The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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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
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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.

본 논문은 PnP(plug and play) 기술을 지원하는 멀티모달 센서 플랫폼을 제안하였다. PnP 기술은 센서 모듈이 연결이 되면 자동으로 인식하여 응용프로그램을 사용하여 손쉬운 센서 제어를 제공한다. 멀티모달 플랫폼을 검증하기 위해, 펌웨어를 사용하여 센서를 실험하였다. 센서 모듈이 연결되면 펌웨어는 센서 모듈을 인지하여 센서 데이터를 읽는다. 따라서, PnP 기술 지원을 통해 소프트웨어 설정 없이 자동으로 센서를 연동할 수 있게 된다. 측정한 센서 데이터는 다양한 왜곡에 의해 오류를 가지고 있다. 따라서, 본 논문은 다항식 계산을 통해 센서의 오류를 보상하고자 한다. 다항식 보상기의 계수를 찾기 위해 유전자 알고리즘 방식을 사용하였다. 실험결과 악조건에서 97%의 오류를 제거하였다. 또한, 제안하는 플랫폼은 다양한 프로토콜의 센서를 지원하기 위해 UART, I2S, I2C, SPI, GPIO를 지원한다.

Keywords

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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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