多符号差分球形译码在无线传感器网络分布式检测中的应用研究

在无线传感器网络分布式检测中,信道条件复杂,难于估计.比较相关检测,差分检测的优势是不需要信道估计,但总是付出一定的性能损失.为了缩短这一差距,本文提出多符号差分检测,并结合低复杂度球形译码算法,对多符号差分球形检测的迭代搜索过程进行了详细分析.算法检测性能和复杂度的分析表明,该检测算法不仅能有效降低计算复杂度,而且能保证较好的检测性能.结果证明该算法可作为一种有效检测算法应用于无线传感器网络分布式检测中.     

遥测PCM/FM信号的低复杂度多符号检测算法

针对遥测PCM/FM信号解调中多符号检测（MSD）算法存在的计算复杂度高、工程实现难度大等问题, 提出了一种低复杂度MSD算法。该算法通过利用分段相关等效较长的相关来降低算法复杂度。仿真结果表明, 该算法在损失较小性能的前提下, 显著降低了MSD算法计算复杂度, 易于硬件实现, 实用性强。     

基于小波去噪的联合频谱感知优化算法

传统的能量检测算法由于受到噪声不确定性的影响,在信噪比较低时检测精度差,理论上较优的循环平稳特征频谱感知算法的计算复杂度偏高。因此,在传统能量检测算法基础上结合小波阈值去噪和差分能量检测模型,提出一种优化的双门限联合检测算法。使用能量检测法来判断双门限区间之外的区域,双门限阈值内的不确定性区域使用小波阈值去噪重构后做差分能量检测,并根据信道实时状态动态地调整双阈值。当信道质量较差时,增大双门限之间的距离,否则缩短双门限之间的距离,从而提高频谱检测效率。通过仿真对比得知,该算法有效地提高了噪声不确定性影响下频谱感知的准确性,并且降低了感知算法的计算复杂度。     

基于模糊化符号复杂度的脑电运动想象识别算法

提出一种基于模糊化符号复杂度的运动想象脑电信号特征提取与识别方法。在脑电信号的复杂度细粒化多符号度量中引入模糊算法,用sigmoid函数模糊化处理,逻辑判断得到模糊化符号复杂度。取细粒化指数n为2,提取模糊化符号复杂度作为特征值,最后利用支持向量机对脑电运动想象任务进行分类识别。实验结果表明,以模糊化符号复杂度为特征的分类方法,对左右手运动想象脑电信号的分类识别率最高达88.67%,优于二值化Lempel-Ziv复杂度算法。     

基于双阈值运动区域分割的AdaBoost行人检测算法

结合单目摄像机静止拍摄的视频序列使用背景差法或AdaBoost算法检测行人时分别存在易受噪声干扰或检测速度慢的问题,提出一种双阈值运动区域分割的AdaBoost快速行人检测算法。首先建立背景帧,利用前景帧与背景帧的差分图像拟合噪声曲线,提取噪声与亮暗运动目标的阈值,消除噪声,分割出运动区域;然后通过AdaBoost学习算法选择少量有效的Haar-like弱矩形特征构造强分类器;最后在运动区域利用强分类器检测是否包含行人。实验结果表明,该方法迅速缩小了检测范围,加快了检测速度,降低了误检率。     

基于FPGA的多特征融合司机疲劳状态检测系统设计

驾驶员的疲劳驾驶是造成交通事故的重要因素,为了实时有效地检测驾驶员的驾驶状态,设计了一种融合多种疲劳特征进行疲劳状态判定的检测算法,并构建了车载的基于现场可编程门阵列(FPGA)的嵌入式检测平台。该多检测算法融合了眼睛和嘴巴的疲劳特征,当某一特征的检测受到影响时可以使用另外的特征进行疲劳状态的判定,较传统的单一特征疲劳检测算法拥有更高的检测效率。实验结果表明:系统的算法简单、可靠、实时性强。     

基于二阶差分Markov特征的LSB匹配隐写检测

针对安全性较高的最不重要位(LSB)匹配隐写算法,通过计算待检测图像像素水平和垂直方向的二阶差分,得到二阶差分矩阵并将其作为敏感特征提取源,提取差分矩阵的二阶Markov转移概率矩阵作为特征,提出了一种隐写检测算法。实验结果表明：与基于一阶差分Markov转移概率矩阵的算法相比,该算法在保证检测较高正确率的情况下,在很大程度上提高了算法的检测速度,增强了算法的性能和实用性。     

A hybrid ant colony optimization algorithm for optimal multiuser detection in DS-UWB system

A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of optimization problems. Hence in this paper, a hybrid ACO algorithm, named ACO-EO algorithm, is proposed by introducing EO to ACO to improve the local-search ability of the algorithm. The ACO-EO algorithm is applied to multiuser detection in DS-UWB communication system, and via computer simulations it is shown that the proposed hybrid ACO algorithm has much better performance than other ACO algorithms and even equal to the optimal multiuser detector.     

