一种新型的无线光通信脉冲波形研究

针对无线光通信系统中高斯脉冲、升余弦脉冲、三角波脉冲等几种常用的脉冲信号通信可靠性相对较低、误码率相对较高的问题,为消除噪声干扰,提高通信可靠性,提出结合无线光通信系统中常用的调制后脉冲波形设计了一种新的合成脉冲波形,合成脉冲用一个持续时间与单个相应脉冲相等的脉冲代替相应的连续脉冲,且与原始脉冲属性相同,能量和原始的连续脉冲相等.理论分析和仿真结果表明,当合成脉冲波形用于几种特定的调制信号,使调制信号的峰值会出现在几个固定的位置,可以映射出原始信源信号.经过仿真证明,误码率性能较原始常用脉冲波形有较大提高.     

用双目型电视摄象机决定运动物体在三维空间的位置

本文提出用双目型电视摄象机决定运动物体的位置,包括用属性链文法表示物体,用透视 变换定基元空间位置和属性,用有限状态自动机剖析基元链.     

倒排索引优化的波形激光雷达数据存储和访问

LAS1.3格式是由美国摄影测量与遥感协会（ASPRS）制定的一种用于存储激光点云及其他相关信息的激光雷达交换数据格式,该版本支持激光雷达波形数据的存储,以适应全波形激光雷达的发展和应用。但由于其继承了LAS格式以点存储为中心的设计思路,波形数据仅作为附加部分挂接在点云数据上,故只提供了一种简单的映射方法实现波形数据访问,而无法直接体现出波形数据作为原始信息与回波点云的逻辑关系,因此在研究和实际应用中不便于波形数据的直接访问。设计实现了一种基于倒排索引的文件格式,建立了从波形数据到属于该束激光脉冲的点云的倒排映射关系,消除了LAS1.3文件中波形数据访问的无序性,也大大降低了波形数据处理过程中程序访问的复杂性。实验表明,新的索引文件组具有标准LAS1.3格式规范不具备的访问特性,可以增加波形数据的访问速度。     

计算机智能输出—语音合成

本文提出了一种语音波形构造法。根据汉语中字音为单音节的特点,选出合成的基本单元(下面简称基元),并根据语音的频率,采用两种方法来处理、合成基元。对频率低的波形,利用正弦函数插值重构;对频率高的波形,利用二值脉冲序列,根据过零信息重构波形。然后,按照汉语拼音准则,由基元进行字的合成。实验表明,合成音质好,可懂度高,各种声调的字均可合成,数据压缩率高。     

基于有向单连通链的表格框线检测算法

表格框线检测是表格识别的基础.现有的表格框线检测算法或者速度慢,或者鲁棒性差,而且没有充分利用表格框线之间的约束信息.提出了一种基于所定义的图像结构基元"有向单连通链"的自底向上表格框线检测算法.在此算法中,有向单连通链是一种黑像素游程序列,作为非常合适的矢量基元,在引入一定表格框线约束信息的条件下合并单连通链,有效地去除伪框线,补全断裂的框线,提高了算法的鲁棒性,可以准确而快速地提取表格框线.通过滤除噪声单连通链,加快单连通链的合并速度,算法速度提高了3~10倍,满足了实用要求.实验证明,该算法具有速度     

基于小波变换的岩体AE波形特征研究

文章对室内声发射事件能率与应力的关系及波形特性进行了分析。通过小波变换,将室内岩体声发射等波形分解成低频和高频信号,对其原始声发射波形、细节信号和近似信号进行比较与分析,结果表明岩体在变形损伤破裂过程中不同时期的声发射波形特征不同。据此,可建立声发射波形特征库,用于预测预报岩体受损变形过程的发展趋势和规律。对含有信号部分的小波系数进行重构后,该信号与原始信号的误差小于10-10,说明小波分析可使岩体声发射波形特征预测预报的准确度和可靠性得以提高。     

一种SAR图像直线提取的线基元组织方式

合成孔径雷达(SAR)图像直线提取中,线基元的组织是一个重要的步骤。该文根据直线在SAR图像经过边缘检测后所体现出的特点,将线基元用只包含水平或垂直两个方向的连续边缘像素的集合来表示。方法是首先提取原始线基元,然后提取出线结构,接着将线结构组织成线段,最后将线段连接提取出直线。原始的线基元看作两个四邻域连接的或者单个边缘像素的集合,再将这些原始线基元按照水平或垂直的方向进行合并、组织,并形成一些短的或长的线段。提取线结构采用一种先搜索水平(垂直)方向再搜索其他方向的八邻域搜索的方法,利用了线基元空间邻域关系,并用最小二乘方法拟合直线。     

