Comparison of Compensation Algorithms for Smart Sensors With Approach to Real-Time or Dynamic Applications

This paper presents the dynamic assessment and the comparison of four compensation algorithms programmed in commercial hardware with a restricted computational capability. For each of the four algorithms implemented in a low cost microcontroller embedded in a data acquisition system, the frequency response was evaluated to find out their performance in real-time or dynamic environments. The data acquisition system is an analysis tool proposed to model the compensation process as the first-order system. With the transfer functions in s and z domains and some experimental analysis, the dynamic features for all the four compensation schemes were estimated. The development of modern measurement systems implies the use of hardware and software; they require evaluation beyond the number of samples per second. Then, it is necessary to determine their capabilities and performance for real-time or dynamic applications, because their development is in part based on the physical features and the computational capability of the hardware used. Also, smart sensors are used in the development of measurement systems, and an important capability is the compensation to eliminate the systematic errors and ease the sensor calibration. Available literature lacks information regarding to the performance of compensation algorithms in real-time or dynamic applications. Then, the analysis process exposed in this paper can be a useful methodology to the designer or the end user to evaluate the performance for dynamic or real-time conditions of any measurement system. The frequency and timing parameters would change according to the algorithm implemented and processor used in the data acquisition system.

[1]  Albert Mo Kim Cheng,et al.  Efficient Verification and Optimization of Real-Time Logic-Specified Systems , 2009, IEEE Transactions on Computers.

[2]  Ernest Otto Doebelin,et al.  Measurement Systems Application and Design , 1966 .

[3]  J. Vega,et al.  PXI-based architecture for real-time data acquisition and distributed dynamic data processing , 2006, IEEE Transactions on Nuclear Science.

[4]  Tahseen Ahmed Jilani,et al.  Levenberg-Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting , 2007 .

[5]  Chun-Chi Chen,et al.  A Linearization Time-Domain CMOS Smart Temperature Sensor Using a Curvature Compensation Oscillator , 2013, Sensors.

[6]  Mariano Carrillo-Romero,et al.  Quantitative evaluation of self compensation algorithms applied in intelligent sensors , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[7]  A.J. Pires,et al.  Real-Time Communication for Smart Sensor Networks: A CAN Based Solution , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[8]  Mihai V. Micea,et al.  PARSECS: A Predictable Data Communication System for Smart Sensors and Hard Real-Time Applications , 2010, IEEE Transactions on Instrumentation and Measurement.

[9]  Luca Mari,et al.  Measurement science: constructing bridges between reality and knowledge , 2014, IEEE Instrumentation & Measurement Magazine.

[10]  Jagdish Chandra Patra,et al.  A novel neural network-based linearization and auto-compensation technique for sensors , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[11]  Daniele Marioli,et al.  Digital signal processing for biaxial position measurement with a pyroelectric sensor array , 2006, IEEE Transactions on Instrumentation and Measurement.

[12]  William J. Miceli,et al.  Real Time Signal Processing , 1985 .

[13]  C.A.F. Varandas,et al.  Real-time multi-DSP based VME system for feedback control on the TCV Tokamak , 2005, IEEE Transactions on Nuclear Science.

[14]  Chin-Shung Hsu.  Digital control systems , 1982, Proceedings of the IEEE.

[15]  P. Bruschi,et al.  Smart Flow Sensor With On-Chip CMOS Interface Performing Offset and Pressure Effect Compensation , 2012, IEEE Sensors Journal.

[16]  Zhezhao Zeng,et al.  A Method of Error Compensation on Sensors Based on Neural Networks Algorithm , 2008, 2008 International Conference on Computational Intelligence and Security.

[17]  Tao Ji,et al.  An Intelligent Pressure Sensor Using Rough Set Neural Networks , 2006, 2006 IEEE International Conference on Information Acquisition.

[18]  S. P. Rajagopalan,et al.  An abstract to calculate big o factors of time and space complexity of machine code , 2011 .

[19]  Samir Mekid,et al.  Further Structural Intelligence for Sensors Cluster Technology in Manufacturing , 2006, Sensors (Basel, Switzerland).

[20]  Adrian Tulbure,et al.  The management of wireless real-time data acquisition processes using Virtual Instruments , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[21]  Jia Guo,et al.  Calibration and Compensation of the Scale Factor Errors in DTG POS , 2013, IEEE Transactions on Instrumentation and Measurement.

[22]  Liu Jialei,et al.  The performance quantitative method of data acquisition system based on model identification , 2010, 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering.

[23]  P Bruschi,et al.  An Offset Compensation Method With Low Residual Drift for Integrated Thermal Flow Sensors , 2011, IEEE Sensors Journal.

[24]  José Rivera,et al.  Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks , 2007, Sensors (Basel, Switzerland).

[25]  Javier Vega-Pineda,et al.  Evaluation of the Progressive Polynomial Compensation Algorithm for dynamic or Real-Time applications , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[26]  Gert van der Horn,et al.  Integrated Smart Sensors: Design and Calibration , 1997 .

[27]  José Alberto Fonseca,et al.  VTP-CSMA: A Virtual Token Passing Approach for Real-Time Communication in IEEE 802.11 Wireless Networks , 2007, IEEE Transactions on Industrial Informatics.

[28]  Mahmood Joorabian,et al.  Design and optimisation of electromagnetic flowmeter for conductive liquids and its calibration based on neural networks , 2006 .

[29]  Yoonmee Doh,et al.  Guaranteeing Real-Time Services for Industrial Wireless Sensor Networks With IEEE 802.15.4 , 2010, IEEE Transactions on Industrial Electronics.

[30]  Jafar Ghaisari,et al.  Intelligent Selection of Calibration Points Using a Modified Progressive Polynomial Method , 2012, IEEE Transactions on Instrumentation and Measurement.

[31]  F. Girosi,et al.  A Connection Between GRBF and MLP , 1992 .

[32]  A. Moreno-Munoz,et al.  DSP algorithm for the real-time detection of power quality surge transients , 2009, 2009 IEEE International Conference on Mechatronics.

[33]  Francis T.K. Au,et al.  Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[34]  José Rivera,et al.  Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors , 2008, Sensors.

[35]  Suneeta Agarwal,et al.  Back-forth sorting algorithm analysis and applications perspective , 2009, 2009 IEEE/ACS International Conference on Computer Systems and Applications.

[36]  Jin Liu,et al.  An accurate current source with on-chip self-calibration circuits for low-voltage current-mode differential drivers , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.