Floc architecture of bioflocculation sediment by ESEM and CLSM

Sediment flocculation is a critical component for the understanding of cohesive sediment dynamics. Traditionally, the referred study has largely been devoted to forming mechanism, influencing factors and physicochemical sediment conditions of all kinds of organic-flocculation and inorganic-flocculation. However, during the last decade, the bioflocculation of sediment by biological activity has been given increasing attention. But most studies have focused on the interrelations between biological and sedimentological variables. With the assistance of a newly developed field kit and correlative microscopy (which includes environmental scanning electron microscopy and confocal laser scanning microscopy), this article begins to bridge the resolution gap between sediment particles and biological activities as well as its metabolic products biofilm, in order to better understand the role of polymeric material biofilm in floc ultrastructure and outward floc behavior of bioflocculation sediment. Results have demonstrated that bioflocculation sediment was observed to be composed of complex networks of biofilm and appeared to be of complicated physical floc structures. The biofilm was found to embed particles and permeate the void space, representing the dominant physical bridging mechanism of the flocs and contributed to the extensive surface area, architecture characteristics, and mechanical properties of bioflocculation sediment.     

Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

Bartels–Stewart algorithm is an effective and widely used method with an O(n ³) time complexity for solving a static Sylvester equation. When applied to time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. Gradient-based recurrent neural network are able to solve the time-varying Sylvester equation in real time but there always exists an estimation error. In contrast, the recently proposed Zhang neural network has been proven to converge to the solution of the Sylvester equation ideally when time goes to infinity. However, this neural network with the suggested activation functions never converges to the desired value in finite time, which may limit its applications in realtime processing. To tackle this problem, a sign-bi-power activation function is proposed in this paper to accelerate Zhang neural network to finite-time convergence. The global convergence and finite-time convergence property are proven in theory. The upper bound of the convergence time is derived analytically. Simulations are performed to evaluate the performance of the neural network with the proposed activation function. In addition, the proposed strategy is applied to online calculating the pseudo-inverse of a matrix and nonlinear control of an inverted pendulum system. Both theoretical analysis and numerical simulations validate the effectiveness of proposed activation function.     

An efficient quantum search engine on unsorted database

We consider the problem of finding one or more desired items out of an unsorted database. Patel has shown that if the database permits quantum queries, then mere digitization is sufficient for efficient search for one desired item. The algorithm, called factorized quantum search algorithm, presented by him can locate the desired item in an unsorted database using O( $log_4N$ ) queries to factorized oracles. But the algorithm requires that all the attribute values must be distinct from each other. In this paper, we discuss how to make a database satisfy the requirements, and present a quantum search engine based on the algorithm. Our goal is achieved by introducing auxiliary files for the attribute values that are not distinct, and converting every complex query request into a sequence of calls to factorized quantum search algorithm. The query complexity of our algorithm is O( $log_4N$ ) for most cases.     

光学薄膜元器件外观特性机器视觉检测系统

本系统利用工业级摄像头及开发包的机器视觉功能与计算软件编程控制技术相结合,通过与输入控制系统中的标准膜片模板外观品质进行对比,实现高速高效、准确识别检测光学薄膜器件中滤光片的外观缺陷。可在生产线上根据外观品质标准模板中设定的类别进行自动分类装盒。     

UDP协议在视频图像传输中的扩展与应用

根据UDP协议的特点,结合视频网络传输的要求对其进行扩展,给UDP数据包定义了一个8个字节的包头结构,用来加载数据包的序列号、时戳等信息。在发送端对传榆进行平滑处理,控制数据包大小和时戳间隔,减小了图像抖动的发生。应用给出了一种在Delphi下实现的视频数据打包流程,能够满足多种网络平台上的视频传输的要求。     

Truth-Telling Reservations

We present a mechanism for reservations of bursty resources that is both truthful and robust. It consists of option contracts whose pricing structure induces users to reveal the true likelihoods that they will purchase a given resource. Users are also allowed to adjust their options as their likelihood changes. This scheme helps users save cost and the providers to plan ahead so as to reduce the risk of under-utilization and overbooking. The mechanism extracts revenue similar to that of a monopoly provider practicing temporal pricing discrimination with a user population whose preference distribution is known in advance.     

使用HTTP Tunneling技术实现穿透网络防火墙的通信

近年来,防火墙在网络中得到了广泛的应用.它能够比较有效地防范病毒与黑客的攻击,保护网络的安全.但是也带来了一些不利影响,使一些需要进行网络通信的应用软件系统不能正常工作.介绍一个解决方案,并且重点介绍通过HTTP Tunneling技术使用基于文本的HTTP协议传送二进制数据的方法.     

基于LabVIEW的远程测控系统视频采集的设计与实现

本文提出了一种在虚拟仪器开发平台LabVIEW上,利用ActiveX技术和普通USB摄像头实现视频采集的新方法;设计了远程系统的总体结构;通过LabVIEW与ActiveX的协同编程,实现了视频预览、录像、拍照及实验数据和视频数据的实时传输等功能;通过一个分压特性测量实验阐明了该视频系统在远程测控中的具体应用。     

集成学习算法在不平衡分类中的应用研究

提出一种基于训练集分解的不平衡分类算法,该算法使用能输出后验概率的支持向量机作为分类器,使用基于测度层次信息源合并规则实现分类器的集成.在4个不同领域的不平衡数据集上的仿真实验表明:该算法有效提高分类器对正类样本的正确率,同时尽量减少对负类样本的误判.实验结果验证集成学习算法处理不平衡分类问题的有效性.