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Genetic Algorithm Calibration Method and PnP Platform for Multimodal Sensor Systems

멀티모달 센서 시스템용 유전자 알고리즘 보정기 및 PnP 플랫폼

  • Received : 2018.11.22
  • Accepted : 2019.02.15
  • Published : 2019.02.28

Abstract

This paper proposes a multimodal sensor platform which supports plug and play (PnP) technology. PnP technology automatically recognizes a connected sensor module and an application program easily controls a sensor. To verify a multimodal platform for PnP technology, we build up a firmware and have the experiment on a sensor system. When a sensor module is connected to the platform, a firmware recognizes the sensor module and reads sensor data. As a result, it provides PnP technology to simply plug sensors without any software configuration. Measured sensor raw data suffer from various distortions such as gain, offset, and non-linearity errors. Therefore, we introduce a polynomial calculation to compensate for sensor distortions. To find the optimal coefficients for sensor calibration, we apply a genetic algorithm which reduces the calibration time. It achieves reasonable performance using only a few data points with reducing 97% error in the worst case. The platform supports various protocols for multimodal sensors, i.e., UART, I2C, I2S, SPI, and GPIO.

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Fig. 1 Block diagram of genetic algorithm processor

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Fig. 2 LFSR-12

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Fig. 3 LFSR-16

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Fig. 4 Two parents (P0, P1)

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Fig. 5 Two children (C0,C1) using 1-point crossover with the 5th point

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Fig. 6 Two children (C0,C1) using 2-point crossover with the 3rd and 6th points

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Fig. 7 Two children (C0,C1) using uniform crossover

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Fig. 8 Block diagram of fitness and selection

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Fig. 9 Sensor module for PnP platform

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Fig. 10 Sensor identification

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Fig. 11 Recognition process

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Fig. 12 Timing diagram for I2C interface

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Fig. 13 Block diagram for multimodal sensor platform

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Fig. 14 Sensor board for the multimodal sensor platform

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Fig. 15 Compensated Results for Ambient Light Sensor (TEMT6000)

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Fig. 16 Firmware for multimodal sensor platform

Table 1. Simulation Results using Genetic Algorithm

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Acknowledgement

Supported by : 중소벤처기업부

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