Intel corporation (20240289930). DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES simplified abstract
Contents
- 1 DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Deep Learning-Based Real-Time Detection and Correction of Compromised Sensors in Autonomous Machines
- 1.11 Frequently Updated Research
- 1.12 Original Abstract Submitted
DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES
Organization Name
Inventor(s)
Xiaopei Zhang of Shanghai (CN)
DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289930 titled 'DEEP LEARNING-BASED REAL-TIME DETECTION AND CORRECTION OF COMPROMISED SENSORS IN AUTONOMOUS MACHINES
Simplified Explanation
The mechanism described in the patent application helps detect and correct compromised sensors in autonomous machines using deep learning technology.
- Detection and capturing logic allows sensors, like cameras, to capture images of a scene, identifying unclear images.
- Classification and prediction logic uses a deep learning model to identify compromised sensors in real-time.
Key Features and Innovation
- Real-time detection and correction of compromised sensors in autonomous machines.
- Utilization of deep learning technology for accurate identification of sensor issues.
- Enhances the reliability and performance of autonomous systems by ensuring sensor integrity.
Potential Applications
- Autonomous vehicles
- Robotics
- Surveillance systems
- Industrial automation
Problems Solved
- Ensures the accuracy and reliability of sensor data in autonomous machines.
- Prevents potential malfunctions or errors due to compromised sensors.
- Improves overall safety and efficiency of autonomous systems.
Benefits
- Increased reliability and accuracy of sensor data.
- Enhanced performance and safety of autonomous machines.
- Real-time detection and correction of sensor issues.
Commercial Applications
"Deep Learning-Based Real-Time Detection and Correction of Compromised Sensors in Autonomous Machines" can be utilized in various industries such as autonomous vehicles, robotics, surveillance systems, and industrial automation. This technology can improve the reliability, accuracy, and safety of autonomous systems, leading to increased efficiency and reduced risks in operations.
Questions about Deep Learning-Based Real-Time Detection and Correction of Compromised Sensors in Autonomous Machines
1. How does deep learning technology help in identifying compromised sensors in real-time? 2. What are the potential implications of this technology in enhancing the performance of autonomous machines?
Frequently Updated Research
Research on deep learning algorithms and sensor fusion techniques in autonomous systems is constantly evolving. Stay updated on the latest advancements in sensor technology and machine learning algorithms to further enhance the capabilities of autonomous machines.
Original Abstract Submitted
a mechanism is described for facilitating deep learning-based real-time detection and correction of compromised sensors in autonomous machines according to one embodiment. an apparatus of embodiments, as described herein, includes detection and capturing logic to facilitate one or more sensors to capture one or more images of a scene, where an image of the one or more images is determined to be unclear, where the one or more sensors include one or more cameras. the apparatus further comprises classification and prediction logic to facilitate a deep learning model to identify, in real-time, a sensor associated with the image.
