Adaptive Playout Buffer Algorithm for Enhancing Perceived Quality of Streaming Applications
Self-adaptive differential evolution algorithm for numerical optimization
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Abstract—In this paper, we propose an extension of Self-adaptive Differential Evolution algorithm (SaDE) to solve optimization problems with constraints. In comparison with the original SaDE algorithm, the replacement criterion was modified for handling constraints. The performance of the proposed method is reported on the set of 24 benchmark problems provided by CEC2006 special session on constrained real parameter optimization.
2006 IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006
Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization
“DE/rand/1”: Vi ,G = Xr ,G + F ⋅ Xr ,G − Xr G
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“DE/best/1”: Vi ,G = Xbest ,G + F ⋅ Xr ,G − X r G 1 2,
一种改进的自适应匹配滤波方法
一种改进的自适应匹配滤波方法王泽玉;李明;卢云龙【摘要】In order to overcome the detection degradation for the conventional detectors in the limited-training environment,a modified adaptive matched filter is proposed by modeling the disturbance as an autoregressive process with unknown parameters.The detector is derived by resorting to a two-step design procedure:first derive the generalized likelihood ratio test under the assumption that the parameters of the autoregressive process are known,and then,the maximum likelihood estimates of the parameters,based on the training data,are substituted in place of the true parameters into the test.The detection performance of the new receiver shows that the proposed receiver can lead to a noticeable performance improvement over the conventional adaptive matched filter. For a moderate size of radar echoes, the proposed detector performs close to the optimum matched filter even in the limited-training environment.%针对雷达目标检测中由于训练数据缺失导致传统自适应检测方法的检测性能下降的问题,提出一种改进的自适应匹配滤波方法.该方法首先将杂波用自回归过程表示;然后假设自回归参数已知,推导出广义似然比检验表达式;最后将采用训练数据估计得到的自回归参数的最大似然估计值代入广义似然比检验表达式中,代替已知的自回归参数.仿真实验结果表明,与传统的自适应方法相比,这种方法能在训练数据不足时提高检测性能.当雷达回波数目较大时,这种方法的检测性能接近理想的匹配滤波方法.【期刊名称】《西安电子科技大学学报(自然科学版)》【年(卷),期】2018(045)001【总页数】6页(P12-16,82)【关键词】雷达检测;自适应匹配滤波;自回归建模【作者】王泽玉;李明;卢云龙【作者单位】西安电子科技大学雷达信号处理国家重点实验室,陕西西安710071;西安电子科技大学雷达信号处理国家重点实验室,陕西西安710071;西安电子科技大学雷达信号处理国家重点实验室,陕西西安710071【正文语种】中文【中图分类】TN953在协方差矩阵未知的杂波环境下对目标进行检测是雷达最基本的任务.通常,假设存在一组不含目标的训练数据来估计未知的杂波协方差矩阵.在高斯杂波环境下,文献[1]提出了基于广义似然比检验(Generalized Likelihood Ratio Test, GLRT)的检测方法,该方法需要求得所有未知参数的最大似然估计.为了减少计算量,文献[2]提出了自适应匹配滤波(Adaptive Matched Filter, AMF)方法,首先假设杂波的协方差矩阵已知,推导出广义似然比检验表达式,然后将采用训练数据估计得到的协方差矩阵的最大似然估计值代入检验表达式中来代替已知的协方差矩阵.随后,其他的检测方法[3-6](如Rao检测,Wald检测等)被相继提出.然而,这些检测方法至少需要两倍系统自由度的训练数据来估计杂波的协方差矩阵[7-9].在实际检测环境中,这个条件难以满足.利用杂波的性质能有效地解决训练数据缺失情况下检测性能下降的问题.众所周知,杂波可以用阶数较低的自回归过程来表示.文献[10]利用自回归频谱估计提出一种自适应滤波方法来抑制杂波.在色高斯噪声环境下,假设目标信号已知,文献[11]将噪声用自回归过程来表示并采用广义似然比检验准则来进行检测.考虑到文献[11]中的目标模型较为简单,文献[12]针对未知幅度的信号,提出一种自回归广义似然比检测方法.渐进性能分析表明,该检测方法具有渐进恒虚警特性.文献[13]在完全均匀场景和不同距离单元的杂波协方差矩阵结构不同的非均匀场景中,针对自回归过程的阶数已知和未知两种情形设计了4种基于广义似然比检验的检测器.近几年,基于多通道自回归过程的检测器也得到了广泛的研究[14-16].针对训练数据缺失情况下传统检测方法检测性能下降的问题,笔者提出基于自回归的自适应匹配滤波方法.该方法首先将杂波用自回归过程来表示,然后假设自回归参数已知,利用一步广义似然比检验准则设计检测器,最后利用训练数据对自回归参数进行估计,并将得到的自回归参数的最大似然估计值代入一步广义似然比检验表达式中,得到最终的基于自回归的自适应匹配滤波器.假设回波包含N个相干脉冲,目标检测问题可以用以下的二元假设检验来表示:其中,z0∈CN×1,表示待检测距离单元的数据;zt∈CN×1,t=1,…,K,表示不含目标的一组训练数据;nt∈ CN×1,t=0,…,K,是均值为零、协方差矩阵为R的独立复高斯向量;p= [1,exp(j Ω),…,exp(j(N-1)Ω)]T,是导向矢量;Ω是目标多普勒;α表示未知的目标幅度.假设杂波信号nt可以用阶数为M的自回归过程来表示:其中,a(m)=[a(1),…,a(M)]T,是复自回归参数向量;wt(l)表示均值为零、方差为σ2的复白高斯噪声,σ2是未知常量.为了解决上述问题,采用自适应匹配滤波(即两步广义似然比准则)进行检测.首先假设自回归参数a和σ2已知,基于广义似然比准则推导检验表达式;然后采用训练数据对a和σ2进行估计,将得到的最大似然估计值代入检验表达式中得到最终的结果.当N≫M时,z0在H0和H1条件下的概率密度函数可以分别表示为[17]其中,(·)H表示共轭转置.ut=[zt(M+1),…,zt(N)]T,t=0,…,K,是 N-M 维的复列向量;q= [p(M+1),…,p(N)]T,是 N-M 维的复列向量是 (N-M)× M维的矩阵是 (N-M)× M维的矩阵.首先假设a和σ2已知,推导广义似然比检验表达式:其中,η表示检测门限.由式(4)和式(5)可以看出,参数α的最大似然估计可以通过对表达式J(α)= [u0+ Y0a- α(q+ P a)]H [u0+ Y0a- α(q+ P a)]求α的最小值得到.将J(α)展开,可以得到其中,Re[·]表示取实部.显然,当包含绝对值的第1项等于0时,表达式J(α)取得最小值.因此,α的最大似然估计值为将α的最大似然估计值即式(7)代入到表达式J(α)中,得到其中,H=I-(q+P a)(q+P a)H/[(q+P a)H(q+P a)],是一个幂等矩阵.将式(3)~(4)和式(8)代入式(5)中,得到基于广义似然比的检验表达式其中,=H u0,=H Y0.利用训练数据对自回归参数a和σ2进行估计,并将得到的自回归参数的最大似然估计值代入一步广义似然比检验表达式(9)中代替已知的a和σ2.训练数据zt的联合概率密度函数可以表示为对联合概率密度函数取对数,得到对式(11)关于σ2求导并令导数等于零,即可得到σ2的最大似然估计值将式(12)代入到式(11)中,可得从式(13)可以看出,参数a的最大似然估计可以通过对表达式求关于a的最小值得到.将表达式Q(a)展开,可得其中,由于SY Y是非负定的,且式(14)中的第2项和第3项与a无关,可以得到[18]参数a的最大似然估计为将a和σ2的最大似然估计值(即式(12)和式(16))代入式(9)中并化简,得到基于自回归的自适应匹配滤波器:其中,ηAR-AMF表示检测门限.对所提出的基于自回归的自适应匹配滤波方法的检测性能进行分析.仿真参数设置为: Ω=1,a= [-0.25+ 0.25j,0.3]T,σ2=2,Pfa= 10-2.信干噪比定义为RSINR= pHR-1p,R表示杂波的协方差矩阵,可以通过a和σ2确定.检测概率和门限分别通过 100/ Pfa和 1 000/ Pfa次独立的蒙特卡罗实验确定.图1和图2分别为K=2和K=20情况下,N取不同值时检测概率随着信干噪比变化的曲线.为了进行对比,理想的匹配滤波器(Matched Filter, MF)[2]的检测概率曲线也在图中画出.虽然理想的匹配滤波器在实际中无法实现,然而其提供了对比的基准.以下的实验结果均采用理想匹配滤波器检测概率的理论值.从图1和图2可以看出,随着脉冲数N的增加,基于自回归的自适应匹配滤波方法的检测性能逐渐提高.由图1可知,当 K=2,N=8,Pd=0.9 时,笔者提出的方法相对于理想的匹配滤波器的性能损失约为 5 dB;当脉冲数N增加到40时,性能损失减小到 1 dB;当脉冲数N增加到100时,性能损失小于 1 dB.因此,当脉冲数较大时,即使在训练数据严重缺失的情况下,笔者提出的方法仍然能获得与理想的匹配滤波器相近的检测性能.从图2可以看出,当 K=20,N>40 时,笔者提出的方法相对于理想匹配滤波器的性能损失可以忽略.因此,当脉冲数不是很小时,笔者提出的方法是训练数据缺失情况下检测目标的一种有效方法.图3表示N=30,K=5,30,60,150的情况下,检测概率随信干噪比变化的曲线.为了进行对比,传统的广义似然比检测器[1]和自适应匹配滤波器[2]的检测性能也在图中画出.从图中可以看出,随着信干噪比的增加,笔者提出的方法(AR-AMF)、传统的广义似然比检测器(GLRT)和自适应匹配滤波器(AMF)的检测性能均逐渐提高.这是由于训练数据的增加使得估计得到的参数值更加精确.在图3(a)中,传统的广义似然比检测器和自适应匹配滤波并没有画出,这是由于 K<N 使得传统方法中的采样协方差矩阵奇异.从图3(b)可以看出,当训练数据较小时,传统的广义似然比检测器和自适应匹配滤波方法的检测性能损失严重,笔者提出的方法相比于传统的方法有明显的性能增益.这是由于笔者提出的方法利用了杂波的性质,使得检测性能有所提高.随着训练数据的增加,传统方法相对于笔者提出的方法的性能损失逐渐减少,而笔者提出的方法仍优于传统的方法.由图3(c)和图3(d)可知,当 K=60,检测概率 Pd=0.9 时,笔者提出的方法相对于传统方法的增益差约为 3 dB;当训练数据 K=5N,Pd=0.9 时,笔者提出的方法与传统自适应匹配滤波器的性能增益差小于 1 dB.从以上仿真可知,在训练数据缺失的情况下,笔者提出的基于自回归的自适应匹配滤波方法通过利用杂波的性质使检测性能有所改善.仿真结果验证了笔者提出方法的有效性.针对训练数据缺失情况下传统自适应检测方法检测性能下降的问题,笔者提出一种基于自回归的自适应匹配滤波方法.