类椭圆特征自动识别及亚像素提取的完整实现

针对视觉检测中高精度、自动化类椭圆图像特征提取的需要,提出一种类椭圆特征自动识别和中心亚像素定位的完整实现方法。方法中采用基于类椭圆边缘属性对特征区域进行自动识别,采用最小二乘椭圆拟合精确求取类椭圆亚像素定位中心。实验表明,基于该解决方案,可以实现类椭圆特征的自动识别及椭圆中心的亚像素定位,定位算法精度较高,鲁棒性强,同时简便易行,不需要人机交互,可以很好地满足视觉检测仪器化需求。     

类椭圆特征自动识别及亚像素提取的完整实现

针对视觉检测中高精度、自动化类椭圆图像特征提取的需要,提出一种类椭圆特征自动识别和中心亚像素定位的完整实现方法.方法中采用基于类椭圆边缘属性对特征区域进行自动识别,采用最小二乘椭圆拟合精确求取类椭圆亚像素定位中心.实验表明,基于该解决方案,可以实现类椭圆特征的自动识别及椭圆中心的亚像素定位,定位算法精度较高,鲁棒性强,同时简便易行,不需要人机交互,可以很好地满足视觉检测仪器化需求.     

光笔式视觉坐标测量中的亚像素图像处理方法

介绍了一种光笔式便携三坐标视觉测量系统,提出了被测特征点像面坐标的提取方法。依据数字图像处理理论并结合测量系统中被测特征点的成像特点,给出了图像预处理过程及实际处理结果;采用基于灰度质心法的径向截面扫描法对图像边缘进行亚像素级检测;应用平面内任意位置椭圆的最小二乘曲线拟合法对椭圆形光斑中心的像面坐标进行了提取,实验结果表明该方法的提取精度为0.1个像素。     

基于欧氏距离图的随机Hough变换椭圆检测方法

为了实现椭圆目标的有效检测,克服椭圆检测过程中对椭圆完整性和边缘梯度精度要求过高的缺点,提出了一种改进的随机Hough变换的椭圆检测方法。首先充分利用椭圆的轴对称特性和极点-极弦性质求取候选椭圆,有效解决了无效采样和累积问题,然后采用欧氏距离图计算椭圆边缘点的欧氏距离之和来确定真实椭圆。实验结果表明,该算法相对于RHT-3算法和CMHT算法具有检测精度高、检测速度快和抗椭圆缺失能力强的优点。     

基于灰度导数加权的椭圆高精度提取算法

为了满足姿态测量时对椭圆合作目标的高精度定位要求,提出了一种基于灰度导数加权的椭圆拟合算法(WGG).首先,在进行预处理之后的二值图像上提取初始边缘点,采用简单的代数拟合方法得到椭圆参数;其次,考虑二值化过程中的不确定因素会对提取边界点带来干扰,根据原始图缘中边缘灰度的渐变姓,利用边缘点的灰度导数值对其进行高斯函数拟合;最后,求取每一个边缘点的正交相切点,并构造优化目标函数求取椭圆最优参数.实验结果表明,对测试图像提取出采的椭圆拟合误差能达到0.013像素以内,与真实值点的平均误差为0.017像素,真实图像中椭圆的平均拟合误差为0.008 6像素.对于不同的外界环境该方法都能获得更精确的亚像素边缘和椭圆中心,具有很高的精确性和鲁棒性.     

用改进的随机Hough变换检测胸水图像中的细胞

针对胸水细胞显微图像的特点提出一种改进的随机Hough变换(MRHT,modified randomized hough transform)检测圆和椭圆的算法.该算法分为两步:利用椭圆的几何性质,确定可能的椭圆中心的位置;在限定区域内,通过多次3点随机抽样,计算椭圆除中心坐标外的其他3个参数.研究表明,该算法可以同时检测多个圆和椭圆,可以从胸水细胞显微图像的复杂背景中较为准确地提取圆形和椭圆形细胞.实验结果表明,该算法有较高的检测效率,检测精度和较强的鲁棒性.     

基于改进弧段切点弦的多椭圆检测

为了提高图像中多椭圆检测的精确度,提出了基于改进弧段切点弦的多椭圆检测算法。使用弧段切点弦方法计算弧对椭圆中心,筛选弧段组。针对基于切点弦计算椭圆中心易受切点数字化误差影响的问题,一方面通过在弧对上多次取不同切点计算多个椭圆中心坐标,采取中值滤波的方法降低数字化误差影响;另一方面使用抑制数字化误差影响的弧段切线斜率估计方法估计弧段上点的切线斜率,控制数字化误差的影响。实验表明,该方法具有较高的检测精确度和较快的运行速度。     

基于图像的点读坐标提取方法

为快速准确地获取点读机的点读坐标,提出一种基于图像的点读坐标提取方法,该方法首先采用邻近像素差分、图像二值化、连通域检测和最小二乘直线拟合等方法建立书本坐标系;其次,利用点读笔颜色和杆状特征,并采用扫描法实现了图像中笔尖坐标的定位;最后,对图像坐标进行标定后,计算得到点读坐标。实验结果表明,该方法具有误差小、抗干扰能力强、通用性好的特点,能达到点读机对坐标精度的要求。     

