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1. |
Record Nr. |
UNINA9910637698903321 |
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Autore |
Laoutaris Nikos |
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Titolo |
DE'22 : proceedings of the 1st International Workshop on Data Economy : December 9, 2022, Rome, Italy / / Nikos Laoutaris, Marco Mellia |
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Pubbl/distr/stampa |
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New York, New York : , : Association for Computing Machinery, , 2022 |
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Descrizione fisica |
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1 online resource (70 pages) : illustrations |
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Disciplina |
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Soggetti |
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Big data |
Personal information management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Data-driven decision making powered by Machine Learning (ML) algorithms is changing how the society and the economy work and is having a profound positive impact on our daily life. With the exception of very large companies that have both the data and the skills to develop powerful ML-driven services, the large majority of provably possible ML services, from e-health, to transportation and predictive maintenance, to name just a few, still remain at the idea or prototype level for the simple reason that data, the skills to manipulate them, and the business models to bring them to market, seldom co-exist under the same roof. Data must somehow meet with the ML and business skills that can unleash its full power for the society and economy. This has given rise to a highly dynamic sector around the Data Economy, involving Data Providers/Controllers, data Intermediaries, often-times in the form of Data Marketplaces or Personal Information Management Systems for end-users to control and even monetise their personal data. |
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2. |
Record Nr. |
UNINA9910484519603321 |
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Autore |
Ma Hongbin |
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Titolo |
Kalman Filtering and Information Fusion / / by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (xvii, 291 pages) : illustrations |
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Disciplina |
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Soggetti |
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Automatic control |
Robotics |
Automation |
Engineering mathematics |
Engineering - Data processing |
System theory |
Control theory |
Electrical engineering |
Control, Robotics, Automation |
Mathematical and Computational Engineering Applications |
Systems Theory, Control |
Electrical and Electronic Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Preface -- Part I Kalman Filtering: Preliminaries -- Part II Kalman Filtering for Uncertain Systems -- Part III Kalman Filtering for Multi-Sensor Systems -- Part IV Kalman Filtering for Multi-Agent Systems. |
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Sommario/riassunto |
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This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like |
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intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques. Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields. To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus. |
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