LEADER 04224nam0 2200529 i 450 001 BVEE056072 005 20170908093228.0 012 $asiLa i-ta u-zo Feza (3) 1681 (A)$2fei 100 $a20140129d1681 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aMemorie della vita, miracoli, e culto di San Gianuario martire vescouo di Beneuento, e principal protettore della città di Napoli. Raccolte da don Camillo Tutini napoletano 205 $aSeconda impressione 210 $aIn Napoli$dper Ottauio Beltrano, 1633. E di nuouo per Gio. Francesco Paci$d1681 215 $a[8], 110, [6] p., [1] c. di tav.$cill.$d4º 300 $aOcchietto 300 $aMarca (O951) a c. π4v 300 $aEffigie calcogr. di San Gennaro sulla c. di tav 300 $aSegn.: π⁴ A-N⁴ O⁶ 300 $aIll. xil. (S. Gennaro. In cornice) sul front 300 $aIniziali e fregi xil. 316 $a1 v. (in v. misc. - Nell'occhietto timbri: Bibliotheca Conventus S. Dominici Majoris)$5IT-NA0079, V.F. 154 H 32 316 $a1 v. (Esemplare mutilo della c. di tav. - Sul contropiatto ant. ex libris Bibliothecae R. Musaei S. 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Raccolte da don Camillo Tutini napoletano$91482153 997 $aUNISANNIO LEADER 05685nam 22007334a 450 001 9910144134803321 005 20201120201350.0 010 $a1-281-76662-3 010 $a9786611766627 010 $a0-470-28315-7 010 $a0-470-28314-9 035 $a(CKB)1000000000539702 035 $a(EBL)353380 035 $a(OCoLC)277561665 035 $a(SSID)ssj0000188018 035 $a(PQKBManifestationID)11167898 035 $a(PQKBTitleCode)TC0000188018 035 $a(PQKBWorkID)10157783 035 $a(PQKB)11153940 035 $a(MiAaPQ)EBC353380 035 $a(EXLCZ)991000000000539702 100 $a20071018d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge-based radar detection, tracking, and classification$b[electronic resource] /$fedited by Fulvio Gini and Muralidhar Rangaswamy 210 $aHoboken, NJ $cWiley$dc2008 215 $a1 online resource (287 p.) 225 1 $aAdaptive and Learning Systems for Signal Processing, Communications and Control Series ;$vv.52 300 $a"A Wiley-Interscience publication." 311 $a0-470-14930-2 320 $aIncludes bibliographical references and index. 327 $aKNOWLEDGE-BASED RADAR DETECTION, TRACKING, AND CLASSIFICATION; CONTENTS; Contributors; 1 Introduction; 1.1 Organization of the Book; Acknowledgments; References; 2 Cognitive Radar; 2.1 Introduction; 2.2 Cognitive Radar Signal-Processing Cycle; 2.3 Radar-Scene Analysis; 2.3.1 Statistical Modeling of Statistical Representation of Clutter- and Target-Related Information; 2.4 Bayesian Target Tracking; 2.4.1 One-Step Tracking Prediction; 2.4.2 Tracking Filter; 2.4.3 Tracking Smoother; 2.4.4 Experimental Results: Case Study of Small Target in Sea Clutter 327 $a2.4.5 Practical Implications of the Bayesian Target Tracker2.5 Adaptive Radar Illumination; 2.5.1 Simulation Experiments in Support of Adjustable Frequency Modulation; 2.6 Echo-Location in Bats; 2.7 Discussion; 2.7.1 Learning; 2.7.2 Applications; 2.7.2.1 Multifunction Radars; 2.7.2.2 Noncoherent Radar Network; Acknowledgments; References; 3 Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview; 3.1 Radar Evolution; 3.2 Taxonomy of Radar; 3.3 Signal Processing; 3.4 Data Processing; 3.5 Introduction to Artificial Intelligence; 3.5.1 Why Robotics and Knowledge-Based Systems? 327 $a3.5.2 Knowledge Base Systems (KBS)3.5.3 Semantic Web Technologies; 3.6 A Global View and KB Algorithms; 3.6.1 An Airborne Autonomous Intelligent Radar System (AIRS); 3.6.2 Filtering, Detection, and Tracking Algorithms and KB Processing; 3.7 Future work; 3.7.1 Target Matched Illumination; 3.7.2 Spectral Interpolation; 3.7.3 Bistatic Radar and Passive Coherent Location; 3.7.4 Synthetic Aperture Radar; 3.7.5 Resource Allocation in a Multifunction Phased Array Radar; 3.7.6 Waveform Diversity and Sensor Geometry; Acknowledgments; References 327 $a4 An Overview of Knowledge-Aided Adaptive Radar at DARPA and Beyond4.1 Introduction; 4.1.1 Background on STAP; 4.1.2 Examples of Real-World Clutter; 4.2 Knowledge-Aided STAP (KA-STAP); 4.2.1 Knowledge-Aided STAP: Back to "Bayes-ics"; 4.2.1.1 Case I: Intelligent Training and Filter Selection (ITFS); 4.2.1.2 Case II: Bayesian Filtering and Data Pre-Whitening; 4.3 Real-Time KA-STAP: The DARPA KASSPER Program; 4.3.1 Obstacles to Real-Time KA-STAP; 4.3.2 Solution: Look-Ahead Scheduling; 4.4 Applying KA Processing to the Adaptive MIMO Radar Problem 327 $a4.5 The Future: Next-Generation Intelligent Adaptive SensorsReferences; 5 Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective; 5.1 Introduction; 5.2 Problem Statement; 5.3 Low Computation Load Algorithms; 5.3.1 Joint Domain Localized Processing; 5.3.2 Parametric Adaptive Matched Filter; 5.3.3 Multistage Wiener Filter; 5.4 Issues of Data Support; 5.4.1 Nonhomogeneity Detection; 5.4.2 Direct Data Domain Methods; 5.4.2.1 Hybrid Approach; 5.5 Knowledge-Aided Approaches; 5.5.1 A Preliminary Knowledge-Based Processor; 5.5.2 Numerical Example; 5.5.3 A Long-Term View 327 $a5.6 Conclusions 330 $aDiscover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems. The book begins with a chapter introducing the concept of Knowledge Based (KB) radar.The remaining nine chapters focus 410 0$aAdaptive and Learning Systems for Signal Processing, Communications and Control Series 606 $aTracking radar 606 $aExpert systems (Computer science) 606 $aAutomatic tracking 606 $aTarget acquisition 606 $aAdaptive signal processing 608 $aElectronic books. 615 0$aTracking radar. 615 0$aExpert systems (Computer science) 615 0$aAutomatic tracking. 615 0$aTarget acquisition. 615 0$aAdaptive signal processing. 676 $a621.389/28 676 $a621.38928 700 $aGini$b Fulvio$0964138 701 $aRangaswamy$b Muralidhar$0964139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144134803321 996 $aKnowledge-based radar detection, tracking, and classification$92186508 997 $aUNINA