该方法首先利用杂波的性质,将杂波用一个自回归过程表示;然后假设自回归参数已知,推导广义似然比检验表达式;最后利用训练数据估计未知的自回归参数,并将得到的自回归参数的最大似然估计值代入广义似然比检验表达式中,得到基于自回归的自适应匹配滤波检测器.仿真实验表明,在只有少量训练数据存在的情况下,笔者提出的方法优于传统的自适应检测方法.同时,当脉冲数较大时,笔者提出方法的检测性能接近理想的匹配滤波器.下一步的工作拟解决脉冲数较小情况下的自适应检测问题.[1] KELLY E J. An Adaptive Detection Algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(1):115-127.[2] ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR Adaptive Matched Filter Detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208-216.[3] de MAIO A. Rao Test for Adaptive Detection in Gaussian Interference with Unknown Covariance Matrix[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3577-3584.[4] de MAIO A. A New Derivation of the Adaptive Matched Filter[J]. IEEE Signal Processing Letters, 2004, 11(10): 792-793.[5] KRAUT S, SCHARF L L. CFAR Adaptive Subspace Detector is a Scale-invariant GLRT[J]. IEEE Transactions on Signal Processing, 1999, 47(9):2538-2541.[6] PULSONE N B, RADER C M. Adaptive Beamformer OrthogonalRejection Test[J]. IEEE Transactions on Signal Processing, 2001, 49(3): 521-529.[7] REED I S, MALLETT J D, BRENNAN L E. Rapid Convergence Rate in Adaptive Arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, 10(6): 853-863.[8] 周宇, 张林让, 刘楠. 贝叶斯雷达自适应检测算法研究[J]. 西安电子科技大学学报, 2012, 39(1): 36-42.ZHOU Yu, ZHANG Linrang, LIU Nan. Research on Bayesian Radar Adaptive Detection[J]. Journal of Xidian University, 2012, 39(1): 36-42.[9] 高永婵, 廖桂生, 朱圣棋. 复合高斯噪声中知识辅助的贝叶斯Rao检测方法[J]. 西安电子科技大学学报, 2013, 40(6): 46-51.GAO Yongchan, LIAO Guisheng, ZHU Shengqi. Knowledge-aided Bayesian Rao Detection Approach in Compound Gaussian Noise[J]. Journal of Xidian University, 2013, 40(6): 46-51.[10] BOWYER D E, RAJASEKARAN P K, GEBHART W W. Adaptive Clutter Filtering Using Autoregressive Spectral Estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1979, 15(4): 538-546.[11] KAY S M. Asymptotically Optimal Detection in Unknown Colored Noise via Autoregressive Modeling[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1983, 31(4): 927-940.[12] SHEIKHI A, NAYEBI M M, AREF M R. Adaptive Detection Algorithm for Radar Signals in Autoregressive Interference[J]. IEE Proceedings: Radar, Sonar and Navigation, 1998, 145(5): 309-314.[13] ALFANO G, de MAIO A, FARINA A. Model-based Adaptive Detectionof Range-spread Targets[J]. IEE Proceedings: Radar, Sonar and Navigation, 2004, 151(1): 2-10.[14] WANG P, WANG Z, LI H B, et al. Knowledge-aided Parametric Adaptive Matched Filter with Automatic Combining for Covariance Estimation[J]. IEEE Transactions on Signal Processing, 2014, 62(18): 4713-4722.[15] BOUKABAA T, ZOUBIRB A M, BERKANIA D. Parametric Detection in Non-stationary Correlated Clutter Using a Sequential Approach[J]. Digital Signal Processing: a Review Journal, 2015, 36(C): 69-81.[16] GAO Y C, LIAO G S, ZHU S Q, et al. Generalised Persymmetric Parametric Adaptive Coherence Estimator for Multichannel Adaptive Signal Detection[J]. IET Radar, Sonar and Navigation, 2015, 9(5): 550-558. [17] HAYKIN S S, STEINHARDT A. Adaptive Radar Detection and Estimation[M]. New York: Wiley-Interscience, 1992.[18] LI J, HALDER B, STOICA P, et al. Computationally Efficient Angle Estimation for Signals with Known Waveforms[J]. IEEE Transactions on Signal Processing, 1995, 43(9): 2154-2163.。
dpm adaptive原理
dpm adaptive原理DPM Adaptive原理概述•DPM Adaptive是指动态传输模式自适应技术(Dynamic Play Mode Adaptive),用于在线视频播放中的自适应码率选择。
•在视频传输过程中,根据网络状况实时调整播放的码率,以提供更好的用户体验。
原理详解1. 动态码率调整•DPM Adaptive通过实时监测网络带宽和延迟情况,动态调整视频的码率。
•当网络带宽较低或延迟较高时,会自动降低码率以适应网络状况;当网络带宽较高或延迟较低时,会自动提高码率以提高观看质量。
2. 媒体文件分片•在DPM Adaptive中,视频文件会被分成多个小片段进行传输。
•每个小片段的时长通常在几秒到十几秒之间,这样可以更精细地控制视频码率的调整。
3. 播放器与服务器的交互•在视频播放过程中,播放器会与服务器进行交互,获取不同码率的小片段。
•播放器会根据当前网络状况选择合适的码率片段进行播放,以实现码率自适应。
•当网络状况改变时,播放器会再次向服务器请求适合当前状况的码率片段,保证播放的稳定性和流畅度。
4. 码率选择算法•DPM Adaptive中常用的码率选择算法有BOLA(Bitrate-Optimization Based on Ladder-shaped Association)和ABR(Adaptive Bitrate)等。
•这些算法根据网络条件和视频质量评估,选择合适的码率片段进行播放。
•BOLA算法在选择码率时综合考虑了当前缓冲区的状态、带宽预测和视频质量;ABR算法则主要根据带宽和缓冲区来动态调整码率。
应用场景1. 在线视频服务•DPM Adaptive广泛应用于各大视频平台和流媒体服务,如YouTube、Netflix等。
•用户通过在线观看视频时,可以获得更好的观影体验,避免了视频卡顿、加载过慢等问题。
2. 直播•在直播场景中,DPM Adaptive也发挥重要作用。
Autodesk Nastran 2023 参考手册说明书
FILESPEC ............................................................................................................................................................ 13
DISPFILE ............................................................................................................................................................. 11
File Management Directives – Output File Specifications: .............................................................................. 5
BULKDATAFILE .................................................................................................................................................... 7
play out造句
play out造句1、We play out an imaginary confrontation in our mind.我们的脑海中浮现出一种想象的冲突。
2、Lift up your eyes and see your dream play out in your mind.抬起双眼你会看到梦想在脑海绽放。
3、How does that play out in love?变化在爱情中起什么作用呢?4、Q: How does your forecast play out for the labor market?《华尔街日报》:你们对于劳动力市场的预测是怎样的?5、Back on the hiking trail in Spain, I saw this play out in myriad ways.回想在西班牙的徒步旅行,我没完没了地看到这样的情景。
6、So yes, you can see it play out in products and you can see it also play out in business models.所以在他的产品和商业模式中,你也能看到这些特点。
这其中的关键就是:有些事物是恒久不变的,而在这个框架下,你可以充分发挥创造力。
7、Packet Audio Adaptive Play out Algorithms of IP NetworkIP网络的实时分组音频回放自适应算法8、Do you learn best by example? See one way freewriting can play out in practice.你通过举例的方式会学的最好吗?看单面的随便可能在实际上用完。
9、Children need to play out in the open.孩子需要在户外玩耍。
10、Greece, one might wager, will play out in similar fashion.有人可能会* 希腊将有类似表现。
AdaptivCRT算法运作演示讲解
Day in the Life of an AdaptivCRTTM Patient
Note: AdaptivCRTTM is available in the Viva XT CRT-D device.
