基于PDES的最小时间桶算法研究

混合策略在一定程度上避免了过分保守或极端乐观的缺点。首先分析了现有同步机制存在的不足,然后论述了视界概念及最小时间桶算法的设计与实现,接着对最小时间桶算法的性能进行了定性定量的深入探讨,最后采用PHOLD仿真应用模型在MTB算法、保守算法和TW乐观算法之间进行了性能对比实验,实验结果表明：MTB算法在某些条件下具有更小的回退开销和更快的事件处理效率。

Abstract：

Hybrid strategy to some certain extent avoids the shortcomings of the excessive conservation and extremely optimism. The shortcoming of the existing time synchronization algorithms was analyzed; then the very important conception of the event horizon,furthermore was proposed,and the design and the realization of the Minimum Time Buckets Algorithm were put forward. The remainder provided the farther research for the performance of this algorithm by qualitative analysis and quantitative analysis. Finally,the PHOLD simulation model was used to compare the performance of conservative algorithm,TW algorithm and MTB algorithm. The experiment results indicate that under some conditions MTB algorithm has lower rollback costs and faster event process speed,which lead to better performance than TW and conservative algorithms.     

A deep learning-based binocular perception system

An obstacle perception system for intelligent vehicle is proposed.The proposed system combines the stereo version technique and the deep learning network model,and is applied to obstacle perception tasks in complex environment.In this paper,we provide a complete system design project,which includes the hardware parameters,software framework,algorithm principle,and optimization method.In addition,special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application.The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles,and suitable for different weather and lighting conditions in complex environment.It announces that the proposed system is flexible and robust to the intelligent vehicle.     

Novel imaging methods of stepped frequency radar based on compressed sensing

The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Experiments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier transform method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.     

Angle estimation in bistatic MIMO radar using improved reduced dimension Capon algorithm

This paper discusses the problem of direction of departure (DOD) and direction of arrival(DOA) estimation for a bistatic multiple input multiple output(MIMO) radar,and proposes an improved reduced-dimension Capon algorithm therein.Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation,the proposed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar.Furthermore,the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays.The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound (CRB) is also derived.Simulation results verify the usefulness of the proposed algorithm.     

Novel antenna selection algorithm based on Tanimoto similarity

A new antenna selection algorithm for multiple input multiple output （MIMO） wireless systems is proposed. The modified Tanimoto coefficient is used to compare the similarity of the rows/columns of the channel matrix. Based on the calculated similarity, the proposed algorithm chooses the antenna subset, which has the maximum product of dissimilarity and Frobenius norm. The proposed algorithm requires low computational complexity as to the optimal selection but with comparative outage capacity and average signal to noise ratio （SNR） performance. It can improve both the outage capacity and the average SNR as compared to random selection. The simulation results are shown to validate our algorithm.     

An ε-domination based two-archive 2 algorithm for many-objective optimization

The two-archive 2 algorithm(Two_Arch2) is a manyobjective evolutionary algorithm for balancing the convergence,diversity,and complexity using diversity archive(DA) and convergence archive(CA).However,the individuals in DA are selected based on the traditional Pareto dominance which decreases the selection pressure in the high-dimensional problems.The traditional algorithm even cannot converge due to the weak selection pressure.Meanwhile,Two_Arch2 adopts DA as the output of the algorithm which is hard to maintain diversity and coverage of the final solutions synchronously and increase the complexity of the algorithm.To increase the evolutionary pressure of the algorithm and improve distribution and convergence of the final solutions,an ε-domination based Two_Arch2 algorithm(ε-Two_Arch2) for many-objective problems(MaOPs) is proposed in this paper.In ε-Two_Arch2,to decrease the computational complexity and speed up the convergence,a novel evolutionary framework with a fast update strategy is proposed;to increase the selection pressure,ε-domination is assigned to update the individuals in DA;to guarantee the uniform distribution of the solution,a boundary protection strategy based on I_ε+ indicator is designated as two steps selection strategies to update individuals in CA.To evaluate the performance of the proposed algorithm,a series of benchmark functions with different numbers of objectives is solved.The results demonstrate that the proposed method is competitive with the state-of-the-art multi-objective evolutionary algorithms and the efficiency of the algorithm is significantly improved compared with Two_Arch2.     

