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 of the deployment to devices and consequently restricting the practical application of 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. In order 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, Kalman Filter (KF) is applied to filter out the disturbances during data acquisition process. In designing 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 10 times still achieves good regression performance with R 2 , MAE and 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.