Showing papers by "Pedro Silva Girao published in 2005"

Smart Sensor Network for Air Quality Monitoring Applications

Abstract: The work presents a network for indoor and outdoor air quality monitoring whose nodes include tin dioxide sensor arrays connected to an acquisition and control system with WiFi communication capabilities. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor-influencing quantities, temperature and humidity, are also measured. Advanced processing based on multi-input single-output neural networks is implemented at the network sensing nodes in order to obtain temperature and humidity compensated gas concentration values. Anomalous operation of network nodes and power consumption are also discussed

A Digitally Programmable A/D Converter for Smart Sensors Applications

Abstract: Nonlinear analog to digital conversion in smart sensor applications is an important topic since signal digitalization and linearization can be performed in a single step nearby the transducer. In this paper, a double pulse width modulated (PWM) scheme for nonlinear analog-to-digital conversion is presented. Calibration or auto-calibration data stored in the smart sensor's memory define the nonlinear profile characteristic of the transducer and provide the required data to obtain the inverse function of the analog-to-digital converter (ADC) transfer curve. Basically, as a function of the transducer's nonlinearity degree, the input voltage range of the ADC is segmented in a continuous set of sub intervals and for each of these subintervals a second order correction term, based on a PWM A/D conversion, is used to obtain a linear characteristic for the smart sensor. Additional advantages of this method results from its easy implementation in low-cost microcontrollers that include generally comparator inputs and PWM outputs. A flexible and programmable A/D conversion solution can be dynamically adapted to variations of the transducer's nonlinearity profile and an increased resolution can be achieved at expenses of a lower conversion rate. Some MATLAB simulations and experimental results obtained with a square-root airflow transducer are presented in the final part of the paper

IEEE 1451 Correction Engine to Temperature-compensation of Magnetoresistive Transducers

Abstract: The paper presents the comparison between polynomial approximation and artificial neural networks (ANNs) to compensate temperature dependence of a magnetic field transducer. The sensing elements are a magnetoresistance whose value can vary almost 20% in the experimental operating temperature range (20degC-100degC) and a two terminal integrated temperature sensor. The first technique to correct the temperature drift in the magnetoresistance is fully compliant with IEEE 1451.2 correction engine. It uses a segmented multinomial (multivariate polynomial) function and the coefficients and offset values stored in TEDS are determined using a least-mean-square error method. The application of an artificial neural network, well adapted to conveniently modeling strongly nonlinear transducer characteristics, is the second technique to be used and leads to an improvement of magnetic transducer's accuracy from 20% to 2%. An approach to a "correction engine" covering this method is proposed

A Self-Adaptable Method to Optimize the Performance of Frequency-To-Code Conversion Based Measurement Systems

Abstract: Accuracy, error compensation and simplicity of transducer's communication and interfacing are three important topics in the design and development of any measurement system. Nowadays, there are a substantial number of transducers and actuators that generate or receive, respectively, frequency modulated signals. The main advantages associated with frequency transducers include its high noise immunity, high output signal power, wide dynamic range and simplicity of signal interfacing and coding [1-2]. The frequency-to-digital conversion (FDC) is easily performed by any microcontroller, or circuits based on commercial off-the- shelf (COTS) components, without need of an analog-to-digital converter (ADC), and the same easiness exists when frequency signals are required for actuators. Eliminating the need of ADCs and DACs reduces the cost of instrumentation and measurement systems and eliminates a large number of error sources associated with these conversion devices. This paper is dedicated to FDC based measurement systems, giving particular attention to calibration issues and self- adaptive measurement capabilities that can be used to select a suitable conversion accuracy for a given signal-to-noise ratio. Some simulation and experimental results for a temperature and humidity measurement system will be included as application examples.