Direct solution for fixed source location using well-posed TDOA and FDOA measurements

Based on the time differences of arrival(TDOA) and frequency differences of arrival(FDOA) measurements of the given planar stationary radiation source, the joint TDOA/FDOA location algorithm which solves the location of the target directly is proposed. Compared with weighted least squares(WLS) methods,the proposed algorithm is also suitable for well-posed conditions,and gets rid of the dependence on the constraints of Earth’s surface. First of all, the solution formulas are expressed by the radi...     

基于协议同步水下传感器网络目标协同定位算法

为了解决水下目标的定位问题,讨论了一种基于水下传感器阵列网络的目标协同定位算法。该算法在实现水下节点同步的基础上,通过建立目标位置与距离差测量值的统计模型后依据最大似然准则完成目标定位。定位算法的实现采用分布-集中相结合的处理方法,在提高定位精度的同时大大节省了水下节点通信能耗。通过仿真实验验证了算法的有效性和可行性,实验结果表明该方法具有较好的同步及目标定位精度。

Abstract：

A collaborative target location algorithm for underwater acoustic sensor networks （UASN） was proposed. The algorithm was achieved based on time synchronization for high transmitting delay for UASN. Target location was estimated by maximum-likelihood methods based on proposed statistical model which was established by the relation between target position and measured range difference. The proposed algorithm adopts the distributed-centralized computation methods,which degrade the transmitting energy comparing with traditional centralized methods. The result of simulation shows the application validity and the more location performance of the algorithm.     

Hybrid algorithm for project scheduling with capacity constraint

Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.     

Learning algorithm and application of quantum BP neural networks based on universal quantum gates

A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.     

Robust digital receiver for EPC sensor network

A robust digital receiver based on a matched filter (MF) is proposed for the radio frequency identification (RFID) reader system to enhance the reliability of signal processing in the electronic product code (EPC) sensor network (ESN). The performance of the proposed receiver is investigated by examining the anti-collision algorithm in the EPC global Class1 Generation2 protocol. The validity and usefulness are demonstrated by both computer simulations and experiments. Based on the verification  results, comparing with the conventional zero crossing detector (ZCD) based receiver, the proposed receiver is very robust against strong amplitude distortions and considerable frequency deviations happening on the backscattered signal from a passive tag.     

供应链采用RFID的标签成本阈值分析及契约协调

无线射频(radio frequency identification,RFID)技术能够消除需求信息不对称并减小需求预测误差,同时增加供应链的运作成本,如何分析RFID标签成本的影响并协调链上成员的收益是本文研究的问题.本文通过建立RFID采用前后分散式和集中式供应链的收益模型,分析了RFID标签成本阈值对批发价、服务水平和协调参数的影响;在此基础上通过两部定价契约进行协调.研究发现:分散式供应链"双重边际化"效应的大小与RFID标签成本负相关;当RFID标签成本位于某一阈值时,采用RFID能够提升分散式供应链的服务水平并实现成员收益的Pareto改进,同时降低制造商的批发价;集中式供应链采用RFID会降低服务水平;两部定价契约均能协调RFID采用前后的供应链,但是采用RFID后契约的批发价参数变大,固定费用参数区间的上下界及范围均与RFID标签成本负相关.     

物联网移动RFID系统隐私保护方案

为了解决标签携带者和阅读器持有者被恶意追踪与监视以及隐私信息泄漏等问题,设计了物联网移动RFID系统隐私保护方案.该方案采用RFID隐私管理服务系统,在标签与阅读器之间根据标签隐私等级实现单向认证或双向认证,在阅读器与信息服务系统之间采用基于模糊身份加密算法实现匿名访问控制.安全性及效能分析表明,此方案不仅增强了标签携带者的隐私保护,还保护了阅读器持有者的身份隐私,且具有一定的效能优势.     

