Toward Embedded Sensing Automation and Miniaturization for Portable Smart Cost-Effective Algae Monitor
Yumin Liao, Ningmei Yu, Dian Tian, Yongchao Wang, Shuaijun Li, Zhengpeng Li
Abstract
As an important indicator of water pollution, algae are highly sensitive to changes in their environment and respond to a wide range of pollutants, they provide an early caution signal of worsening ecological condition. In this article, a kind of portable microfluidic lensless depth neural network algae monitor is proposed. The lensless algae image acquisition module, algae segmentation and classification circuit, and touch panel were integrated into the equipment. Therefore, the equipment can collect and analyze algae automatically in wild water body without laboratory. In order to miniaturize the equipment and reduce the cost, lensless microfluidic channel sampling is adopted. In addition, a dual asymmetric quantization algorithm and circuit structure are proposed for the implementation of deep neural network hardware. Finally, the prototype system construction was completed. Compared with the current analysis equipment, the equipment size and hardware cost are greatly reduced, and the accuracy reduction is kept in a small range, which makes a better compromise between the accuracy, hardware cost and circuit power consumption. The performance of the equipment fully meets the needs of the current portable algae monitor equipment, and the cost is greatly reduced compared with the current equipment. The accuracy of the equipment achieves 94.27%, the size achieves 11 * 11 * 17.5cm. These advances in portability and cost are conducive to promoting the transformation of water algae analysis based on artificial intelligence from large servers in the laboratory to portable algae analysis equipment, and promoting the rapid early analysis of water monitoring.