Embedded Electronic Nose and Supporting Software Tool for its Parameter Optimization

Botre, B. A.; Gharpure, Damayanti; Shaligram, A. D.

doi:10.1016/j.snb.2009.11.033

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…They include: the principal component analysis (PCA) and linear discriminant analysis [3], self-organizing maps [16], the k-nearest neighbor algorithm [15], neuro-fuzzy systems [14], different types of neural networks [1,3], the support vector machine [2,17,18]. Recently, the random forest has been also tried in recognition of orange beverage and Chinese vinegar [18].…”

Section: The Electronic Nose Measurementsmentioning

confidence: 99%

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Osowski

Siwek

2017

Metrology and Measurement Systems

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The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

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Section: The Electronic Nose Measurementsmentioning

confidence: 99%

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Osowski

Siwek

2017

Metrology and Measurement Systems

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“…k is the output nodes (k: 1-7). j is the hidden nodes (j: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]: Because the output function is a Tansig, the output has a value in the range [-1,1] so they must be made to fit among [0,1] using Eq. (2).…”

Section: Implementation Of the Multi-layer Feed-forward Neural Networkmentioning

confidence: 99%

“…The best method for achieving a single system is the use of microcontrollers in systems which, in addition to the measurement of potential, are able to perform the analysis of relevant data using a software program implemented in the microcontroller memory. Thus portable electronic tongues are becoming popular as they offer simplicity, reliability and use in field [11]. Some systems using microprocessors have been presented as electronic tongues [12] but the system presented in this communication has as its main novelty the development and comparison of three types of pattern recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

García‐Breijo

Atkinson

Gil-Sánchez

et al. 2011

Sensors and Actuators A: Physical

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a b s t r a c tA portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 microcontroller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.

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“…En la actualidad diferentes fabricantes de estos instrumentos de medida, instituciones y centros de investigación dedican grandes esfuerzos con el fin de optimizar este tipo de instrumentos de medida. A continuación se mencionan brevemente algunos de estos estudios: un método para optimizar la matriz de sensores de gases [5]; implementación de algoritmos de procesamiento de datos [6]; uso de algoritmos genéticos para aumentar la eficiencia de la matriz de sensores [7]; mejoras de una nariz electrónica mediante métodos de selección característica [8], y mediante la optimización de una red neuronal en sistemas olfativos [9]. Para el desarrollo de esta investigación, se estudian cada una de las etapas principales de las narices electrónicas, sobre todo las etapas de adquisición y procesamiento de datos, con el fin de conocer a grandes rasgos la estructura y funcionamiento de estos equipos.…”

Section: Introductionunclassified

Desarrollo de un sistema embebido con tecnología DSP para un sistema multisensorial (Nariz electrónica)<br />Development of embedded system with DSP technology for multisensor system (Electronic nose)

Acevedo¹,

Torres-López²

2014

Iteckne

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Resumen-Este artículo consiste en el desarrollo de un sistema integrado con tecnología DSP para ser aplicado a un sistema multi-sensorial (es decir, nariz electrónica). La idea de este estudio fue mejorar la eficiencia de estos sistemas multisensoriales en aplicaciones portátiles, usando diferentes algoritmos para clasificar tres clases de compuestos volátiles detectados por una matriz de sensores de gases químicos. El software CodeComposer Studio (CCS) fue acoplado con Matlab para la programación de la tarjeta DSP TMS320F28335 de Texas Instruments. Los resultados se obtuvieron a partir de muestras de vino de tres denominaciones diferentes (es decir, manzana, rojo y casillero), que luego fueron clasificadas mediante algoritmos de procesamiento (redes neuronales artificiales). El sistema fue validado mediante la técnica de análisis de componentes principales (ACP), para verificar la reproducibilidad y selectividad del sistema de medición. En los resultados se logró un 83,4 % de tasa de éxito en la calificación de las medidas utilizando DSP Hardware,a través de la implementación de una Red NeuronalArtificial (RNA). Palabras clave-Sensor de gases, adquisición de datos, procesamiento, Filtros digitales, Redes neuronales, PCA

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Embedded Electronic Nose and Supporting Software Tool for its Parameter Optimization

Cited by 15 publications

References 12 publications

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

Desarrollo de un sistema embebido con tecnología DSP para un sistema multisensorial (Nariz electrónica)<br />Development of embedded system with DSP technology for multisensor system (Electronic nose)

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