*AV conduction checks will be delayed if AV block is suspected.
2. Determine Pacing Method
Determines the pacing method based on the intrinsic conduction assessment and heart rate
1 Martin, DO, et. al. Heart Rhythm (2012), doi: 10.1016/j.hrthm.2012.07.009. 2 Medtronic Viva XT CRT-D manual.
A daptive LV pacing
What Is Adaptive LV Pacing?
Menu Selection
This slide deck provides an overview of the AdaptivCRTTM feature as well as animations of the feature in action for different patient scenarios. Click on one of the selections below or advance to the next slide to review the entire presentation:
面向ECG的二分法稀疏度自适应匹配追踪重构算法
2021年第40卷第4期传感器与微系统(Transducer and Microsystem Technologies)131DOI : 10.13873/J. 1000-9787(2021)04-0131-04面向ECG 的二分法稀疏度自适应匹配追踪重构算法**收稿日期:2019-09-29*基金项目:中央高校基本科研业务费专项资金资助项目(JUSRP51510);江苏省研究生科研与实践创新计划项目(SJCX18-0647);江苏省物 联网应用技术重点建设实验室资助项目王 涛▽,田青】,虞致国孙益洲-顾晓峰|(1.物联网技术应用教育部工程研究中心江南大学电子工程系,江苏无锡214122;2.无锡太湖学院,江苏无锡214064)摘要:为了减少无线传感器网络(WSNs)心电信号的压缩感知重构时间,提出一种面向心电(ECG)信号的二分法稀疏度自适应匹配追踪重构算法。
基于二分法快速接近真实稀疏度的值,并通过相邻迭代之间残差范数值差的绝对值确定下一轮迭代计算区间。
实验结果表明:与传统稀疏自适应匹配追踪重构算法相比较,改进算法可显著降低重构时间并接近子空间追踪算法和正交匹配追踪算法;与子空间追踪算法和正交匹配追踪算法相比,峰值信噪比平均提升了 6.29%和5.43 %。
关键词:压缩感知;心电(ECG);重构;自适应匹配追踪中图分类号:TP391; TP212文献标识码:A 文章编号:1000-9787(2021)04-0131-04Dichotomy sparsity adaptive matching pursuit in compressedsensing reconstruction algorithm for ECG *WANG Tao 1'2, TIAN Qing 1, YU Zhiguo 1, SUN Yizhou 1, GU Xiaofeng 1(1・ Engineering Research Center of Internet of Things Technology Applications , Ministry of Education ,Department of Electronic Engineering , Jiangnan University , Wuxi 214122, China ;2. Taihu University of Wuxi,Wuxi 214064,China)Abstract : To reduce the time consumption in the reconstruction of electrocardiograph ( ECG ) signals for thewireless sensor networks ( WSNs ) , a dichotomy spars 让y adaptive matching pursuit ( DSAMP ) algorithm forcompressed sensing without knowing sparsity is proposed ・ The algorithm calculates the stage value for approachingthe real sparsity based on dichotomy , and determines the direction of stage switching by the absolute value of thedifference in residual norm between two adjacent iterations. Results show that the runtime of DSAMP can be reduced compared with that of sparsity adaptive matching pursuit and is similar to those of subspace pursuit andorthogonal matching pursuit. The peak signal to noise ratio of the DSAMP is improved 6. 29 % and 5. 43 % , compared with the that of orthogonal matching pursuit and subspace pursuit on average , respectively.Keywords : compressed sensing ; electrocardiograph (ECG ) ; reconstruction ; adaptive matching pursuit0引言随着人工智能和无线传感器技术的飞速发展,现代医 学已经进入了智能医疗和远程医疗的时代⑴。
Buffer Insertion with Adaptive Blockage Avoidance:自适应阻塞避免缓冲器插入
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Find an unblocked n’ closest to n, between n and parent_node( n )
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Drive long wire Shield load from critical
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度米文库汇编之杨坚副教授个人简历[1]
度米文库汇编之杨坚副教授个人简历[1]杨坚,男,中国科学技术大学自动化系副教授,博士生导师。
2001年7月中国科学技术大学自动化系本科毕业,2005年12月于中国科学技术大学自动化系,获工学博士。
2010年在英国南安普顿大学电子与计算机科学学院从事访问研究,与英国皇家科学院院士lajos hanzo教授合作研究无线视频传输。
主要研究方向为网络系统建模与优化、多媒体通信、随机优化理论以及信号处理。
2008年入选教育部新世纪优秀人才支持计划,现为ieee会员,中国科学院青年创新促进会会员。
在国内外学术刊物发表论文50余篇,授权专利4项。
作为项目负责人主持“十一五”国家863计划,国家自然科学基金,安徽省自然科学基金等,作为技术骨干曾参加并完成国家863重大专项、国家自然科学基金等多项,包括863重大专项3tnet宽带网络应用支撑环境两项子课题,即“媒体服务器系统的研制”和“媒体服务器系统的工程化”,国家发改委“cngi基于p2p的视频多媒体点播系统”研制,国家广电总局“海南省iptv示范网络的建设”项目,上海嘉定地区有线电视双向改造等项目。
论文著作书:杨孝先,杨坚著,《信息论基础》,中国科学技术大学出版社,2011年7月。
专利:[1]杨坚等,负载均衡系统及多种业务实现负载均衡的方法,专利号:zl200710087417.6.[2]杨坚等,实现上下行数据分离的方法和装置,专利号:zl200810103393.3.[3]杨坚等,多协议适配器及其对多种业务实现适配处理的方法,专利号:zl20071008417.6.[4]杨坚等,媒体流在线服务迁移的方法和装置,专利号:zl200810104086.7.代表性论文:[1] jian yang, h. hu, and h. s. xi, nonlinear criterion based adaptive generalized eigen-decomposition, iet signal processing. (accept)[2]jian yang, q. zheng, h. s. xi, and l. hanzo, receiver-driven adaptive enhancement layer switching algorithm for scalablevideo transmission over link-adaptive networks, ieee signal processing letters, vol. 20, no. 1, pp. 47-50, jan. 2013.[3]jian yang, x. chen, and h. s. xi, fast adaptive extraction algorithm for multiple principal generalized eigenvectors, international journal of intelligent systems (wiley), vol. 28, no. 3, pp. 289-306, mar. 2013.[4]jian yang, k. zeng,h. hu and h. s. xi, dynamic cluster reconfiguration for energy conservation in computation intensive service, ieee transactions on computers, vol. 61, no. 10, pp. 1401-1416, oct. 2012.[5]jian yang, h. hu, h. s. xi and l. hanzo, online buffer fullness estimation aided adaptive media playout for video streaming, ieee transactions on multimedia, vol. 13, no. 5, pp. 1141-1153, oct. 2011.[6]jian yang,y. zhao and h.s. xi,weighted rule based adaptive algorithm for simultaneously extracting generalized eigenvectors, ieee transact。
TeraMax TM TurboCell OFDM点对多点系统产品说明书
Specifications subject to change without noticeTeraMaxTMT URBO C ELL ®OFDM P OINT -TO -M ULTI P OINT S YSTEMNetwork FeaturesDescription(FP)Flat Panel Enclosure (EX)Ruggedized EnclosureTeraMax™ is Terabeam’s flagship broadband wirelessnetworking solution series, offering an optimal combination of throughput, range, suitability for outdoor environments, network scalability and value. A powerful feature set – includingorthogonal frequency division multiplexing (OFDM), adaptive dynamic polling, packet aggregation, bandwidth management at the client, and enhanced security including AES – provides TeraMax with much of the functionality of WiMAX today.OFDM: enables communication even without total line of sight.Polling: by actively providing equal time to all clients in the network, the system prevents interference among nodes, and thus maximizes network scalability.Packet aggregration and bandwidth management: ensure optimal distribution of bandwidth throughout the network.TeraMax point-to-multipoint systems are ideally suited forwireless Internet service providers seeking to enhance network performance; cable and DSL operators seeking to extend service into remote areas; municipalities requiring innovative ways to support city services such as utility monitoring or surveillance;and enterprises building metropolitan or regional private data networks. A 4.9 GHz version specifically enables public safety communications in the frequency band dedicated by the FCC for that use in the United States. These systems can be combined with TeraMax point-to-point systems (see separate specification sheet) or products throughout Terabeam’s extensive portfolio to comprehensively address your network requirements.TeraMax point-to-multipoint systems comprise base stations and clients; are available in either 5.8 GHz or 4.9 GHz versions;include mounting equipment, Power over Ethernet andmanagement software; and can be ordered with various external antennas and lengths of cable to complete the turnkey solutions.5.8 GHz clients are provided with flat-panel antennas, either affixed to a ruggedized, carrier-grade enclosure, or integrated into a flat-panel outdoor-rated enclosure.TeraMaxTMP-MP System Includes:•Outdoor radio with mounting hardware •Surge protected Cat 5 DC Power Injector •110/240 VAC to 48 VDC power supply•CD-ROM with Windows-based Terabeam Configurator software •User’s ManualA Terabeam outdoor Ethernet cable must be ordered separately per unit. Available lengths are 50, 100, 200, or 300 feet.Models with external antennas include one 6 ft LMA-600 coax cable per radio.Specifications subject to change without noticeRF FeaturesJul 2005-01Ordering Information。
y轴自适应算法
y轴自适应算法As a programmer, designing an algorithm for y-axis adaptive scaling is a challenging task that requires a deep understanding of data visualization principles and user experience. The goal is to create a responsive and intuitive system that can automatically adjust the y-axis scale based on the data being displayed, ensuring that the information is presented in a clear and meaningful way. This algorithm needs to take into account various factors such as the range of values in the data, the desired level of detail, and the overall aesthetic of the visualization.作为一名程序员,设计一个y轴自适应缩放的算法是一项具有挑战性的任务,需要对数据可视化原则和用户体验有深入的了解。
目标是创建一个响应灵活且直观的系统,它能够根据所显示的数据自动调整y轴刻度,确保信息以清晰且有意义的方式呈现。
此算法需要考虑各种因素,如数据中的值范围、所需的详细级别以及可视化的整体美感。
One approach to y-axis adaptive scaling is to analyze the data before rendering the visualization and determine the appropriate y-axis scale based on the range of values present. This pre-processing stepcan involve calculating the minimum and maximum values in the dataset and then defining the y-axis range to encompass these values in a visually pleasing manner. By setting the y-axis scale dynamically, the algorithm can ensure that the data is displayed optimally without any unnecessary distortion or truncation.y轴自适应缩放的一种方法是在渲染可视化之前分析数据,并根据现有值范围确定适当的y轴刻度。
adaptive_filters_theory_and_applications_2nd