基于几何特征的圆形标志点亚像素中心定位

视觉测量系统中圆形标志点中心定位的精度易受噪声的影响。为了增强其抗噪性从而提高定位精度,提出了一种利用几何特征以降低噪声干扰的中心定位算法。首先将自适应阈值分割法与质心法相结合,对点目标进行圆心粗定位。利用粗定位的圆心和半径对Canny算子检测到的边缘进行半径约束,以消除孤立点和噪声点。然后根据理想的圆成像后边缘点分布的几何特征和链接规律,采用一种基于分区原理的方法获取点目标的理想边缘。最后,采用Zernike正交矩对像素级边缘点进行亚像素定位,并用最小二乘椭圆拟合法计算得到中心坐标。实验结果表明,该方法的定位精度可以达到0.023 7pixel,算法的运行时间为2~3ms,基本满足测量系统对于圆形标志点中心定位在精度、稳定性和实时性上的要求。     

基于标记点的直升机旋翼动态三维测量方法

为快速方便地获取直升机旋翼动态三维坐标,提出了一种基于编码标记点和立体视觉的旋翼动态三维测量方法,该方法首先采用平面标定法及Bouguet校正算法实现立体标定和图像校正;其次,依据标记点形状和灰度特征,采用图像二值化和连通域标记等方法检测并定位编码标记点的中心,确定标记点像素坐标;然后,采用邻域扫描法对编码点进行解码,得到标记点的码值,并依据码值实现左右视图中同名标记点匹配;最后,利用投影方程求出标记点三维坐标,实现直升机旋翼动态三维测量。仿真实验结果表明该方法精度高,实际测量误差不超过0.3mm,可满足旋翼动态三维测量的要求。     

DSP control of high-power UPS systems feeding nonlinear loads

Many facilities, such as patient health care centers, data processing systems, and critical telecommunication links, rely on uninterruptible power supplies (UPS) to maintain a continuous supply of power in case of a line outage. In addition to requiring continuous power, many critical nonlinear loads are sensitive to incoming line transients and input harmonic voltage distortion. Conventional UPS systems operate to protect against such disturbances using complex filtering schemes, often employing large passive components. This paper presents the advantages of using real time digital signal processing (DSP) control of UPS systems. A DSP controlled UPS inverter and harmonic conditioning system is described and the performance is verified on a 150 kVA system     

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.     

实时时钟芯片DS1302在DSP嵌入式系统中的应用

在许多嵌入式应用场合需要实时时钟的功能,通常DSP（数字信号处理器）芯片并不具备此功能,因此在实际使用过程中需要外扩实时时钟芯片来解决该问题。文中介绍了串行时钟芯片DS1302的功能以及在DSP嵌入式实时处理系统中的应用,给出了基于CPLD（复杂可编程逻辑器件）的控制器逻辑设计和DSP底层软件驱动编写的方法,指出了逻辑设计中需要注意的问题。实现一种分层、高效和可移植的嵌入式系统。经过长期的试验．实时时钟运行稳定准确,取得了良好的效果。     

实时硬件系统中控制方法的研究

由于实时硬件系统的规模和复杂度不断增大，系统结构的模块化设计已成为硬件系统开发的发展趋势。研究了硬件系统中模块控制的方法和特点。结合MPEG2电视编码器中各功能模块的特点，提出了适合该硬件系统的模块控制方案，说明了控制模块的硬件设计思想和软件实现。实践证明，该方案满足系统实时性和可靠性的设计要求。     

Secure VM Migration in Cloud: Multi-Criteria Perspective with Improved Optimization Model

Distributed computing has risen as a well-known worldview for facilitating an assortment of online applications and services. The present business distributed computing stages utilize a semi concentrated design, where cloud resources, such as servers and storage are hosted in a few large global data centers. Virtualization in computing is a creation of virtual (not real) of something such as hardware, software, platform or an operating system or storage, or a network device. Further, Virtual Machine (VM) technology has recently emerged as an essential building block for data centers and cluster systems, mainly due to its capabilities of isolating, consolidating, and migrating workload. Migration of VM seeks to improve the manageability, performance, and fault tolerance of systems. In a virtual cloud computing environment, a set of submitted tasks from different users are scheduled on a set of Virtual Machines (VMs), and load balancing has become a critical issue for achieving energy efficiency. Thus to solve this issue and to achieve a good load balance, a new improved optimization algorithm is introduced namely Dual Conditional Moth Flame Algorithm (DC-MFA) that takes into account of proposed multi-objective functions defining the multi-constraints like CPU utilization, energy consumption, security, make span, migration cost, and resource cost. The performance of the proposed model will be analyzed by determining migration cost, energy consumption, and response time, and security analysis as well.

