A Model for the Digital Method of Measuring LED Incident Photocurrent

Pulse oximeters and other light based sensor types are used to monitor arterial blood oxygen levels, heart rate, and much more that rely on LEDs and photodiodes. The conventional method of using photodiodes to detect light signals is accurate, but requires relatively expensive hardware processing to extract the signal. Digital sensing of light using an LED provides a low-cost alternative by using a voltage threshold timing method. However, the accuracy of this method is dependant on the microcontroller clock speed and suffers from variable sample rate (100 us to 10 ms). This paper develops a model for a digital light sensing method using only a microcontroller’s ADC and timer, and an LED. Using the voltage discharge curve of a reverse biased LED, the sensor is capable of accurately detecting light intensities ranging from 0–3885 mcd at a sample period of 500 us. A linear relationship was found through the incident light intensity ranges of 0 to 3880 mcd. The model fit the expected experimental values, with an estimated photocurrent ranging from 10 pA to 55 nA. With an R2 of 0.9997, the model demonstrates the digital sensing method linearly responds to incident light intensity and can simplify the design of pulse oximeters and similar light based sensor types.

Novel Algorithm for Mutual Alignment of Railway Track Geometry Measurements

Track geometry measurements are regularly collected to monitor the condition of a railway network. To detect deterioration patterns and enable predictive maintenance, sequential measurement runs must be mutually aligned which has been proven a serious challenge. This paper presents a novel algorithm for mutual alignment of track geometry signal data. It resolves several previously intractable alignment problems: highly segmented data with variable sample rate, spatially correlated and uncorrelated measurement errors, convergence to true locations, and consistency over time. The algorithm adjusts spatial measurement errors by splitting signals in continuous segments. Re-sampled, error-corrected signals are mutually aligned using cross correlation, and this process is repeated until the mutual alignment meets a pre-defined precision threshold. Missing measurement values are handled by imputing an interpolated offset from nearby segments, ensuring that the signals remain continuous. By using weighted average offsets over all aligned signals, the law of large numbers guarantees convergence and consistency. The practical feasibility of the algorithm is demonstrated on empirical track geometry measurement data from the British railway network, owned and operated by Network Rail.