多线程技术在LIA中两路束流测量的实现

强流脉冲电子束作为一种高亮度的光源,具有广阔的应用前景,而双轴束流测量的控制对于研究产生电子束的加速器技术具有重要意义;利用多线程技术在CVI环境下的实现方法,研究了双路束流参数特点、双路束流测量的时序同步控制方法,在此基础上采用线程池多线程技术实现基于TCP/IP的加速器束流测量的数据采集软件;结果表明采用多线程技术可以很好地实现多任务的同时工作,有助于提高束测量的响应速度和测量的执行效率。     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

高速可配置Rijndael算法的设计与实现

针对网络通信的安全要求,给出了在集成电路设计中实现Rijndael算法的高吞吐率解决方案。对算法实现进行了优化,具有支持密钥长度和数据长度的可变选择与组合配置,加密和解密工作通道完全独立,全双工安全高速通信等特点。已通过了FPGA的验证与电路实现。通过对比分析综合仿真结果,该实现相对于目前已知的实现方案在性能和速度上具有很大的优势。     

基于Struts的Web开发

Struts是目前比较流行的一个基于MVC设计模式的JavaWeb应用框架,文章详细地阐述了Struts框架实现MVC的构架机制及其核心组件和框架特点,介绍了集成在Struts框架内部的Validator框架在具体Web系统中的开发应用,比较了基于该验证框架的两种验证的优缺点。     

Friendly spectrum jamming against MIMO eavesdropping

Wireless Networks - Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.