Concordia liberi arbitrii cum gratiae donis, diuina praescientia, prouidentia, praedestinatione, et reprobatione, ad nonnullos primae partis D. Thomae articulos. Doctore Ludouico Molina ... autore. Adiecti sunt duo indices, rerum alter, alter eorum scripturae locorum, ..

• Record Nr.: UNISANNIOBVEE019560
• Autore: Molina, Luis : de <1536-1600>
• Pubbl/distr/stampa: Olyssipone : apud Antonium Riberium typographum regium : expensis Ioannis Hispani & Michaelis de Arenas bibliopolarum, 1588
• Descrizione fisica: 2 pt. (\12!, 512, \48!; 44 p.) ; 4º
• Collocazione: BUZ.B. 0368
• Lingua di pubblicazione: Latino
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Segue con proprio front.: Appendix ad concordiam liberi arbitrii cum gratiae donis, diuina praescientia, prouidentia, praedestinatione, et reprobatione. Doctore Ludouico Molina ... autore; Olyssippone : apud Emman. de Lyra typographum, 1589; Cors. ; rom; Segn.: a⁶A-2M⁸; A-B⁸C⁶; Iniziali e fregi xil; Insegna dei Gesuiti sui front.

Near-sensor and in-sensor computing / / Yang Chai and Fuyou Liao, editors

• Record Nr.: UNISA996495560503316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-11506-6
• Descrizione fisica: 1 online resource (237 pages)
• Disciplina: 006.22
• Soggetti: Cooperating objects (Computer systems); Sensor networks; Neural networks (Computer science)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Contents -- Chapter 1: Neuromorphic Computing Based on Memristor Dynamics -- 1.1 Introduction -- 1.2 Artificial Synapses -- 1.2.1 Long-Term Plasticity -- 1.2.2 Short-Term Plasticity -- 1.3 Artificial Neuron -- 1.3.1 H-H Neuron -- 1.3.2 LIF Neurons -- 1.3.3 Oscillation Neuron -- 1.3.4 Artificial Dendrites -- 1.4 Memristor-Based Neuromorphic Computing Systems -- 1.4.1 Memristive Reservoir Computing Systems -- 1.4.2 Memristor-Based Coupled Oscillator Network -- 1.4.3 Memristor-Based Continuous Attractor Neural Network -- 1.4.4 Memristive Spiking Neural Network -- 1.4.5 Memristor-Based Chaotic Computing -- 1.5 Conclusions and Outlook -- References -- Chapter 2: Short-Term Plasticity in 2D Materials for Neuromorphic Computing -- 2.1 Introduction -- 2.2 Sound Localization via 2D Synaptic Devices -- 2.2.1 Short-Term Plasticity -- 2.2.2 Joule Heating for STP -- 2.2.3 Tunable STP in 2D Material-Based Synaptic Devices -- 2.2.4 Synaptic Computation for Sound Localization -- 2.2.5 2D Materials Engineering for Optimized STP -- 2.3 In-Sensor Reservoir Computing for Language Learning via 2D Memristors -- 2.3.1 Recurrent Neural Networks and Reservoir Computing -- 2.3.2 SnS Memristor for Optoelectronic RC -- 2.3.3 Spatiotemporal Signal Processing in a Circuit Based on SnS Memristors -- 2.3.4 Optoelectronic RC for the Learning of the Korean Language -- 2.3.5 ; Inference of Korean Sentences via Optoelectronic RC -- 2.4 Conclusion and Outlook -- References -- Chapter 3: Bioinspired In-Sensor Computing Devices for Visual Adaptation -- 3.1 Introduction -- 3.2 The Visual Adaptation of the Retina -- 3.3 In-Sensor Visual Adaptation Based on Emerging Devices -- 3.3.1 Two-Terminal Optoelectronic Devices -- 3.3.2 Three-Terminal Phototransistors -- 3.3.3 Optoelectronic Circuits -- 3.4 Conclusion and Future Prospects -- References.; Chapter 4: Neuromorphic Vision Based on van der Waals Heterostructure Materials -- 4.1 Introduction -- 4.2 Retinomorphic Sensor -- 4.2.1 Mimicking Retinal Cells with Vertical Heterostructure Devices -- 4.2.2 Reconfigurable Retinomorphic Vision Sensor -- 4.3 Neuromorphic Vision System -- 4.3.1 The Architecture of Neuromorphic Vision System -- 4.3.2 The Function of Neuromorphic Vision System -- 4.4 Conclusion -- References -- Chapter 5: Neuromorphic Vision Chip -- 5.1 Introduction -- 5.2 Vision Chip Architectures -- 5.2.1 Early Frame-Driven Vision Chips -- 5.2.2 Dynamically Reconfigurable Vision Chips -- 5.2.2.1 Architecture Design of Dynamically Reconfigurable Vision Chips -- 5.2.2.2 Architecture Characteristics of Dynamically Reconfigurable Vision Chips -- 5.2.3 Convolutional Neural Network-Oriented Vision Chip Architecture -- 5.2.3.1 Convolutional Neural Network-Oriented Vision Chip Architecture Design -- 5.2.3.2 Optimization Strategy -- 5.2.3.3 Development Status of the Convolutional Neural Network Accelerator -- 5.2.4 Programmable Parallel Vision Chip -- 5.2.4.1 Design of Programmable Parallel Architecture -- 5.2.4.2 Features of Programmable Parallel Architecture -- 5.3 Visual Tasks and Software on Vision Chips -- 5.3.1 Categories of Various Visual Tasks and Model Selection -- 5.3.2 Network Architecture Determination -- 5.3.2.1 On-Chip Memory -- 5.3.2.2 On-Chip Computing Resources -- 5.3.2.3 Specialized Hardware Units -- 5.3.2.4 Other Constraints -- 5.3.3 Model Compression on Vision Chips -- 5.3.3.1 Lightweight Model Pruning -- 5.3.3.1.1 Fine-Grained Pruning and Structured Pruning -- 5.3.3.1.2 Regularization-Based Structured Pruning -- 5.3.3.2 Network Quantization -- 5.3.3.2.1 Post-training Quantization and Quantization-Aware Training -- 5.3.3.3 Precision Alignment After Quantization.; 5.3.3.3.1 Model Accuracy Verification on a Vision Chip Simulator -- 5.3.3.3.2 Solutions for Accuracy Mismatch -- 5.3.4 Model Mapping on Vision Chips -- 5.3.4.1 Dataflow on Vision Chips -- 5.3.4.1.1 Integer Inference Dataflow -- 5.3.4.1.2 Overflow Handling and Bit-Shifting -- 5.3.4.2 Manually Designed Operator Library -- 5.3.4.2.1 Interface Design of the Library -- 5.3.4.2.2 Performance Optimization of the Library -- 5.3.4.2.3 Mapping of the Model to the Library -- 5.4 Conclusion -- References -- Chapter 6: Collision Avoidance Systems and Emerging Bio-inspired Sensors for Autonomous Vehicles -- 6.1 Introduction -- 6.2 Sensors for CAS -- 6.2.1 Light Detection and Ranging (LiDAR) -- 6.2.2 Radio Detection and Ranging (Radar) -- 6.2.3 Ultrasonic Sensors -- 6.2.4 Image Sensors -- 6.3 Bio-inspired Sensors -- 6.4 Sensor Fusion Technology -- 6.5 Summary -- References -- Chapter 7: Emerging Devices for Sensing-Memory-Computing Applications -- 7.1 Introduction -- 7.1.1 History and Emerging Technology -- 7.1.2 The Challenge for Sensing-Memory-Computing -- 7.1.2.1 Material System and Device Structure -- 7.1.2.2 Array Integration -- 7.2 Metal Oxide-Based Sensing-Memory-Computing Device -- 7.2.1 Research Background and Status -- 7.2.2 Several Device Types Based on Metal Oxide Film -- 7.2.2.1 Single Two-Terminal Memristor -- 7.2.2.2 Memristor Array -- 7.2.3 Issues ; and Challenges -- 7.2.4 Conclusion and Outlook -- 7.3 Two-Dimensional Sensing-Memory-Computing Device -- 7.3.1 Research Background and Status -- 7.3.1.1 Two-Dimensional Materials -- 7.3.1.2 Library of 2D Materials and Their Heterostructures -- 7.3.1.3 Preparation of 2D Materials and Their Heterostructures -- 7.3.2 Several Device Types Based on Two-Dimensional Film -- 7.3.2.1 Charge-Based Device -- 7.3.2.2 Resistive Switching Device -- 7.3.3 Issues and Challenges -- 7.3.4 Conclusion and Outlook.; 7.4 Organic Sensing-Memory-Computing Device -- 7.4.1 Research Background and Status -- 7.4.2 Several Device Types Based on Organic Film -- 7.4.2.1 Tactile Sensing-Memory-Computing Device -- 7.4.2.2 Visual Sensing-Memory-Computing Device -- 7.4.2.3 Olfaction Sensing-Memory-Computing Device -- 7.4.2.4 Auditory Sensing-Memory-Computing Device -- 7.4.3 Issues and Challenges -- 7.4.4 Conclusion and Outlook -- 7.5 Phase Change Sensing-Memory-Computing Device -- 7.5.1 Research Background and Status -- 7.5.2 Several Device Types Based on Phase Change Material -- 7.5.3 Issues and Challenges -- 7.5.4 Conclusion and Outlook -- 7.6 Ferroelectric Sensing-Memory-Computing Device -- 7.6.1 Research Background and Status -- 7.6.2 Several Device Types Based on Ferroelectric Material -- 7.6.2.1 Piezoelectric Device -- 7.6.2.2 Logic Device -- 7.6.2.3 Storage Device -- 7.6.2.4 Optical Device -- 7.6.3 Issues and Challenges -- 7.6.4 Conclusion and Outlook -- 7.7 Conclusion -- References -- Chapter 8: Neural Computing with Photonic Media -- 8.1 Introduction -- 8.2 Nanophotonic Medium for Neural Computing -- 8.2.1 Implementation -- 8.2.2 Training Process -- 8.3 Fabrication Constraints -- 8.3.1 B-Splines -- 8.4 Neuromorphic Metasurfaces -- 8.4.1 System Setup -- 8.4.2 Training Process -- 8.4.3 Results -- 8.5 Conclusion -- References -- Chapter 9: Multimodal Sensory Computing -- 9.1 Introduction -- 9.2 Multisensory Integration Modeling -- 9.2.1 Optimal Cue Integration -- 9.2.2 Normalization Model -- 9.2.3 Dynamic Adjustment -- 9.3 Hardware Implementation -- 9.4 Outlook -- References -- Index.

