基于NiosⅡ用户自定义指令的AES算法实现

提出一种采用AES算法和RSA算法相结合的混合加解密算法,并采用Altera的NiosII软核用户自定义指令功能实现该混合加解密算法。文中主要对该混合加解密算法中的AES算法进行了设计、论述,通过对AES算法的轮变换和密钥扩展两部分算法的分析,并在NiosII软核上实现其自定义指令,就可以使用简单的几条语句快速地实现AES算法,大大地提高了算法实现的灵活性,最后给出了使用NiosII用户自定义指令实现与使用VerilogHDL实现AES算法效果的对比分析。     

基于NiosⅡ用户自定义指令的AES算法实现

提出一种采用AES算法和RSA算法相结合的混合加解密算法,并采用Altera的NiosII软核用户自定义指令功能实现该混合加解密算法。文中主要对该混合加解密算法中的AES算法进行了设计、论述,通过对AES算法的轮变换和密钥扩展两部分算法的分析,并在NiosII软核上实现其自定义指令,就可以使用简单的几条语句快速地实现AES算法,大大地提高了算法实现的灵活性,最后给出了使用NiosII用户自定义指令实现与使用VerilogHDL实现AES算法效果的对比分析。     

RSA和RSA数字签名的实现

RSA算法是一种公钥密码算法.实现RSA算法包括生成RSA密钥,加密和解密数据.RSA数字签名算法利用RSA算法实现数字签名.本文详述了RSA算法的基本原理、RSA算法的实现以及如何利用RSA实现数字签名.     

蚁群算法用于TSP的并行策略及模型*

蚁群算法是一种元启发式算法,其经典应用是解决旅行商问题。该算法有着先天的并行特性。介绍了该算法的两种并行实现策略,给出了蚁群算法的并行实现模型,分析了该算法并行实现需要解决的问题。     

表插入排序算法的改进

介绍表插入排序的数据结构设计、算法等方面的内容。给出表插入排序的具体算法实现。特别是阐述算法实现的整个设计过程,并给出一个具体实例的实现过程及相应的算法分析。     

图像中值滤波算法的优化及其硬件实现

讨论了FPGA图像处理算法的几种实现途径,在分析和研究中值滤波算法的基础上提出了一种优化的算法,该算法既能满足硬件的流水实现,又可在效率上得到明显提高。设计以FPGA为硬件平台,用Verilog语言实现了中值滤波的优化算法。通过与软件中值滤波进行比较,可以看到硬件实现的效率优势和算法可行性。     

SWIFFT算法效率分析

以SWIFFT算法为重要组成部分的SWIFFTX杂凑算法因实现效率问题未能进入SHA-3第二轮竞选。为此,研究提高SWIFFTX杂凑算法效率的方法,分析SWIFFT算法的实现过程。通过绘制快速傅里叶变换(FFT)流向图,估算实现SWIFFT算法的加/减、乘法运算量。此外,还提出一种计算中间参数ω的方法。分析结果表明:当存储空间较少时,选用16点FFT实现SWIFFT算法效率更高;当存储空间充足时,选用8点FFT实现SWIFFT算法效率更高。     

导弹控制技术FPGA实现仿真

利用Max PlusⅡ软件平台对导弹模糊控制算法的FPGA硬件实现进行仿真，分析控制算法FPGA实现的设计过程，并与基于PC机的算法软件实现结果进行比较。仿真结果表明导弹模糊控制算法的FPGA实现是可行的，满足实时性要求。此外对FPGA优缺点的分析进一步为算法工程实现的可行性提供依据。     

A~*算法的改进及其PROLOG实现

在人工智能领域中,A~*算法是实现有向图最佳优先搜索的一种启发式算法。本文介绍了A~*算法和对A~*算法的改进——B算法,并叙述了用Turbo Prolog实现B算法的方法。     

RSA公钥密码体制的实现研究

RSA公钥算法是目前被广泛采用的公钥体制密码算法,但RSA算法的运算复杂度极大,如果能针对个人用户,用专用集成电路快速而又低成本地实现RSA算法,将有助于RSA算法的推广。该文对目前应用较好的RSA公钥算法的软、硬件算法进行了详细的实现分析,指出采用合理的软硬件算法并增加部分硬件,可以用专用集成电路较快地实现RSA算法。     

Face recognition algorithms surpass humans matching faces over changes in illumination

There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control-humans.     