adaptive_filters_theory_and_applications_2ndBrochureMore information from /doc/e9d77d4fdd3383c4ba4cd292.html /reports/2379637/Adaptive Filters. Theory and Applications. 2nd EditionDescription:This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of thetheory to illustrate the much broader range of adaptive filters applications developed in recent years. Thebook offers an easy to understand approach to the theory and application of adaptive filters by clearlyillustrating how the theory explained in earlier chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource forgraduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers.Key features:- Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material ontransform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noisecontrol.- Provides an in–depth study of applications which now includes extensive coverage of OFDM, MIMO andsmart antennas.- Contains exercises and computer simulation problems at the end of each chapter.- Includes a new companion website hosting MATLAB? simulation programs which complement thetheoretical analyses, enabling the reader to gain an in–depth understanding of the behaviours andproperties of the various adaptive algorithms.Contents:Preface xviiAcknowledgments xxi1 Introduction 11.1 Linear Filters 11.2 Adaptive Filters 21.3 Adaptive Filter Structures 31.4 Adaptation Approaches 71.5 Real and Complex Forms of Adaptive Filters 91.6 Applications 92 Discrete–Time Signals and Systems 282.1 Sequences and z–Transform 282.2 Parseval s Relation 322.3 System Function 332.4 Stochastic Processes 35Problems 443 Wiener Filters 483.1 Mean–Squared Error Criterion 483.2 Wiener Filter Transversal, Real–Valued Case 503.3 Principle of Orthogonality 553.4 Normalized Performance Function 573.5 Extension to Complex–Valued Case 583.6 Unconstrained Wiener Filters 613.7 Summary and Discussion 79Problems 804 Eigenanalysis and Performance Surface 904.1 Eigenvalues and Eigenvectors 904.2 Properties of Eigenvalues and Eigenvectors 914.3 Performance Surface 104Problems 1125 Search Methods 1195.1 Method of Steepest Descent 1205.2 Learning Curve 1265.3 Effect of Eigenvalue Spread 1305.4 Newton s Method 1315.5 An Alternative Interpretation of Newton s Algorithm 133 Problems 1356 LMS Algorithm 1396.1 Derivation of LMS Algorithm 1396.2 Average Tap–Weight Behavior of the LMS Algorithm 141 6.3 MSE Behavior of the LMS Algorithm 144 6.4 Computer Simulations 1566.5 Simplified LMS Algorithms 1676.6 Normalized LMS Algorithm 1706.7 Affine Projection LMS Algorithm 1736.8 Variable Step–Size LMS Algorithm 1776.9 LMS Algorithm for Complex–Valued Signals 1796.10 Beamforming (Revisited) 182Problems 190Appendix 6A: Derivation of Eq. (6.39) 2057 Transform Domain Adaptive Filters 2077.1 Overview of Transform Domain Adaptive Filters 2087.2 Band–Partitioning Property of Orthogonal Transforms 2107.3 Orthogonalization Property of Orthogonal Transforms 2117.4 Transform Domain LMS Algorithm 2137.5 Ideal LMS–Newton Algorithm and Its Relationship with TDLMS 2157.6 Selection of the Transform T 2167.7 Transforms 2297.8 Sliding Transforms 2307.9 Summary and Discussion 242Problems 2438 Block Implementation of Adaptive Filters 2518.1 Block LMS Algorithm 2528.2 Mathematical Background 2558.3 The FBLMS Algorithm 2608.4 The Partitioned FBLMS Algorithm 2678.5 Computer Simulations 276Problems 279Appendix 8A: Derivation of a Misadjustment Equation for the BLMS Algorithm 285 Appendix 8B: Derivation of Misadjustment Equations for the FBLMS Algorithms 288 9 Subband Adaptive Filters 2949.1 DFT Filter Banks 2959.2 Complementary Filter Banks 2999.3 Subband Adaptive Filter Structures 3039.4 Selection of Analysis and Synthesis Filters 3049.5 Computational Complexity 3079.6 Decimation Factor and Aliasing 3089.7 Low–Delay Analysis and Synthesis Filter Banks 3109.8 A Design Procedure for Subband Adaptive Filters 3139.9 An Example 316Problems 31910 IIR Adaptive Filters 32210.1 Output Error Method 32310.2 Equation Error Method 32710.3 Case Study I: IIR Adaptive Line Enhancement 33210.4 Case Study II: Equalizer Design for Magnetic Recording Channels 343 10.5 Concluding Remarks 349 Problems 35211 Lattice Filters 35511.1 Forward Linear Prediction 35511.2 Backward Linear Prediction 35711.3 Relationship Between Forward and Backward Predictors 35911.4 Prediction–Error Filters 35911.5 Properties of Prediction Errors 36011.6 Derivation of Lattice Structure 36211.7 Lattice as an Orthogonalization Transform 36711.8 Lattice Joint Process Estimator 36911.9 System Functions 37011.10 Conversions 37011.11 All–Pole Lattice Structure 37611.12 Pole–Zero Lattice Structure 37611.13 Adaptive Lattice Filter 37811.14 Autoregressive Modeling of Random Processes 38311.15 Adaptive Algorithms Based on Autoregressive Modeling 385 Problems 400Appendix 11A: Evaluation of E[ua(n)xT(n)K(n)x(n)uTa (n)] 407Appendix 11B: Evaluation of the parameter 40812 Method of Least–Squares 41012.1 Formulation of Least–Squares Estimation for a Linear Combiner 411 12.2 Principle of Orthogonality 41212.3 Projection Operator 41512.4 Standard Recursive Least–Squares Algorithm 41612.5 Convergence Behavior of the RLS Algorithm 421Problems 43013 Fast RLS Algorithms 43313.1 Least–Squares Forward Prediction 43413.2 Least–Squares Backward Prediction 43513.3 Least–Squares Lattice 43713.4 RLSL Algorithm 44013.5 FTRLS Algorithm 453Problems 46014 Tracking 46314.1 Formulation of the Tracking Problem 46314.2 Generalized Formulation of LMS Algorithm 46414.3 MSE Analysis of the Generalized LMS Algorithm 46514.4 Optimum Step–Size Parameters 46914.5 Comparisons of Conventional Algorithms 47114.6 Comparisons Based on Optimum Step–Size Parameters 475 14.7 VSLMS: An Algorithm with Optimum Tracking Behavior 477 14.8 RLS Algorithm with Variable Forgetting Factor 48514.9 Summary 486Problems 48815 Echo Cancellation 49215.1 The Problem Statement 49215.2 Structures and Adaptive Algorithms 49515.3 Double–Talk Detection 51215.4 Howling Suppression 52115.5 Stereophonic Acoustic Echo Cancellation 524Appendix 15A: Multitaper method 542Appendix 15B: Derivation of the Two–Channel Levinson Durbin Algorithm 54916 Active Noise Control 55116.1 Broadband Feedforward Single–Channel ANC 55316.2 Narrowband Feedforward Single–Channel ANC 55916.3 Feedback Single–Channel ANC 57316.4 Multichannel ANC Systems 577Appendix 16A: Derivation of Eq. (16.46) 582Appendix 16B: Derivation of Eq. (16.53) 58317 Synchronization and Equalization in Data Transmission Systems 584 17.1 Continuous Time Channel Model 585 17.2 Discrete Time Channel Model and Equalizer Structures 58917.3 Timing Recovery 59317.4 Equalizers Design and Performance Analysis 60617.5 Adaptation Algorithms 61717.6 Cyclic Equalization 61817.7 Joint Timing Recovery, Carrier Recovery, and Channel Equalization 628 17.8 Maximum Likelihood Detection 629 17.9 Soft Equalization 63117.10 Single–Input Multiple–Output Equalization 64317.11 Frequency Domain Equalization 64517.12 Blind Equalization 649Problems 65418 Sensor Array Processing 65918.1 Narrowband Sensor Arrays 66018.2 Broadband Sensor Arrays 67818.3 Robust Beamforming 683Problems 69219 Code Division Multiple Access Systems 69519.1 CDMA Signal Model 69519.2 Linear Detectors 69919.3 Adaptation Methods 707Problems 70920 OFDM and MIMO Communications 71120.1 OFDM Communication Systems 71120.2 MIMO Communication Systems 73020.3 MIMO OFDM 743Problems 743References 746Index 761Ordering:Order Online - /doc/e9d77d4fdd3383c4ba4cd292.html /reports/2379637/ Order by Fax - using the form belowOrder by Post - print the order form below and send toResearch and Markets,Guinness Centre,Taylors Lane,Dublin 8,Ireland.Fax Order FormTo place an order via fax simply print this form, fill in the information below and fax the completed form to 646-607-1907 (from USA) or +353-1-481-1716 (from Rest of World). 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稀疏恢复和傅里叶采样
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leslie A. Kolodziejski Chair, Department Committee on Graduate Students
2
Sparse Recovery and Fourier Sampling by Eric Price
Submitted to the Department of Electrical Engineering and Computer Science on August 26, 2013, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Department of Electrical Engineering and Computer Science August 26, 2013
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Piotr Indyk Professor Thesis Supervisor
深圳台电公司 HPA-0202DP 反馈抑制器说明书
为了安全和方便安装请仔细阅读并妥善保管本说明书Please read this manual before using the apparatus and keep it for future referenceHPA-0202DP Feedback Suppressor连接Connection深圳台电公司保留更改数据资料权,恕不事先通知。
Contents may change without prior announcement.Printed in Shenzhen 04/2022TAIDEN INDUSTRIAL CO.,LTD.反馈抑制器User's Manual 用户手册V 1.0P A会议室N Room N注:开机时,建议先开启HPA -0202DP ,再开启功放;关机时,建议先关闭功放再关闭HPA -0202DP 。
