A comparative study on some navigation schemes of a real robot tackling moving obstacles

A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.     

Pattern analysis of dermoscopic images based on Markov random fields

In this paper a method for detecting different patterns in dermoscopic images is presented. In order to diagnose a possible skin cancer, physicians assess the lesion based on different rules. While the most famous one is the ABCD rule (asymmetry, border, colour, diameter), the new tendency in dermatology is to classify the lesion performing a pattern analysis. Due to the colour textured appearance of these patterns, this paper presents a novel method based on Markov random field (MRF) extended for colour images that classifies images representing different dermatologic patterns. First, each image plane in L^*a^*b^* colour space is modelled as a MRF following a finite symmetric conditional model (FSCM). Coupling of colour components is taken into account by supposing that features of the MRF in the three colour planes follow a multivariate Normal distribution. Performance is analysed in different colour spaces. The best classification rate is 86% on average.     

Evenly Spaced Streamlines for Surfaces: An Image-Based Approach

We introduce a novel, automatic streamline seeding algorithm for vector fields defined on surfaces in 3D space. The algorithm generates evenly spaced streamlines fast, simply and efficiently for any general surface-based vector field. It is general because it handles large, complex, unstructured, adaptive resolution grids with holes and discontinuities, does not require a parametrization, and can generate both sparse and dense representations of the flow. It is efficient because streamlines are only integrated for visible portions of the surface. It is simple because the image-based approach removes the need to perform streamline tracing on a triangular mesh, a process which is complicated at best. And it is fast because it makes effective, balanced use of both the CPU and the GPU. The key to the algorithm's speed, simplicity and efficiency is its image-based seeding strategy. We demonstrate our algorithm on complex, real-world simulation data sets from computational fluid dynamics and compare it with object-space streamline visualizations.     

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture,Layout, and Context

This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). J. Shotton is now working at Toshiba Corporate Research & Development Center, Kawasaki, Japan.     

教育机器人仿真系统中的声音设计

现有教育机器人仿真系统在进行物理仿真时缺少对声音的支持,为弥补这一缺点,有必要设计一种声音系统,使仿真更加逼真。分析仿真过程对声音的需求状况,并结合FMOD声音引擎的特点,设计系统的实现流程,讨论其中的关键问题及解决方法。测试结果显示,该系统可以实现多种音效,能够满足教育机器人物理仿真对声音的需求,对多声源能够进行较好的管理,并且有效地节约计算机资源。     

基于分治算法的ECC乘法器结构及实现

针对椭圆曲线密码体制中的有限域乘法运算,讨论基本的串行结构、并行结构以及串并混合结构乘法器的硬件实现及存在的缺陷,提出一种改进的乘法器结构。该结构利用分治算法,通过低位宽乘法运算级联,降低运算复杂度,减少所需的时钟数。FPGA实验结果证明新结构在相同频率下有更小的面积和时间乘积。GF（2^233）域上椭圆曲线点乘采用此结构一次计算仅需0．811ms,满足椭圆曲线密码体制的应用要求。     

基于数据场的SVM技术在雷暴预报中的应用

针对天气预报中样本不平衡造成漏报率高的问题,提出一种基于数据场的C加权支持向量机（SVM）技术。该技术对不平衡天气数据进行分类,采用叠加数据场势值作为数据重采样依据,筛选出最利于SVM分类器学习的样本作为训练样本,结合C加权方法进行训练。实验结果证明,在样本数量较多且不平衡性显著的雷暴天气中,该技术能缩减训练集规模,减少漏报,提升预报系统的g-means值。     

微型折叠式三维电场传感器

提出一种基于柔性衬底和三维组装方法的新型三维电场传感器.利用柔性衬底的折叠,构建一种互相垂直的新型三维电场传感器,研究了测量空间电场时的耦合,提出解耦算法,标定灵敏度矩阵,实现了空间电场三个方向分量的准确测量;通过一系列的温湿度实验研究了三维结构的形变,证明了三维电场传感器结构稳定可靠.实验结果表明:所研制的三维电场传感器不仅能消除耦合干扰,实现空间电场分量的准确测量,测量误差在3.61％以内;而且通过新的三维组装方法,大大减少了三维电场传感器的质量、体积,折叠工艺简化了三维传感器的组装,有利于批量化生产.     

基于旋转谐振结构的单芯片二维电场传感器

提出并研制了一种二维电场检测传感芯片,将四个电场测量微型单元和旋转式驱动微结构集成在3. 5 mm ×3. 5 mm的敏感结构上,实现了单芯片的电场二维测量.介绍了传感器的工作原理、敏感结构的设计,以及基于绝缘体上硅( SOI)工艺的单芯片微型二维电场传感器制备工艺技术.成功研制出传感器原理样机,研究了微型二维电场传感器的标定方法,开发了用于电场二维标定的测试装置,并在室温常压下对传感器进行了二维标定.实验结果表明:该传感器能够有效减小电场的轴间耦合干扰,测量误差优于7. 04%,线性度可达到1. 25%.