一种引入延迟的语音增强算法

针对传统语音增强算法中,只采用当前帧和当前帧以前的信息对当前帧语音谱进行估计而造成变电平噪声和音乐噪声的问题,采用一种改进的引入延迟的语音增强算法。通过引入延迟,可以在对当前帧语音谱进行估计时使用当前帧以后帧的信息,在噪声估计中采用类似路径搜索的双向搜索方法消除变电平噪声的影响,在先验信噪比估计中采用改进的非因果先验信噪比估计算法,消除低信噪比平滑不足带来的音乐噪声,在此基础上构建了一个完整的语音增强算法。实验结果表明,该算法基本不受变电平噪声的影响,而且音乐噪声和残留背景噪声都得到了很好的抑制。     

Computationally Optimal Metric-First Code Tree Search Algorithms

Code tree search algorithms find wide applicability in source encoding, channel decoding, pattern recognition, and maximum likelihood sequence estimation. These algorithms search code trees and may be classified as depth-first, breadth-first, and metric-first depending on the search criterion employed. We define here a criterion for metric-first algorithms to be optimal. We show that implementations of metric-first searches proposed heretofore are not optimal, and we propose and analyze two algorithms which are. Experimental data obtained by encoding a voiced speech sound point to superiority of the proposed implementation over earlier versions.     

A Bionic Optimization Technique with Cockroach Biological Behavior

Many practical engineering problems can be abstracted as corresponding function optimization problems.During the last few decades,many bionic algorithms have been proposed for this problem.However,when optimizing for large scale problems,such as 1000 dimensions,many existing search techniques may no longer perform well.Inspired by the social model of cockroaches,this paper presents a novel search technique called Cooperation cockroach colony optimization (CCCO).In the CCCO algorithm,two kinds of special biological behavior of cockroach,wall-following and nest-leaving,are simulated and the whole population is divided into wall-following and nest-leaving populations.By the collaboration of the two populations,CCCO accomplishes the computation of global optimization.The crucial parameters of CCCO are set by the self-adaptive method.Moreover,a discussion on group model design is provided in this paper.The CCCO algorithm is evaluated with shifted test functions (1000 dimensions).Three state-of-the-art cockroach-inspired algorithms are used for the comparative experiments.Furthermore,CCCO is applied to a real-world optimization problem concerning spread spectrum radar poly-phase.Experiment results show that the CCCO algorithm can be applied to optimize large-scale problems with the good performance.     

基于循环谱对称性的新型频谱判决算法

采用循环平稳信号特性检测法能够更好地分辨出不同的调制信号,其对噪声具有很好的抑制,但在循环频谱工程上使用存在数据截断效应,它会降低频谱感知的检测概率,结合频谱已有的频谱搜索策略,提出了一种新的频谱判决算法,利用相对偏差、双门限和权重来对抗传统算法上方差的差值抖动问题。在使用FPGA搭建的硬件测试系统下,证明与传统算法相比,提出的算法大大提高了单节点的检测概率。     

Performance Analysis of an Expanding-Search Algorithm for Coarse Acquisition of DSSS Signals

For coarse acquisition of Direct Sequence Spread Spectrum (DSSS) signals, a search of the possible code phases is required. In cases such as signal reacquisition, where a priori information is available, the serial search may be abandoned in favor of an expanding search. In this paper, an expanding-search algorithm is presented and compared to the expanding-sweep algorithms which have been treated in the literature. Performance results for the serial-search and the expanding-search algorithms are given. A discussion of the optimality of the serial-search and the expanding-search algorithms is presented. Performance comparisons of the serial search and the expanding search are made for different a priori code phase probability density functions (pdfs) and the results are discussed. A technique for determining when the expanding search is an optimal search algorithm is developed and applied to the performance curves shown previously. The results indicate that the expanding search provides a quantifiable improvement over the serial search when the pdf is somewhat peaked. The results also indicate that in many cases the expanding search may be a more effective search algorithm than the more common expanding sweep.     

