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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Hardware Architectures / / edited by Sudeep Pasricha, Muhammad Shafique
| Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Hardware Architectures / / edited by Sudeep Pasricha, Muhammad Shafique |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (418 pages) |
| Disciplina | 006.22 |
| Soggetto topico |
Embedded computer systems
Cooperating objects (Computer systems) Artificial intelligence Embedded Systems Cyber-Physical Systems Artificial Intelligence |
| ISBN |
9783031195686
303119568X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning. |
| Record Nr. | UNINA-9910760273503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems / / edited by Vipin Kumar Kukkala, Sudeep Pasricha
| Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems / / edited by Vipin Kumar Kukkala, Sudeep Pasricha |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (782 pages) |
| Disciplina | 006.22 |
| Altri autori (Persone) |
KukkalaVipin Kumar
PasrichaSudeep |
| Soggetto topico |
Embedded computer systems
Electronic circuit design Cooperating objects (Computer systems) Embedded Systems Electronics Design and Verification Cyber-Physical Systems |
| ISBN |
9783031280160
3031280164 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Reliable Real-time Message Scheduling in Automotive Cyber-Physical Systems -- Chapter 2 Evolvement of Scheduling Theories for Autonomous Vehicles -- Chapter 3 Distributed Coordination and Centralized Scheduling for Automobiles at Intersections -- Chapter 4 Security Aware Design of Time-Critical Automotive Cyber-Physical Systems -- Chapter 5 Secure by Design Autonomous Emergency Braking Systems in Accordance with ISO 21434 -- Chapter 6 Resource Aware Synthesis of Automotive Security Primitives -- Chapter 7 Gradient-free Adversarial Attacks on 3D Point Clouds from LiDAR Sensors -- Chapter 8 Internet of Vehicles- Security and Research Road map -- Chapter 9 Protecting Automotive Controller Area Network: A Review on Intrusion Detection Methods Using Machine Learning Algorithms -- Chapter 10 Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders -- Chapter 11 Stacked LSTMs based Anomaly Detection in Time-Critical Automotive Networks -- Chapter 12 Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems -- Chapter 13 Physical Layer Intrusion Detection and Localization on CAN bus -- Chapter 14 Spatiotemporal Information based Intrusion Detection Systems for In-vehicle Networks -- Chapter 15 In-Vehicle ECU Identification and Intrusion Detection from Electrical Signaling -- Chapter 16 Machine Learning for Security Resiliency in Connected Vehicle Applications -- Chapter 17 Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open Challenges -- Chapter 18 Scene-Graph Embedding for Robust Autonomous Vehicle Perception -- Chapter 19 Sensing Optimization in Automotive Platforms -- Chapter 20 Unsupervised Random Forest Learning for Traffic Scenario Categorization -- Chapter 21 Development of Computer Vision Models for Drivable Region Detection in Snow Occluded Lane Lines.-Chapter 22 Machine Learning Based Perception Architecture Design for Semi-Autonomous Vehicles -- Chapter 23 -- Predictive Control During Acceleration Events to Improve Fuel Economy -- Chapter 24 Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic -- Chapter 25 Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering -- Chapter 26 Safety-assured Design and Adaptation of Connected and Autonomous Vehicles -- Chapter 27 Identifying and Assessing Research Gaps for Energy Efficient Control of Electrified Autonomous Vehicle Eco-driving. |
| Record Nr. | UNINA-9910743687003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning for Indoor Localization and Navigation / / edited by Saideep Tiku, Sudeep Pasricha
| Machine Learning for Indoor Localization and Navigation / / edited by Saideep Tiku, Sudeep Pasricha |
| Autore | Tiku Saideep |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (563 pages) |
| Disciplina | 621.384191 |
| Altri autori (Persone) | PasrichaSudeep |
| Soggetto topico |
Embedded computer systems
Cooperating objects (Computer systems) Microprocessors Computer architecture Embedded Systems Cyber-Physical Systems Processor Architectures |
| ISBN |
9783031267123
3031267125 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Indoor Localization and its Challenges -- Advanced Pattern-Matching Techniques for Indoor Localization -- Machine Learning Approaches for Resilience to Device Heterogeneity -- Enabling Temporal Variation Resilience for ML based Indoor Localization -- Deploying Indoor Localization Frameworks for Resource Constrained Devices -- Securing Indoor Localization Frameworks. |
| Record Nr. | UNINA-9910734847903321 |
Tiku Saideep
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||