MLP neural network based gas classification system on Zynq SoC

Systems based on Wireless Gas Sensor Networks (WGSN) offer a powerful tool to observe and analyse data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex Micro hotplates (MHP). The overall system acquires the gas sensor data through RFID, and processes the sensor data with the proposed MLP classifier implemented on a System on Chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and achieved results have shown that an accuracy of 97.4% has been obtained

Analysis and classification of drift susceptible chemosensory responses

textThis report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents basic decision tree models and advanced ensemble versions. The contribution of this report is to show that basic decision trees perform reasonably well on the gas sensor data, however their accuracy can be drastically improved by employing ensemble decision tree classifiers. The report presents bagged trees, Adaboost trees and Random Forest models in addition to basic entropy and Gini based trees. It is shown that ensemble classifiers achieve a very high degree of accuracy of 99% in classifying gases even when the sensor data is drift ridden. Finally, the report compares the accuracy of all the models developed.Electrical and Computer Engineerin

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments’ proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures

Sistema de sensado mediante Arduino y matriz de sensores de gases industriales

[ES] El presente Trabajado de Fin de Grado titulado “Sistema de sensado mediante Arduino y una matriz de sensores de gases industriales”tiene como objetivo el desarrollo de un sistema electrónico capaz de detectar sustancias volátiles en el aire. Este sistema está destinado a ser implementado en una nariz electrónica funcional, apta para realizar estudios e investigaciones.Para la realización del proyecto, y una vez establecidos los requisitosque ha de cumplir el sistemade sensado, se ha comenzado por determinar y diseñar los principales subsistemas por los que está formado. Así, las partes que lo forman son una matriz compuesta por sensores de gases de la familia MQ y dos sensores de temperatura y humedad DHT11, el sistema de alimentación de esta matriz, un microcontrolador Arduino que procesa y envía los datos obtenidos de los sensoresa un ordenador, y un software diseñado con Processing que permite la visualización a tiempo real de esa información, además de dar la posibilidad al usuario de exportar los datos en un fichero de texto con formato .csv.Se ha llevado a cabo una simulación del sistema utilizando el softwareProteus, con lo que se ha podido comprobar que ambos programas, el de Arduino y el de Processing, funcionan correctamente.A continuación, se ha implementado un prototipo de la matriz de sensores y se han llevado a cabo dos ensayos con los que se ha podido corroborar que el sistema en su totalidad es viable y proporciona los resultados esperados.Por último, se han diseñado las placas de circuito impreso en las que se montan tanto la matriz de sensores como la alimentación del sistema y que se usarán en la futura implementación de la nariz electrónica.[EN] The present Final Degree project titled "Sistema de sensado mediante Arduino y una matriz de sensores de gases industriales" –“Sensing system using Arduino and a matrix of industrial gas sensors” –aims to develop an electronic system capable of detectingvolatile substances in the air. This system is intended to be implemented in a functional electronic nose, suitable for studies and research.For the realization of the project, and once established the requirements that the sensing system must fulfill, its main subsystems by which it is formed have been determinedand designed. Its main parts are a matrix composed of gas sensors of the MQ family and two temperature and humidity DHT11 sensors,its power supply system,an Arduino microcontroller that processes and sends the data obtained from the matrixto a computer, and a softwaredesigned with Processingthat allows a real time visualizationof the sensorized dataand gives the user the possibility to export this information to a .csv text file.Asimulation of the system in Proteus was carried out, in orderto verify that both programs, that of Arduino and that of Processing, work correctly.Next, a prototype of the sensor array has been implemented and two experiments have been carried out. By doing that, it has been possible to corroborate that the system is viable and provides the expected results.Finally, the printed circuit boards have been designed in which both the sensor array and the system powersupplyare mounted. This circuit boards will be used in the future implementation of the electronic nose.Díaz Paredes, A. (2019). Sistema de sensado mediante Arduino y matriz de sensores de gases industriales. Universitat Politècnica de València. http://hdl.handle.net/10251/123654TFG

Conception et fabrication d'un prototype de nez électronique basé sur un système d'apprentissage et de reconnaissance évolutif des composants organiques volatiles

Plusieurs actions sont faites par l’être humain sur la base de la perception d’odeur comme sortir les ordures, changer les couches de bébé, prendre de mesures de la sécurité en cas de fuite de gaz, etc. Mais, le sens d’odorat de l’homme est limité, car il y a des gaz qui sont très toxiques et l’être humain ne peut pas les détecter par le nez comme le monoxyde de carbone. Ainsi, le sens d’odeur est utilisé dans plusieurs applications industrielles dans la production (industries des parfums) ou bien dans la sécurité (industrie de pétrole et du gaz), des applications médicales (détection de bactéries) et des applications de sécurité nationale (détection de cannabis). Depuis plusieurs décennies, la communauté des capteurs essaie de reproduire artificiellement la capacité de l’odorat. La première apparition de nez électroniques ou nez artificiel a été dans les années 1980. Cet appareil est un ensemble de capteurs de gaz et de techniques d’apprentissage et de reconnaissance utilisés pour distinguer de nombreuses odeurs. Plusieurs travaux ont été publiés sur l’utilisation du nez électronique dans des applications spécifiques. Cependant, il n’y a pas un grand nombre des travaux sur les nez artificiels qui peuvent être utilisés dans plusieurs applications. Ce projet a comme objectif la conception et la fabrication d’un nez électronique qui peut être utilisé dans plusieurs applications selon les besoins d’utilisateur. Une conception et une fabrication de la partie matérielle ont été faites à partir de zéro. Elle contient le système d’échantillonnage qui facilite la réaction des gaz avec les capteurs et la carte électronique qui traduit ces réactions en valeurs compréhensibles par la partie logicielle. Une conception, dans l’ensemble, optimale pour toutes les applications a été fabriquée à la fin de cette partie. Pour la partie logicielle, un processus d’apprentissage et de reconnaissance a été proposé en utilisant un système d’apprentissage évolutif basé sur des règles floues (FRB). L’évolution de la partie logicielle assure une flexibilité de l’ensemble (partie matérielle et partie logicielle) aux besoins d’utilisateurs. Afin de diminuer la dépendance du système à l’égard d’utilisateurs, une méthode de supervision active a été utilisée avec le système d’apprentissage et de reconnaissance

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

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