Low-frequency approach to target identification

This paper presents a low-frequency method for target identification, and its effectiveness is demonstrated for a large variety of objects varying in complexity from spheres and cubes to modern airplanes. The selection of an appropriate discrete set of frequencies led to a low misclassification error. A number of classification methods are examined using this discrete set of frequencies. It is shown that simple objects can be adequately classified by a linear discriminant method. For more complex targets, such as aircraft, a nearest neighbor approach is required. The introduction of phase and orthogonal polarization components further decreased misclassification error. A discussion of the tradeoff between the increased complexity and improved performance of various classification alternatives is provided.     

SVM based approach for complexity control of HEVC intra coding

The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.     

A New K-Ary Crisp Decision Tree Induction with Continuous Valued Attributes

The simplicity and interpretability of decision tree induction makes it one of the more widely used machine learning methods for data classification.However,for continuous valued (real and integer) attribute data,there is room for further improvement in classification accuracy,complexity,and tree scale.We propose a new K-ary partition discretization method with no more than K-1 cut points based on Gaussian membership functions and the expected class number.A new K-ary crisp decision tree induction is also proposed for continuous valued attributes with a Gini index,combining the proposed discretization method.Experimental results and non-parametric statistical tests on 19 real-world datasets showed that the proposed algorithm outperforms four conventional approaches in terms of both classification accuracy,tree scale,and particularly tree depth.Considering the number of nodes,the proposed methods decision tree tends to be more balanced than in the other four methods.The complexity of the proposed algorithm was relatively low.     

基于最小距离的音频分类方法的研究

基于内容的音频分类是一个有趣并有重要意义的问题。音频分类技术包括音频特征抽取和分类器两个基本部分。如今,基于内容的音频自动分类技术已经有了很大的发展。然而,现有的基于内容的音频自动分类方法在分类的准确性、有效性和算法复杂度等诸多方面存在一定的不足,探索性能更佳的方法就成为了该领域的研究热点。提取了基于内容的音频分类所使用的音频特征,得到了基于帧的音频特征和基于片段的音频特征两个层次的特征,并提出了一种基于MFCC的简化的特征;选取了最小距离分类器中的最近邻分类器和K近邻分类器,对这几种典型的音频分类器进行研究,进行仿真实验,分析了实验结果;最后设计并仿真了经过改进的最小距离音频分类器,它的性能相对于原有的最近邻和K近邻分类器有一定的提高,并具有很低的系统复杂度和很短的分类时间。     

基于三种不同绝对对齐方法的分类器分析

雷达高分辨距离像识别时的一个重要问题是其平移对齐。相对于相关对齐方法,绝对对齐方法具有计算简单的优点,虽然对齐精度不如相关对齐法,但识别率并没有降低,并且由于利用最简单的分类准则———最小欧氏距离准则来进行分类,运算量与运算复杂度有明显的降低。文中介绍了三种基于不同绝对对齐方法———零相位对齐法、零线性相位对齐法、重心法的分类器,并对它们进行了理论分析与性能比较。实验结果表明,这三种分类器都能在低运算量的情况下获得了较好的识别性能。     

Statistical textural features for detection of microcalcificationsin digitized mammograms

Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. Texture-analysis methods can be applied to detect clustered microcalcifications in digitized mammograms. In this paper, a comparative study of texture-analysis methods is performed for the surrounding region-dependence method, which has been proposed by the authors, and conventional texture-analysis methods, such as the spatial gray-level dependence method, the gray-level run-length method, and the gray-level difference method. Textural features extracted by these methods are exploited to classify regions of interest (ROI's) into positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissues. A three-layer backpropagation neural network is used as a classifier. The results of the neural network for the texture-analysis methods are evaluated by using a receiver operating-characteristics (ROC) analysis. The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity.     

