Recently, a growing interest in artificial implementations of biological systems has been arising. In particular, several research groups have been working in mimicking the mammalian olfactory system with the so-called electronic noses (e-noses). The e-noses, which are based on a sensor array, a fluid-dynamic system, and a data processing unit, are systems devoted to detecting and analyzing volatiles, where a deep knowledge of the target application is needed. In order to achieve effective results the sampling system, the measurement protocols, the sensor array, and the pattern recognition techniques have to be carefully designed. The increasing complexity of such design poses issues in sensory feature extraction and fusion, drift compensation, and data processing, especially when high efficiency is required for real-time applications. The interconnection and cooperation of several modules devoted to processing different tasks, such as control, data acquisition, data filtering interfaces, feature selection, and pattern analysis, are already mandatory. Moreover, heterogeneous techniques used to implement such tasks may introduce module interconnection and cooperation issues. In this paper, we address the development of a dedicated instrument able to perform real-time transduction, fusion, and processing of chemoresistive sensor array signals. In particular, this instrument realizes a dynamic and efficient management of data processing techniques and automatically controls the measurement protocols and the sampling system. An array of conducting poly (alkoxy-bithiophenes) sensors, the fluid-dynamic system, the electronic section, the framework's base architecture, and the implementation of dedicated application processes are described. The classification task is based on a self-organizing map where models for artificial neurons and connections were derived from the base structures available in the framework core. According to the target application, this instrument is portable and easily tailored, calibrated, and trained. Classification of olive oil headspaces supports its utility in supplying high-efficiency routine for volatile organic compounds detection and analysis.
Towards a Real-Time Transduction and Classification of Chemo-Resistive Sensor Array Signals
PIOGGIA, GIOVANNI;FERRO, MARCELLO;DI FRANCESCO, FABIO
2007-01-01
Abstract
Recently, a growing interest in artificial implementations of biological systems has been arising. In particular, several research groups have been working in mimicking the mammalian olfactory system with the so-called electronic noses (e-noses). The e-noses, which are based on a sensor array, a fluid-dynamic system, and a data processing unit, are systems devoted to detecting and analyzing volatiles, where a deep knowledge of the target application is needed. In order to achieve effective results the sampling system, the measurement protocols, the sensor array, and the pattern recognition techniques have to be carefully designed. The increasing complexity of such design poses issues in sensory feature extraction and fusion, drift compensation, and data processing, especially when high efficiency is required for real-time applications. The interconnection and cooperation of several modules devoted to processing different tasks, such as control, data acquisition, data filtering interfaces, feature selection, and pattern analysis, are already mandatory. Moreover, heterogeneous techniques used to implement such tasks may introduce module interconnection and cooperation issues. In this paper, we address the development of a dedicated instrument able to perform real-time transduction, fusion, and processing of chemoresistive sensor array signals. In particular, this instrument realizes a dynamic and efficient management of data processing techniques and automatically controls the measurement protocols and the sampling system. An array of conducting poly (alkoxy-bithiophenes) sensors, the fluid-dynamic system, the electronic section, the framework's base architecture, and the implementation of dedicated application processes are described. The classification task is based on a self-organizing map where models for artificial neurons and connections were derived from the base structures available in the framework core. According to the target application, this instrument is portable and easily tailored, calibrated, and trained. Classification of olive oil headspaces supports its utility in supplying high-efficiency routine for volatile organic compounds detection and analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