     

Probabilistic response‐time analysis for real‐time systems in body area sensor networks

Advances in real‐time system and wireless communication have led to the deployment of body area sensor networks (BASNs) for effective real‐time healthcare applications. Real‐time systems in BASNs tend increasingly to be probabilistic and mixed critical to meet stringent requirements on space, weight, and power consumption. Response‐time analysis is an important and challenging task for BASNs to provide some critical services. In this paper, we propose a request‐based compositional probabilistic response‐time analysis framework for probabilistic real‐time task models with fixed‐priority preemptive scheduling in BASNs. In this method, each probabilistic real‐time task is abstracted as a probabilistic request function. Rough response‐time distribution is computed first based on the cumulative request distribution and then exact response‐time distribution is obtained by refinement based on the request increase distribution. Our strategy can effectively improve performance by reducing repetitive computational overhead for the probabilistic response‐time analysis of all tasks in the system. Our evaluation demonstrates that our proposed method significantly outperforms the existing probabilistic response‐time analysis algorithm in terms of analysis duration. Copyright © 2015 John Wiley & Sons, Ltd.     

Maintenance of reliable real time systems: Hardware versus software tradeoffs

The maintenance features in a high reliability real time control system form an important component in the total system. It is essential that software and hardware maintenance features function in an effective manner to ensure a high level of reliability in real time systems. Hardware maintenance features include redundant processors, self-checking circuits, diagnostic microcode etc., while software maintenance features include fault recovery programmes, audits, diagnostics etc. In this paper, we examine briefly the relationship of maintenance software to hardware in current high reliability real time systems, and how this relationship is likely to change in the future. Discussion in the paper centres around issues such as: factors that contribute to effective maintenance design; hardware characteristics that affect software; partitioning of maintenance functions between software, firmware and hardware; and current trends in maintenance of real time systems.     

DATCP: deadline-aware TCP for the commoditized data centers

Many flows in data centers have deadlines and missing deadlines would hurt application performance such as affecting response quality in Web applications or delaying computing jobs in MapReduce-like systems.However,transmission control protocol(TCP) which is widely used in data centers now cannot provide deadline-aware transmission service.Service differentiation only distinguishes flows with different priority but is unable to guarantee completion time.In this paper,we propose a new protocol named deadline-aware TCP(DATCP) to provide deadline-aware transmission service for the commoditized data centers,which can be used as a flexible method for flow scheduling.DATCP combines flow urgency and importance to calculate precedence.Flow urgency is dynamically adjusted according to the gap between desired rate and actual throughput.Setting importance can avoid starving the important but no-urgent flows.Furthermore,a flow quenching method is presented which allows as many high precedence flows as possible to meet deadlines under heavy network load.By extensive simulations,the performance of DATCP was evaluated.Simulation results show that DATCP can make flows meet deadlines effectively.     

Cross-track three apertures millimeter wave SAR side-looking three-dimensional imaging

The airborne cross-track three apertures MilliMeter Wave (MMW) Synthetic Aperture Radar (SAR) side-looking three-Dimensional (3D) imaging is investigated in this paper. Three apertures are distributed along the cross-track direction, and three virtual phase centers will be obtained through one-input and three-output. These three virtual phase centers form a sparse array which can be used to obtain the cross-track resolution. Because the cross-track array is short, the cross-track resolution is low. When the system works in side-looking mode, the cross-track resolution and height resolution will be coupling, and the low cross-track resolution will partly be transformed into the height uncertainty. The beam pattern of the real aperture is used as a weight to improve the Peak to SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the cross-track sparse array. In order to suppress the high cross-track sidelobes, a weighting preprocessing method is proposed. The 3D images of a point target and a simulation scene are achieved to verify the feasibility of the proposed method. And the imaging result of the real data obtained by the cross-track three-baseline MMW InSAR prototype is presented as a beneficial attempt.     

A self-immune to 3D masks attacks face recognition system

Due to their advantages, facial recognition systems are among the most widely used biometric systems in recent years. However, their drawback is that they can easily be deceived by using 3D masks, which are replicas of real faces. To confirm this fact, we have tested the vulnerability to 3D masks attacks of the already approved Legendre moments invariants (LMI)-based face recognition method. This has been achieved by using the 3D mask attack database (3DMAD), which consists of real faces and faces with 3D masks. The obtained spoof false acceptance rate (SFAR) was close to 65%, which proves that this recognition system is vulnerable to 3D masks attacks. This is generally the case of other face recognition systems, with no anti-spoofing provision. In this paper, a face recognition method is proposed to prevent hackers from deceiving face recognition systems by using 3D masks of people belonging to the system database. This method combines the LMI and the linear discriminant analysis for characteristic features extraction, and the maximum likelihood for classification. With a 97.6% recognition rate and a SFAR equal to 0.83%, the results obtained may be considered as very satisfactory. These results, while obtained with a lower computational time, compare favorably with those of the state-of-the-art method that uses the same 3DMAD database.