Quartz crystal microbalance based electronic nose system implemented on Field Programmable Gate Array (original) (raw)

A Framework for an Artificial-Neural-Network-Based Electronic Nose

Electronic Nose Technologies and Advances in Machine Olfaction

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

An electronic nose (E-nose) system's ability to recognize multivariate responses from gas sensors in a variety of applications necessitates gas recognition. Principal component analysis (PCA) and other traditional gas recognition methods have been widely used in E-nose systems for decades. ANNs have transformed the field of E- nose, particularly spiking neural networks (SNNs), significantly in recent years. In this paper, we compare and contrast recent E-nose gas recognition techniques in terms of algorithms and hardware implementations. Each classical gas recognition method has a relatively fixed framework and few parameters, making it easy to design. It works well with few gas samples but poorly with multiple gas recognition when noise is present. Keywords: Gas detection, electronic nose, artificial neural network, and spiking neural network.

Quartz Crystal Microbalance Device for Electronic Nose Application

Quartz crystal microbalance technology was developed for measurement of a tiny amount of mass put on the sensor in a form of thin layer. Since then many other applications were developed, including measurement in liquid phase, sorption etc.. The main idea is to measure frequency and other parameters of vibrating quartz crystal as dependent on the amount and properties of thin layer of cover material. The quartz crystal vibration frequency in a resonant mode is very stable. Utilizing this property one can easily measure influence of external mass addition of an order of below 1ng/cm 2 . The dependence of frequency on mass adsorbed on the crystal is linear and is given by the equation:

Estimating Gas Concentration using Artificial Neural Network for Electronic Nose

Procedia Computer Science, 2017

E-nose is a sensor used to detect the existence of gas in the air. Some types of sensor has the ability to detect certain gas and also has different datasheet. Slope deflection is the method to determine the suitable sensor for the experiment. E-nose with MQ Family produces the ratio of existing air and base line air resistance, and it is usually equipped with a datasheet containing the consecration of detected gas in a certain value of the sensor to convert the output to the concentration of detected gas. The ratio is used to estimate the concentration of a gas. In this paper, Artificial neural network is used to estimate the concentration of a gas in the air based on the ratio. Providing the accurate calculation of the ratio is very important to increase the Electronic nose performance, and the result of this experiment showed that the Artificial neural network method achieves a good performance with smaller RMSE of 0.0433 compared with the existing methods.

Microcontroller Based E-Nose for Gas Classification without Using ADC

2016

This paper illustrates a technique that facilitate direct-interfacing (DI) a sensor array to a microcontroller for analog output voltage measurement without the use of an ADC (Analog-to-digital Converter). Even though all earlier reports on direct interface is successfully implemented for a single sensor, it provided a gateway for interfacing a sensor array. We successfully demonstrate the direct-interfacing of MOS (metal oxide semiconductor) based gas sensor array to an inexpensive 8-bit microcontroller. Further to accentuate the discriminative capability of the system two pattern classification paradigmsFFBP (feed forward back propagation) ANN (artificial neural network) and LDA (linear discriminant analysis) are associated with the direct interface circuit. We also corroborate gas identification in the microcontroller by implementing FFBP ANN which shows an accuracy of 98.75 %. The effectiveness of DI methodology established will serve as a viable tool for online gas monitoring a...

Article Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

2013

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO 2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.

Gas Sensors Characterization and Multilayer Perceptron (MLP) Hardware Implementation for Gas Identification Using a Field Programmable Gate Array (FPGA)

Sensors, 2013

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO 2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.