H. Saha

A low power MEMS gas sensor based on nanocrystalline ZnO thin films for sensing methane.

Abstract Nanocrystalline ZnO based sensor using micromachined silicon substrate has been reported for efficient detection of methane as opposed to conventional SnO2 based micromachined sensors for its higher compatibility to silicon IC technology and greater response. A suitably designed nickel microheater has been fabricated on to the micromachined Si platform. The optimum temperature for highest response magnitude and lowest response time were found to be 250xa0°C although relatively high (76.6%) response is obtained even at as low as 150xa0°C. Our study showed quite high response magnitude (87.3%), appreciably fast response time (8.3xa0s) and recovery time (17.8xa0s) to 1.0% methane at 250xa0°C. The sensor showed appreciably fast response (14.3xa0s) and recovery time (28.7xa0s) at 150xa0°C. The power consumption at an operating temperature of 250xa0°C was 120xa0mW and at 150xa0°C is only ∼70xa0mW. Moreover, this type of sensor was found to give fairly appreciable response for lower methane concentrations (0.01%) also. For higher methane concentrations (>0.5%) response is detectable even at 100xa0°C where the power consumption is only ∼40xa0mW.

Schottky Junction Methane Sensors Using Electrochemically Grown Nanocrystalline-Nanoporous ZnO Thin Films

Nanocrystalline-nanoporous ZnO thin films were prepared by an electrochemical anodization method, and the films were tested as methane sensors. It was found that Pd-Ag catalytic contacts showed better sensing performance compared to other noble metal contacts like Pt and Rh. The methane sensing temperature could be reduced to as low as 100°C by sensitizing nanocrystalline ZnO thin films with Pd, deposited by chemical method. The sensing mechanism has been discussed briefly.

ANN based CMOS ASIC design for improved temperature-drift compensation of piezoresistive micro-machined high resolution pressure sensor

Abstract The paper investigates the temperature-drift compensation of a high resolution piezoresistive pressure sensor using ANN based on conventional neuron model as also the inverse delayed function model of neuron. Using the delayed neuron model, an improvement in temperature-drift compensation has been obtained compared to the conventional neuron model. The CMOS analog ASIC design of a feed forward neural network using the inverse delayed function model of self connectionless neuron for the precise temperature-drift compensation has been presented. The inverse tan-sigmoid function is realized in CMOS implementation by Gilbert multiplier, differential adder and a cubing circuit. The entire design of the circuit has been done using AMS 0.35xa0μm CMOS model and simulated using Mentor Graphics ELDO simulator. Using the inverse delayed function model of neuron a mean square error of the order of 10−7 of the neural network has been obtained against a mean square error of the order of 10−3 using conventional neuron model for the same architecture of ANN. This brings down the error from 9% for uncompensated sensor to 0.1% only for compensated sensor using the delayed model of neuron in the temperature range of 0–70xa0°C. Using conventional neuron based ANN compensation, the error is reduced to 1% error.

Development of an FPGA based smart embedded system for rural telediagnostic applications

The convergence of artificial intelligence based data processing techniques with information and communication technologies has enabled the development of a smart diagnostic system that can be used to provide health care support in remote rural areas. The paper focuses on the development of a smart diagnostic system that can predict the physiological state of a patient given the past physiological data. The smart system has been implemented on an Altera Cyclone EP1K6Q240C8 FPGA chip. Since patients data randomly vary, therefore no crisp opinion can be made about the physiological state of a patient knowing only the present data. The system employs a smart agent whose role is to monitor and diagnose on a regular time basis and assist the health care professionals in the process of therapeutic decisions. The paper provides an introduction to the notion of smart agent based telemedicine. An extended example on the problem of monitoring renal patients using Body Mass Index (BMI), blood glucose, urea, creatinine, systolic and diastolic blood pressure has been presented in this paper. The system has been tested with height, weight, blood glucose, urea, creatinine, systolic and diastolic blood pressure data of patients where all data other than height have been taken at 10 days interval. Applying the methodology, the chance of attainment of critical renal condition of a patient before the patient actually reaches a critical state has been predicted with confidence.

MEMS based nano crystalline zinc oxide methane gas sensors

Detection and estimation of methane gas for continuous environmental monitoring is of growing importance for prevention of explosions inside coalmines and asphyxiations in cramped localities and green house warning. For continuous monitoring in field locations, low power and sometimes low temperature operation of methane sensors are essential. This could be achieved by employing MEMS structure for low power operations and nanocrystalline Zinc oxide for low temperature activation. The present communication reports the design, fabrication, characterization and laboratory testing of MEMS based nano ZnO methane sensors. Power consumption range 100-150 mW and temperature range of 150-200degC have been achieved with moderately high (Gt50%) sensitivity and response time (les15 sec). Further improvement is expected through optimization.