ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Islam, Tarikul; Ghosh, Sujan; Saha, Hiranmay

doi:10.1016/j.snb.2006.02.001

Cited by 23 publications

(16 citation statements)

References 19 publications

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“…Humidity sensors have been developed using different sensing materials such as ceramic, polymer and porous silicon [5][6][7]. This kind of sensors can be grouped into two types: resistive and capacitive types according to the output form of the sensor.…”

Section: Introductionmentioning

confidence: 99%

Thin plasma-polymerized layers of hexamethyldisiloxane for humidity sensor development

et al. 2009

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Section: Introductionmentioning

confidence: 99%

Thin plasma-polymerized layers of hexamethyldisiloxane for humidity sensor development

et al. 2009

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“…However, it also exhibits non-idealities like offset, temperature drift, nonlinearity, hysteresis and aging in addition to the drift due to environmental effects, and, thus, the piezoresistive pressure sensor [4,5] can not be directly applied to meteorological measurements. For the sake of solving the above problems, a compensation technique based on the software/hardware co-design is a desirable option from the perspective of large-scale usage, where a data fusion compensating algorithm is developed by using software, and it is finally hardware implemented by utilizing a microcontroller or field programming gate array (FPGA) chip [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the ANN is now a well-known software compensating technique for modeling the sensor behavior to approximate functional relationship between input and output of a sensor [6][7][8][9][10][11][12][13]. Specifically, the ANN has been widely used to compensate the temperature drift errors [9,10], the nonlinear error [6,11,12] and hysteresis errors [8,11,13] of MEMS humidity, temperature and pressure sensors. The main advantage of the ANN algorithm for error compensation is that one need not have full knowledge of the physics of the sensor, and an iterative procedure is applied over the measured data in the interest of obtaining an accurate expression utilized for error compensation [8].…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the ANN has been widely used to compensate the temperature drift errors [9,10], the nonlinear error [6,11,12] and hysteresis errors [8,11,13] of MEMS humidity, temperature and pressure sensors. The main advantage of the ANN algorithm for error compensation is that one need not have full knowledge of the physics of the sensor, and an iterative procedure is applied over the measured data in the interest of obtaining an accurate expression utilized for error compensation [8]. To the best of our knowledge, the neural network optimized by a genetic algorithm possesses better accuracy, stability and versatility compared with the traditional ANN [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

Zhang

Liu

et al. 2015

Micromachines

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This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN) which is improved by genetic algorithm (GA) to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in OPEN ACCESSMicromachines 2015, 6 555 hardware by using the 32-bit STMicroelectronics (STM32) microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement.

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“…It changes the electric characteristics of materials and acts on the answers of the systems carried out. Humidity measurement is one of the important tasks in many industrial processes for manufacturing of products such as textiles, food, paper, semiconductors and petrochemical [1].…”

Section: Introductionmentioning

confidence: 99%

ANN modeling of a smart MEMS-based capacitive humidity sensor

Kouda

Dibi

Barra

et al. 2011

Int. J. Control Autom. Syst.

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This paper presents a design of a smart humidity sensor. First we begin by the modeling of a Capacitive MEMS-based humidity sensor. Using neuronal networks and Matlab environment to accurately express the non-linearity, the hysteresis effect and the cross sensitivity of the output humidity sensor used. We have done the training to create an analytical model CHS 'Capacitive Humidity Sensor'. Because our sensor is a capacitive type, the obtained model on PSPICE reflects the humidity variation by a capacity variation, which is a passive magnitude; it requires a conversion to an active magnitude, why we realize a conversion capacity/voltage using a switched capacitor circuit SCC. In a second step a linearization, by Matlab program, is applied to CHS response whose goal is to create a database for an element of correction 'CORRECTOR'. After that we use the bias matrix and the weights matrix obtained by training to establish the CHS model and the CORRECTOR model on PSPICE simulator, where the output of the first is identical to the output of the CHS and the last correct its nonlinear response, and eliminate its hysteresis effect and cross sensitivity. The three blocks; CHS model, CORRECTOR model and the capacity/voltage converter, represent the smart sensor.

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ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor

Cited by 23 publications

References 19 publications

Thin plasma-polymerized layers of hexamethyldisiloxane for humidity sensor development

Thin plasma-polymerized layers of hexamethyldisiloxane for humidity sensor development

Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

ANN modeling of a smart MEMS-based capacitive humidity sensor

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