Machine learning-coupled tactile recognition with high spatiotemporal resolution based on cross-striped nanocarbon piezoresistive sensor array.

Flexible pressure sensor arrays have been playing important roles in various applications of human-machine interface, including robotic tactile sensing, electronic skin, prosthetics, and human-machine interaction. However, it remains challenging to simultaneously achieve high spatial and temporal resolution in developing pressure sensor arrays for tactile sensing with robust function to achieve precise signal recognition. This work presents the development of a flexible high spatiotemporal piezoresistive sensor array (PRSA) by coupling with machine learning algorithms to enhance tactile recognition. The sensor employs cross-striped nanocarbon-polymer composite as an active layer, though screen printing manufacture processes. A miniaturized signal readout circuit and transmission board is developed to achieve high-speed acquisition of distributed pressure signals from the PRSA. Test results indicate that the developed PRSA platform simultaneously possesses the characteristics of high spatial resolution up to 1.5 mm, fast temporal resolution of about 5 ms, and long-term durability with a variation of less than 2%. The PRSA platform also exhibits excellent performance in real-time visualization of multi-point touch, mapping embossed shapes, and tracking motion trajectory. To test the performance of PRSA in recognizing different shapes, we acquired pressure images by pressing the finger-type device coated with PRSA film on different embossed shapes and implementing the T-distributed Stochastic Neighbor Embedding model to visualize the distinction between images of different shapes. Then we adopted a one-layer neural network to quantify the discernibility between images of different shapes. The analysis results show that the PRSA could capture the embossed shapes clearly by one contact with high discernibility up to 98.9%. Collectively, the PRSA as a promising platform demonstrates its promising potential for robotic tactile sensing.

The complete mitochondrial genome of Diagramma pictum (Perciformes: Haemulidae).

The painted sweetlips Diagramma pictum (Thunberg 1792) is an important fish for commercial fisheries which is widely distributed in the Indo-West Pacific Ocean. It can change its external coloration and pattern during their lives. The complete mitochondrial genome of D. pictum was determined in this study. The genome was 16,531 bp in length and consisted of 13 protein coding genes, 22 transfer RNA (tRNA), 2 ribosomal RNA (rRNA), and one noncoding control region. The overall base composition was estimated to be A: 27.5%; T: 24.7%; C: 30.9% and G: 16.9% with AT bias of 52.2%. The molecular phylogenetic result revealed that D. pictum did not form an independent branch but was tightly clustered inside the Plectorhinchus groups, closely related to the species Plectorhinchus chaetodonoides, indicating the close relationships between genera Diagramma and Plectorhinchus. These results may provide important genomic information for species evolution and mitogenome based phylogenetic analyses of D. pictum in the family Haemulidae.