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关于汉语/英语AMR语音编码参数统计特性的研究 被引量:1
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作者 于薇 赵胜辉 匡镜明 《电讯技术》 北大核心 2002年第2期80-83,共4页
自适应多速率 (AMR)算法是第三代移动通信系统中语音业务所采用的语音编码标准。为研究AMR算法应用于汉语语音和英语语音所产生的差异 ,本文针对AMR语音编码算法的特点 ,在处理分析了大量汉语语音数据的基础上 ,对算法中的线谱频率 (LSF... 自适应多速率 (AMR)算法是第三代移动通信系统中语音业务所采用的语音编码标准。为研究AMR算法应用于汉语语音和英语语音所产生的差异 ,本文针对AMR语音编码算法的特点 ,在处理分析了大量汉语语音数据的基础上 ,对算法中的线谱频率 (LSF)、基音周期等关键编码参数进行了比较并给出相应结论。 展开更多
关键词 汉语 英语 语音编码 AMR算法 线谱频率 基时周期 参数统计特性
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Electron Transport in Graphene-Based Double-Barrier Structure under a Time Periodic Field
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作者 卢伟涛 王顺金 《Communications in Theoretical Physics》 SCIE CAS CSCD 2011年第7期163-167,共5页
The transport property of electron through graphene-based double-barrier under a time periodic field is investigated. We study the influence of the system parameters and external field strength on the transmission pro... The transport property of electron through graphene-based double-barrier under a time periodic field is investigated. We study the influence of the system parameters and external field strength on the transmission probability. The results show that transmission exhibits various kinds of behavior with the change of parameters due to its angular anisotropy. One could control the values of transmission and conductivity as well as their distribution in each band by tuning the parameters. 展开更多
关键词 graphene-based double-barrier structure Klein tunneling external field
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Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches
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作者 Mohammad R. Hamidizadeh Mohammad E. Fadaeinejad 《Journal of Modern Accounting and Auditing》 2012年第2期232-240,共9页
The paper's aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data (i... The paper's aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data (i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations), they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best "track records" in generating data. Paper's models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na'fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na'fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data. 展开更多
关键词 Naive forecasting models smoothing techniques Fibonacci and Golden section search line search bycurve fit
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