Fast‐moving coin recognition using deep learning

Multimedia Tools and Applications - Deep learning has made essential contributions to the development of visual object detection and recognition. Identifying fast-moving objects from the viewpoint...     

An ultrahigh-resolution image encryption algorithm using random super-pixel strategy

Multimedia Tools and Applications - Almost all existing image encryption algorithms are only suitable for low-resolution images in the standard image library. When they are used to encrypt...     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

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.

     

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Machine Intelligence Research - One of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have...     

大内跨小边跨框架结构预应力筋优化布置方法

预应力筋的优化布置是大内跨小边跨的多跨预应力混凝土框架结构合理设计计算的关键.为满足控制截面承载力最大的需要,以往常将大跨所需预应力筋布置为连续曲线,并按水平直线延伸通过其相邻的两小跨,按这种布置方式仅使大跨内产生竖向向上的预应力等效荷载,可较好平衡大跨的竖向外荷载;而若将大跨及相邻的两小跨统一考虑,预应力筋按连续曲线布置,张拉产生的预应力等效荷载不但可有效平衡大跨的竖向外荷载而且也可平衡小跨的竖向外荷载,可以使荷载平衡得到优化实现.以一典型工程为例,分析了在相同条件下两种预应力筋布置过程中张拉引起的综合弯矩、主弯矩和次弯矩的分布和量值上差异,比较了两类布筋形式对正截面承载力计算结果的影响,验证了对大内跨小边跨框架结构统一考虑后整体上按三段连续正反抛物线预应力筋布置的合理性.     

求解声波动方程的隐格式有限体积法

为研究声传播问题,提出一种声波动方程的隐格式有限体积法,该方法将格点型有限体积法与Newmark格式相结合.模拟平面波的传播过程,对比分析隐格式有限体积法和文献中显格式有限体积法的精度、稳定性及计算消耗等方面的性能.数值结果表明:当λ/Δx≥10时,两种算法均能得到满足精度要求的解;采用无条件稳定的隐格式算法,当满足ω0Δt≤0.3时,预测声压的相对峰值误差1%;当采用相同时间、空间步长时,隐格式算法精度高于显格式算法;隐格式算法对吸收边界的处理精度高于显格式算法,但对全反射边界的处理精度低于显格式算法;两种算法内存消耗比较接近,显格式算法的CPU耗时较少.     

动态网络中行业因素与股市波动性研究

股票市场的波动性一直是金融领域的一个研究重点,行业因素对股市波动性的影响也是投资者关注的一个焦点.从复杂网络视角出发,以行业内股票相关性强弱(RIL)为指标实证研究了行业因素与股票波动性之间的联系.研究发现不同行业的RIL指标有不同的参考标准,于是对原有的RIL指标进行了改进得到新指标RIL*.在以沪深300样本股为例的实证研究中发现采矿业、金融业和房地产业内的股票受行业因素影响最强.研究还发现股票网络结构变化和股市波动性之间存在联系,当行业因素影响急剧降低时,行业内的股票走势会猛烈上升或下降.