Algorithms for packet classification

The process of categorizing packets into “flows” in an Internet router is called packet classification. All packets belonging to the same flow obey a predefined rule and are processed in a similar manner by the router. For example, all packets with the same source and destination IP addresses may be defined to form a flow. Packet classification is needed for non-best-effort services, such as firewalls and quality of service; services that require the capability to distinguish and isolate traffic in different flows for suitable processing. In general, packet classification on multiple fields is a difficult problem. Hence, researchers have proposed a variety of algorithms which, broadly speaking, can be categorized as basic search algorithms, geometric algorithms, heuristic algorithms, or hardware-specific search algorithms. In this tutorial we describe algorithms that are representative of each category, and discuss which type of algorithm might be suitable for different applications     

基于重叠缓冲预解跳的DS/FH扩频信号快速捕获

DS/FH(Direct Sequence/Frequency Hopping)扩频信号捕获相对直接序列扩频(Direct Sequence Spread Spectrum,DSSS)需要更多维度的搜索,捕获电路复杂。针对现有DS/FH扩频信号捕获算法复杂度高、电路结构与波形参数强相关,以及通用性差、灵活度低的问题,提出了一种基于重叠缓冲预解跳的DS/FH扩频信号快速捕获方法,利用双缓冲补零(Double Block Zero Padding,DBZP)缓冲单元与预解跳单元的灵巧结合,将时域-频域-多普勒域高度耦合的扩跳频图案搜索进行降维分离,在保证积分增益的同时降低了捕获实现的复杂度。数值分析与仿真结果表明,所提方法兼顾了捕获性能与运算资源,相比现有基于预解跳方法的DS/FH扩频信号捕获算法的运算量减小了50%。     

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.     

Intelligent learning algorithms for adaptive digital filters

It is wellknown that gradient search fails in adaptive IIR filters, since their mean-square error surfaces may be multi-modal. In the letter a new approach based on learning algorithms is shown to be capable of performing global optimisation. The new algorithms are suitable for both adaptive FIR and IIR filters.<>     

Fast and Accurate Detection of Malicious Users in Cooperative Spectrum Sensing Network

Cooperative spectrum sensing allows users to detect available spectrum and utilize it. However, it is known that a few users can easily affect the cooperative decision at the fusion center by reporting falsified sensing data. The problem of detecting malicious users in cooperative spectrum sensing has been studied by numerous researchers. The basic approach is to compute the credibility of the reported data of each user and declare those with low credibility (below some threshold) as malicious. The computation of credibility can be based on several things such as pattern of historic behavior, entropy of the reported data, signal-to-noise ratio, etc. The credibility of the reported data of some user node can be expressed as an attack probability of that node. A node with higher attack probability will have lower credibility while a node with lower attack probability a higher credibility. Then, the problem of computing credibility of a node becomes the problem of computing attack probability of a node given a set of reported data. We can enumerate a list of all possible attack probability vectors for a set of nodes and the list of all possible energy level vectors of the channel for the observed time period, and compute which combination of attack probability and channel energy level vector can have the maximum probability to produce the reported channel energy level. However, the search space is quite large and grows exponentially as the number of user nodes and the number of time slots to observe increase. In this paper, we suggest algorithms that reduce the search space considerably and detect malicious users in linear time instead of exponential time. The suggested algorithms have been implemented and show promising results.

     

二维谱估计算法的空间探测性能分析及验证

针对二维谱估计算法的空间探测性能分析的需求,从计算速度、计算量、顽健性、计算精度以及实际工程应用的角度出发,对基于L型阵列的二维MUSIC、二维干涉仪、二维增广矩阵束的谱估计算法进行了简要介绍,并对上述二维谱估计算法的性能进行了仿真分析,得到了3种算法的角度RMSE的对比分析,可知在同样仿真条件下,二维增广矩阵束算法最优,二维MUSIC算法次之,二维干涉算法最差。同时,构建了相应的试验场景,通过试验分析上述二维谱估计算法的空间探测性能,得到的试验结论与仿真结论一致。在此基础上,提出了二维增广矩阵束算法可扩展应用到雷达测控一体化系统的思路。     

Optimization algorithm of periodical cooperative spectrum sensing in cognitive radio

To decrease the interference to the primary user (PU) and improve the detected performance of cognitive radio (CR), a single‐band sensing scheme wherein the CR periodically senses the PU by cooperative spectrum sensing is proposed in this paper. In this scheme, CR first senses and then transmits during each period, and after the presence of the PU is detected, CR has to vacate to search another idle channel. The joint optimization algorithm based on the double optimization is proposed to optimize the periodical cooperative spectrum sensing scheme. The maximal throughput and minimal search time can be respectively obtained through the joint optimization of the local sensing time and the number of the cooperative CRs. We also extend this scheme to the periodical wideband cooperative spectrum sensing, and the joint optimization algorithm of the numbers of the sensing time slots and cooperative CRs is also proposed to obtain the maximal throughput of CR. The simulation shows that the proposed algorithm has lower computational quantity, and compared with the previous algorithms, when SNR = 5 dB, the throughput and search time of the proposed algorithm can respectively improve 0.3 kB and decrease 0.4 s. The simulation also indicates that the wideband cooperative spectrum sensing can achieve higher throughput than the single‐band cooperative spectrum sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

