MIPS内存管理单元的设计与实现

设计MIPS32 4kc处理器内存管理单元(MMU),该模块对处理器地址进行合法性检查,并按照不同的地址空间对虚拟地址进行静态或动态映射。在硬件上采用三级流水线方式实现JTLB,并为处理器指令端口和数据端口设计相应的快表以提高TLB的查询速度。MMU与总线接口模块的时序采用简化的AMBA协议,与处理器进行联合调试并运行Linux操作系统,同时在功能上通过FPGA验证。该模块经过DC综合后,面积约为32K等效逻辑门。     

E500 MMU架构研究及VxWorks下的优化方案

MMU/TLB（存储器管理单元/转换旁置缓冲区）是影响嵌入式操作系统实时性能的关键因素之一。VxWorks嵌入式操作系统为E500处理器内核的TLB默认配置有效率低、易发生缺页中断的问题。针对E500处理器内核TLB的架构特点和VxWorks系统运行时的内存布局,提出了VxWorks系统下优化E500 TLB配置的方法;实验表明,该优化方法降低了CPU利用率,提高了系统的性能。     

中低端微处理器平台软件仿真MMU的设计与实现

嵌入式软件目前已广泛应用于生活和工业中,由于其硬件开发的局限性,在微控制器(单片机)利用软件的方式来仿真嵌入式硬件平台的物理结构和硬件功能目前在工业和软件开发过程中的应用越来越普遍,以软件方式模拟MMU对内存的操作,实现低端微处理器管理大量内存,来代替高端的ARM内核的微处理器能以比较快的速度进行访问的功能,实现应用上层接口比较方便的系统。本文先重点介绍了ARM系列微处理器中MMU的硬件结构、MMU相关的缓存结构以及MMU的事务处理过程。在此基础上对MMU访问的缓存硬件TLB、Cache、WriteBuffer等结构组成和属性进行描述,并进行软件仿真实现。最后针对s3c2440微处理器与MMU所相关的行为给予软件实现,并进行平台测试。     

微处理器存储管理单元的功能验证方法

在分析存储管理单元(MMU)验证方法的基础上设计一种验证专用操作系统(VPOS)。采用静态存储管理、静态用例调度和伪中断处理等技术,为编写可执行、可控制的MMU测试程序提供了软件平台。验证表明,基于VPOS的仿真能在早期的软仿真阶段覆盖94%的MMU设计错误,在FPGA验证中覆盖剩余的错误,保证了移植通用操作系统一次成功。     

减少TLB失效开销提高64位Linux系统性能的方法

针对64位的Linux提出了一个减少TLB失效开销的方法——FAST_TLB_REFILL（快速TLB重载入）。测试结果表明,这种方法可以将TLB失效处理时间减少30％以上,对TLB失效比较频繁的程序会有1％～7％的性能提高。     

构建基于以太网的uClinux开发环境

uClinuX是针对没有MMU微控制器的嵌入式Linux操作系统.本文主要介绍如何利用NFS,在一个局域网中构建一个开放的uClinux开发环境.它简化了uClinux应用程序的开发过程.     

uCLinux与标准Linux在内存管理中的分析与比较

标准Linux系统在内存管理中使用了虚拟内存技术,使得Linux系统的内存管理功能相当强大和安全,而在嵌入式Linux系统中,uCLinux的内存没有相应的MMU处理器,也无法实现虚拟内存管理,针对这种情况,uCLinux采用了针对没有MMU的特殊处理,以实现在嵌入式设备中的需求。     

Linux/IA64存储管理的研究

安腾处理器家族是Intel公司新一代64位处理器,它已成功应用于科学计算和事务处理等领域,Linux是支持IntelIA-64平台的主流操作系统之一。该文研究了Linux/IA64存储管理,包括Linux/IA64灵活的地址空间划分、TLB等机制,并着重讨论了与IA64VHPT(VirtualHashPageTable)walker紧密结合的虚实地址变换、基于IA64区ID的懒惰TLB刷新等新技术。     

基于Linux的虚拟试妆系统设计

结合SoPC技术裁剪灵活的特点和嵌入式Linux操作系统高性能多任务的特性,设计了基于Linux和轻量级图形库FTK的虚拟试妆系统。以带MMU的Nios II处理器和Linux操作系统为基础,采用自行设计的模块实现影像采集和存储,通过在触摸屏上移植轻量级图形库FTK实现交互界面开发。最终实现了具有商品浏览、模拟化妆、真人上妆等功能的虚拟试妆系统。     

构建基于以太网的uClinux开发环境

uClinux是针对没有MMU微控制器的嵌入式Linux操作系统。本文主要介绍如何利用NFS,在一个局域网中构建一个开放的uClinux开发环境。它简化了uClinux应用程序的开发过程。     

基于身份的签名和可验证加密签名方案*

基于双线性对,提出了一个基于身份的签名方案,在计算性D iffie-Hellman问题困难的假设下,证明了该方案在随机预言机模型下抗适应性选择消息和身份攻击。基于提出的方案,构造了一个可证安全的可验证加密签名方案,其不可伪造性依赖于提出的基于身份的签名方案,不透明性依赖于基于身份的签名方案和BLS短签名。与已有方案相比,该方案的优势是基于身份,不需要证书,从而简化了密钥管理。     

