M Vani

Bio: M Vani is an academic researcher. The author has contributed to research in topics: Linearization & Log amplifier. The author has an hindex of 1, co-authored 1 publications receiving 34 citations.

Linearising dual slope digital converter suitable for a thermistor

Abstract: To measure temperature using a thermistor as the sensing element, linearization to compensate for the inverse exponential nature of the resistance-temperature characteristic of the thermistor is required. A linearizing dual-slope digital converter (LDSDC) that accepts a thermistor sensor as input and provides a digital output that is directly proportional to the temperature being sensed is presented here. A logarithmic amplifier at the input of the LDSDC compensates for the exponential characteristics. The conversion logic of the underlying dual-slope converter is suitably modified to implement the required inversion and offset correction and thus obtain linearization over a wide range of input temperature. The efficacy of the proposed LDSDC is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested. Analysis of the proffered method to identify possible sources of errors is also presented.

Linearization of NTC Thermistor Characteristic Using Op-Amp Based Inverting Amplifier

Abstract: A low cost linearizing circuit is developed, placing the NTC thermistor in a widely used inverting amplifier circuit using operational amplifier. The performance of the system is verified experimentally. A linearity of approximately ± 1% is achieved over 30 °C -120 °C. When used for a narrower span, a much better linearity of ± 0.5% is obtained. The gain of the arrangement can be adjusted over a wide range by simply varying the feedback resistance. The simplicity of the configuration promises a greater reliability, and also curtails the deterioration in the stability of performance, by reducing the cumulation of drifts in the different circuit components and devices.

A Linearization Scheme for Thermistor-Based Sensing in Biomedical Studies

Abstract: Temperature is one of the basic biophysical quantity monitored for various biomedical systems. Moreover, the variation of temperature is also an important parameter which can be used for estimating other measurands, such as respiratory airflow. This paper proposes a simple operational amplifier-based astable multivibrator circuit for linearization of the characteristic of a negative temperature coefficient thermistor constituting one of the timing resistors. The circuit has been combined with a lookup table to get the unknown temperature value from multivibrator output. Moreover, the same system topology can be used as a linearizer for measurement of respiratory airflow. The performance of the composite system has been verified experimentally. A linearity of approximately ±0.75% has been achieved over 30 °C–110 °C in the case of temperature measurement and ±1.2% for airflow of 10–60 LPM. Better results can also be achieved with the introduction of interpolation algorithms, but at a higher computational and component cost. The compactness of the complete system makes it a good candidate for embedded sensing applications in biomedical systems, such as point-of-care monitoring or in sleep study.

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

Abstract: This paper presents the development of an artificial neural network (ANN)-based linearization technique for the voltage controlled oscillator (VCO) thermistor circuit. VCO thermistor circuit exhibits a linear analog temperature-frequency relation over a temperature range of 0 °C-100 °C with good response and reasonable error. An ANN-based direct modeling technique is used for a nonlinearity estimation of VCO thermistor circuit to further improve the linearity. The performance of the developed technique has been experimentally verified. A linearity of approximately ±0.2% and sensitivity of 5 kHz/°C are achieved over a temperature range of 0 °C-100 °C, with a high thermal stability. A notable feature of the developed technique is linearity and sensitivity of the temperature measurement, which are quite high. The efficacy of the technique is established through simulation studies and its practicality is demonstrated on a prototype unit.

Development of an Intelligent Temperature Transducer

Abstract: This paper presents the development of an intelligent temperature transducer to measure temperature in the range of 0 °C–100 °C using a negative temperature coefficient (NTC) thermistor. The NTC thermistor is connected in a timer circuit to convert the temperature change into frequency. The timer circuit acts as a signal conditioning circuit (SCC) for the NTC thermistor and exhibits a stable temperature-frequency characteristic with a reasonable error. The Levenberg-Marquardt training algorithm is used in a multilayer perceptron neural network to further reduce the nonlinearity error of the SCC. The trained artificial neural network (ANN) improved the linearity, sensitivity, and precision of the SCC to an appreciable range. A linearity of approximately ±0.8% and the sensitivity of about 5 kHz/°C are achieved. The intelligence of the trained ANN is embedded in a microcontroller unit, and the performance of the developed transducer is experimentally studied on a prototype board.

Development of thermistor signal conditioning circuit using artificial neural networks

Abstract: Thermistor is most widely used sensor in the temperature measurement due to its high sensitivity and fast response. The non-linearity of the thermistor gives rise to several difficulties for on-chip interface, direct digital readout, wireless transmission and so on. Hence, an effective lineariser is needed to overcome the difficulties. In this study, an artificial neural network-based lineariser has been developed for the thermistor connected in operational amplifier circuit. Operational amplifier-based thermistor signal conditioning circuit exhibits a stable temperature–voltage relation over a range of 0–100°C with low linearity. A multilayer perceptron feed-forward neural network is used for non-linearity compensation of thermistor circuit to further improve the linearity. A linearity of ±0.3% is achieved over 0–100°C with high temperature stability. A notable feature of the proposed method is the non-linearity error remains low over the entire dynamic range of the thermistor. The efficacy of the method is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested.