一种改进的G.729标准固定码本快速搜索算法*

在G.729建议的共轭结构代数码激励线性预测编码（CS-ACELP）中,固定码本搜索在整个语音编码算法中占有较大比重,直接影响编码算法复杂度。全搜索算法准确度很高,但搜索量过大,而传统的脉冲序列替换搜索,搜索次数减少,但合成语音质量较差。为解决该问题,提出一种基于脉冲序列替换的改进码本搜索算法。设定循环阈值门限,对脉冲序列重置后的部分脉冲组合进行全搜索,引入双脉冲位置替换,有效地减少了搜索次数,同时提高了搜索准确度。实验结果证明,该算法在增加算法复杂度较少的情况下,合成语音质量有明显的改进。     

G.729中固定码本快速搜索算法研究

虽然G.729中采用的集中搜索和G.729a中采用的深度优先树搜索可以有效减少固定码本搜索复杂度,但固定码本搜索在整个语音编码算法中仍占有较大比重.为了在基本维持语音质量的前提下,减少搜索运算量,研究了几种快速搜索算法,脉冲替代和预选替代一个脉冲搜索算法可以大大减少搜索次数,但语音质量明显下降,因此提出每次替代两个脉冲搜索算法,得到比替代一个脉冲较为完整的搜索,产生较好的语音质量.仿真结果表明,该算法可以大大减少搜索运算量,并且保持了和G.729a深度优先树搜索算法相同的语音质量.     

改进的引力搜索算法及在波束赋形中的应用

针对引力搜索算法在优化复杂的波束赋形问题时,准确率低的问题,提出了一种改进算法:伪反向学习引力搜索算法.首先设计了一种随迭代次数变化的反向概率,将其用于算法中来优化反向学习的作用时机,进一步提高了算法搜索最优解的速度;其次,定义了"精英粒子",并将其保留至下一代种群中,替换掉种群中适应度值较差的粒子,从而改善了算法易陷入局部最优解的问题.利用改进算法对不同阵列天线进行优化,结果显示,和多种同类高性能算法的优化结果相比,伪反向学习引力搜索算法无论是优化精度还是收敛速度均为最佳,验证了所提改进算法在解决复杂波束赋形问题时的有效性.     

TD-LTE系统中基于码本的快速波束赋形算法

为解决时分双工长期演进(TD-LTE)系统中下行单流波束赋形算法计算复杂度高的问题,在基于有效功率最大化准则和信道平均思想的基础上,提出了一种基于码本的波束赋形算法。该算法通过构建下行波束赋形权值码本,并根据信道估计信息对预设码进行搜索,从而实现快速单流波束赋形。仿真结果表明,该算法使单流波束赋形的复杂度大大降低,并且误块率性能较最优的奇异值分解算法损失不大。     

基于负载平衡的搜索算法研究

本文针对语义拆分的平衡算法提出了基于请求者反馈的搜索算法,然后对算法的搜索长度进行了分析,通过限定负载平衡时语义类移动的最大次数和聚类中节点的数目可以控制搜索服务的最大时间延时。     

一种基于H264的改进的运动估计三步搜索算法

保证搜索质量满足要求的前提下实现快速的搜索,在要求实时的编解码器中成为了最关键的问题之一.提出了一种改进的运动估计三步搜索算法,是在原快速三步算法的基础上,重新定义了两个不同的搜索起始点和搜索窗,以并行的方式同时进行三步搜索,并将二者的搜索结果进行比较,取其最优的一个作为最终的最佳匹配块.这种搜索算法,具有三步搜索算法的快速搜索特点,同时可以减小陷入局部最优的可能性,能够实现更高性能的快速搜索.     

改进的粒子群算法在VRP中的应用

将粒子群算法和禁忌搜索算法相结合构造禁忌搜索粒子群算法.提出一种对粒子群算法中全局最优解进行禁忌搜索的混合算法,扩展了粒子群算法进化方式.将其用于车辆路径优化问题求解.与基本粒子群算法相比较,结合禁忌搜索算法的粒子群算法明显提高了算法收敛速度和优化性能.     

基于全零检测的改进小菱形搜索算法

为减小H.264/AVC编码器中的运动搜索时间并降低计算量,提出一种基于全零检测技术的改进小菱形搜索算法。在搜索中心及周围4个点进行匹配计算,计算过程中插入全零检测,提高搜索速度。与其他几种典型运动搜索算法的比较结果表明,该算法在保证一定峰值性噪比的条件下,能降低运动搜索次数、减少编码时间,适用于背景相对固定或中低运动强度的视频序列。     

基于全零检测的改进小菱形搜索算法

为减小H.264/AVC编码器中的运动搜索时间并降低计算量,提出一种基于全零检测技术的改进小菱形搜索算法。在搜索中心及周围4个点进行匹配计算,计算过程中插入全零检测,提高搜索速度。与其他几种典型运动搜索算法的比较结果表明,该算法在保证一定峰值性噪比的条件下,能降低运动搜索次数、减少编码时间,适用于背景相对固定或中低运动强度的视频序列。     

G.723.1语音编码算法闭环基音搜索的低复杂度算法

由于ITU-TG.723.1语音编码算法具有较高的算法复杂度,故而在应用与实现时受到了很多的限制。该文提出一种低复杂度闭环基音搜索算法,该算法仍以5阶基音预测器为基础,但在求取5个基音预测增益时不是采用原算法中对20维矢量码本进行搜索的方法,而是利用这个20维矢量组成一个Wiener-Hopf方程,并利用语音的短时平稳特性将该方程简化为一个Toeplitz线性代数方程组,方程组的解就是所求的基音预测增益。对该增益进行5维码本矢量量化,从而用5维矢量码本搜索代替了原来的20维矢量码本搜索。这样使闭环基音搜索部分的运算量降低了一半,语音质量只有略微下降,同时与G.723.1算法码流兼容。     

一种降低ACELP码书搜索复杂度的高效算法

为了降低代数码激励线性预测(algebraic code-excited linear prediction, ACELP)语音编码算法的复杂度, 以便更好地实时实现, 提出了一种有效的改进算法。在自适应码书搜索上提出了不连续的开环基音搜索算法, 利用时间抽取因子对不同时延段语音样点进行不连续抽取; 在代数码书的搜索上提出了一致脉冲替换法, 采用脉冲位置预选和循环判断机制控制码书搜索的次数。以G. 729A为实验平台进行仿真, 仿真结果表明, 改进的算法在保证语音质量的情况下, 有效降低了ACELP码书搜索的复杂度。     

Fast additive quantization for vector compression in nearest neighbor search

Vector quantization has been widely employed in nearest neighbor search because it can approximate the Euclidean distance of two vectors with the table look-up way that can be precomputed. Additive quantization (AQ) algorithm validated that low approximation error can be achieved by representing each input vector with a sum of dependent codewords, each of which is from its own codebook. However, the AQ algorithm relies on computational expensive beam search algorithm to encode each vector, which is prohibitive for the efficiency of the approximate nearest neighbor search. In this paper, we propose a fast AQ algorithm that significantly accelerates the encoding phase. We formulate the beam search algorithm as an optimization of codebook selection orders. According to the optimal order, we learn the codebooks with hierarchical construction, in which the search width can be set very small. Specifically, the codewords are firstly exchanged into proper codebooks by the indexed frequency in each step. Then the codebooks are updated successively to adapt the quantization residual of previous quantization level. In coding phase, the vectors are compressed with learned codebooks via the best order, where the search range is considerably reduced. The proposed method achieves almost the same performance as AQ, while the speed for the vector encoding phase can be accelerated dozens of times. The experiments are implemented on two benchmark datasets and the results verify our conclusion.     

An improved search algorithm for vector quantization using mean pyramid structure

Vector quantization (VQ) is a well-known data compression technique. In the codebook design phase as well as the encoding phase, given a block represented as a vector, searching the closest codeword in the codebook is a time-consuming task. Based on the mean pyramid structure and the range search approach, an improved search algorithm for VQ is presented in this paper. Conceptually, the proposed algorithm has the bandpass filter effect. Each time, using the derived formula, the search range becomes narrower due to the elimination of some portion of the previous search range. This reduces search times and improves the previous result by Lee and Chen (A fast search algorithm for vector quantization using mean pyramids of codewords. IEEE Trans. Commun. 43(2/3/4), (1995) 1697–1702). Some experimental results demonstrate the computational advantage of the proposed algorithm.     

Improved adaptive vector quantization algorithm using hybrid codebook data structure

Recently, Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95] presented an efficient adaptive vector quantization (AVQ) algorithm and their proposed AVQ algorithm has a better peak signal-to-noise ratio (PSNR) than that of the previous benchmark AVQ algorithm. This paper presents an improved AVQ algorithm based on the proposed hybrid codebook data structure which consists of three codebooks—the locality codebook, the static codebook, and the history codebook. Due to easy maintenance advantage, the proposed AVQ algorithm leads to a considerable computation-saving effect while preserving the similar PSNR performance as in the previous AVQ algorithm by Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95]. Experimental results show that the proposed AVQ algorithm over the previous AVQ algorithm has about 75% encoding time improvement ratio while both algorithms have the similar PSNR performance.     

基于802.ac网络的混合速率自适应算法

IEEE 802.11ac技术已经成为下一代局域网的主要技术,其通过扩增多输入多输出技术、信道宽度、编码与调制策略的种类,使得传输速率获得了巨大的提升.然而种类的扩增也导致了速率选择时搜索空间过大,搜索时间过长,算法计算复杂度高等方面的问题,而已有的针对IEEE802.11a/b/g/n的速率自适应算法无法解决该问题.为了解决该问题,提出了一种在高速无线局域网模式下混合速率自适应算法VhRa.该算法利用MIMO模式、信道宽度的最佳设置与RSSI之间的单调关系来进行特征提取,在MCS （编码与调制策略）选择上通过利用二分法基于zigzag探测的模式进行选择,从而缩小算法搜索的空间,提高搜索效率,进一步提升传输吞吐量.结果表明,VhRa的搜索效率与RRAA、Minstrel-HT相比分别提高了42%、20%,同时在不同场景下对VhRa进行吞吐量分析,移动环境下VhRa相对比RRAA、Minstrel-HT、Samplelite吞吐量分别提升90%、10%、34%.     

FASTEST: a practical low-complexity algorithm for compile-timeassignment of parallel programs to multiprocessors

In the area of parallelizing compilers, considerable research has been carried out on data dependency analysis, parallelism extraction, as well as program and data partitioning. However, designing a practical, low complexity scheduling algorithm without sacrificing performance remains a challenging problem. A variety of heuristics have been proposed to generate efficient solutions but they take prohibitively long execution times for moderate size or large problems. In this paper, we propose an algorithm called FASTEST (Fast Assignment and Scheduling of Tasks using an Efficient Search Technique) that has O(e) time complexity, where e is the number of edges in the task graph. The algorithm first generates an initial solution in a short time and then refines it by using a simple but robust random neighborhood search. We have also parallelized the search to further lower the time complexity. We are using the algorithm in a prototype automatic parallelization and scheduling tool which compiles sequential code and generates parallel code optimized with judicious scheduling. The proposed algorithm is evaluated with several application programs and outperforms a number of previous algorithms by generating parallelized code with shorter execution times, while taking dramatically shorter scheduling times. The FASTEST algorithm generates optimal solutions for a majority of the test cases and close-to-optimal solutions for the rest     

基于改进人工蜂群的图像矢量量化码书设计算法

为了进一步提高图像矢量量化的码书质量,提出了一种新的图像压缩矢量量化码书设计算法。该算法采用均方误差(MSE)作为码书设计的适应度函数,利用改进的人工蜂群算法进行适应度函数的优化求解,增强了算法的自组织性和收敛性,大大减少了陷入局部收敛的可能性。将一种基于和值特性的快速码字搜索思想引入到码书设计算法中,使算法计算量明显降低。仿真结果表明,该算法具有计算时间短、收敛速度快的优点,并且生成的码书质量好、稳定性强。     

Self-configuration single particle optimizer for DNA sequence compression

This paper presents a novel self-configuration single particle optimizer (SCSPO) for DNA sequence compression. Particularly, SCSPO searches an optimal compression codebook of all unique repeat patterns and then DNA sequences are compressed by replacing the duplicate fragments with the indexes of the corresponding matched code vectors in the codebook. Featured with a crucial self-configuration process, SCSPO optimizes the codebook with no predefined parameter settings required. Experimental results on benchmark numerical functions and real-world DNA sequences demonstrate that SCSPO is capable of attaining better fitness value than many other PSO variants and the proposed DNA sequence compression algorithm based on SCSPO attains encouraging compression performance.