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Record Nr. |
UNINA990002704060403321 |
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Autore |
Kafer, Karl <1898- > |
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Titolo |
Theory of accounts in double -Entry book keeping. / by Kafer K. |
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Pubbl/distr/stampa |
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Illinois : Center for Intern. Educ |
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Locazione |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9911019714703321 |
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Autore |
Lei Tao |
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Titolo |
Image segmentation : principles, techniques, and applications / / Tao Lei |
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Pubbl/distr/stampa |
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Hoboken, NJ : , : Wiley-Blackwell, , 2023 |
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ISBN |
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9781119859024 |
1119859026 |
9781119859031 |
1119859034 |
9781119859048 |
1119859042 |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Image segmentation |
Image segmentation - Mathematical models |
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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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Sommario/riassunto |
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Image Segmentation Summarizes and improves new theory, methods, |
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and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors-such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression-to assist graduate students and researchers apply and improve image segmentation in their work. * Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. * Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. * Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. * Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods. |
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3. |
Record Nr. |
UNINA9910743687003321 |
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Titolo |
Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems / / edited by Vipin Kumar Kukkala, Sudeep Pasricha |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (782 pages) |
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Altri autori (Persone) |
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KukkalaVipin Kumar |
PasrichaSudeep |
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Disciplina |
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Embedded computer systems |
Electronic circuit design |
Cooperating objects (Computer systems) |
Embedded Systems |
Electronics Design and Verification |
Cyber-Physical Systems |
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Nota di contenuto |
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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 |
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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. |
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Sommario/riassunto |
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This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles. The book describes state-of-the-art solutions to design secure, robust, and time-critical automotive systems; Various approaches are discussed that will impact the design of emerging autonomous vehicle systems; The content is relevant to researchers and industry practitioners interested in future automotive platforms. . |
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