Efficient parallel hierarchical clustering algorithms

Clustering of data has numerous applications and has been studied extensively. Though most of the algorithms in the literature are sequential, many parallel algorithms have also been designed. In this paper, we present parallel algorithms with better performance than known algorithms. We consider algorithms that work well in the worst case as well as algorithms with good expected performance.     

Genetic algorithms for task scheduling problem

The scheduling and mapping of the precedence-constrained task graph to processors is considered to be the most crucial NP-complete problem in parallel and distributed computing systems. Several genetic algorithms have been developed to solve this problem. A common feature in most of them has been the use of chromosomal representation for a schedule. However, these algorithms are monolithic, as they attempt to scan the entire solution space without considering how to reduce the complexity of the optimization process. In this paper, two genetic algorithms have been developed and implemented. Our developed algorithms are genetic algorithms with some heuristic principles that have been added to improve the performance. According to the first developed genetic algorithm, two fitness functions have been applied one after the other. The first fitness function is concerned with minimizing the total execution time (schedule length), and the second one is concerned with the load balance satisfaction. The second developed genetic algorithm is based on a task duplication technique to overcome the communication overhead. Our proposed algorithms have been implemented and evaluated using benchmarks. According to the evolved results, it has been found that our algorithms always outperform the traditional algorithms.     

Some algorithms for interval interpolating polynomial

Some algorithms for the computation of an Interval Interpolating Polynomial have been proposed. The algorithms have been compared with the existing algorithms. One of the algorithms has been shown to be superior to all others.     

On the dominance of non-parametric Bayes rule discriminant algorithms in high dimensions

Considerable attention has been given to the relative performance of the various commonly used discriminant analysis algorithms. This performance has been studied under varying conditions. This author and others have been particularly interested in the behavior of the algorithms as dimension is varied. Here we consider three basic questions: which algorithms perform better in high dimensions, when does it pay to add or delete a dimension, and how discriminant algorithms are best implemented in high dimensions.

One of the more interesting results has been the relatively good performance of non-parametric Bayes theorem type algorithms compared to parametric (linear and quadratic) algorithms. Surprisingly this effect occurs even when the underlying distributions are “ideal” for the parametric algorithms, provided, at least, that the true covariance matrices are not too close to singular. Monte Carlo results presented here further confirm this unexpected behavior and add to the existing literature (particularly Van Ness⁽⁹⁾ and Van Ness et al.⁽¹¹⁾ by studying a different class of underlying Gaussian distributions. These and earlier results point out certain procedures, discussed here, which should be used in the selection of the density estimation windows for non-parametric algorithms to improve their performance. Measures of the effect on the various algorithms of adding dimensions are given graphically. A summary of some of the conclusions about several of the more common algorithms is included.     

Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image

The brain magnetic resonance (MR) image has an embedded bias field. This field needs to be corrected to obtain the actual MR image for classification. Bias field, being a slowly varying nonlinear field, needs to be estimated. In this paper, we have proposed three schemes and in turn three algorithms to segment the given MR image while estimating the bias field. The problem is compounded when the MR image is corrupted with noise in addition to the inherent bias field. The notions of possibilistic and fuzzy membership have been combined to take care of the modeling of the bias field and noise. The weighted typicality measure together with the weighted fuzzy membership has been used to model the image. The above resulted in the proposed Bias Corrected Possibilistic Fuzzy C-Means (BCPFCM) strategy and the algorithm. Further reinforcing the neighbourhood data to the modeling aspect has resulted in the two other strategies namely Bias Corrected Possibilistic Neighborhood Fuzzy C-Means (BCPNFCM) and Bias Corrected Separately weighted Possibilistic Neighborhood Fuzzy C-Means (BCSPNFCM). The proposed algorithms have successfully been tested with synthetic data with bias field of low and high spatial frequency. Noisy brain MR images with Gaussian Noise of varying strength have been considered from the BrainWeb database. The algorithms have also been tested on real brain MR data set with axial and sagittal view and it has been found that the proposed algorithms produced segmentation results with less percentage of misclassification errors as compared to the Bias Corrected Fuzzy C-Means (BCFCM) algorithm proposed by Ahmed et al. [4]. The performance of the proposed algorithms has been compared with algorithms from other paradigm in the context of Tanimoto's index.     

改进的粒子滤波算法在多目标跟踪中的应用研究

由于基于序贯重要性采样的粒子滤波算法存在着样本退化的问题,因此文章在几种常用的重采样算法的基础上提出了一种改进的重采样算法,通过在初始化阶段对粒子集的优化处理,在重采样阶段使用基于特定权值的改进重采样算法,从而得到了一种改进的粒子滤波算法。最后根据仿真实验表明改进的算法不但在跟踪精度上有所提高,而且对于样本退化和枯竭问题也进行了一定程度的改善,更为重要的是在多机动目标跟踪中也得到了很好的应用。     

A reconfigurable architecture for autonomous visual-navigation

Abstract. This paper describes the design of a reconfigurable architecture for implementing image processing algorithms. This architecture is a pipeline of small identical processing elements that contain a programmable logic device (FPGA) and double port memories. This processing system has been adapted to accelerate the computation of differential algorithms. The log-polar vision selectively reduces the amount of data to be processed and simplifies several vision algorithms, making possible their implementation using few hardware resources. The reconfigurable architecture design has been devoted to implementation, and has been employed in an autonomous platform, which has power consumption, size and weight restrictions. Two different vision algorithms have been implemented in the reconfigurable pipeline, for which some experimental results are shown. Received: 30 March 2001 / Accepted: 11 February 2002 RID="*" ID="*" This work has been supported by the Ministerio de Ciencia y Tecnología and FEDER under project TIC2001-3546 Correspondence to: J.A. Boluda     

Regulation and control of LARM Hand III

This paper addresses the problem of regulation and control of the grasp in a three finger robotic hand. In particular, the operation of a built prototype of LARM Hand III has been investigated. This robotic hand has been designed and built at LARM: Laboratory of Robotics and Mechatronics in Cassino. Two control algorithms have been developed for its controlled operation. The first one is a position control with a force threshold that limits the maximum grasping force to a given value. The second control algorithm is a force control with inner position loop that keeps the grasping force at a given value. The control algorithms have been implemented in the robotic hand by means of a virtual instrument in LabVIEW environment. A test-bed has been set up at LARM for experimentally validating the proposed algorithms. Results of experimental tests demonstrate the effectiveness and engineering feasibility of the proposed algorithms.     

A NASA perspective on quantum computing: Opportunities and challenges

In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, however, it is currently unknown whether quantum algorithms can provide an advantage, and if so by how much, or how to design quantum algorithms that realize such advantages. Many of the most challenging computational problems arising in the practical world are tackled today by heuristic algorithms that have not been mathematically proven to outperform other approaches but have been shown to be effective empirically. While quantum heuristic algorithms have been proposed, empirical testing becomes possible only as quantum computation hardware is built. The next few years will be exciting as empirical testing of quantum heuristic algorithms becomes more and more feasible. While large-scale universal quantum computers are likely decades away, special-purpose quantum computational hardware has begun to emerge, which will become more powerful over time, as well as small-scale universal quantum computers.