时间域两网格全波形反演

全波形反演同时利用波场的振幅、相位和走时信息来推测介质物性参数,是一个极小化模拟数据与已知观测数据之间残量的优化迭代过程.由于正问题是关于模型参数的一个非线性算子及波形拟合中的周期跳跃现象,全波形反演是一个典型的不适定性问题.本文我们在前期频率多尺度反演工作的基础上,提出了多重网格全波形反演方法,文中详细描述了有限差分正演方法及两层网格全波形反演的策略,并对复杂构造Marmousi模型,基于MPI并行编程语言,进行了大规模反演计算,得到了较高精度的反演结果.数值计算表明两网格全波形反演具有提高计算效率和加速收敛的优点.     

指纹图像帧序列拼接的波形匹配算法

为了获得完整的指纹图像,提出一种基于波形匹配的指纹图像帧序列拼接算法.该算法从每个图像帧的参考区域提取波形信息,通过计算波形相似度并引入逐步求精的拼接策略,分3个阶段获得拼接参数,确定图像帧之间的重叠和水平移位.实验结果表明,文中算法运算量小、运算速度快、占用系统资源少,特别适用于资源有限环境中的自动指纹识别系统.     

基于几何相似特征的石窟造像装饰图案生成方法

针对石窟造像装饰图案设计中图案基元的组合方式不够灵活多变、画面布置与调整耗时、图案基元间的交叉重叠问题,提出一种基于几何相似特征的图案生成方法.首先明确图案属性,利用Freeman链码描述图案基元轮廓信息;然后利用最长公共子序列计算图案基元间的几何相似性特征;接着基于计算结果对图案基元进行设计布局,完成子图案的效果生成;最后依据检测和识别出的图案空间分布模式将子图案进行扩充生成.采用响堂山石窟中的装饰图案基元数据进行实验的结果表明,所提方法不仅解决了图案基元组合优化问题,而且能实现复杂图案的快速生成设计,该方法在图案基元的匹配时间为19.2 s,比人工方法的36.1 s更优.     

Attributed string matching with merging for shape recognition

A new structural approach to shape recognition using attributed string matching with merging is proposed. After illustrating the disadvantages of conventional symbolic string matching using changes, deletions, and insertions, attributed strings are suggested for matching. Each attributed string is an ordered sequence of shape boundary primitives, each representing a basic boundary structural unit, line segment, with two types of numerical attributes, length and direction. A new type of primitive edit operation, called merge, is then introduced, which can be used to combine and then match any number of consecutive boundary primitives in one shape with those in another. The resulting attributed string matching with merging approach is shown useful for recognizing distorted shapes. Experimental results prove the feasibility of the proposed approach for general shape recognition. Some possible extensions of the approach are also included.     

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

In this paper, we present a methodology for automatic diagnosis of systems characterized by continuous signals. For each condition considered, the methodology requires the development of an alphabet of signal primitives, and a set of hierarchical fuzzy automatons (HFAs). Each alphabet is adaptively obtained by training an adaptive resonance theory (ART2) architecture with signal segments from a particular condition. Then, the original signal is transformed into a string of vectors of primitives, where each vector of primitives replaces a signal segment in the original signal. The string, in turn, is presented to the HFA characterizing that particular condition. Each set of HFA consists of a main automaton identifying the entire signal, and several sub-automata each identifying a particular significant structure in the signal. A transition in the main automaton occurs (i.e., the main automaton moves from one state to another) if the corresponding subautomaton recognizes a token where a token is a portion of the string of vectors of signal primitives with a significant structure. The fuzziness in automaton operation adds flexibility to the operation of the automaton, enabling the processing of imperfect input, allowing for toleration measurement noise and other ambiguities. The methodology is applied to the problem of automatic electrocardiogram diagnosis.     

View-invariant gesture recognition using 3D optical flow and harmonic motion context

This paper presents an approach for view-invariant gesture recognition. The approach is based on 3D data captured by a SwissRanger SR4000 camera. This camera produces both a depth map as well as an intensity image of a scene. Since the two information types are aligned, we can use the intensity image to define a region of interest for the relevant 3D data. This data fusion improves the quality of the motion detection and hence results in better recognition. The gesture recognition is based on finding motion primitives (temporal instances) in the 3D data. Motion is detected by a 3D version of optical flow and results in velocity annotated point clouds. The 3D motion primitives are represented efficiently by introducing motion context. The motion context is transformed into a view-invariant representation using spherical harmonic basis functions, yielding a harmonic motion context representation. A probabilistic Edit Distance classifier is applied to identify which gesture best describes a string of primitives. The approach is trained on data from one viewpoint and tested on data from a very different viewpoint. The recognition rate is 94.4% which is similar to the recognition rate when training and testing on gestures from the same viewpoint, hence the approach is indeed view-invariant.     

