Recurrent Neural Network Cross-Sensor Data Abstraction
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Solution Overview
Problem
Artificial neural networks are limited in their ability to process data that deviates in form or content from the training set, making them ineffective when inputting data from different sensors, such as audio data into an image classifier or telecommunications signals into a heart arrhythmia classifier.
Innovation Solution
The implementation of a recurrent artificial neural network that identifies topological patterns of activity in response to input from multiple sensors, allowing for the abstraction of shared characteristics from diverse data sources and the output of a collection of digits representing these patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained on specific data types (e.g., images), then it achieves high accuracy for that specific task, but it fails to process data from different sensors (e.g., audio, telecommunications signals)
Solution Approach 1:
The patent implements a universal neural network architecture that can process data from multiple sensor types (images, audio, telecommunications signals) through a unified topological pattern recognition framework. The network uses sensor-agnostic feature extraction and topological analysis to achieve cross-domain adaptability while maintaining high classification accuracy across different data types.
Solution Approach 2:
The patent employs dynamic parameter adjustment and normalization techniques that allow the neural network to adapt to different data formats and sensor characteristics. By transforming diverse sensor data into a common topological representation space with standardized parameters, the network can maintain consistent performance across different domains without requiring separate specialized models.
2Productivity
If a neural network is designed to process only one type of data, then the processing is simple and efficient, but the network cannot handle diverse data from multiple sensors
Solution Approach 1:
The patent divides the neural network into specialized modules for different sensor types while maintaining a unified topological processing core. Each sensor has dedicated input processing layers that preserve data-specific characteristics, followed by a common topological pattern recognition engine that integrates information across sensors. This modular segmentation enables efficient specialized processing while achieving versatile multi-sensor integration.
3Adaptability or versatility
If a neural network uses complex processing to handle diverse data types, then it achieves versatility, but the computational complexity and processing time increase
Solution Approach 1:
The patent introduces a topological pattern recognition layer as an intermediary that mediates between diverse sensor inputs and final classification outputs. This intermediate representation layer transforms heterogeneous sensor data into a unified topological feature space, simplifying the overall processing architecture. The intermediary layer handles the complexity of cross-domain integration centrally, allowing simpler specialized layers for each sensor type.
Data Source
AI summary
Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.