Neural Network Signal Correction for Sensor Stabilization
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Solution Overview
Problem
Existing signal processing technologies face challenges in effectively correcting input signals from sensors, particularly in removing unwanted components like centrifugal force and noise, which affects the performance of stabilization processing in imaging devices.
Innovation Solution
A signal processing device equipped with a feature quantity extraction unit using a neural network to identify and extract specific features such as centrifugal force or imaging noise, allowing for targeted correction of input signals, thereby improving stabilization performance and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional filter processing is used to remove unnecessary components from sensor signals, then the processing can be implemented with simple mathematical equations, but the correction performance is insufficient for complex events like centrifugal force and vibration
Solution Approach 1:
The patent replaces traditional mathematical filter processing with a neural network-based AI system. The neural network learns to extract feature quantities representing complex events (centrifugal force, vibration, imaging noise) from sensor signals, enabling superior correction performance that cannot be achieved with conventional mathematical equations alone.
Solution Approach 2:
The patent introduces a neural network as an intermediary component between the sensor and the correction processing. This intermediary learns to represent complex events through feature quantity extraction, serving as a bridge that enables effective correction of difficult-to-model phenomena without requiring complex mathematical formulations.
2Reliability
If complex correction processing is implemented to handle difficult events like centrifugal force and focus, then the correction performance improves, but the processing becomes more difficult to implement and adjust
Solution Approach 1:
The neural network performs self-learning during the training phase, automatically extracting feature quantities for complex events from labeled data. This self-service learning process eliminates the need for manual programming of complex correction algorithms, making the system easier to implement while maintaining high correction performance for difficult events.
Solution Approach 2:
The patent transforms the correction problem from manipulating complex mathematical equations to adjusting neural network parameters through training. By changing the approach from equation-based processing to parameter-learning-based processing, the system achieves better correction performance while simplifying implementation and adjustment procedures.
3Measurement precision
If neural network-based feature quantity extraction is used, then the extraction of complex feature quantities like centrifugal force and focus is enabled, but the computational resources and processing time increase
Solution Approach 1:
The patent segments the correction task into two stages: (1) feature quantity extraction using a neural network that processes sensor signals into meaningful representations of complex events, and (2) correction processing that operates on these extracted features. This segmentation allows the computationally intensive neural network to focus solely on extraction, while the correction stage benefits from the pre-processed features, improving overall efficiency.
Solution Approach 2:
The neural network performs preliminary feature quantity extraction during a training phase using labeled data, learning to represent complex events efficiently. Once trained, the network can rapidly extract features during actual operation without requiring the same computational resources, as the learning has already been performed in advance during the training phase.
Data Source
AI summary
A signal processing device according to the present technology includes a feature quantity extraction unit including a neural network and trained to extract a feature quantity for a specific event with respect to an input signal from a sensor, and a correction unit that performs correction of the input signal on the basis of the feature quantity extracted by the feature quantity extraction unit.