04393nam 22007095 450 991048436610332120250610110307.03-319-20985-X10.1007/978-3-319-20985-2(CKB)3710000000476907(EBL)4178361(SSID)ssj0001585028(PQKBManifestationID)16264785(PQKBTitleCode)TC0001585028(PQKBWorkID)14864273(PQKB)10832780(DE-He213)978-3-319-20985-2(MiAaPQ)EBC4178361(PPN)190518464(MiAaPQ)EBC29205589(EXLCZ)99371000000047690720150915d2015 u| 0engur|n|---|||||txtccrDescribing Verb Valency Practical and Theoretical Issues /by Mário Alberto Perini1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (300 p.)Description based upon print version of record.3-319-20984-1 Includes bibliographical references and index.Chapter 1 Constructions, Diatheses, Valency -- Chapter 2 Syntactic Functions -- Chapter 3 Semantic Roles in Grammatical Description -- Chapter 4 Delimitation and Definition -- Chapter 5 Core CSRs -- Chapter 6 Coding Semantic Relations -- Chapter 7 Prepositions, Transparency, and Prototypes -- Chapter 8 Hierarchies and Human Subjects -- Chapter 9 Direct Schema Connections -- Chapter 10 Other Aspects of the Role-coding Process -- Chapter 11 Paring Diatheses Down -- Chapter 12 Summary -- Appendix 1 Some Verbs and their Valencies -- Appendix 2 The FrameNet Project (with Larissa Ciríaco) -- Appendix 3 The ADESSE Database -- Appendix 4 The Erlangen Valency Patternbank -- Appendix 5 Other Databases and Valency Dictionaries -- Appendix 6 Why the Passive Construction is a Diathesis of ser.  .The elaboration of linguistic theories depends on the existence of adequate descriptions of particular languages; otherwise theories will be poorly grounded on empirical data. This book starts from theoretical points of wide acceptance among linguists and goes on to present a descriptive metalanguage, able to express the facts of verb valency, which constitute one of the core areas in linguistic description. Most of the data come from an extensive survey under way of the valency of Portuguese verbs; but the present work’s relevance goes well beyond that, and incorporates a proposal applicable to other European languages, illustrated by the wealth of English examples included in the exposition. Among the topics discussed are the syntactic component of constructions (following here a proposal recently published in Culicover and Jackendoff’s Simpler Syntax); delimitation and definition of semantic roles; the role of linking rules and their relation to prototypes; and the connection between linguistic expressions and cognitive units such as frames and schemata. The result is a notational system flexible and robust enough to describe all aspects of verb valency.Applied linguisticsGrammarRomance languagesSemanticsSyntaxApplied Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N13000Grammarhttps://scigraph.springernature.com/ontologies/product-market-codes/N24000Romance Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/N36000Semanticshttps://scigraph.springernature.com/ontologies/product-market-codes/N39000Syntaxhttps://scigraph.springernature.com/ontologies/product-market-codes/N45000Applied linguistics.Grammar.Romance languages.Semantics.Syntax.Applied Linguistics.Grammar.Romance Languages.Semantics.Syntax.410Perini Mário Albertoauthttp://id.loc.gov/vocabulary/relators/aut199325BOOK9910484366103321Describing Verb Valency2855517UNINA10410nam 22006013 450 991101947900332120240220080208.09781119847656111984765697811198476491119847648(MiAaPQ)EBC31167448(Au-PeEL)EBL31167448(OCoLC)1423038685(OCoLC-P)1423038685(CaSebORM)9781119847465(CKB)30404827700041(OCoLC)1422743186(EXLCZ)993040482770004120240220d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Autonomous Vehicles The Future of Driverless Technology1st ed.Newark :John Wiley & Sons, Incorporated,2024.