Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

悬挂式重力补偿系统精密跟踪方法

介绍了一种用于研究空间机器人微重力条件下的工作特性的悬挂系统——悬挂式重力补偿系统,并给出了悬挂模块的动力学模型.通过数值求解与仿真,运用非线性规化求出了一定条件下的最优控制参数;用正交试验寻优、基于时间最优控制的试凑法证明了优化模型的解正确性.该研究为空间机器人地面试验系统模拟微重力环境下精密跟踪运动提供了最优控制方法.     

改进型LOG算子在偏振片绝对角度中的应用

光学成像系统自身通常都具有一定的偏振效应,严重影响仪器偏振测量精度,而提高仪器中偏振片绝对角度精度能提高偏振定标精度,对校正光学系统自身的偏振特性具有重要的意义。对偏振片直线透光轴Raw图像高精度边缘定位和直线检测是偏振片绝对角度计算的关键步骤。针对传统LOG算子各向同性及虚假边缘与边缘定位精度相矛盾的缺陷,提出改进型一阶导阈值旋转式椭圆对称LOG算子。该算子不仅保留了经典LOG算子的优点,还具有对不同角度的边缘更加有效地进行检测,在保持边缘定位精度下,减少虚假边缘的特点。在处理得到的边缘二值图像上,进行直线检测求出偏振片绝对角度,通过对比分析最小二乘法线性拟合和霍夫变换的直线检测效果,得出霍夫变换能精确计算出偏振片绝对角度。实验证明改进型LOG算子与霍夫变换相结合提高了偏振片绝对角度测量精度,对偏振定标精度具有很大的提高。     

Mechanical analysis of double-layered circular graphene sheets as building material embedded in an elastic medium

Journal of Central South University - Possessing the unique and highly valuable properties, graphene sheets (GSs) have attracted increasing attention including that from the building engineer due...     

多尺度卷积神经网络检测睡眠呼吸暂停

睡眠呼吸暂停综合征作为一种常见的与睡眠相关的呼吸障碍性疾病,受到众多的关注。由于其复杂的检诊断过程及昂贵的价格,吸引了众多研究学者探索基于单通道信号的快速、便捷检测方法。基于心电信号(ECG)提出了一种多尺度卷积神经网络睡眠呼吸暂停快速检测方法,与常规的单尺度卷积神经网络方法相比,该方法可以有效地结合信号的细节信息和抽象信息,提升卷积神经网络的特征呈现能力。通过PhysioNet提供的Apnea-ECG数据库进行验证,多尺度卷积神经网络获得了85.2%准确率、83.1%敏感性和86.5%特异性。与现有方法相比,该方法进一步提升了睡眠呼吸暂停的检测性能。     

三轴机床数控系统软件的设计与开发

在成功开发了Windows环境下基于通用PC的计算机数控软件的基础上，介绍了数控系统的模块组成和具体实现。经过实践证明，所研制的计算机数控系统软件是成功的，可靠的。能够准确，有效，实时的控制机床进行工作。     

基于步法的跑步机速度跟踪控制方法

目前,电动跑步机的加减速都是通过手动按钮控制,制约了健身者改变速度的自由性,对于电动跑步机应用拓展也产生了诸多不利影响。本文提出了一种基于步法的跑步机速度跟踪控制方法：通过在电动跑步机跑步带下安装压力传感器,获取运动时脚底与跑步机之间的作用力信号,分析运动时的步法信息,判别人体的运动趋势,据此控制电机加速或减速,使得跑步机速度随着人体运动速度的变化而改变。实验表明,该方法能够达到预期的速度跟踪效果,可为今后的进一步研发和电动跑步机技术升级提供有益参考。     

结合手指检测和HOG特征的分层静态手势识别

目的 基于手势的交互方式在人机交互中发挥着越来越重要的作用,手势识别是大多数手势交互系统的核心技术.当手势种类较多时,目前已有的大多数手势识别方法往往无法获得足够高的识别率.为此,提出了一种结合手指检测和梯度方向直方图(HOG)特征的分层静态手势识别方法.方法 提出一种基于形态学操作的手指检测算法作为手势识别方法的基础.首先由肤色模型从输入图像中提取出手部区域,然后利用手指检测算法识别出手势包含的手指个数,并根据手指个数从事先训练好的支持向量机分类器集合中选取一个,最后提取手部区域的HOG特征,并利用选择好的分类器完成识别任务.结果 对25种常用手势进行了识别实验,将本文方法与单独使用HOG特征的方法进行对比.本文方法可以将传统HOG方法的识别率提高20%左右.结论 基于手指个数的分层识别策略可以有效地解决传统单层识别方法在手势种类较多时识别率不高的问题.在手部区域能被成功检测的情况下,提出的结合手指检测和HOG特征的方法可以取得较理想的手势识别结果,且能达到实时性要求.     

A HMM-based Mandarin Chinese Singing Voice Synthesis System

We propose a mandarin Chinese singing voice synthesis system, in which hidden Markov model (HMM)-based speech synthesis technique is used. A mandarin Chinese singing voice corpus is recorded and musical contextual features are well designed for training. F0 and spectrum of singing voice are simultaneously modeled with context-dependent HMMs. There is a new problem, F0 of singing voice is always sparse because of large amount of context, i.e., tempo and pitch of note, key, time signature and etc. So the features hardly ever appeared in the training data cannot be well obtained. To address this problem, difference between F0 of singing voice and that of musical score (DF0) is modeled by a single Viterbi training. To overcome the over-smoothing of the generated F0 contour, syllable level F0 model based on discrete cosine transforms (DCT) is applied, F0 contour is generated by integrating two-level statistical models. The experimental results demonstrate that the proposed system outperforms the baseline system in both objective and subjective evaluations. The proposed system can generate a more natural F0 contour. Furthermore, the syllable level F0 model can make singing voice more expressive.