基于形体组合关系的基元体识别技术研究

提出了一种新的基于组合因子的基元体识别方法 .从构成复杂形体的基元体之间的组合关系类型入手 ,将组合关系分为显式组合关系和隐式组合关系 ;接着 ,针对显式组合关系在二维图纸中的投影特性 ,将其在二维视图中的相关投影定义为组合因子 ,并由此建立相应的组合关系表 ,最后基于组合关系表对具有不同组合关系的基元体进行识别 .该方法将存在于二维视图中的组合因子与其对应的三维空间中基元体之间的组合关系信息联系起来 ,具有搜索空间小、识别范围广等优点 ,并在原型系统中得到了应用     

采用指令集扩展和随机调度的AES算法实现技术

在随机掩码技术基础上,定义了若干细粒度的随机掩码操作,将AES（Advanced Encryption Standard）算法中各种变换分解为细粒度随机掩码操作的序列,并使得所有的中间结果均被不同的随机量所掩码。为高效实现基于细粒度随机掩码操作分解的AES算法,定义了三种扩展指令,结合指令随机调度方法,给出了AES算法的完整实现流程,并指出这种实现技术可以抗一阶和高阶功耗攻击。实验结果表明,与其他典型防护技术相比,这种实现技术具有安全性、运算性能以及硬件复杂度等方面的综合优势。     

Syntactic recognition of ECG signals by attributed finite automata

A syntactic pattern recognition method of electrocardiograms (ECG) is described in which attributed automata are used to execute the analysis of ECG signals. An ECG signal is first encoded into a string of primitives and then attributed automata are used to analyse the string. We have found that we can perform fast and reliable analysis of ECG signals by attributed automata.     

From dynamic movement primitives to associative skill memories

In recent years, research on movement primitives has gained increasing popularity. The original goals of movement primitives are based on the desire to have a sufficiently rich and abstract representation for movement generation, which allows for efficient teaching, trial-and-error learning, and generalization of motor skills (Schaal 1999). Thus, motor skills in robots should be acquired in a natural dialog with humans, e.g., by imitation learning and shaping, while skill refinement and generalization should be accomplished autonomously by the robot. Such a scenario resembles the way we teach children and connects to the bigger question of how the human brain accomplishes skill learning. In this paper, we review how a particular computational approach to movement primitives, called dynamic movement primitives, can contribute to learning motor skills. We will address imitation learning, generalization, trial-and-error learning by reinforcement learning, movement recognition, and control based on movement primitives. But we also want to go beyond the standard goals of movement primitives. The stereotypical movement generation with movement primitives entails predicting of sensory events in the environment. Indeed, all the sensory events associated with a movement primitive form an associative skill memory that has the potential of forming a most powerful representation of a complete motor skill.     

Multifont size-resilient recognition system for Ethiopic script

This paper presents a novel framework for recognition of Ethiopic characters using structural and syntactic techniques. Graphically complex characters are represented by the spatial relationships of less complex primitives which form a unique set of patterns for each character. The spatial relationship is represented by a special tree structure which is also used to generate string patterns of primitives. Recognition is then achieved by matching the generated string pattern against each pattern in the alphabet knowledge-base built for this purpose. The recognition system tolerates variations on the parameters of characters like font type, size and style. Direction field tensor is used as a tool to extract structural features.     

Finding Syntactic Structures from Human Motion Data

We present a new approach to motion rearrangement that preserves the syntactic structures of an input motion automatically by learning a context‐free grammar from the motion data. For grammatical analysis, we reduce an input motion into a string of terminal symbols by segmenting the motion into a series of subsequences, and then associating a group of similar subsequences with the same symbol. To obtain the most repetitive and precise set of terminals, we search for an optimial segmentation such that a large number of subsequences can be clustered into groups with little error. Once the input motion has been encoded as a string, a grammar induction algorithm is employed to build up a context‐free grammar so that the grammar can reconstruct the original string accurately as well as generate novel strings sharing their syntactic structures with the original string. Given any new strings from the learned grammar, it is straightforward to synthesize motion sequences by replacing each terminal symbol with its associated motion segment, and stitching every motion segment sequentially. We demonstrate the usefulness and flexibility of our approach by learning grammars from a large diversity of human motions, and reproducing their syntactic structures in new motion sequences.