©2024.1 online resource (268 pages)Advances in Data Engineering and Machine Learning Series9781119847465 111984746X Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence in Autonomous Vehicles-A Survey of Trends and Challenges -- 1.1 Introduction -- 1.2 Research Trends of AI for AV -- 1.3 AV-Pipeline Activities -- 1.3.1 Vehicle Detection -- 1.3.2 Rear-End Collision Avoidance -- 1.3.3 Traffic Signal and Sign Recognition -- 1.3.4 Lane Detection and Tracking -- 1.3.5 Pedestrian Detection -- 1.4 Datasets in the Literature of Autonomous Vehicles -- 1.4.1 Stereo and 3D Reconstruction -- 1.4.2 Optical Flow -- 1.4.3 Recognition and Segmentation of Objects -- 1.4.4 Tracking Datasets -- 1.4.5 Datasets for Aerial Images -- 1.4.6 Sensor Synchronization Datasets -- 1.5 Current Industry Standards in AV -- 1.6 Challenges and Opportunities in AV -- 1.6.1 Cost -- 1.6.2 Security Concerns -- 1.6.3 Standards and Regulations -- 1.7 Conclusion -- References -- Chapter 2 Age of Computational AI for Autonomous Vehicles -- 2.1 Introduction -- 2.1.1 Autonomous Vehicles -- 2.1.2 AI in Autonomous Vehicles -- 2.1.2.1 Functioning of AI in Autonomous Vehicles -- 2.2 Autonomy -- 2.2.1 Autonomy Phases -- 2.2.2 Learning Methodologies for Incessant Learning in Real-Life Autonomy Systems -- 2.2.2.1 Supervised Learning -- 2.2.2.2 Unsupervised Learning -- 2.2.2.3 Reinforcement Learning -- 2.2.3 Advancements in Intelligent Vehicles -- 2.2.3.1 Integration of Technologies -- 2.2.3.2 Earlier Application of AI in Automated Driving -- 2.3 Classification of Technological Advances in Vehicle Technology -- 2.4 Vehicle Architecture Adaptation -- 2.5 Future Directions of Autonomous Driving -- 2.6 Conclusion -- References -- Chapter 3 State of the Art of Artificial Intelligence Approaches Toward Driverless Technology -- 3.1 Introduction -- 3.2 Role of AI in Driverless Cars -- 3.2.1 What is Artificial Intelligence? -- 3.2.2 What are Autonomous Vehicles?.3.2.3 History of Artificial Intelligence in Driverless Cars -- 3.2.4 Advancements Over the Years -- 3.2.5 Driverless Cars and the Technology they are Built Upon -- 3.2.6 Advancement of Algorithms -- 3.2.7 Case Study on Tesla -- 3.3 Conclusion -- References -- Chapter 4 A Survey on Architecture of Autonomous Vehicles -- 4.1 Introduction -- 4.1.1 What is Artificial Intelligence? -- 4.1.2 What are Autonomous Vehicles? -- 4.2 A Study on Technologies Used in AV -- 4.2.1 Artificial Vision -- 4.2.2 Varying Light and Visibility Conditions -- 4.2.3 Scenes with a High Dynamic Range (HDR) -- 4.2.3.1 3 Dimensional Technology -- 4.2.3.2 Emerging Vision Technologies -- 4.2.4 Radar -- 4.2.4.1 Emerging Radar Technologies -- 4.2.5 LiDAR -- 4.2.5.1 Emerging LiDAR Technologies -- 4.3 Analysis on the Architecture of Autonomous Vehicles -- 4.3.1 Hardware Architecture -- 4.3.2 Software Architecture -- 4.4 Analysis on One of the Proposed Architectures -- 4.5 Functional Architecture of Autonomous Vehicles -- 4.6 Challenges in Building the Architecture of Autonomous Vehicles -- 4.6.1 Road Condition -- 4.6.2 Weather Condition -- 4.6.3 Traffic Condition -- 4.6.4 Accident Responsibility -- 4.6.5 Radar Interference -- 4.7 Advantages of Autonomous Vehicles -- 4.8 Use Cases for Autonomous Vehicle Technology -- 4.8.1 Five Use Cases -- 4.9 Future Aspects of Autonomous Vehicles -- 4.9.1 Levels of Vehicle Autonomy -- 4.9.2 Safer Mobility Technology -- 4.9.3 Industry Collaboration and Policy Matters -- 4.10 Summary -- References -- Chapter 5 Autonomous Car Driver Assistance System -- 5.1 Introduction -- 5.1.1 Traffic Video Surveillance -- 5.1.2 Need for the Research Work -- 5.2 Related Work -- 5.3 Methodology -- 5.3.1 Intelligent Driver Assistance System -- 5.3.2 Traffic Police Hand Gesture Region Identification -- 5.3.3 Vehicle Brake and Indicator Light Identification.5.4 Results and Analysis -- 