Design and Implementation of Portable Electronic Nose System Based on Lightweight Convolutional Neural Network

Deep learning is widely used in electronic nose (E-nose) pattern recognition algorithms. However, deep-learning algorithms are complex in structure and require a large amount of memory resources, inducing difficulty in the deployment to devices and consequently restricting the practical application of an E-nose. In this work, a lightweight convolutional neural network (LCNN) is designed by weight pruning and quantization and then deployed the E-nose to solve this problem. The whole process including hardware fabrication, data sampling and processing, model design and compression, and deployment to the portable E-nose system has been accomplished. To realize low cost and portability, only a microcontroller unit (MCU) is utilized as the main control chip of the E-nose system to complete model inference and system control. Furthermore, the Kalman filter (KF) is applied to filter out the disturbances during the data acquisition process. In designing a network model for gas concentration prediction, the designed network model consists of only four layers to keep the model size as small as possible, followed by further compression using 80% sparsity weight pruning and dynamic range quantization. The experimental results show that the model after being compressed by nearly ten times still achieves good regression performance with
${R}^{{2}}$
, mean absolute error (MAE), and mean square error (MSE) of 0.9914, 2.932, and 21.052, respectively. Finally, the optimized model was deployed into the MCU to obtain a low-cost and reliable portable E-nose system.