Procli Diadochi Tria Opuscula : de Providentia, Libertate, Malo : Latine Guilelmo de Moerbeka Vertente et Graece Ex Isaacii Sebastocratoris Aliorumque Scriptis Collecta / / [ed. H. Boese]

• Record Nr.: UNINA9910973061403321
• Autore: Proclus <approximately 410-485.>
• Pubbl/distr/stampa: Berlin, : De Gruyter, 1960
• ISBN: 3-11-163087-0
• Edizione: [Reprint 2011]
• Descrizione fisica: 1 online resource (375 p.)
• Collana: Quellen und Studien zur Geschichte der Philosophie ; ; 1
• Altri autori (Persone): BoeseHelmut
• Disciplina: 888
• Soggetti: Providence and government of God
• Lingua di pubblicazione: Latino
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Front matter -- TABULA -- PRAEFATIO -- I. DE TRIBUS PROCLI OPUSCULIS -- II. DE VERSIONE LATINA -- ΙII. DE MEMORIA TEXTUS -- IV. DE TESTIMONIIS TEXTUS GRAECI -- V. DE RATIONE EDENDI -- INDICULUS COMPENDIORUM ET OPERUM IN ADNOTATIONIBUS ALLATORUM -- PROCLI DIADOCHI. TRIA OPUSCULA -- CONSPECTUS SIGLORUM -- I. PROCLI DIADOCHI. DE DECEM DUBITATIONIBUS CIRCA PROVIDENTIAM -- II. EIUSDEM PROCLI. DE PROVIDENTIA ET FATO ET EO QUOD IN NOBIS AD THEODORUM MECHANICUM -- III: INCIPIT LIBER EIUSDEM PROCLI DE MALORUM SUBSISTENTIA -- APPENDIX: MARGINALIA GRAECA CODICIS VATICANI (Vat. lat. 4568) -- INDICES -- I. INDEX AUCTORUM -- II. INDICES VERBORUM