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by Keyword: Reservoir computing


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Fonollosa, J., Sheik, S., Huerta, R., Marco, S., (2015). Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring Sensors and Actuators B: Chemical 215, 618-629

Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors - provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies.

Keywords: Chemical sensors, Continuous gas prediction, Electronic nose, Real-time detection, Reservoir computing


Sheik, S., Marco, S., Huerta, R., Fonollosa, J., (2014). Continuous prediction in chemoresisitive gas sensors using reservoir computing Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 843-846

Although Metal Oxide (MOX) sensors are predominant choices to perform fundamental tasks of chemical detection, their use has been mainly limited to relatively controlled scenarios where a gas sensor array is first exposed to a reference, then to the gas sample, and finally to the reference again to recover the initial state. In this paper we propose the use of MOX sensors along with Reservoir Computing algorithms to identify chemicals of interest. Our approach allows continuous gas monitoring in simple experimental setups without the requirement of acquiring recovery transient of the sensors, thereby making the system specifically suitable for online monitoring applications.

Keywords: Chemical sensing, Reservoir computing, Gas sensors, Dynamic gas mixtures, Electronic nose