Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application (eBook, ePUB)
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Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application explores the power of artificial intelligence (AI) in advancing sensor technologies and computer vision for healthcare and automation. Covering both machine learning (ML) and deep learning (DL) techniques, the book demonstrates how AI optimizes prediction, classification, and data visualization through sensors like IMU, Lidar, and Radar. Early chapters examine AI applications in object detection, self-driving vehicles, human activity recognition, and robot automation, featuring reinfor...
Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application explores the power of artificial intelligence (AI) in advancing sensor technologies and computer vision for healthcare and automation. Covering both machine learning (ML) and deep learning (DL) techniques, the book demonstrates how AI optimizes prediction, classification, and data visualization through sensors like IMU, Lidar, and Radar. Early chapters examine AI applications in object detection, self-driving vehicles, human activity recognition, and robot automation, featuring reinforcement learning and simultaneous localization and mapping (SLAM) for autonomous systems. The book also addresses computer vision techniques in healthcare and automotive fields, including human pose estimation for rehabilitation and ML in augmented reality (AR) for automotive design. This comprehensive guide provides essential insights for researchers, engineers, and professionals in AI, robotics, and sensor technology.
Key Features:
- In-depth coverage of AI-driven sensor innovations for healthcare and automation.
- Applications of SLAM and reinforcement learning in autonomous systems.
- Use of computer vision in rehabilitation and vehicle automation.
- Techniques for managing prediction uncertainty in AI models.
Readership:
Graduate, undergraduate students, researchers, working professionals, and general readers.
Key Features:
- In-depth coverage of AI-driven sensor innovations for healthcare and automation.
- Applications of SLAM and reinforcement learning in autonomous systems.
- Use of computer vision in rehabilitation and vehicle automation.
- Techniques for managing prediction uncertainty in AI models.
Readership:
Graduate, undergraduate students, researchers, working professionals, and general readers.
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