A smart and distributed measurement system to acquire and analyze mechanical motion parameters

This paper presents a low-cost and smart measurement system to acquire and analyze mechanical motion parameters. The measurement system integrates several measuring nodes that include one or more triaxial accelerometers, a temperature sensor, a data acquisition unit and a wireless communication unit. Particular attention was dedicated to measurement system accuracy and compensation of measurement errors caused by power supply voltage variations, by temperature variations and by accelerometers’ misalignments. Mathematical relationships for error compensation were derived and software routines for measurement system configuration, data acquisition, data processing, and self-testing purposes were developed. The paper includes several simulation and experimental results obtained from an assembled prototype based on a crank-piston mechanism.info:eu-repo/semantics/publishedVersio

Analog to Digital Conversion Methods for Smart Sensing Systems

The new capabilities of smart sensing systems namely, adaptability, reconfiguration, lowenergy consumption and cost, between others, require a wisely selection of the methods that are use to perform analog to digital conversion. It is very important to optimize the trade-offs between, resolution, accuracy, conversion rate, and energy consumption, between others, and above all to adapt dynamically the conversion parameters for different signals characteristics and applications\\u27 purposes. Establishing the best trade-offs are even more important when signals to be digitized have different signal-to-noise ratios (S/N) ratios, different requirements of measuring accuracy and acquisition rate, their characteristics are time-variant and above all if they are sharing the same digitalization device. Very low resolution or conversion rate of data acquisition (DAQs) systems are generally not compliant with measurement systems\\u27 requirements since signal information is lost without any possible recovery procedure. Otherwise, if resolution or data acquisition rate are excessively high that means the sampling rate is much higher than its minimum value (Nyquist rate), the excessive amplitude and time resolutions provided by A/D conversion or frequency-to-digital conversion (FDC) does not improve measurements system\\u27s performance. Moreover, the excessive resolution or data acquisition rate implies an increase of hardware and software complexity, data processing load and a higher implementation cost, without any benefits. So, for any A/D or FDC conversion method the best trade-off between different conversion characteristics must be established considering applications\\u27 purposes. For example, in wireless sensing and actuating networks (WSAN) energy wastes are particularly important because a wrong choice of conversion method can affect deeply measurement system autonomy. Whenever possible, classical A/D conversion methods are being replaced by discrete A/D conversion methods that are supported by low cost microcontroller (C) (Microchip, 2010) connected to a few external resistive or capacitive components. This solution takes full advantage of Cs benefits, namely specific hardware and software capabilities and it provides a conversion rate that can be higher that several hundreds of kHz that is sufficien

DESIGN AND IMPLEMENTATION OF POTENTIOMETER-BASED NONLINEAR TRANSDUCER EMULATOR

This work attempts to design and implement in hardware a transducer with a nonlinear response using potentiometer. Potentiometer is regarded as a linear transducer, while a the response of a nonlinear transducer can be treated as a concatenation of linear segments made out of the response curve of an actual nonlinear transducer at the points of inflections being exhibited by the nonlinear curve. Each straight line segment is characterized by its slope and a constant, called the y-intercept, which is ultimately realized by a corresponding electronic circuit. The complete circuit diagram is made of three stages: (i) the input stage for range selection, (ii) a digital logic to make appropriate selection, (iii) a conditioning circuit for realizing a given straight-line segment identified by its relevant slope and reference voltage. The simulation of the circuit is carried using MULTISIM, and the designed circuit is afterward tested to verify that variations of the input voltage give us an output voltage very close to the response pattern envisaged in the analytical stage of the design. The utility of this work lies in its applications in emulating purpose built transducers that could be used to nicely emulate a transducer in a real world system that is to be controlled by a programmable digital system

Improved PWM A/D conversion technique: working principle and model validation

Analog-to-digital conversion plays a central role in any application of digital sensors and sensor systems that require an interface between analog devices, namely analog sensors, and digital devices, namely, microprocessors, digital signal processors or microcontrollers. With the advent of smart sensing, the integration of signal conditioning, analog-to-digital and digital data processing in single hardware devices became a reality. Moreover, the usage of low-cost discrete A/D conversion techniques for applications that are not critic in terms of accuracy, resolution or conversion rate, are considering increasingly mixed hardware and software A/D solutions tailored for specific application demands. In this context, this chapter presents a discrete low-cost A/D conversion solution based on pulse width modulation particularly suited for microcontrollers' integration with smart sensing devices.info:eu-repo/semantics/publishedVersio

Microelectronic cmos implementation of a machine learning technique for sensor calibration

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 er 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