Lookup Table Optimization for Sensor Linearization in Small Embedded Systems

Bengtsson, Lars

doi:10.4236/jst.2012.24025

Cited by 32 publications

(17 citation statements)

References 21 publications

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“…There exist best protocol settings in terms of the best radio adjustment, the best redundancy level for multipath routing, the best number of voters, and the best intrusion invocation interval used for intrusion detection to maximize the system lifetime. Leveraging the analysis techniques developed in this paper, one can obtain optimal protocol settings at design time, store them in a table, and apply a simple table lookup operation at runtime with interpolation [23], [24], [25] to determine optimal settings for adaptive network defense management to maximize the system lifetime without runtime complexity. This paper considers three defenses against selective capture attacks.…”

Section: Resultsmentioning

confidence: 99%

“…The optimal design settings in terms of optimal 0 , , , , and are determined at design time and pre-stored in a table over perceivable ranges of input parameter values. The BS applies a table lookup operation with extrapolation techniques [23][24][25] to determine the optimal design parameter settings. The complexity is O(1) because of the table lookup technique employed.…”

Section: Problem Definition Solution and Algorithm Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

Hamadi

Chen

2015

IEEE Trans. Netw. Serv. Manage.

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We propose and analyze adaptive network defense management for countering smart attack and selective capture which aim to cripple the basic data delivery functionality of a base station based wireless sensor network. With selective capture, the adversaries strategically capture sensors and turn them into inside attackers. With smart attack, an inside attacker is capable of performing random, opportunistic and insidious attacks to evade detection and maximize their chance of success. We develop a model-based analysis methodology with simulation validation to identify the best defense protocol settings under which the sensor network lifetime is maximized against selective capture and smart attack.Index Terms-Wireless sensor networks, selective capture, smart attack, multipath routing, intrusion tolerance, intrusion detection, MTTF. H. Al-Hamadi is with the

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Section: Resultsmentioning

confidence: 99%

Section: Problem Definition Solution and Algorithm Descriptionmentioning

confidence: 99%

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

Hamadi

Chen

2015

IEEE Trans. Netw. Serv. Manage.

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“…Engin used an eight-bit microcontroller, its on-chip programmable gain amplifier and 24-bit ADC to amplify and digitize the measured type T T/C output, and built-in temperature sensor for compensation, finally linearized the output by first-and second-degree polynomials, and piecewise linear interpolation methods [11]. Some researchers utilized a T/C amplifier for amplification and cold-junction compensation and linearized the output by look-up table (LUT) embedded in the microcontroller [11,16,17]. Wang et al used B-spline method for linearizing the output of nonlinear sensors [18].…”

Section: Methods For Linearizationmentioning

confidence: 99%

Sensors and Digital Signal Conditioning in Mechatronic Systems

Engin¹,

Engin²

2017

Design, Control and Applications of Mechatronic Systems in Engineering

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Essential part of a mechatronics system is the measurement system that senses the variations in the physical parameters, such as temperature, pressure, displacement, and so on, and converts it to voltage or current. The control of industrial processes and automated manufacturing systems requests accurate, moreover, linearized sensor measurements, where numerous sensors have nonlinear characteristics. In mechatronic systems, accurate measurement of the dynamic variables plays a vital role for the actuators to function properly. This chapter presents linearization methods and a measurement system in mechatronics consisting of temperature sensors and the signal-conditioning circuits, providing detailed information on design process of an embedded measurement and linearization system. This system uses a 32-bit microcontroller for thermocouple (T/C) cold junction compensation, amplification of low output voltage, then conversion to digital, and linearization of the type K thermocouple's output by software to output a desired signal. Piecewise and polynomial methods are used in linearization software, and the implemented embedded system for the linearization of a type K T/C is presented as a case study. The obtained results are compared to give an insight to the researchers who work on measurement systems in mechatronics.

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“…However, to obtain a high accuracy of the estimated input value, a high number of linearization nodes should be implemented in the LUT, making it memory consuming. To reduce memory requirement, a sparse LUT can be combined with an interpolation method [8]. A simple method is piece-wise linear interpolation which connects each two adjacent LUT values with an appropriate linear function.…”

Section: Introductionmentioning

confidence: 99%

On the node ordering of progressive polynomial approximation for the sensor linearization

et al. 2019

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Many sensors exhibit nonlinear dependence between their input and output variables and specific techniques are often applied for the linearization of their transfer characteristics. Some of them include additional analog circuits, while the others are based on different numerical procedures. One commonly used software solution is Progressive Polynomial Approximation. This method for sensor transfer function linearization shows strong dependence on the order of selected nodes in the linearization vector. There are several modifications of this method which enhance its effectiveness but require extensive computational time. This paper proposes the methodology that shows improvement over Progressive Polynomial Approximation without additional increase of complexity. It concerns the order of linearization nodes in linearization vector. The optimal order of nodes is determined on the basis of sensor transfer function concavity. The proposed methodology is compared to the previously reported methods on a set of analytical functions. It is then implemented in the temperature measurement system using a set of thermistors with negative temperature coefficients. It is shown that its implementation in the low-cost microcontrollers integrated into the nodes of reconfigurable sensor networks is justified.

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Lookup Table Optimization for Sensor Linearization in Small Embedded Systems

Cited by 32 publications

References 21 publications

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

Sensors and Digital Signal Conditioning in Mechatronic Systems

On the node ordering of progressive polynomial approximation for the sensor linearization

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