基于DESO的Mean Shift目标跟踪算法研究

将基于DESO的运动预测算法和Mean Shift算法相结合,形成一种新的基于Mean Shift的快速目标跟踪算法.该算法以DESO预测位置作为Mean Shift算法下一帧候选模型的计算中心,实现了对快速运动目标的跟踪,并通过DESO对目标运动轨迹进行预测,较好地解决了目标完全遮挡时的跟踪问题.实验结果表明,该算法具有预测精度高、实时性好、抗遮挡能力强的优点.     

A reduced state SISO iterative decoding algorithm for serially concatenated continuous phase modulation

In this paper, serially concatenated continuous phase modulation (SCCPM) system is analyzed and a reduced state soft input soft output (SISO) a posteriori probability algorithm is proposed. Based on the reduced state sequence detection (RSSD), it has the more general form compared with other reduced state SISO algorithms. The proposed algorithm can greatly reduce the state number, thus leads to the compu- tation complexity reduction. It also minimizes the degradation in Euclidean dis- tance with decision feedback in the reduced state trellis. Analysis and simulation results show that the performance degradation is little with proper reduction scheme.     

基于流交互三态模型的DDoS攻击检测*

针对传统方法在检测DDoS攻击时的不足,提出了一种新的IP流交互行为特征算法(IFF),该方法利用IP地址和端口表示IP流的交互性。采用IFF特征,将网络流定义为三种状态,即健康、亚健康和异常,提出了基于IFF特征的三态模型检测方法(DASA),该方法采用了基于滑动平均方法的自适应双阈值算法和报警评估机制,提高了检测DDoS攻击的准确度。仿真实验结果表明,该方法不但能快速、有效地检测DDoS攻击,而且具有较低漏报率和误报率。     

Parallel collision detection of ellipsoids with applications in large scale multibody dynamics

This contribution describes a parallel approach for determining the collision state of a large collection of ellipsoids. Collision detection is required in granular dynamics simulation where it can combine with a differential variational inequality solver or discrete element method to approximate the time evolution of a collection of rigid bodies interacting through frictional contact. The approach proposed is structured on three levels. At the lowest level, the collision information associated with two colliding ellipsoids is obtained as the solution of a two-variable unconstrained optimization problem for which first and second order sensitivity information is derived analytically. Although this optimization approach suffices to resolve the collision problem between any two arbitrary ellipsoids, a less versatile but more efficient approach precedes it to gauge whether two ellipsoids are actually in contact and require the more costly optimization approach. This intermediate level draws on the analytical solution of a 3rd order polynomial obtained from the characteristic equation of two arbitrary ellipsoids. Finally, this intermediate level is invoked by the outer level only when a 3D spatial binning algorithm indicates that two ellipsoids share the same bin (box) and therefore could potentially collide. This multi-level approach is implemented in parallel and when executed on a ubiquitous Graphics Processing Unit (GPU) card scales linearly and yields a two orders of magnitude speedup over a similar algorithm executed on the Central Processing Unit (CPU). The GPU-based ellipsoid contact detection algorithm yields a 14-fold speedup over a CPU-based sphere contact detection algorithm implemented in the third party open source Bullet Physics Library (BPL). The proposed methodology provides the efficiency demanded by granular dynamics applications, which routinely handle scenarios with millions of collision events.     

