Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
To simulate the firing pattern of biological grid cells, this paper presents an improved computational model of grid cells based on column structure. In this model, the displacement along different directions is processed by modulus operation, and the obtained remainder is associated with firing rate of grid cell. Compared with the original model, the improved parts include that: the base of modulus operation is changed, and the firing rate in firing field is encoded by Gaussian-like function. Simulation validates that the firing pattern generated by the improved computational model is more consistent with biological characteristic than original model. Besides, the firing pattern is badly influenced by the cumulative positioning error, but the computational model can also generate the regularly hexagonal firing pattern when the real-time positioning results are modified. 相似文献
Perovskite light-emitting diodes (PeLEDs) show promising prospects in the wide color gamut display owing to their ultra-narrow full width at half maximum (FWHM). However, up to now, all perovskite white LEDs integrated by standard red, green, and blue perovskite emitters, namely, monolithic white PeLEDs (WPeLEDs), have been rarely reported, owing to facing some issues, e.g., solvent incompatibility in solution technique, ion exchange, and energy transfer between different emission centers. Herein, centered on these issues, an optimal intermediate connection layer (ICL) of Po-T2T/LiF/Ag/HAT-CN/MoO3 is adopted to successfully develop monolithic tandem multicolor PeLEDs and WPeLEDs for the first time. The multicolor PeLEDs can achieve the best external quantum efficiency of 1.8% and the highest luminance of 4844 cd m−2. Besides, the red/green/blue (R/G/B) monolithic tandem WPeLED shows a standard white International Commission on Illumination coordinate of (0.33, 0.33) and achieves an extremely wide color gamut reaching National Television Standards Committee of 130%. This study is the first to realize the standard R/G/B co-electroluminescence in a monolithic perovskite device and offers a feasible strategy for developing wide-color gamut perovskite displays. 相似文献