Note: when starting up, it is recommended to turn on the HPA-0202DP first, and then turn on the power amplifier; when shutting down, it is recommended to turn off the power amplifier first and then turn off the HPA-0202DP.产品概述Overview功能及指示Functions and instructions■适用于本地和远程会议系统的专用音频处理器■使用高性能DSP 处理器和业界前沿的自适应反馈抑制和回声消除算法,有效抑制声学反馈的发生,处理速度更快,音质更佳■采用AI 算法,极强的自学习能力,有效滤除房间混响,实现消除声学反馈的目的,同时大幅度改善了直通和反馈处理后的音色差异■Line In/Out :适用于现场音频信号输入/输出■VCS In/Out :适用于远程会议音频信号输入/输出■内置自适应反馈抑制(AFC )和自动回声消除(AEC )开关■自适应滤波器根据环境自动收敛,无需调试,即接即用,在声学反馈发生之前可以额外获得多达12dB 的增益■采用自适应全双工全频段回声消除算法,提高通讯音频频率响应,改善通信质量■自适应噪音消除算法,可滤除背景噪音,但不影响发言人的声音,降噪能力可达15 dB ,有效提高信噪比,改善音质■广泛适用于现场会议、远程会议、教育教学等需要提升环境声压并防止系统啸叫的场所技术参数频率响应0 ~ 20000 Hz采样频率 48 kHz 信噪比≥90 dBA 总谐波失真≤0.1%输入阻抗10 k Ω输出阻抗 1 k Ω反馈抑制时间≤400 ms 信号延时≤11 ms 噪音消除量≤15 dB 回声消除能力≥65 dB 电源AC 100 V - 240 V, 50 Hz/60 Hz 最大功耗 5 W 颜色尺寸 宽×深×高(mm)桌面放置,含胶脚480 × 295 × 48 mm 19"机柜安装,不含胶脚480 × 295 × 45 mm 重量 3.9 kg............................................8..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................银灰色面板+黑色机身................................................................................................Specifications1.Power 电源开关2.Line in 增益调节旋钮,带指示灯当输入信号超过“-23dBV ”时,指示灯亮绿色 当输入信号超过“-4dBV ”时,指示灯亮橙色3.Power 电源指示灯电源开关接通,指示灯亮绿色4.VCS in 增益调节旋钮,带指示灯当输入信号超过“-23dBV ”时,指示灯亮绿色 当输入信号超过“-4dBV ”时,指示灯亮橙色5.Line in 输入接口6.VCS in 输入接口7.Line out 输出接口8.VCS out 输出接口9 AFC(拨码1):关/开LOW CUT(拨码2):关/开(300Hz ,-3dB ) AEC(拨码3和4):关(00),低(01), 中(10),高(11)10.电源接口AC 100-240 V ,50/60Hz1.Power switch2.Line in gain control with indicatorWhen the input signal exceeds “-32dBV”, theindicator will turn to green;When the output signal distortion of thecorresponding channel exceeds 1%, the indicator will turn to orange 3.Power indicatorThe "Power" indicator turns to green when poweron4.VCS in gain control with indicatorWhen the input signal exceeds “-32dBV”, theindicator will turn to green;When the output signal distortion of thecorresponding channel exceeds 1%, the indicator will turn to orange 5.Line in interface 6.VCS in interface 7.Line out interface 8.VCS out interface 9. AFC (switch 1): OFF /ONLOW CUT (switch 2): OFF /ON (300Hz ,-3dB ) AEC (switch 3&4): OFF (00) Low (01) Medium (10) High (11)10.Power connectorAC 100-240 V ,50/60Hz■Apply to local and teleconferencing congresssystem■High-performance DSP processor,cutting edgefeedback suppression and echo cancellation algorithms suppress acoustic feedbackeffectively, for making fast processor speed and better sound quality■AI algorithm with strong self-learning ability,effectively filter out room reverberation to achieve the purpose of eliminating acoustic feedback, and greatly improve the timbre difference between through and feedback processing ■Line In/Out: for Live audio signal input/output ■VCS In/Out: for Teleconferencing audio signalinput/output■Build-in Adaptive Feedback Cancellation(AFC)and Automatic Echo Cancellation(AEC) switch ■According with the environment, the automaticadaptive filter adjust amount of computation to achieve best performance, get extra 12dB gain before acoustic feedback happens■Adpot self adaptive full duplex and all frequencybands echo elimination algorithm■Adaptive noise cancellation algorithm, which canfilter out background noise without affecting the speaker's voice, the 15 dB noise reductionFrequency response.............................80 ~ 20000 Hz Sampling Rate.................................................. 48 kHz SNR..............................................................≥90 dBA THD..................................................................≤0.1%Input impedance (balanced)...............................10 k ΩOutput impedance (balanced)..............................1 k ΩFeedback suppression time........................≤400 ms Signal delay....................................................≤11 ms Noise cancellation...........................................≤15 dB Echo cancellation capability............................≥65 dB Power supply...............AC 100 V - 240 V, 50 Hz/60 Hz Max. consumption..................................................5 W Color...........................Black box with silver gray panel Dimensions (h × w × d)For table use, with feet................480 × 295 × 48 mm For 19" rack use..........................480 × 295 × 45 mm ................................................................3.9 kgWeight。
变步长求积算法 英文
变步长求积算法英文Title: The Variable Step-Size Integration AlgorithmIntegration is an indispensable tool in the realm of mathematics, serving as the cornerstone for modeling and solving problems in various scientific disciplines. Among the myriad methods developed to approximate integrals, the variable step-size integration algorithm emerges as a pivotal technique, renowned for its precision and adaptability.The variable step-size integration algorithm represents a sophisticated evolution in numerical integration methods. It dynamically adjusts the size of steps based on predefined criteria, thereby enhancing accuracy where necessary while optimizing computational efficiency. This method finds extensive applications in fields as diverse as engineering, physics, economics, and beyond, where precisequantitative analysis is imperative.The allure of the variable step-size integration algorithm lies in its core principle: the adaptation of step sizes according to the function's behavior. Unlike traditional fixed-step algorithms that use a uniform step size, potentially leading to impractical computation times or inaccurate results, the variable step-size algorithm tailors its approach based on thefunction's contour. In regions where the function exhibits subtle changes, broader steps are taken, whereas zones of rapid change necessitate smaller, more precise steps. This dynamic adjustment ensures that computational resources are judiciously utilized, concentrating effort where it matters most.The implementation of the variable step-size integration algorithm typically involves a recursive or iterative process. Initially, a coarse approximation is made using large steps. As the algorithm progresses, it identifies intervals where the function varies significantly and refines the integration by reducing the step size. This process continues iteratively until the desired level of accuracy is achieved or a predetermined step size limit is reached. Through this adaptive refinement, the algorithm strikes a balance between computational cost and result precision.One quintessential example of the variable step-size algorithm is Simpson's rule with adaptive step sizes. Simpson's rule itself offers a notable improvement over basic numerical integration techniques due to its error minimization properties. When combined with a variable step-size strategy, Simpson's rule becomes even more potent. Initially, a broad estimate isobtained with large segments. The algorithm then divides the intervals with substantial variations into smaller sub-intervals, applying Simpson's rule with enhanced granularity to these specific sections. This adaptive application ensures high accuracy without needless computational expense.Another prominent method within the variable step-size integration family is Romberg integration. Romberg integration starts with the trapezoidal rule applied with wide steps and progressively halve the step size, retaining and reusing previous calculations to enhance precision. While not inherently variable in the step size, the concept can be extended to focus more computational power on intricate sections of the function by dynamically adjusting the step pattern based on function analysis.The variable step-size integration algorithm is underpinned by robust mathematical principles, ensuring both reliability and rigor. The algorithm often engages error estimation techniques, such as comparing results from successive iterations or employing different integration rules to cross-verify accuracy. These error analyses are pivotal in gauging the convergence of the algorithm and deciding whether further refinement is necessary.Error estimation in variable step integration commonly involves estimating local truncation errors and using these estimates to adjust step sizes. For instance, inRomberg integration, the Richardson extrapolation method is employed to enhance accuracy by systematically reducing errors through extrapolation rather than merely refining step sizes. This extra layer of sophistication ensures that the algorithm does not just blindly reduce step sizes but does so based on an informed error analysis.Beyond its mathematical elegance, the variable step-size integration algorithm holds immense practical significance across multiple domains. In engineering, it is instrumental in structural analysis, fluid dynamics, and thermal conduction problems where precise simulations are crucial for design and safety. In economics, it aids in the forecasting models and risk assessments where data volatility requires adaptive analytical tools. Even in modern technology, such as machine learning and data analytics, variable step-size algorithms provide robust solutions for integrating complex and dynamic systems.In the ever-evolving landscape of scientific research and technological advancement, the variable step-size integrationalgorithm stands out as a testament to human ingenuity. It encapsulates the essence of flexibility and precision, offering a nuanced approach to tackling intricate problems. As we continue to push the boundaries of knowledge, such algorithms will undoubtedly play a vital role in advancing our understanding and capabilities across diverse fields.In conclusion, the variable step-size integration algorithm exemplifies the harmonious blend of mathematical sophistication and practical utility. Its ability to fine-tune integration processes based on real-time analysis makes it an indispensable tool for scientists and engineers alike. As research progresses and computational technologies evolve, we can anticipate even more innovative enhancements to this foundational algorithm, further solidifying its status as a cornerstone of numerical analysis.。
改进NSGA-Ⅱ算法的自动变速箱离合器接合控制
改进NSGA-Ⅱ算法的自动变速箱离合器接合控制禹云1唐翠娥2(1娄底职业技术学院电子信息工程系,湖南娄底417000)(2贵州医科大学计算机教育与信息技术中心,贵州贵阳550025)摘要为了提高车辆在起步过程中的平稳性,并延长离合器的使用寿命,首先,根据离合器的主动盘和从动盘的运动状态,建立了接合过程的动力学模型;然后,通过引入NSGA-Ⅱ算法来解决多目标优化问题,并采取了正态分布交叉和自适应变异算子对其进行改进,保持了种群的多样性,能有效防止陷入局部最优;最后,将离合器的接合时长和接合速度作为设计变量,并将冲击程度和滑动摩擦功为优化目标函数进行了仿真实验。
结果表明,改进的NSGA-Ⅱ算法具有更优的Pareto前沿和更快的收敛速度,且分布更加均匀,明显优于其他两种比较算法;在相同接合时长的条件下,能够通过平稳控制离合器的接合速度获得更小的冲击程度和滑动摩擦,为设计离合器接合控制策略提供了技术支撑。
关键词离合器接合控制动力学模型NSGA-Ⅱ算法冲击程度滑动摩擦功正态分布交叉自适应变异平稳性和耐用性Automatic Transmission Clutch Engagement Control based onImproved NSGA-ⅡAlgorithmYu Yun1Tang Cuie2(1Department of Electronic Information Engineering,Loudi Vocational&Technical College,Loudi417000,China)(2Computer Education and Information Technology Center,Guizhou Medical University,Guiyang550025,China)Abstract To improve the stability of the vehicle in the starting process and prolong the service life of the clutch,a dynamics model of the engagement process is established according to the motion state of the active and driven discs of the clutch.The NSGA-Ⅱalgorithm is introduced to solve the multi-objective optimization problem,and the crossover operator of normal distribution and adaptive mutation operator are adopted to im⁃prove it,which keeps the diversity of population and effectively prevents falling into local optimum.Finally,the engagement speed and time of clutch are taken as the design variable,and the impact degree and sliding friction work are taken as the optimization objective function,and the simulation experiment is carried out.The simula⁃tion results show that the improved NSGA-Ⅱalgorithm has better Pareto front and faster convergence speed,and distribution is more uniform,obviously superior to the other two comparison algorithms,and the smaller im⁃pact degree and sliding friction work can be obtained by smoothly controlling the engagement speed of clutch in the condition of the same joint time,which provides technical support for the design of clutch engagement con⁃trol strategy.Key words Clutch engagement control Dynamics model NSGA-Ⅱalgorithm Impact degree Slid⁃ing friction work Normal distribution crossover Adaptive variation Stability and durability0引言自动变速箱已经成为乘用车的标配,极大方便了驾驶操作。
三星HM1700蓝牙耳机使用说明书
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Adaptive Playout Buffer Algorithm for Enhancing Perceived Quality of Streaming ApplicationsKohei Fujimoto Shingo Ata Masayuki MurataGraduate School of Engineering Science,Osaka University1-3Machikaneyama,Toyonaka,Osaka560–8531,JapanPhone:+81–6–6850–6588,Fax:+81–6–6850–6589E-mail:k-fujimo@nal.ics.es.osaka-u.ac.jpGraduate School of Engineering,Osaka City University 3–3–138Sugimoto,Sumiyoshi-ku,Osaka558–8585,JapanPhone,Fax:+81–6–6605–2191E-mail:ata@info.eng.osaka-cu.ac.jpCybermedia Center,Osaka University1–30,Machikaneyama,Toyonaka,Osaka560-0043,JapanPhone:+81-6-6879-8793,Fax:+81-6-6879-8794 E-mail:murata@cmc.osaka-u.ac.jpAbstractAn end-to-end packet delay in the Internet is an important performance parameter,because it heavily affects the quality of real-time applications.In the current Internet,however,because the packet trans-mission qualities(e.g.,transmission delays,jitters,packet losses)may vary dynamically,it is not easy to handle a real-time traffic.In UDP based real-time applications,a smoothing buffer(playout buffer)is typically used at a client host to compensate for variable delays.The issue of playout control has been studied by some previous works,and several algorithms controlling the playout buffer have been pro-posed.These studies have controlled the network parameters(e.g.,packet loss ratio and playout delay), not considered the quality perceived by users.In this paper,wefirst clarify the relationship between Mean Opinion Score(MOS)of played audio and network parameters(e.g.,packet loss,packet transmission delay,transmission rate).Next,utilizing the MOS function,we propose a new playout buffer algorithm considering user’s perceived quality of real-time applications.Our simulation and implementation tests show that it can enhance the perceived quality,compared with existing algorithms.All correspondence should be directed to:Dr.Shingo AtaDepartment of Information and Communication Engineering Graduate School of Engineering,Osaka City University3–3–138Sugimoto,Sumiyoshi-ku,Osaka558–8585,JapanE-mail:ata@info.eng.osaka-cu.ac.jpPhone,Fax:+81–6–6605–21911IntroductionAccording to the fast growth of the Internet,there are an increasing number of network applications.The real-time application is one of such applications.The use of real-time application includes IP telephony, voice conference,internet radio,video on demand(V oD).Now,these applications become widely used.In the current Internet,however,because the packet transmission qualities(e.g.,transmission delays,jit-ters,packet losses)may vary dynamically,it is not easy to handle a real-time traffic.In UDP based real-time applications,a smoothing buffer is typically used at a client host to compensate for variable delays.Re-ceived packets arefirst queued into the smoothing buffer.After several packets are queued,actual decoding is started.Then,the influences of the delay variations within the network can be minimized.(We refer to this delay as the playout delay.)Choosing the playout delay is important because it directly affects the communi-cation quality of the application;if the playout delay is set to be too short,the client application treats packets to be lost even if those packets eventually arrive.On the contrary,the large playout delay may introduce an unacceptable delay that the client users cannot be tolerant.That is,a difficulty exists in determining the playout delay.The packet transmission delay between the server and client may be varied according to the network condition in the Internet,and hence,the adequate playout delay is heavily dependent on variations of packet transmission delays.The issue of playout control has been studied by some previous works(Ramjee, Kurose,Towsley&Schulzrinne1994,Dempsey&Zhang1996,Moon,Kurose&Towsley1998,Mohamed, Cervantes-P´e rez&Afifi2001),and several algorithms controlling the playout buffer(which we will refer to as the playout buffer algorithm(PBA))have been proposed.Most of those PBAs are however based on a cal-culation method of the time-out threshold in TCP(Postel1981).For example,Moon et al.(Moon et al.1998) trace the packet delays and suggest the playout delay from the distribution of traced delays.However,they only focus on adjustments of the playout delay,and do not consider to control the packet loss ratio.In our prior work(Fujimoto,Ata&Murata2001b),we analyzed the characteristics of the packet trans-mission delays.We measured the one-way transmission delay as well as the round-trip delay,and provided the determination of the suitable distribution function through a statistical analytic approach.We next in-troduced the use of the distribution function to estimate the playout delay for real-time applications.we proposed a new playout buffer algorithm,which keeps the packet loss ratio according to users’willingness while minimizing the playout delay.However,neither the packet loss ratio nor playout delay is not a user-friendly metric for the perceived quality in streaming applications.There are many factors affecting the perceived quality in playing audio or speech of the streaming applications.Actually,in addition to the packet loss ratio,other network parameters such as type of codecs,access lines would affect the perceived quality.One important issue is how to map the network metrics into the users’perceived quality of the real-time traffic.Accordingly,we propose a new PBA to maximize the MOS(Mean Opinion Score)index directly for given network parameters.Our approach is to utilize the data set shown in(Savolaine2001),which clarified the relationship between MOS of played audio and network parameters(e.g.,packet loss,packet transmission delay,and transmission rate).This paper is organized as follows.Wefirst show a brief summary of existing PBAs and our prior work in Section2.In Section3,we examine the relationship between MOS of played audio and network parameters. Then,we propose a new PBA in order to maximize the MOS.In Section4,we evaluate the proposed algorithms through the simulation and implementation.Finally,we summarize our work and describe our future research topics in Section5.2Introduction of Adaptive Playout Buffer Algorithms based on Network ParametersIn this section,we review some existing playout buffer algorithms for the comparison with our proposed algorithm.Then we describe our prior work,which proposed an adaptive playout buffer algorithm based on the analysis of packet delays.2.1Existing Playout Buffer AlgorithmsFor comparison purpose,we examine four algorithms which have been proposed in(Ramjee et al.1994, Moon et al.1998).Before introducing our proposed playout buffer algorithm in prior work,we describe the brief overviews of each playout buffer algorithm.Exponential-Average(Exp-Avg):In this algorithm,the playout delay of the th arrived packet is determined from the approximated values of the mean and variance of one-way delays,which are given by(1)(2)(3) where means the one-way delay of th packet.The value of is chosen to be according to(Ramjee et al.1994).Thus,playout time is determined from playout delay and time when the sender host sent packet according to the equation;.Here,the playout time means the time when the application client actually starts playing audio data recorded in the packet.Thus,Exp-Avg estimates the playout time from means and variances,and does not consider the distribu-tion of the delays.Fast Exp-Avg(F-Exp-Avg):This algorithm is a modified version of Exp-Avg.F-Exp-Avg computesthe weighted mean of one way delays()asif(4)otherwise,where and are constant values,satisfying.Spike Detection(SPD):This algorithm focuses on spike which represents a sudden and large increase in delays over a sequence number of packets.Examples of spikes are shown at3,850in Figure4(a).SPD usually obtains the playout delay from Eq.(2),which is same as Exp-Avg.During spike,however,SPD uses the following equation;to catch up the sudden increase of delays.Window:This algorithm proposed in(Moon et al.1998)intends to detect a spike as SPD.During the spike,thefirst packet in the spike is used as a playout delay.After spike,the playout delay is chosen by finding the delay corresponding to the th quantile of the distribution of the last packets received by the receiver.2.2Prior WorkTo provide a high-quality communication in streaming applications,it is desirable that the packet loss ratio and playout delay are kept small.However,there is a critical trade-off between packet loss ratio and the length of the playout delay.In our prior work(Fujimoto et al.2001b,Fujimoto,Ata&Murata2001a),we hence measured packet transmission delays and analyzed their characteristics by taking into account the network parameters.We then proposed a method modeling the tail distribution of the delays,which is available for applications.From the results of statistical analysis,we found that the Pareto distribution is most appropriate as the model of one-way delay distribution in any network conditions.The Pareto distribution is widely known to be able to represent a self-similarity(M.E.Crovella&A.Bestavros1996),whose cumulative distribution function (CDF)is given by(5)where and are the parameters of a Pareto cumulative distribution function.Next,we proposed a new playout buffer algorithm based on our statistical analysis.The proposed algorithm determines the playout delay so that the packet loss ratio specified by the users is satisfied.We show the design of our proposed playout buffer algorithm more specifically.Our playout buffer algorithm records the history of one-way delays of packets.On each packet arrival,parameters(,)of the Pareto cumulative distribution function are updated to estimate the playout delay from the equation ,where is a target value.The target value means the reproduction ratio of packets preferred by the user.From the Pareto CDF,our proposed algorithm determines the playout delay by(6) We consider95,99,and99.9%as the target value through our numerical results.We refer to our proposed playout buffer algorithm as the loss control playout buffer algorithm(Loss-Control).Numerical examples showed that Loss-Control can control the playout buffer with satisfying the target packet loss probability. See Section4for evaluation results of the above control method including our new proposal described in the next section.3Proposed Playout Buffer Algorithm to Maximize the Perceived QualityIn our previous work(Fujimoto et al.2001b),we have demonstrated that Loss-Control can control the packet loss ratio as users like.However,there is a high possibility that the delay and other network parameters(type of codecs,access lines)in addition to the PLR(packet loss ratio)would affect the perceived quality.The end users can choose the preferable playout quality,but there are still many configurable parameters left to the end users.It is hence necessary to introduce a simpler index for the end users,which directly indicates the perceived quality in multimedia communications.Today,many metrics to express the playout quality are proposed and evaluated.The subjective metrics(e.g.,MOS)that we adopt in this paper are more user-friendly because it is based on scores made by users according to listening and/or watching the played media.Our objective in this section is to maximize the subjective index of the perceived quality for given network parameters,which are automatically measured.Wefirst model relations between the MOS and network parameters shown in(Savolaine2001)into mathematical formulas.After modeling,we obtain the MOS-relative form,which gives the appropriate packet loss ratio and playout delay according to the MOS value. We then modify our Loss-Control PBA to apply above MOS-relative function.Numerical comparisons are shown in the next section.3.1Effects of Packet Loss Ratio and Delay on MOSTo clear the relation between the MOS value and network parameters,we pick out the data from(Savolaine 2001),which show the effects of network parameters on MOS.We show one referred data in Figure1. Each plot shows the relation between MOS and end-to-end one-way delay in given loss ratio.From the model of one-way delay distribution clarified in our previous work(Fujimoto et al.2001b),we can get the feasible combinations of playout delay and the packet loss ratio to maximize the MOS index.We describe our modeling method in the next subsection.012345050100150200250300350400M O S End-to-End One-way Delay [msec]0% Loss 1% Loss 3% Loss 5% Loss Figure 1:Effects of PLR and Delay (Encoder:G.711)3.2Modeling Methods of MOS FunctionsThe first step of our modeling is to formulate relations among MOS,packet loss ratio,and playout delay approximately.That is,we plot MOS curves shown in Figure 1with mathematical notations given by our modeling method.Of course,our formulas depends on the relation shown in Figure 1.However,our modeling approach is also applicable to another result of relation.From Figure 1,we can have the following assumptions.Four curves shown in Figure 1are in parallel.It means that the packet loss ratio and one-way delay,and hence the playout delay,affect MOS independently.From this assumption,we can consider the effect of packet loss ratio and one-way delay on modeling MOS separately.The degree of degradation in MOS values is proportional to the packet loss ratio,and it does not depend on the playout delay.Under above-mentioned assumptions,we can obtain the MOS functionfor given packet loss ratio and the playout delay from and separately.We now determine the MOS function as follows.We first model the MOS function for given playout delaywith a three-dimensional polynomial approximation,where the packet loss ratio is assumed to be zero (shown in the cross point in Figure 1).Parameters of the polynomial are obtained by curvefitting.We then obtain the MOS function as(7)We then get the MOS curve by sliding inversely in the horizontal direction.By applying the second assumption described above,the degree of degradation of MOS values is proportional to the packet loss ratio.We calculate the parameter of the function by the least linear square method.The MOS function for given packet loss ratio is hence expressed as(8) where the playout delay is set to.Because we assume that the packet loss ratio and the playout delay affect the MOS value independently, we can obtain for given and by combining Eqs.(7)and(8),i.e.,(9) In Figure2we add the solid curves by Eq.(9)to Figure1,and we can observe our approximate modeling provides an agreement with the original ones.In the real network,however,there is a correlation between the packet loss ratio and the playout delay .In streaming applications,the packet loss ratio is a summation of(1)packet loss ratio caused by packet drops within the network(referred to as),and(2)ratio of late arriving packets exceeding playout threshold ().That is,.From Eq.(5)in Section2,we have a relation between and as follows;(10) By applying Eq.(10),Eq.(9)can be rewritten as12345050100150200250300350400M O SPlayout Delay [msec]0% Loss 1% Loss 3% Loss 5% Loss M(0,d)M(1,d)M(3,d)M(5,d)Figure 2:Results of Modeling MOS values and Network Parameters (Encoder:G.711)(11)As shown in Eq.(11),two parameters andaffect the MOS value.For the streaming application,however,only the playout delay is controllable.We therefore consider Eq.(11)as a function of ,denotedby,i.e.,(12)We now examine.If,all packets are treated as packet loss,and no packet is played.Thus,we set.As the playout delay is increased,takes smaller value.However,when the playout delayis too large,is again degraded due to the large delay for playing.Therefore,there exists an optimumpoint ofthat provides the maximum value of.Figure 3shows the example of variation independent on the playout delay ,where andin Eq.