Tag Reader Machine Learning Decoding Speed Variations
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Solution Overview
Problem
The accuracy of decoding tag data from wireless tags can be compromised due to variations in encoding speed, which can lead to incorrect decoding of the clock signal generated by the preamble part, especially with the price reduction of wireless tags causing decreased accuracy.
Innovation Solution
A tag reading apparatus that includes a processor and antenna, which learns the data signal from the wireless tag by generating correct answer data and using machine learning to associate the data signal with the correct answer data, allowing for accurate decoding even if the encoding speed varies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the encoding speed of wireless tags is reduced to lower costs, then the price of wireless tags decreases, but the decoding accuracy deteriorates due to variations in encoding speed
Solution Approach 1:
The system performs preliminary learning by acquiring tag data from multiple wireless tags and generating correct answer data before actual decoding operations. This pre-learning phase creates a reference model that compensates for encoding speed variations, allowing accurate decoding even when tag encoding speeds vary due to cost reduction
Solution Approach 2:
The system uses machine learning to create a feedback mechanism where the processor compares received data signals against learned correct answer data. This feedback loop enables the system to adapt to encoding speed variations and correct decoding errors that occur with lower-cost tags
2Ease of manufacture
If the encoding speed varies during data transmission, then the cost of tags can be reduced, but the reliability of data decoding deteriorates
Solution Approach 1:
The system performs preliminary learning by acquiring tag data from multiple wireless tags and generating correct answer data before actual decoding operations. This pre-learning phase creates a reference model that compensates for encoding speed variations, allowing accurate decoding even when tag encoding speeds vary due to cost reduction
Solution Approach 2:
The system changes the approach from using fixed clock signals based on preamble parts to using machine learning models that adapt to varying encoding speeds. The processor learns the relationship between data signals and correct answers, allowing it to decode reliably despite parameter variations in encoding speed
3Measurement precision
If machine learning is used to learn data signals, then decoding accuracy improves, but device complexity increases
Solution Approach 1:
The system creates a digital copy or model of the correct answer data through learning from multiple tags. This learned model serves as a reference that can be reused for decoding multiple tags, reducing the need for complex real-time analysis while maintaining high decoding accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The apparatus effectively increases the possibility of correctly decoding tag data by learning the data signal patterns and adapting to variations in encoding speed, ensuring reliable data retrieval.
Implementation Method 1
an antenna, which emits a radio wave to start reding the tag data
Implementation Method 2
A wireless tag backscatters the radio wave emitted from an antenna of a tag reading apparatus and wirelessly transmits a data signal
Data Source
AI summary
According to an embodiment, a tag reading apparatus acquires tag data of a wireless tag to be learned without transmission from the wireless tag to be learned. The tag reading apparatus extracts a data signal from a reception signal output from an antenna that has received a radio wave from the wireless tag to be learned. The tag reading apparatus learns the extracted data signal in association with correct answer data of the wireless tag to be learned.