用于高阶MPPSK 信号检测的SVM新算法

为了降低支持向量机(SVM)算法在高阶多元位置相移键控（M-ary Position Phase Shift Keying,MPPSK）系统的信号检测复杂度,在分析常用SVM多分类算法的基础上,提出了一种新的具有更低复杂度的类二分法SVM。为了进一步提高高阶MPPSK信号检测性能,提出一种新的SVM特征向量提取方法,调制矩阵法,并将两种方法结合起来,用于高阶MPPSK系统的信号检测。仿真结果表明:类二分法SVM能显著降低多分类SVM的算法复杂度,调制矩阵选取特征向量法能够显著提高高阶MPPSK系统的检测性能,两种方法结合用于高阶MPPSK系统,可以在有效降低复杂度的前提下保证期望的检测性能。      

可计算图像复杂度评价方法综述

可计算的图像复杂度评价是让计算机模拟人类视觉感知,从而对图像视觉复杂度进行决策的研究,该研究属于多学科交叉的创新性研究课题,在图像工程领域具有重要意义.本文针对可计算的图像复杂度评价方法进行了全面的梳理和分析,文中首先回顾了图像复杂度的应用领域,并详细阐述了图像复杂度评价方法,从信息论、图像压缩理论、图像特征分析、眼动数据等方面进行总结;随后,着重阐述基于图像特征的图像复杂度评价方法中所使用的图像特征;归纳图像复杂度建模中的分类和回归问题;最后,总结当前图像视觉复杂度评价方法存在的问题和挑战,展望图像复杂度的计算化发展方向.     

Low Latency,Area and Optimal Power Hybrid Lightweight Cryptography Authentication Scheme for Internet of Things Applications

The Internet of Things (IoT) is proved as technologically competent connecting many devices via the internet. Now in networks transmitting a large quantity of data in a secure manner is of vital concern as communication is on the increase. Hence an efficient cryptographic methodology is essential to transmit securely. However, conventional encryption algorithms are having high computational complexity, more power consumption and high memory occupation. In this paper, low latency, area and optimal power Hybrid Lightweight Cryptography Authentication Scheme which is utilizing the 8-bit manipulation principle (HLCAS) is proposed and implemented. For such a method verification is done and validated for speech signal utilizing MATLAB. The correlation and histogram attributes have been computed to validate the security level. The complexity of the hardware is validated utilizing devices of FPGA of Spartan3E XC3S500E. From the implementation result, it has been found that the proposed HLCAS method has 5.4 ns latency, 0.9 K bytes RAM and consumes 202 mW power. From the comparison with a few reported methods it has been observed that the proposed HLCAS method outperform other methods.

     

面向作战需求的战场电磁环境复杂度评估

战场电磁环境是战场中的一个重要因素,其复杂度对作战指挥人员的作战决策和作战运用具有重要的参考意义。本文以战场电磁环境复杂度在作战指挥中的应用为研究背景,在分析作战指挥对战场电磁环境复杂度的作战需求的基础上,研究建立了适合不同作战指挥层次应用的分层分类的战场电磁环境复杂度的评估方法,并给出了电磁环境复杂度评估指标和相应的表征方法。     

基于遥感影像的土地覆盖分类及变化监测研究

目前人们使用遥感数据获取土地利用分类信息的主要方法仍是依靠目视判读、人工解译。该方法可以获得较高的分类结果精度,但是其工作效率很低,难以满足日益增长的应用需求。以广东省为例,重点研究了基于遥感影像的土地利用分类信息自动化提取及变化监测方法,开发了土地利用分类及变化信息提取软件,实现了自动化或半自动化土地分类。此项研究成果能够满足土地二次调查的需求,为土地利用规划提供了必要指导。     

基于二值神经网络的大场景点云分类

近年随着3维数据采集技术不断发展,大场景 点云数据的获取越来越方便。目前深 度学习网络框架在2维图像处理领 域越来越成熟,而大场景点云是一种3维无规则化的数据,3维卷积神经网络直接处理大场 景3维数据会存在分类精度低和计 算复杂等问题。因此为了有效解决基于深度学习的点云分类任务中存在的计算时间长和分类 精度低的问题,本文提出基于二值 神经网络的大场景点云分类方法,针对不规则的3维点云数据设计特征值计算方法,基于IR -Net二值神经网络处理输入的点云 特征图像,进一步采用Dynamic ReLU激活函数,提高神经网络的计算效率,最后得出点云分 类结果。实验结果表明,所提出 的方法在Oakland数据集上分类精度达到97.6%,在GML数据集中取得 了92.3%和97.2%的分类精度,实验结果证明Dy -ResNet 能够有效提升了点云分类的精度,减少计算的复杂度,并提高了训练效率。     