(12)are set to 9.10and 15.53from measured data,respectively.Calculating the optimalis realized by the false position method (Press,Flannery,Teukolsky&Vetterling 1988)utilizing the differential equation of .Becauseis the convex function,wecan determine the optimal from the -intercept of the differential equation of by the false positionmethod.123450100200300400500600M O SPlayout Delay [msec]Q(d)Figure 3:MOS Function(Encoder:G.711)3.3Modified Playout Buffer Algorithm for Enhancing MOS IndexWe modified our Loss-Control PBA to realize the MOS-based control.In our Loss-Control algorithm,the playout delay was determined from the target packet loss ratio.On the other hand,our new algorithm is to control the playout delay to maximize the MOS value .More specifically,our new PBA consists of thefollowing steps;1.Measure the transmission delays of arrived packets2.Calculate the parameter of Pareto distributionby the MLE (Maximum Likelihood Estimation)method (see our prior work (Fujimoto et al.2001b ))3.Assign the value ofinto the MOS function4.Obtain the optimal value of ,maximizing,by utilizing the false position method applied to thedifferential equation of 5.Set the playout delay to 6.Return to Step.1We refer to this new playout buffer algorithm as enhanced MOS-based playout buffer algorithm (E-MOS ).4Evaluation of Playout Buffer AlgorithmIn this section,we evaluate the playout buffer algorithms by the trace-driven simulation,and we investigate an effectiveness of the proposed algorithm.4.1Simulation MethodWe prepared a set of one-way delays of packets for our trace-driven simulation.For this purpose we mea-sured the one-way delays with various network parameters.Second,in our simulation,the recorded one-way delays are used one by one,and the playout delay of the th packet is estimated according to each algo-rithm for all measured delays.Then,we check whether the delay of the next packet is smaller than the estimated playout delay or not.If the delay is larger than the estimated playout delay,the packet is treated to be lost.After tracing all measured delays,average playout delay and packet loss ratio are computed as the output.Some PBAs require pre-defined parameters.For F-Exp-Avg,we set andfollowing(Ramjee et al.1994).In SPD,we use following(Ramjee et al.1994).In Window,a value of0.99is used for,and10,000for,which are used in(Moon et al.1998).4.2Simulation ResultsNow,we evaluate the performance of E-MOS by simulations.Table1compares packet loss ratios(PLRs), mean values of the playout delays,and MOS evaluated by simulation in the following three cases;Thefirst case is“dynamic”,in which the values of one-way delays often change,and many spikes are observed.The packets were sent by the G.723.1encoder at2:00pm and delivered to the receiver via the dial-up line.The second is a“moderate”case,in which there are a several number of spikes.We used the G.711encoder on ADSL and the delays were measured at1:00pm.The last case is“quiet”,where no dynamic change of delays were observed.These delays were sent by the G.723.1encoder at2:00pm and delivered to thereceiver through LAN.In Loss-Control,we used95,99,and99.9%as the target values.The MOS values shown in the last column of Table1are evaluated by Eq.(9)from the PLR and playout delay.The maximum value of MOS among all PBAs is shown in bold.Results in Table1indicate that E-MOS can provide the highest perceived quality for users in any network conditions.Looking at the playout delays and PLRs of E-MOS,we canfind that E-MOS has a tendency to minimize the PLR when the one-way delays are small(“moderate”and“quite”cases in Table1).From Figure1,we can observe that the effect of introducing the playout delay is quite limited when the playout delay is small(less than200msec).In this region,it is effective to prevent the packet loss by lengthening the playout delay.However,as the one-way delay becomes large,E-MOS tries to intentionally bear the increasing PLR for reducing the playout delay.It is a good solution for improving the users’perceived quality.Other PBAs have their own ground.For example,Window gives a good result in the“dynamic”case,but it is worse than F-Exp-Avg in other cases.However,F-Exp-Avg provides quite poor performance in the“dynamic”condition.Exp-Avg and Loss-Control(99.9%)give a passable performance in all cases. However,these methods cannot attain the improvement of the perceived quality as E-MOS.Furthermore, the Loss-Control method has a disadvantage that it tries to shorten the playout delay and forces to abandon packets even if the playout delay is enough short(less than200msec).Thus,Loss-Control is not suitable in low packet transmission delay environments.Figure4shows the time-dependent behavior of the playout delay,PLR,and MOS for PBAs.Here,the target value of Loss-Control is set to99%.The playout delays of E-MOS are larger than the other algorithms except F-Exp-Avg,where the one-way delays are small.From thisfigure,wefind that E-MOS intends to minimize the PLR when the one-way delay is less than200msec.On the other hand,in the condition that the one-way delay is over200msec,E-MOS tries to increase the PLR for reducing the playout delay in order to enhance the MOS.That is,E-MOS can achieve a good balance of the playout delay and PLR based on Eq.(12).3040506070809010011012013036003800400042004400P l a y o u t D e l a y [m s e c ]S equence NumberDelayExp-Avg F -Exp-AvgS PDWindowLoss -C ontrol (99%)E-MOS(a)Comparison of Playout Delay24681036003800400042004400L o s s R a t i o [%]Sequence N umberExp-AvgF -Exp-AvgS PD WindowLoss -C ontrol(99%)E-MOS (b)Comparison of PLR1234536003800400042004400M O SS equence NumberExp-AvgF -Exp-AvgS PDWindowLoss -C ontrol (99%)E-MOS (c)Comparison of MOSTable1:Comparison of PLR and Mean Playout Delay and MOSCase Algorithm Target PLR[%]Mean of[msec]MOS95% 5.7227.92 2.22Loss-Control99%0.94387.12 2.4199.9%0.12770.440.59“dynamic”E-MOS- 2.95294.75 2.49Exp-Avg- 4.54247.91 2.38F-Exp-Avg-0.1970.340.10SPD- 5.44198.74 2.33Window99% 1.34362.57 2.4795% 6.0240.61 2.99Loss-Control99% 1.7758.45 3.8399.9%0.60375.28 3.61“moderate”E-MOS-0.1077.71 4.17Exp-Avg- 4.9339.79 3.21F-Exp-Avg-0.04102.26 4.13SPD- 3.0839.74 3.57Window99% 2.3348.60 3.7295% 3.949.40 2.94Loss-Control99%0.729.87 3.6099.9%0.2210.53 3.70“quiet”E-MOS-0.0051.92 3.77Exp-Avg-0.1810.49 3.71F-Exp-Avg-0.0129.53 3.76SPD-0.7710.19 3.59Window99% 1.059.76 3.53 4.3Evaluation through Implementation ExperimentsFigure5:Operation Window of ClientWinamp(NULLSOFT n.d.),which is one of major frontends in real-time applications today.We placed the streaming server at Osaka University,which sends audio packets generated by the G.711 or G.728encoder(the size of the packet and transmission interval are160byte and20msec for G.711and 40byte,20msec for G.728,respectively).Packets are transmitted via the Internet and transferred to our developed client.In the client,the smoothing buffer is adjusted based on the playout delay calculated by our PBA(E-MOS).Arrived packets are stored into the buffer and then the client starts playing after the playout interval.Figure5shows the operation window of our client.Our implementation experiments include(1)to check whether our PBA tries to maximize the MOS,(2) to verify whether the computational overhead of calculating the playout delays is enough small to operate our PBA in real-time.The platform was Microsoft Windows98operating system on Intel Pentium III750MHz CPU.In this case,the computation overhead was about0.02msec for each packet arrival;0.1%of packet transmission interval in G.711,which is sufficiently small overhead.Note that we also confirmed that the audio playing is not interrupted by any other factors except packet losses.c Nullsoft Inc.20025Concluding RemarksIn this paper,we have attended to the perceived quality of streaming applications and then,modified the proposed algorithm so that perceived quality may become maximum.Simulation and implementation ex-periments have shown that the modified algorithm performs the highest quality in all of PBAs For future research topics,it is necessary to improve the accuracy of our model for representing the delay distributions.To achieve it,it might be useful to test another heavy-tailed probability functions as the model of delay distributions.Moreover,though no serious problem occurs at the client of E-MOS,less CPU load would be more comfortable for users.The more effective calculation method for E-MOS is necessary.ReferencesDempsey,B.J.&Zhang,Y.(1996),Destination buffering for low-bandwidth audio transmissions using redundancy-based error control,,in‘Proceedings of LCN,21st Annual Conference on Local Computer Networks’,pp.345–354.Fujimoto,K.,Ata,S.&Murata,M.(2001a),Playout control for streaming applications by statistical delay analysis,in‘Proceedings of IEEE International Conference on Communications(ICC2001)’,V ol.8, Helsinki,pp.2337–2342.Fujimoto,K.,Ata,S.&Murata,M.(2001b),‘Statistical analysis of packet delays in the Internet and its application to playout control for streaming applications’,IEICE Transactions on Communications E84-B(6),1504–1512.M.E.Crovella&A.Bestavros(1996),Self-similarity in World Wide Web;traffic evidence and possible causes,in‘Proceedings of ACM SIGMETRICS’96’,pp.160–169.Mohamed,S.,Cervantes-P´e rez,F.&Afifi,H.(2001),Integrating networks measurements and speech quality subjective scores for control purpose,in‘Proceedings of IEEE INFOCOM2001’.Moon,S.B.,Kurose,J.&Towsley,D.(1998),‘Packet audio playout delay adjustment:performance bounds and algorithms’,ACM/Springer Multimedia Systems5(1),17–28.NULLSOFT(n.d.),‘ now featuring self-transforming mechanical elves’.available at .Postel,J.(1981),‘Transmission control protocol specification’,RFC793.Press,W.H.,Flannery,B.P.,Teukolsky,S.A.&Vetterling,W.T.(1988),Numerical Recipes in C;The Art of Scientific Computing,Cambridge University Press,chapter9.2,pp.263–266.Ramjee,R.,Kurose,J.,Towsley,D.&Schulzrinne,H.(1994),Adaptive playout mechanisms for packetized audio applications in wide–area networks,in‘Proceedings of IEEE INFOCOM’94’,pp.680–688. Savolaine,C.(2001),QoS/V oIP overview,in‘IEEE Communications Quality&Reliability(CQR2001) International Workshop’.。