Determination of Head Pose and Facial Expression from a Single Perspective View by Successive Scaled Orthographic Approximations

Human faces are the main organs for expressing human emotion. In this study, a new iterative approach to analyzing the head pose and the facial expression of a human face from a single image is proposed. The proposed approach extends the concept of successive scaled orthographic approximations, which was used to estimate the pose of a rigid object, to develop a method to estimate the parameters for a non-rigid object, namely, a human face. The implementation of the proposed method is simple; furthermore, no initial guess is required. The convergency property of the proposed method is also analyzed theoretically and experimentally. Experimental results show that the proposed method is robust and has a high percentage of convergency, and thus prove the feasibility of the proposed approach.     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

A chaotic framework and its application in image encryption

A novel image encryption framework is proposed in this article. A new chaotic map and a pseudorandom bit generator are proposed. Apart from this, a novel image encryption system is designed based on the proposed map and the proposed pseudorandom bit generator. These three are the major contributions of this work that makes a complete cryptosystem. The proposed new chaotic map is proposed which will be known as the ‘RCM map’ and its chaotic property is studied based on Devaney’s theory. The proposed pseudorandom bit generator is tested using the NIST test suite. The proposed method is simple to implement and does not involve any highly complex operations. Moreover, the proposed method is completely lossless, and therefore cent percent of data can be recovered from the encrypted image. The decryption process is also simple to implement i.e. just reverse of the encryption procedure. A scrambling algorithm is also proposed to further enhance the security of the overall system. The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it. The experimental results are very promising and show the prospects of chaos theory and its usage in the field of data security.

     

A boosting approach for supervised Mahalanobis distance metric learning

Determining a proper distance metric is often a crucial step for machine learning. In this paper, a boosting algorithm is proposed to learn a Mahalanobis distance metric. Similar to most boosting algorithms, the proposed algorithm improves a loss function iteratively. In particular, the loss function is defined in terms of hypothesis margins, and a metric matrix base-learner specific to the boosting framework is also proposed. Experimental results show that the proposed approach can yield effective Mahalanobis distance metrics for a variety of data sets, and demonstrate the feasibility of the proposed approach.     

Autonomous Bayesian Search and Tracking,and its Experimental Validation

We present a technique that uniformly controls a team of autonomous sensor platforms charged with the dual task of searching for and then tracking a moving target within a recursive Bayesian estimation framework. The proposed technique defines the target detectable region, and uniformly formulates observation likelihoods with detection and no-detection events. The unified likelihood function allows the proposed technique to update and maintain the target belief, regardless of the target detectability. For unified search and tracking (SAT), the proposed technique further predicts the belief in a finite-time horizon, and decides control actions by maximizing a unified objective function consisting of local and global measures derived from the predicted belief. Using the objective function, the proposed technique can smoothly change its control actions even during transitions between SAT. The numerical results first show successful SAT by the proposed technique in tests using a sensor platform with different detectability and comparison with conventional searching techniques under different prior knowledge, and then identifies the superiorities of the proposed technique in SAT. The experimental results finally validate the applicability and extendability of the proposed technique via coordinated SAT in a field experiment.     

带一步随机延迟量测非线性序列贝叶斯估计的条件后验克拉美罗下界

为了解决带一步随机延迟量测非线性状态估计器可获得最优性能的评价问题,提出了一种适用于带一步随机延迟量测非线性系统的条件后验克拉美罗下界(Conditional posterior Cramr-Rao lower bound, CPCRLB),且现有的CPCRLB仅是所提出的CPCRLB在延迟概率为零时的一种特例. 为了递归地计算提出的CPCRLB,本文提出了一种带一步随机延迟量测的粒子滤波器(Particle filter, PF),继而推导了提出的CPCRLB 一般近似解和在高斯噪声情况下的特殊近似解. 单变量非平稳增长模型、纯方位跟踪和频率调制信号模型的数值仿真证明了本文提出方法与现有方法相比的有效性和优越性.     

解无约束全局优化问题的一个新的进化算法及其收敛性

文章利用一维搜索与局部极小点的消去技术设计了一个新的进化算法。此算法在迭代过程中,可不断消除那些比目前已找到的最好点差的局部极小点,从而使局部极小点的数目随着迭代的进行大量地减少,使算法更易找出全局极小点。另外,将一维搜索巧妙地用于算法之中,加快了收敛速度。并且证明了算法的全局收敛性,最后的数值实验也表明新算法十分有效。     

Computing simple and complex matchings between XML schemas for transforming XML documents

This paper presents a schema matching method for the transformation of XML documents. The proposed method consists of two steps: computing preliminary matching relationships between leaf nodes in the two XML schemas based on proposed ontology and leaf node similarity, and extracting final matchings based on a proposed path similarity. Particularly, for a sophisticated schema matching, the proposed ontology is incrementally updated by users' feedback. Furthermore, since the ontology can describe various relationships between concepts, the proposed method can compute complex matchings as well as simple matchings. Experimental results with schemas used in various domains show that the proposed method performs better than previous methodologies, resulting in a precision of 97% and a recall of 83% on the average.     

A measurement fusion method for nonlinear system identification using a cooperative learning algorithm

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.