Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit

Kumar, Vaegae Naveen; Narayana, Komanapalli Venkata Lakshmi; Bhujangarao, Annepu; Sankar, S.

doi:10.1109/jsen.2014.2357173

Cited by 26 publications

(4 citation statements)

References 31 publications

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“…Several research articles have reported to compensate nonlinearity, temperature, hysteresis, drift due to aging of various sensors by different ANN structures such as adaptive linear neural network (Islam et al, 2006;Islam and Saha, 2007), multilayer perceptron neural network (Khan et al, 2003;Khan and Islam, 2011;Kumar et al, 2015;Tarikul Islam et al, 2015), computationally efficient Chebyshev neural network , Laguerre neural network (LaNN) [137], fully connected cascade (FCC) neural network (Cotton and Wilamowski, 2011), fuzzy logic (Teodorescu), neuro-fuzzy architecture (Bouhedda, 2013), support vector machine (Xiaodong, 2008;Patra et al, 2011), covariance 3. Some of the works reported the hardware implementation of the optimized ANN models using a microcontroller, or FPGA (O'Droma and Mgebrishvili, 2005;Islam and Saha, 2007;Patra et al, 2011), or basic analog signal conditioning block.…”

Section: Soft Computing Methods Of Linearizationmentioning

confidence: 99%

Linearization of the sensors characteristics: a review

Islam¹,

Mukhopadhyay

2019

International Journal on Smart Sensing and Intelligent Systems

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Today, the sensing devices play an important role for various system automation and monitoring of different physical and chemical parameters. Nonlinearity is an important long-time issue for most of the sensors, so to compensate nonlinearity, various linearization schemes are reported in the literature. The accuracy of linearization schemes depends on the type and the nonlinearity value of the sensor output. Since it is difficult to find an exact polynomial equation or other functions to represent the response curve; it gives more error when the measurement parameter is determined from the inverse approximation functions. As many sensors are used for different applications, the linearized characteristics will simplify the design, calibration, and accuracy of the measurement. This paper presents a review of different methods applied to linearize sensor characteristics reported in the literature. Due to availability of high-performance analog devices, analog methods are still popular among many researchers. However, due to the advancement of IC technologies, hardware implementation of the software methods can be done easily with reduced time, cost, and more accuracy, so the digital methods combined with software techniques perform the job with better flexibility and efficiency.

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Section: Soft Computing Methods Of Linearizationmentioning

confidence: 99%

Linearization of the sensors characteristics: a review

Islam¹,

Mukhopadhyay

2019

International Journal on Smart Sensing and Intelligent Systems

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“…Digital implementations show a good linearity over a narrow temperature range, however, power consumption or complexity data are not specified [12], [13]. Note that a software solution requires memory cells and sequential machines, thus resulting in a larger occupied area and higher power consumption.…”

Section: A Comparison With Other Implementationsmentioning

confidence: 99%

Microelectronic CMOS Implementation of a Machine Learning Technique for Sensor Calibration

et al. 2020

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An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18µm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error e r below 3% within an input span of 130 • C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm 2 . The digital programmability of the network weights provides flexibility when a sensor change is required.INDEX TERMS Adaptive signal processing, artificial neural networks, CMOS, sensor conditioning.

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“…A distinctive feature of thermistors is the highly non-linear dependence of resistance with temperature, which is a fact to consider even with modern cutting-edge materials and manufacturing technologies [19]. To compensate for this, various approaches have been made to linearise the response-from hardware-based signal conditioning circuits to computer-based solutions or their combinations, implementing modern techniques, such as artificial neural networks [20] and field-programmable arrays (FPGA) [21][22][23]. On the hardware side, common solutions mainly rely on conditioning circuits implementing standalone operational amplifiers (OP ap) circuits [23,24] or voltage-controlled oscillators using dedicated timer ICs (555) [25][26][27], where a thermistor is connected to a frequency-determining input stage of the timing circuit, thus resulting in a more or less linear dependence of output frequency with temperature.…”

Section: Introductionmentioning

confidence: 99%

Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

2021

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This paper addresses the challenges of selecting a suitable method for negative temperature coefficient (NTC) thermistor-based temperature measurement in electronic devices. Although measurement accuracy is of great importance, the temperature calculation time represents an even greater challenge since it is inherently constrained by the control algorithm executed in the microcontroller (MCU). Firstly, a simple signal conditioning circuit with the NTC thermistor is introduced, resulting in a temperature-dependent voltage UT being connected to the MCU’s analog input. Next, a simulation-based approximation of the actual temperature vs. voltage curve is derived, resulting in four temperature notations: for a look-up table principle, polynomial approximation, B equation and Steinhart–Hart equation. Within the simulation results, the expected temperature error of individual methods is calculated, whereas in the experimental part, performed on a DC/DC converter prototype, required prework and available MCU resources are evaluated. In terms of expected accuracy, the look-up table and the Steinhart–Hart equation offer superior results over the polynomial approximation and B equation, especially in the nominal temperature range of the NTC thermistor. However, in terms of required prework, the look-up table is inferior compared to the Steinhart–Hart equation, despite the latter having far more complex mathematical functions, affecting the overall MCU algorithm execution time significantly.

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Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit

Cited by 26 publications

References 31 publications

Linearization of the sensors characteristics: a review

Linearization of the sensors characteristics: a review

Microelectronic CMOS Implementation of a Machine Learning Technique for Sensor Calibration

Algorithm Execution Time and Accuracy of NTC Thermistor-Based Temperature Measurements in Time-Critical Applications

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