01148 am a2200265 i 4500991003177729707536040719 1998 aob 0856981036b13035666-39ule_instJeffreys, D.G.488497The Anubieion at Saqqara 1. :the Settlement and the Temple Precinct /by D.G. Jeffreys and H.S. Smith ; with a chapter by M. Jessop PriceLondon :Egypt exploration society,1988XII, 115 p., 123 p. : ill. ; 32 cmExcavation Memoirs ; 54ScaviScavi archeologiciEgittoSaqqarahSmith, H.S.Price, M. Jessop.b1303566607-06-1219-07-04991003177729707536LE007 932 JEF 01.0112007000080733le007-E0.00-l- 00000.i1365303919-07-04LE002 Museo Papirologico BELT Coll. ExcMem 05412002000844705le002gE45.00-no 00000.i1541923x07-06-12Anubieion at Saqqara 1.1747297UNISALENTOle007le00219-07-04ma -enguk 4002190nam 2200373 450 991063769890332120230830145010.0(CKB)5720000000119634(NjHacI)995720000000119634(EXLCZ)99572000000011963420230830d2022 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDE'22 proceedings of the 1st International Workshop on Data Economy : December 9, 2022, Rome, Italy /Nikos Laoutaris, Marco MelliaNew York, New York :Association for Computing Machinery,2022.1 online resource (70 pages) illustrations1-4503-9923-1 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.Big dataCongressesPersonal information managementCongressesBig dataPersonal information management005.7Laoutaris Nikos1421718Mellia MarcoNjHacINjHaclBOOK9910637698903321DE'223543810UNINA04285nam 22007335 450 991048451960332120251127082720.0981-15-0806-210.1007/978-981-15-0806-6(CKB)4100000009940020(MiAaPQ)EBC5986811(DE-He213)978-981-15-0806-6(PPN)243768400(EXLCZ)99410000000994002020191127d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierKalman Filtering and Information Fusion /by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu1st ed. 2020.Singapore :Springer Nature Singapore :Imprint: Springer,2020.1 online resource (xvii, 291 pages) illustrations981-15-0805-4 Includes bibliographical references.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.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 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.Automatic controlRoboticsAutomationEngineering mathematicsEngineeringData processingSystem theoryControl theoryElectrical engineeringControl, Robotics, AutomationMathematical and Computational Engineering ApplicationsSystems Theory, ControlElectrical and Electronic EngineeringAutomatic control.Robotics.Automation.Engineering mathematics.EngineeringData processing.System theory.Control theory.Electrical engineering.Control, Robotics, Automation.Mathematical and Computational Engineering Applications.Systems Theory, Control.Electrical and Electronic Engineering.629.8312Ma Hongbinauthttp://id.loc.gov/vocabulary/relators/aut720640Yan Lipingauthttp://id.loc.gov/vocabulary/relators/autXia Yuanqingauthttp://id.loc.gov/vocabulary/relators/autFu Mengyinauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910484519603321Kalman Filtering and Information Fusion2855130UNINA