Low-complexity data reusing methods in adaptive filtering

Most adaptive filtering algorithms couple performance with complexity. Over the last 15 years, a class of algorithms, termed "affine projection" algorithms, have given system designers the capability to tradeoff performance with complexity. By changing parameters and the size/scale of data used to update the coefficients of an adaptive filter but without fundamentally changing the algorithm structure, a system designer can radically change the performance of the adaptive algorithm. This paper discusses low-complexity data reusing algorithms that are closely related to affine projection algorithms. This paper presents various low-complexity and highly flexible schemes for improving convergence rates of adaptive algorithms that utilize data reusing strategies. All of these schemes are unified by a row projection framework in existence for more than 65 years. This framework leads to the classification of all data reusing and affine projection methods for adaptive filtering into two categories: the Kaczmarz and Cimmino methods. Simulation and convergence analysis results are presented for these methods under a number of conditions. They are compared in terms of convergence rate performance and computational complexity.     

An integrative and interactive framework for improving biomedical pattern discovery and visualization

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.     

分布式信源数据域直接位置估计方法

针对目前信号数据域直接位置估计方法对分布式信号源进行直接定位存在精度下降问题,该文提出分布式信源数据域直接位置估计方法。首先构建分布式信源直接位置估计模型,然后分别基于最大似然准则和特征结构分解思想给出分布式信源高精度直接位置估计的两种方法分布源最大似然估计方法和广义子空间方法。最后通过多维搜索完成对于分布式信源的直接位置估计。仿真分析表明,该文算法对分布式信源进行直接位置估计的精度较传统直接位置估计算法明显提升,能够在较低信噪比下逼近克拉美罗界;分布源最大似然估计方法在低信噪比下定位精度优于广义子空间方法,而广义子空间方法复杂度更低。     

Source/channel coding of still images using lapped transforms andblock classification

A novel scheme for joint source/channel coding of still images is proposed. By using efficient lapped transforms, channel-optimised robust quantisers and classification methods it is shown that significant improvements over traditional source/channel coding of images can be obtained while keeping the complexity low     

Genetic Sequence Classification and its Application to Cross-Species Homology Detection

Although large-scale classification studies of genetic sequence data are in progress around the world, very few studies compare different classification approaches, e.g. unsupervised and supervised, in terms of objective criteria such as classification accuracy and computational complexity. In this paper, we study such criteria for both unsupervised and supervised classification of a relatively large sequence data set. The unsupervised approach involves use of different sequence alignment algorithms (e.g., Smith-Waterman, FASTA and BLAST) followed by clustering using the Maximin algorithm. The supervised approach uses a suitable numeric encoding (relative frequencies of tuples of nucleotides followed by principal component analysis) which is fed to a Multi-layer Backpropagation Neural Network. Classification experiments conducted on IBM-SP parallel computers show that FASTA with unsupervised Maximin leads to best trade-off between accuracy and speed among all methods, followed by supervised neural networks as the second best approach. Finally, the different classifiers are applied to the problem of cross-species homology detection.     

对无线传感器网络寻址方式的研究

寻址方式是影响WSN(无线传感器网络)工作效率的重要因素,如何根据WSN的业务特点,设计低开销、低存储、低计算复杂度的高效寻址方式,是值得深入研究解决的问题.本文在深入分析WSN一般业务类型和寻址特点的基础上,重点对当前WSN中的一些主要寻址方式做了系统、深入的分析,并得出了几点重要结论.     

联合NDRI特征和空间相关性的机载MS-LiDAR数据分类

对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明：算法的平均总体精度和Kappa系数分别可达90.17%和...