序列偶扩频通信系统性能分析

将一种新的扩频信号形式——序列偶引入到扩频通信系统中。依据扩频通信系统对扩频序列的技术要求,用遗传算法搜索出40至50长的次优二元序列偶,发现存在几个性能优于Gold序列的序列偶。提出了基于序列偶的扩频通信系统的构思,对这种扩频通信系统的性能进行了仿真,并同基于Gold码的扩频通信系统的性能进行了比较。实验结果表明,序列偶扩频通信系统的性能在某些方面优于Gold码扩频通信系统。     

感知无线电中频谱共享技术的研究

文章研究了感知无线电网络中联合频谱分配和调度的问题,给出了基于物理干扰模型的频谱分配算法。调度算法是对由频谱分配算法得到的一系列传输模式进行调度,从而实现频谱共享。本文基于提出的频谱分配算法通过仿真比较了三种调度算法,结果表明考虑了公平性的调度算法的网络吞吐量有稍微的降低（可达到最大吞吐量的96%）,但感知用户的整体需求满足度却得到了很大的提高,即达到了很好的公平性。     

基于相干对消的噪声子空间自适应估计

MUSIC算法的运算量主要集中在特征值分解和空间谱的搜索两部分,为避免进行特征 值分解,将相干信号自适应对消的思想用于噪声子空间的估计。由于阵元输出信号完全相干 ,视一个阵元输出为参考信号,其它阵元输出能够完全对其进行对消,得到的系数矩阵作为 噪声子空间的估计。基于LMS算法,给出了算法的矩阵形式,得到的噪声子空间估计算法的 运算量大大降低,且以迭代的方式进行,适合应用于运动信号源的跟踪,在阵元数较大时能 很好地逼近MUSIC算法性能。为减少空间谱搜索过程的计算量,对搜索过程先用系数矩阵的 一列进行搜索,然后采用其它列对搜索峰值进行验证。仿真结果显示算法具有很好的空间谱 估计性能和DOA跟踪性能。     

一种粗到精分块金字塔预测连续排除算法

提出一种粗到精分块金字塔连续排除算法用于减少块匹配运动估计所需计算量.金字塔可以方便地从当前搜索窗的积分帧构建,连续的排除从金字塔顶层到底层逐级进行,将最小SAD值与金字塔提供的一系列越来越紧的界值相比较,可以跳过不必要的SAD计算.这样,算法在保证获得与全搜索算法相同精度的前提下,计算复杂度大大降低.仿真实验结果验证了本文算法与全搜索算法和几种快速搜索算法相结合的有效性.     

三种基于进化技术的优化算法及其对比

进化算法（EA）是模仿自然界生物进化或物种的社会行为而产生和发展的随机搜索方法，这种算法能够解决许多传统方法无法解决的大规模优化问题。本文简明介绍了三种基于进化的优化算法：遗传算法、蚁群优化算法和微粒群优化算法，并在原理、参数和应用方面对它们进行了对比。     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

圆阵二维MUSIC谱快速计算方法研究

针对二维波达方向估计时MUSIC谱的快速计算问题,研究了均匀圆阵变换到虚拟线阵的MUSIC算法(UCA-ULA-MUSIC)、流形分离MUSIC算法(MS-MUSIC)、傅立叶域线性求根MUSIC算法(FD-Line-Search-MUSIC)、基于FFT的 元均匀圆阵MUSIC算法( -UCA-FFT-MUSIC)与基于FFT的任意圆阵MUSIC算法(ACA-FFT-MUSIC)。对各种算法快速计算二维MUSIC谱的实现步骤进行了总结。在此基础上,给出了各算法计算二维MUSIC谱的计算复杂度表达式,并以传统方法为参考,对比了各种快速算法相对于传统方法的计算复杂度比值;同时,针对不同的阵列形式,对适用的快速算法的测向性能进行了仿真对比。根据分析和对比的结果,指出MS-MUSIC算法与ACA-FFT-MUSIC算法具有更高的工程应用价值,由具体的情况单独或分频段联合使用MS-MUSIC算法与ACA-FFT-MUSIC算法,可以使测向系统较好的兼顾测向性能与时效性。