Midori64的相关密钥不可能差分分析*

Midori算法是由Banik等人在AISACRYPT2015上提出的一种具有SPN结构的轻量级的加密算法。Midori的分组长度有64bit和128bit两种,分别为Midori64和Midori128,本文主要研究的Midori64。目前攻击者已经使用了不可能差分分析、中间相遇攻击、相关密钥差分分析等方法对Midori进行了分析,却没有使用相关密钥不可能差分分析进行分析。为了验证Midori算法的安全性,本文使用了相关密钥不可能差分分析了Midori算法,构造了一个Midori算法的9轮区分器,进行了Midori算法的14轮攻击,总共猜测了84bit密钥。     

融合智能算法的变形体碰撞检测算法研究

针对变形体碰撞检测算法的准确性与实时性问题,提出了一种融合智能算法的变形体碰撞检测算法。在随机碰撞检测的基础上,使用层次包围技术缩小粒子搜索空间,采用一种融合基于量子行为的粒子群算法与差分进化算法的混合智能算法进行搜索。该方法以局部吸引子作为差分变异基础,在扩大种群多样性的同时加快了算法收敛速度,有效地解决了传统智能算法不适应离散空间计算问题以及早熟收敛问题。针对随机碰撞粒子搜索空间特点,混合算法的引入大大提高了碰撞检测算法的检测效率,解决了检测过程中的穿刺与遗漏现象。经实验验证该方法在很大程度上提高了变形体碰撞检测的实时性与准确性。     

基于人工蜂群的业务流异常状态检测方法

针对日益严重的网络安全问题,基于人工蜂群与聚类方法提出一种新的状态检测算法--DASA。该算法首先根据SKETCH方法和Hash函数建立业务流异常状态模型,并且利用人工蜂群技术实现对异常状态的检测。最后,以实际数据进行仿真实验,对比分析了样本数据与DASA算法检测的结果,发现DASA具有较好的适应性,而且聚类个数、丢弃阈值和邻域半径等因素对状态检测产生较大影响。     

基于多方向的各向异性边缘检测算法

针对传统边缘检测算法抗噪性差的不足,提出了一种多方向的各向异性边缘检测算法。该算法构造了4个具有各向异性的5阶差分模板,对其进行归一化处理后,分别对待检测图像进行卷积处理,根据检测算法在各方向上卷积结果的幅值和方向信息得到灰度边缘图,最后采用最大类间方差法确定阈值进行边缘二值化。多组仿真实验结果表明,该方法能有效实现边缘提取,比传统方法具有更高的检测精度和更强的噪声鲁棒性。     

基于DSP的单车道车流量实时监测算法

针对传统的车流量检测系统采用感应器设备硬件安装繁杂及通用车流量检测算法无法判别车辆行驶方向的问题,提出一种基于数字信号处理器（DSP）的单车道车流量实时监测算法,并应用于停车场。首先,在虚拟检测带上使用背景差分法完成车辆检测,并对均值法背景建模进行改进;其次,提出一种邻帧二值归类算法对车辆行驶方向进行判别;最后,在虚拟检测带上进行车流量计数并将车位情况实时显示于LED显示屏上。通过模拟实验验证了所提算法的可行性,并在实际测试实验中,得到邻帧二值归类算法方向判别的准确率为96.5%,车位监控算法准确率为92.2%。实验结果表明,该单车道车流量实时监测算法准确率较高,节省了检测系统设备,可以应用于单车道停车场进行车流量实时监测。     

基于差异距离反馈的多执行体裁决算法与调度算法

针对现有多执行体裁决算法和调度算法主要将执行体受攻击的次数作为可信度的参考依据,难以区分每次攻击行为的恶意程度,也难以应对执行体输出为数值且允许存在误差的应用场景的问题,以电力系统状态估计异常检测作为应用场景,提出根据归一化的执行体输出差异距离调整其可信度的裁决算法,以及基于运行时长、可信度和切换开销等构造收益函数的调度算法。仿真实验结果表明,相比同等权重的裁决算法和随机切换的调度算法,所提算法在系统执行开销基本相同的情况下,可以将系统平均失效率降低43.8%,能够有效地提升工业网络防护设备的防御能力。     

Multi-object trajectory tracking

The majority of existing tracking algorithms are based on the maximum a posteriori solution of a probabilistic framework using a Hidden Markov Model, where the distribution of the object state at the current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by distractions such as occlusions, background clutters and multi-object confusions. In this paper, we propose a multiple object tracking algorithm that seeks the optimal state sequence that maximizes the joint multi-object state-observation probability. We call this algorithm trajectory tracking since it estimates the state sequence or “trajectory” instead of the current state. The algorithm is capable of tracking unknown time-varying number of multiple objects. We also introduce a novel observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector. The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently. The proposed algorithm has been implemented and tested extensively in a complete CCTV video surveillance system to monitor entries and detect tailgating and piggy-backing violations at access points for over six months. The system achieved 98.3% precision in event classification. The violation detection rate is 90.4% and the detection precision is 85.2%. The results clearly demonstrate the advantages of the proposed detection based trajectory tracking framework.