Implementation of software-based sensor linearization algorithms on low-cost microcontrollers

Erdem, Hamıt

doi:10.1016/j.isatra.2010.04.004

Cited by 33 publications

(24 citation statements)

References 11 publications

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“…As is shown in Fig. 3, the final output of the ANFIS linearizer is given by (4) and the expression of the Tri i (x) function is given by (5). 1 1…”

Section: A Anfis Circuit Digital Elementsmentioning

confidence: 99%

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Neuro-fuzzy sensor's linearization based FPGA

Bouhedda

2013

2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS)

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Nonlinear sensors and digital solutions are used in many embedded system designs. As the input/output characteristic of most sensors is nonlinear in nature, obtaining data from a nonlinear sensor by using an optimized device has always been a design challenge. This paper aims to propose a new Adapive Neuro-Fuzzy Inference System (ANFIS) digital architecture based Field Programmable Gate Array (FPGA) to linearize the sensor's characteristic. The ANFIS linearizer in synthesized and optimized in view to digital linearization. The performance of the developed architecture is examined with comparison to ANFIS software model and with two other designed architectures based FPGA using classical techniques. The results show the successful of the proposed architecture by demonstrating the capability of the operation to linearize a temperature sensor characteristic.

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“…As is shown in Fig. 3, the final output of the ANFIS linearizer is given by (4) and the expression of the Tri i (x) function is given by (5). 1 1…”

Section: A Anfis Circuit Digital Elementsmentioning

confidence: 99%

“…These techniques can be organized into three main classes. Analog hardware-based linearization circuit [2][3], software based linearization algorithms [4][5][6][7][8] and hybrid analog to digital conversion solutions [9].…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy sensor's linearization based FPGA

Bouhedda

2013

2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS)

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“…In particular, Artificial Neural Networks (ANN) constitute the most powerful solution, as they are able to adapt their input–output characteristics without previous knowledge of the particular sensor response [9,10,11,12,13,14,15,16,17]. In this approach, training algorithms are used where sensor input-conditioned output data pairs, which represent the expected input–output characteristic, are iteratively fed to the system, so that the ANN-free parameters (called weights) are adjusted until a maximum permissible error between the expected and the actual output is reached [18,19,20]. The use of registers for weight storage eases the training processes.…”

Section: Introductionmentioning

confidence: 99%

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks

Martínez-Nieto

Marques

Sanz-Pascual

et al. 2019

Sensors

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For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.

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“…A sensor converts the physical unit (the measurand) into some electrical unit (preferably volt) and the embedded measurement system converts the sensor output into a digital value (typically an integer) [19]. The main errors in most measurement systems are related to the transducer's offset, gain and non-linearities [20], and for that reason the process of sensor linearization is a crucial step in the design of an embedded measurement system [21]. The linearization process must compensate for the non-linear relationship between the sensor's input and the output signals [21], see Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Several other linearization methods have been suggested in literature. The most common linearization methods can be classified as follows [21,25,26]: 1) Analog hardware-based; 2) Software-based; 3) Analog hardware-software mixed approach.…”

Section: Introductionmentioning

confidence: 99%

Lookup Table Optimization for Sensor Linearization in Small Embedded Systems

Bengtsson¹

2012

JST

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This paper treats the problem of designing an optimal size for a lookup table used for sensor linearization. In small embedded systems the lookup table must be reduced to a minimum in order to reduce the memory footprint and intermediate table values are estimated by linear interpolation. Since interpolation introduces an estimation uncertainty that increases with the sparseness of the lookup table there is a trade-off between lookup table size and estimation precision. This work will present a theory for finding the minimum allowed size of a lookup table that does not affect the overall precision, <i>i.e.</i> the overall precision is determined by the lookup table entries’ precision, not by the interpolation error

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Implementation of software-based sensor linearization algorithms on low-cost microcontrollers

Cited by 33 publications

References 11 publications

Neuro-fuzzy sensor's linearization based FPGA

Neuro-fuzzy sensor's linearization based FPGA

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks

Lookup Table Optimization for Sensor Linearization in Small Embedded Systems

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