In recent years large scientific interest has been devoted to joint data decoding and parameter estimation techniques. In this paper, iterative turbo decoding joint to channel frequency and phase estimation is proposed. The phase and frequency estimator is embedded into the structure of the turbo decoder itself, taking into consideration both turbo interleaving and puncturing. Results show that the proposed technique outperforms conventional approaches both in terms of detection capabilities and implementation complexity.
標(biāo)簽： scientific parameter interest decoding
上傳時(shí)間： 2015-12-30
上傳用戶：894898248
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標(biāo)簽： monitoring sequential industria accurate
上傳時(shí)間： 2013-12-17
上傳用戶：familiarsmile
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標(biāo)簽： meta-learning classifiers combining Boosting
上傳時(shí)間： 2016-01-30
上傳用戶：songnanhua
A new cable fault location method based on wavelet reconstruction is proposed. In this method the difference between the currents of faulty phase and sound phase under the high voltage pulse excitation is used as the measured signal and is decomposed in multi-scale by wavelet transform, then reconstructed in single scale. Comparing with traditional fault location method by travelling wave, the presented method will not be interfered by the reflected wave from the branch joint of cables or from other positions where the impedances are not matched and not be influenced by fault types, otherwise, the reflected waves can be recognized even the faulty position is near to the measuring terminal, at the same time, the influence of the wave speed uncertainty can be reduced. The correctness of the proposed method is proved by simulation results.
標(biāo)簽： method reconstruction location proposed
上傳時(shí)間： 2016-02-04
上傳用戶：maizezhen
《SPSS 11統(tǒng)計(jì)分析教程》（高級(jí)篇）樣章，第一章：征服一般線性模型――General Linear Model菜單詳解（上）第一節(jié)：方差分析模型簡(jiǎn)介，PDF格式。
標(biāo)簽： SPSS 統(tǒng)計(jì)分析 教程
上傳時(shí)間： 2016-02-04
上傳用戶：semi1981
數(shù)據(jù)庫(kù)系統(tǒng)實(shí)現(xiàn)，linear hash算法
標(biāo)簽： 數(shù)據(jù)庫(kù)系統(tǒng)
上傳時(shí)間： 2016-02-05
上傳用戶：aa17807091
Traveling Salesman Problem (TSP) has been an interesting problem for a long time in classical optimization techniques which are based on linear and nonlinear programming. TSP can be described as follows: Given a number of cities to visit and their distances from all other cities know, an optimal travel route has to be found so that each city is visited one and only once with the least possible distance traveled. This is a simple problem with handful of cities but becomes complicated as the number increases.
標(biāo)簽： interesting Traveling classical Salesman
上傳時(shí)間： 2016-02-06
上傳用戶：rocwangdp
PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data analysis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you have had some preliminary experience with linear/matrix algebra.
標(biāo)簽： Component Principal Analysis bilinear
上傳時(shí)間： 2016-02-07
上傳用戶：zuozuo1215
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
標(biāo)簽： performance equalizers Adaptive several
上傳時(shí)間： 2016-02-16
上傳用戶：yan2267246
在DAB系統(tǒng)中的頻率同步，載頻同步，參考一下Carrier frequency offset estimation of DAB receiver based on phase reference symbol
上傳時(shí)間： 2014-11-23
上傳用戶：songrui
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