we also describe an example application, a model we designed to identify the objective causes of subjective driving perceptions. Finally, we describe and analyse quantized neural networks, artificial neural networks which use finite sets for the parameters and step activation functions. These limitations pose challenging mathematical problems, but quantized neural networks can be executed extremely efficiently on dedicated hardware accelerators, making them ideal candidates to deploy machine learning on edge computers. In particular, we show that quantized neural networks are equivalent to classical artificial neural networks (at least on the set of targets represented by continuous functions defined on compact domains); we also present a novel gradient-based learning algorithm for, named additive noise annealing, based on the regularisation effect of additive noise on the argument of discontinuous functions, reporting state-of-the-art results on image classification benchmarks.
PARMIGGIANI, Nicolò. "Metodi per l’analisi e la gestione dei dati dell’astrofisica gamma in tempo reale." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2021. http://hdl.handle.net/11380/1239980.
Повний текст джерелаThe context of this Ph.D. is the data analysis and management for gamma-ray astronomy, which involves the observation of gamma-rays, the most energetic form of electromagnetic radiation. From the gamma-ray observations performed by telescopes or satellites, it is possible to study catastrophic events involving compact objects, such as white dwarves, neutron stars, and black holes. These events are called gamma-ray transients. To understand these phenomena, they must be observed during their evolution. For this reason, the speed is crucial, and automated data analysis pipelines are developed to detect gamma-ray transients and generate science alerts during the astrophysical observations or immediately after. A science alert is an immediate communication from one observatory to other observatories that an interesting astrophysical event is occurring in the sky. The astrophysical community is experiencing a new era called "multi-messenger astronomy", where the astronomical sources are observed by different instruments, collecting different signals: gravitational waves, electromagnetic radiation, and neutrinos. In the multi-messenger era, astrophysical projects share science alerts through different communication networks. The coordination of different projects done by sharing science alerts is mandatory to understand the nature of these physical phenomena. Observatories have to manage the follow-up of these external science alerts by developing dedicated software. During this Ph. D., the research activity had the main focus on the AGILE space mission, currently in operation, and on the Cherenkov Telescope Array Observatory (CTA), currently in the construction phase. The follow-up of external science alerts received from Gamma-Ray Bursts (GRB) and Gravitational Waves (GW) detectors is one of the AGILE Team's current major activities. Future generations of gamma-ray observatories like the CTA or the ASTRI Mini-Array can take advantage of the technologies developed for AGILE. This research aims to develop analysis and management software for gamma-ray data to fulfill the context requirements. The first chapter of this thesis describes the web platform used by AGILE researchers to prepare the Second AGILE Catalog of Gamma-ray sources. The analysis performed for this catalog is stored in a dedicated database, and the web platform queries this database. This was preparatory work to understand how to manage detections of gamma-ray sources and light curve for the subsequent phase: the development of a scientific pipeline to manage gamma-ray detection and science alerts in real-time. The second chapter presents a framework designed to facilitate the development of real-time scientific analysis pipelines. The framework provides a common pipeline architecture and automatisms that can be used by observatories to develop their own pipelines. This framework was used to develop the pipelines for the AGILE space mission and to develop a prototype of the scientific pipeline of the Science Alert Generation system of the CTA Observatory. The third chapter describes a new method to detect GRBs in the AGILE-GRID data using the Convolutional Neural Network. With this Deep Learning technology, it is possible to improve the detection capabilities of AGILE. This method was also integrated as a science tool in the AGILE pipelines. The last chapter of the thesis shows the scientific results obtained with the software developed during the Ph.D. research activities. Part of the results was published in refereed journals. The remaining part was sent to the scientific community through The Astronomer's Telegram or the Gamma-ray Coordination Network.
Книги з теми "Elaborazione dei dati"
Girolamo, Irene Di. Indici biotici per la valutazione della qualità delle acque: Un approccio alle tecniche di campionamento e di elaborazione dei dati. Roma: Istituto superiore di sanità, 1986.
Знайти повний текст джерелаBusacca, Helle. Diario epistolare a Corrado Pavolini. Edited by Serena Manfrida. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-583-4.
Повний текст джерелаElaborazione dei dati sperimentali. Milan: Springer-Verlag, 2005. http://dx.doi.org/10.1007/b139070.
Повний текст джерелаVisite pastorali ed elaborazione dei dati: Esperienze e metodi. Bologna: Società editrice il Mulino, 1993.
Знайти повний текст джерелаDapor, M., and M. Ropele. Elaborazione dei dati sperimentali (UNITEXT / Collana di Fisica e Astronomia). Springer, 2005.
Знайти повний текст джерелаImpianti sportivi, quanti, dove, come, per chi: Indagine sugli impianti sportivi nei comuni della Provincia di Roma ed elaborazione dei dati rilevati. Roma: Officina, 1985.
Знайти повний текст джерелаGuerrieri, Gianluca, Giovanni Luchetti, Michele Angelo Lupoi, Paola Manes, Marco Martino, and Thomas Tassani, eds. Fiducia e destinazione patrimoniale. Bologna University Press, 2022. http://dx.doi.org/10.30682/sg312.
Повний текст джерелаЧастини книг з теми "Elaborazione dei dati"
Baldassarri, Monica, Giuseppe Naponiello, and Giuliana Pagni. "Elaborazione di un sistema di schedatura dati e sviluppo di un web GIS per la consultazione dei dati archeologici: iI caso di Montescudaio in Val di Cecina (PI)." In Open Source Free Software e Open Format nei processi di ricerca archeologica, 51–74. Ubiquity Press, 2013. http://dx.doi.org/10.5334/bae.f.
Повний текст джерелаТези доповідей конференцій з теми "Elaborazione dei dati"
Albissini, Piero, Antonio Catizzone, Laura De Carlo, Laura Carlevaris, Vittorio Di Stefano, and Alessandro Micucci. "Le trasformazioni dello spazio urbano: la quarta dimensione nella georeferenziazione dell’iconografia storica di Rome." In International Conference Virtual City and Territory. Barcelona: Centre de Política de Sòl i Valoracions, 2009. http://dx.doi.org/10.5821/ctv.7549.
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