we erhjk oir kljf oierwp
標(biāo)簽： oierwp erhjk kljf oir
上傳時(shí)間： 2015-12-22
上傳用戶：czl10052678
MC145540,支持G.726的硬件芯片。適用于采用G.726硬件壓縮
上傳時(shí)間： 2015-12-22
上傳用戶：Divine
eRDP 電子潛水計(jì)算機(jī) 快速上手手冊(cè) We’ll explain how you can start teaching student divers to use the eRDPin your PADI courses
標(biāo)簽： teaching explain student divers
上傳時(shí)間： 2014-01-10
上傳用戶：大三三
％ because we do not truncate and shift the convolved input ％ sequence, the delay of the desired output sequence wrt ％ the convolved input sequence need only be the delay ％ introduced by the ideal weight vector centred at n=5
標(biāo)簽： the convolved truncate sequence
上傳時(shí)間： 2015-12-27
上傳用戶：www240697738
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.
標(biāo)簽： frequency-offset estimation quadrature amplitude
上傳時(shí)間： 2014-01-22
上傳用戶：牛布牛
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
標(biāo)簽： sequential simulation posterior overview
上傳時(shí)間： 2015-12-31
上傳用戶：225588
文件當(dāng)中題意如下：已知一個(gè)二階欠阻尼閉環(huán)系統(tǒng),其傳遞函數(shù)G(s)=20/s2+3s+20 .輸入信號(hào)為單位階躍信號(hào).求: (1):解析解 (2):用四階R—K方法及Adams預(yù)估校正法求數(shù)值解. (3):比較用四階R—K方法不同步長時(shí)計(jì)算精度與解析解進(jìn)行比較,結(jié)果以圖形給出. (4):給出用Adams做出的階躍響應(yīng)圖形. 本文檔解析透徹，有完整的源程序和仿真圖形，對(duì)學(xué)習(xí)的朋友很有借鑒意義。
上傳時(shí)間： 2015-12-31
上傳用戶：WMC_geophy
hahahh g elngnelgn engniengnl
標(biāo)簽： engniengnl elngnelgn hahahh
上傳時(shí)間： 2014-01-22
上傳用戶：chenbhdt
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標(biāo)簽： approach combines particle tracking
上傳時(shí)間： 2016-01-02
上傳用戶：yy541071797
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments.
標(biāo)簽： relational filtering consider problem
上傳時(shí)間： 2016-01-02
上傳用戶：海陸空653
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