Retinal Sensor Stream Processing for Real-Time Noise-Robust Image Analysis
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Solution Overview
Problem
Conventional image analysis systems face limitations in real-time processing of continuous data streams from high-speed sensors due to noise interference and inefficiencies, leading to decreased accuracy and challenges in providing timely notifications.
Innovation Solution
A system utilizing a retinal-type sensor that processes continuous data streams by removing noise, normalizing the data, and performing image analysis with deep learning models to enable efficient, accurate, and real-time image recognition and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional frame-by-frame processing is used for image analysis, then processing simplicity is maintained, but real-time processing capability and efficiency deteriorate
Solution Approach 1:
The patent segments the continuous data stream into fixed-time intervals (e.g., 1 second periods) and processes each segment independently through dedicated processing units. This segmentation enables parallel processing of multiple time segments simultaneously, achieving real-time processing capability while maintaining manageable processing complexity through modular architecture
Solution Approach 2:
The patent performs preliminary actions by pre-configuring multiple processing units with specific processing algorithms before data arrival. Each processing unit is pre-prepared to handle specific types of analysis (e.g., motion detection, object recognition), allowing immediate processing of incoming data segments without sequential preparation delays, thus enabling real-time processing
2Reliability
If continuous data streams from high-speed sensors are processed, then real-time analysis is achieved, but noise interference from environment and sensor hardware increases
Solution Approach 1:
The patent extracts and separates noise components from the continuous data stream using signal processing techniques. Dedicated noise filtering modules identify and remove environmental interference and sensor hardware noise while preserving the underlying meaningful signal, thereby improving data accuracy without sacrificing real-time processing capability
Solution Approach 2:
The patent introduces intermediary processing layers between the high-speed sensor and the analysis system. These intermediary modules include analog-to-digital converters with built-in filtering, buffer memory with smoothing algorithms, and preprocessing units that condition the data before main processing, effectively mediating the noise problem while maintaining real-time data flow
3Measurement precision
If traditional processing methods are used for image analysis, then system simplicity is maintained, but processing efficiency and accuracy for deep learning deteriorate
Solution Approach 1:
The patent implements dynamic processing where the system adapts its processing parameters, algorithms, and resource allocation based on the characteristics of the incoming data stream. Processing complexity is dynamically adjusted - simple traditional methods are used for routine analysis while deep learning models are activated when higher accuracy is needed or when specific patterns are detected, optimizing the balance between accuracy and complexity
Solution Approach 2:
The patent replaces traditional mechanical/image processing methods with deep learning-based computational approaches for specific analysis tasks. Neural networks and convolutional neural networks substitute conventional image processing algorithms, providing superior accuracy for complex pattern recognition while being integrated into a modular system architecture that manages overall complexity
4Loss of time
If frame-by-frame processing is implemented, then processing overhead is reduced, but notification timeliness and reliability deteriorate
Solution Approach 1:
The patent maintains continuous processing action by implementing overlapping processing windows where the next time segment is prepared and pre-processed while the current segment is still being analyzed. This continuous pipeline approach eliminates idle time between frames and ensures uninterrupted processing, reducing notification delay while the modular architecture manages the increased complexity through systematic organization
Data Source
AI summary
A system includes a processor that receives a continuous data stream from a retinal-type sensor, removes noise from the received data stream, normalizes the data from which the noise has been removed, performs image analysis using the normalized data, and transmits an analysis result to a terminal.