A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

蝇蛹金小蜂雌雄蜂触角感器的扫描电镜比较

应用扫描电镜对蝇蛹金小蜂(Pachycrepoideus vindemmiae)雌蜂和雄蜂触角的感受器形态进行了观察,并对雌、雄蜂触角上各个感受器的长度及数量进行详细比较。结果表明,蝇蛹金小蜂雌、雄触角上均有B9hm氏鬃毛、板型感器(I型和II型)、锥形乳头状感器、薄壁化学感器、厚壁化学感器、毛型感器(I型和II型)、刺型感器和感觉孔等8种类型感受器,小蜂触角在雌雄间存在形态和结构上的差异,主要表现在:B9hm氏鬃毛在雄峰梗节基部有分布,雌蜂梗节未见分布;板型感器I型和毛型感器II型仅分布在雌蜂触角,雄峰未见分布。雌蜂的毛型感器的数量显著多于雄峰,雄蜂的薄壁化学感器的数量显著多于雌蜂。

高校科研经费的使用和管理问题研究

据统计,我国高校每年立项的科研项目数量在逐渐提高,这就说明高校的科研水平也在逐步提高。而科研经费一直是困扰大多数高校的难题,如存在支出不合理等问题,使得部分科研经费并没有发挥应有的作用。因此,本文主要探究高校科研经费的使用和管理问题。

极近距离煤层固体充填充实率协同控制覆岩运移规律研究

针对南屯煤矿工广区域下极近距离煤层难以安全回收的问题,采用物理模拟、数值模拟和概率积分法相结合的方法,研究了极近距离煤层固体充填开采充实率协同控制覆岩运移规律。通过对比垮落法与固体充填法开采条件下的覆岩运移特征,得到了极近距离煤层覆岩运移控制关键。通过模拟分析不同充实率条件下极近距离煤层固体充填开采的覆岩运移规律,揭示了上下煤层充实率协同控制覆岩运移机理,从而实现极近距离煤层充实率的优化设计。最后利用概率积分法预计了极近距离煤层固体充填开采下的协同控制效果。结果表明:上下煤层充实率均为80%时可保证工广煤柱回收时地表建筑物的安全稳定。