Rolling Shutter Light Communication Signal Decoding via Curvature Analysis
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Solution Overview
Problem
Rolling shutter image capture devices in mobile computing devices sample light-based communication signals at a rate below the Nyquist rate, leading to under-sampling and impaired signal decoding due to overlapping raster lines, which limits the accuracy of decoding light-based communication signals.
Innovation Solution
A method for decoding light-based communication signals using a rolling shutter light receiver involves determining the curvature of a data buffer containing entering and exiting bits, calculating a threshold value, and comparing it to determine the value of the entering bit, even when the exposure time duration of overlapping raster lines is not an integer multiple of the bit period, thereby accurately reconstructing the signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rolling shutter image capture device is used, then device complexity is reduced and ease of operation is improved, but measurement precision deteriorates due to under-sampling
Solution Approach 1:
The system uses curvature detection as a feedback mechanism to monitor the running average of light data. By calculating the curvature of the data buffer and comparing it against threshold values, the system can determine when entering bits should be set to specific values (0 or 1) based on the observed signal behavior, thereby compensating for the under-sampling effect inherent in rolling shutter devices.
Solution Approach 2:
The invention changes the parameter of how bit values are determined by introducing curvature-based logic. Instead of directly sampling the signal, the system analyzes the curvature of the running average and uses this information to infer bit values, effectively transforming the sampling problem into a curvature analysis problem that can be solved with lower sampling rates.
2Manufacturing precision
If overlapping raster lines are used, then manufacturing precision is improved, but loss of information increases due to effective running average
Solution Approach 1:
The system extracts the curvature information from the running average data buffer. By taking out the curvature characteristic from the mixed signal data, the system can isolate the bit value information that would otherwise be lost in the effective running average created by overlapping raster lines.
Solution Approach 2:
The system performs preliminary action by continuously monitoring the curvature of the data buffer in advance. This allows the system to prepare and determine bit values before the overlapping raster lines complete their sampling, compensating for the information loss that would occur with simple direct sampling.
Data Source
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AI summary
Methods and systems are described for sampling an LCom message and accurately decoding the entire LCom message using a light receiver (e.g., digital camera) of a typical mobile computing device, such as a smartphone, tablet, or other mobile computing device. In one embodiment, a curvature method is disclosed to determine LCom signal bit values from a curvature value of a running average calculation of light sensor data. In another embodiment, a signal reconstruction method is disclosed to determine LCom signal bit values from a comparison of modeled data buffers to light sensor data.