JPEG2000 T2编码快速算法及硬件实现

对JPEG2000中T2编码器的率失真优化算法和码流组织方法进行了深入分析,提出了一种易于硬件实现的T2编码器快速算法并详细给出了其硬件结构。通过降低率失真斜率估计的计算复杂度、简化优化截取方法和码流组织中标记树(tagtree)编码方法,降低了T2编码器硬件实现的难度,减少了硬件资源,提高了JPEG2000硬件系统处理的并行度。实验结果表明,输出码流符合JPEG2000标准格式,而图像质量下降很小,系统已通过了FPGA验证。     

水声信道快速收敛自适应均衡算法

从提高自适应均衡算法水声信号收敛性能的角度出发,提出了一种新的快速收敛水声信道自适应均衡算法。该算法将改进的归一化均方误差算法和判决反馈均衡器结构有机结合,在不增加计算量的前提下,很好地实现了不同水声信道的自适应均衡,易于算法的硬件实现。仿真结果表明,该算法计算量同归一化最小均方(normalized least mean square, NLMS)误差算法的计算量相当,但在稳态误差和收敛速度上有很大优势;收敛性能与自适应调整最小二乘回归〖CD*2〗判决反馈均衡器(variable tap-length decision feed-back equalizer based on recursive least square, RLS-DFE)算法接近,却克服了RLS-DFE算法计算量大,不利于硬件实现的实际问题。提出的算法为水声通信提供了一种性能优良的可实现方法,具有较高的应用价值。     

硬件免疫系统中避免自身免疫的算法研究

硬件免疫系统的研究尚处在起步阶段，有大量的问题需要解决，包括如何避免自身免疫问题等。针对硬件免疫系统设计中无法避免的自身免疫问题提出的一种新算法能够借助协同刺激信号自动删除与自身匹配的检测器，使硬件免疫系统具有自身耐受能力，提高了检测正确率并降低了误报率。模拟实验结果证明了算法的有效性。     

面向个性化交通信息服务的车辆行驶路径关联规则挖掘

为了向驾驶者自动提供个性化的交通服务信息，需要对车辆行驶路径进行分析和预测.面向基于RFID的个性化交通服务系统，提出改进的车辆行驶路径关联规则挖掘方法，挖掘车辆历史行驶路径数据中的频繁序列模式，由频繁序列模式产生序列关联规则，根据当前行程车辆已行驶的路径，实现对车辆未来行驶路径的预测.本方法主要通过0-N数据结构和候选2-序列产生方法的改进，提高车辆行驶路径序列模式挖掘的效率.最后，通过数据测试验证了改进算法在运行效率上与GSP相比的性能优越性.     

一种时域并行差分相关捕获算法

提出了一种基于硬件实现、在时域对全球导航卫星系统信号进行相关捕获的新算法。算法采用并行差分结构,通过对相关结果的复用,实现了对多个卫星信号的同时捕获。还分析了该算法、传统的相关算法和现有的两种改进型时域相关算法以及基于FFT的频域相关算法的算法复杂度和所需寄存器数量。分析和仿真结果表明,该算法在计算单个复现码的1次相关时,运算量并不随着过采样率的增加而增加。相比于传统的相关算法和现有的两种改进型时域相关算法以及基于FFT的频域相关算法,该算法在运算复杂度上具有优势,有利于在实际硬件系统中实现。     

一种基于AR模型的大频偏估计算法

给出了一种适用于卫星通信的大频偏估计算法,文中称之为基于AR模型的大频偏估计算法。该算法结合相应的频率跟踪算法能够在低信噪比条件下实现多普勒频移远大于符号速率的信号捕获与跟踪。同时,还给出了该算法的计算机仿真与硬件实现。结果表明,该算法实现复杂度低,频率估计的准确高,适合于一般的非连续相位调制信号。     

可重构计算平台上软硬件任务划分与调度算法

软硬件任务划分和任务调度是保证任务集合在可重构计算平台上高效运行的重要技术。传统上软硬件任务划分与任务调度独立进行,划分方案和调度方案不一定匹配,加速效果难以保证。提出了一种将软硬件任务划分与任务调度相结合的算法。该算法综合考虑了任务的执行时间、任务间的通信开销、任务间的依赖关系以及计算资源间的并发关系,可以同时获得给定任务集的软硬件任务划分方案和任务调度方案,加速效果良好。算法开销与传统的任务调度算法相当,时间复杂度为O(V²log₂V+PVlog₂V)实验结果表明了该算法的可行性和有效性。