Design and Development of Intelligent Sensors

Abstract

In this project, we make an extensive study of Intelligent Sensors and devise methods for analyzing them through various proposed algorithms broadly classified into Direct and Inverse Modeling. Also we look at the analysis of Blind Equalization in any sensor. A regular sensor is a device which simply measures a signal and converts it into another signal which can be read by an observer and an instrument. A sensor's sensitivity indicates how much the sensor's output changes when the measured quantity changes. Ideal sensors are designed to be linear. The output signal of such a sensor is linearly proportional to the value of the measured property. The sensitivity is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and gives a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linear because the ratio is constant at all points of measurement. If the sensor is not ideal, several types of deviations can occur which render the sensor results inaccurate. On the other hand, an intelligent sensor takes some predefined action when it senses the appropriate input (light, heat, sound, motion, touch, etc.).A sensor is intelligent when it is capable of correcting errors occurred during measurement both at the input and output ends. It generally processes the signal by means of suitable methods implemented in the device before communicating it.
As we discussed an ideal sensor should have linear relationship with the measures quantity. But since in practice there are several factors which introduce non-linearity in a system, we need intelligent sensors. This particular project concentrates on the compensation of difficulties faced due to the non-linear response characteristics of a capacitive pressure sensor (CPS).It studies the design of an intelligent CPS using direct and inverse modeling switched-capacitor circuit(SCC) converts the change in capacitance of the pressure-sensor into an equivalent voltage output . The effect of change in environmental conditions on the CPS and subsequently on the output of the SCC is such that it makes the output non-linear in nature. Especially change in ambient temperature causes response characteristics of the CPS to become highly nonlinear, and complex signal processing may be required to obtain correct results.
The performance of the control system depends on the performance of the sensing element. It is observed that many sensors exhibit nonlinear input-output characteristics. Due to such nonlinearities direct digital readout is not possible. As a result we are forced to employ the sensors only in the linear region of their characteristics. In other words their usable range gets restricted due to the presence of nonlinearity. If a sensor is used for full range of its nonlinear characteristics, accuracy of measurement is severely affected. Similar effect is also observed in case of LVDT. The nonlinearity present is usually time-varying and unpredictable as it depends on many uncertain factors. Nonlinearity also creeps in due to change in environmental conditions such as
temperature and humidity. In addition ageing of the sensors also introduces nonlinearity. The proposed scheme incorporates intelligence into the sensor. We use many algorithms and ANN models to make the sensor ‘intelligent’. Also there is an analysis of the Blind Deconvolution Techniques that maybe used for Channel Estimation. As it is a relatively new field of work, the challenges are huge but opportunities are many as well. We try to make sensors more intelligent as they would allow a varied application of them in industry, academic and domestic environments.