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1. |
Record Nr. |
UNINA990004450910403321 |
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Titolo |
HISTORY of Philosophy eastern and Western / editorial board Sarvepalli Radhakrishnan |
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Pubbl/distr/stampa |
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London, : George Allen, s.d. |
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Descrizione fisica |
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Locazione |
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Collocazione |
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5/ VI I 4(1) |
5/ VI I 4(2) |
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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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2. |
Record Nr. |
UNINA9910842292703321 |
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Autore |
Niu Haoyu |
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Titolo |
Smart Big Data in Digital Agriculture Applications : Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence / / by Haoyu Niu, YangQuan Chen |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (243 pages) |
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Collana |
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Agriculture Automation and Control, , 2731-3506 |
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Disciplina |
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Soggetti |
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Agriculture |
Plant physiology |
Quantitative research |
Engineering design |
Plant Physiology |
Data Analysis and Big Data |
Engineering Design |
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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 and index. |
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Nota di contenuto |
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Part I Why Big Data Is Not Smart Yet? -- 1. Introduction -- 2. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? -- Part II Smart Big Data Acquisition Platforms -- 3. Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 4. The Edge-AI Sensors and Internet of Living Things (IoLT) -- 5. The Unmanned Ground Vehicles (UGVs) for Digital Agriculture -- Part III Advanced Big Data Analytics, Plant Physiology-informed Machine Learning, and Fractional-order Thinking -- 6. Fundamentals of Big Data, Machine Learning, and Computer VisionWorkflow -- 7. A Low-cost Proximate Sensing Method for Early Detection of Nematodes inWalnut Using Machine Learning Algorithms -- 8. Tree-level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- 9. Individual Tree-level Water Status Inference Using High-resolution UAV Thermal Imagery and Complexity-informed Machine Learning -- 10. Scale-aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning -- Part IV Towards Smart Big Data in Digital Agriculture -- 11. Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers -- 12. A Non-invasive Stem Water Potential Monitoring Method Using Proximate Sensor and Machine Learning Classification Algorithms -- 13. A Low-cost Soil Moisture Monitoring Method by Using Walabot and Machine Learning Algorithms -- 14. Conclusions and Future Research. |
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Sommario/riassunto |
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In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent “complexity” and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it “smart” is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization? 2.) How can we achieve a more optimal optimization? 3.) Is it possible to demand “more optimal machine learning,” exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture? This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the "Agriculture Automation and Control" book series published by Springer. |
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3. |
Record Nr. |
UNINA9910741163103321 |
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Titolo |
Foundations of Software Science and Computation Structures : 16th International Conference, FOSSACS 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013, Proceedings / / edited by Frank Pfenning |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (XXIV, 451 p. 51 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 7794 |
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Disciplina |
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Soggetti |
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Machine theory |
Computer science |
Compilers (Computer programs) |
Software engineering |
Formal Languages and Automata Theory |
Computer Science Logic and Foundations of Programming |
Compilers and Interpreters |
Software Engineering |
Theory of Computation |
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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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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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Pattern Graphs and Rule-Based Models: The Semantics of Kappa -- History-Register Automata -- Fatal Attractors in Parity Games -- On Unique Decomposition of Processes in the Applied π-Calculus -- Bounded Context-Switching and Reentrant Locking -- Reachability of Communicating Timed Processes -- Modular Bisimulation Theory for Computations and Values -- Checking Bisimilarity for Attributed Graph Transformation -- Comodels and Effects in Mathematical Operational Semantics -- Preorders on Monads and Coalgebraic Simulations -- A Proof System for Compositional Verification of Probabilistic Concurrent Processes -- Partiality and Recursion in Higher-Order Logic -- Some Sahlqvist Completeness Results for Coalgebraic Logics -- Cut |
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Elimination in Nested Sequents for Intuitionistic Modal Logics -- On Monadic Parametricity of Second-Order Functionals -- Deconstructing General References via Game Semantics -- Separation Logic for Non-local Control Flow and Block Scope Variables -- The Parametric Ordinal-Recursive Complexity of Post Embedding Problems -- Deciding Definability by Deterministic Regular Expressions -- Type-Based Complexity Analysis for Fork Processes -- Pure Pointer Programs and Tree Isomorphism -- A Language for Differentiable Functions -- Computing Quantiles in Markov Reward Models -- Parameterized Weighted Containment -- Weighted Specifications over Nested Words -- An Algebraic Presentation of Predicate Logic -- Strategies as Profunctors -- Generalised Name Abstraction for Nominal Sets. |
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Sommario/riassunto |
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This book constitutes the proceedings of the 16th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2013, held as part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2013, which took place in Rome, Italy, in March 2013 The 28 papers presented in this volume were carefully reviewed and selected from 109 submissions. They are organized in topical sections named: models of computation; reasoning about processes; bisimulation; modal and higher-order logics; reasoning about programs; computational complexity; quantitative models; and categorical models. |
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