5.5 Conclusion -- References -- Chapter 6 AI-Powered Drones for Healthcare Applications -- 6.1 Introduction -- 6.1.1 Role of Artificial Intelligence in Drone Technology -- 6.1.2 Unmanned Aerial Vehicle-Drone Technology -- 6.2 Kinds of Drones Used by Medical Professionals -- 6.2.1 Multirotor -- 6.2.2 Only One Rotor -- 6.2.3 Permanent-Wing Drones -- 6.2.4 Drones for Passenger Ambulances -- 6.3 Medical and Public Health Surveillance -- 6.3.1 Telemedicine -- 6.3.2 Drones as Medical Transportation Devices -- 6.3.3 Advanced System for First Aid for the Elderly People -- 6.4 Potential Benefits of Drones in the Healthcare Industry -- 6.4.1 Top Medical Drone Delivery Services -- 6.4.2 Limitations of Drones in Healthcare -- 6.4.3 The Influence of COVID on Drones -- 6.4.4 Limitations of Drone Technology in the Healthcare Industry -- 6.4.4.1 Privacy -- 6.4.4.2 Legal Concerns -- 6.4.4.3 Rapid Transit-One of the Biggest Drawbacks of Drones is Time -- 6.4.4.4 Bugs in the Technology -- 6.4.4.5 Dependence on Weather -- 6.4.4.6 Hackable Drone Technology -- 6.5 Conclusion -- References -- Chapter 7 An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments -- 7.1 Introduction -- 7.1.1 A Brief Overview of Driverless Cars -- 7.1.2 Objectives -- 7.1.3 Possible Uses for a Car Without a Driver -- 7.2 Related Works -- 7.3 Methodology of the Proposed Work -- 7.4 Experimental Results and Analysis -- 7.5 Results and Analysis -- 7.6 Conclusion -- References -- Chapter 8 Drivers' Emotions' Recognition Using Facial Expression from Live Video Clips in Autonomous Vehicles -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Face Detection -- 8.2.2 Facial Emotion Recognition -- 8.3 Proposed Method -- 8.3.1 Dataset -- 8.3.2 Preprocessing -- 8.3.3 Grayscale Equalization.8.4 Results and Analysis -- 8.5 Conclusions -- References -- Chapter 9 Models for the Driver Assistance System -- 9.1 Introduction -- 9.2 Related Survey -- 9.3 Proposed Methodology -- 9.3.1 Proposed System -- 9.3.2 Data Acquisition -- 9.3.3 Noise Reduction -- 9.3.4 Feature Extraction -- 9.3.5 Histogram of Oriented Gradients -- 9.3.6 Local Binary Pattern -- 9.3.7 Feature Selection -- 9.3.8 Classification -- 9.4 Experimental Study -- 9.4.1 Quantitative Investigation on the NTHU Drowsy Driver Detection Dataset -- 9.5 Conclusion -- References -- Chapter 10 Control of Autonomous Underwater Vehicles -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Control Problem in AUV Control System -- 10.4 Methodology -- 10.5 Results -- References -- Chapter 11 Security and Privacy Issues of AI in Autonomous Vehicles -- 11.1 Introduction -- 11.2 Development of Autonomous Cars with Existing Review -- 11.3 Automation Levels of Autonomous Vehicles -- 11.4 The Architecture of an Autonomous Vehicle -- 11.5 Threat Model -- 11.6 Autonomous Vehicles with AI in IoT-Enabled Environments -- 11.7 Physical Attacks Using AI Against Autonomous Vehicles -- 11.8 AI Cybersecurity Issues for Autonomous Vehicles -- 11.9 Cyberattack Defense Mechanisms -- 11.9.1 Identity-Based Approach -- 11.9.2 Key-Based Solution -- 11.9.3 Trust-Based Solution -- 11.9.4 Solution Based on Behavior Detection -- 11.10 Solution Based on Machine Learning -- 11.11 Conclusion -- References -- Index -- EULA.With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles. There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology. The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.Advances in Data Engineering and Machine Learning SeriesAutomated vehiclesTechnological innovationsAutomated vehiclesTechnological innovations.629.2/046028563Rajendran Sathiyaraj1840039Sabharwal Munish1840040Hu Yu-Chen891622Balusamy Balamurugan1340583MiAaPQMiAaPQMiAaPQBOOK9911019479003321Artificial Intelligence for Autonomous Vehicles4419504UNINA