动态饱和鉴相鉴频器

在鉴相鉴频器的复位路径中插入可变延迟单元,利用反馈动态调节延迟时间,可消除死区并且不受环境变化的影响;采用多级鉴相鉴频器,可扩展鉴相鉴频器的工作范围,改善输入输出的非线性,提高鉴相鉴频性能。文章综合两者的优势,提出了一种动态饱和鉴相鉴频器,并把其应用在锁相环中,结果表明该电路在增加的功耗开销小于0.44%的情况下,使得锁相环的锁定时间减小了14%。     

AADL 测试模型的构造研究

目前,结构分析和设计语言AADL在任务关键嵌入式系统领域有着良好的应用.为了保障任务关键软件的质量,文章提出基于AADL测试模型对AADL设计模型进行测试,以发现设计模型中存在的错误.研究给出了体现系统拓扑结构的AADL测试模型的形式定义及由AADL构造该测试模型的算法,基于该测试模型可以对AADL设计模型中构件交互的输入、输出端口序列和连接的正确性进行测试,并以飞行控制系统的AADL设计模型为例,阐释了研究成果.     

关于构建长庆油田生产建设和谐安全监管的思考

通过对长庆油田生产建设和谐安全监管意义的思考,分析当前在生产建设中存在的不和谐因素,指出构建和谐安全监管的切入点,进而提出构建长庆油田生产建设和谐安全监管的建议。     

运动汽车图像静态视点平滑特征优化仿真

由于运动汽车图像在特征采集过程中,汽车处于高速运动状态,采集的过程无法与汽车完全同步,造成汽车的特征像素分量在高速运动中丢失,产生较大的像素运动噪声,使得汽车图像边缘高频特征因为运动干扰而模糊.传统的平滑过渡方法在对运动汽车图像进行平滑处理时,按照图像中像元灰度值计算突变特性,但受到像素丢失与噪声无法抑制的影响,无法解决运动汽车图像高频分量对图像像元灰度值的干扰问题.提出基于替身运动DR算法的运动汽车图像静态视点平滑过渡方法,用替身像素描述汽车在场景中的运动特征,替身像素在场景中运动并同其它汽车的替身进行交互,采用局部化的车辆模型提高运动汽车场景的逼真度,通过依据替身运动的平滑过渡算法完成归一化线性运动汽车图像静态视点的平滑过渡,解决了运动汽车图像的平滑过渡问题.实验结果说明,所提方法可确保运动汽车图像边缘锐化,有效处理平滑噪声和锐化边缘.     

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.