基于CCS和DSK5402板的音频采集和回放程序设计

为了给程序设计作下基础,本文首先介绍了TI公司的TMS320VC5402和AIC(模拟接口电路)芯片TLC320AD50C的特点,最后着重介绍了利用DSK板上的TMS320C5402和TLC320AD50C实现音频采集并实时回放的软件设计过程,并利用CCS进行了模拟.     

基于非线性规划的指数自回归模型的辨识

讨论了指数自回归模型的辨识问题，证明了该模型最小二乘估计的目标函数的非凸性，并给出了使该函数为凸的条件，最后给出了辨识该模型的算法及该算法的收敛性，并以数值例子加以说明。     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Model selection for zero-inflated regression with missing covariates

Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find out whether the zero-inflation exists or not and how to select the most suitable model. However, the classic AIC criterion for model selection is invalid when the observations are missing. In this paper, we develop a new model selection criterion in line with AIC for the zero-inflated regression models with missing covariates. This method is a modified version of Monte Carlo EM algorithm which is based on the data augmentation scheme. One of the main attractions of this new method is that it is applicable for comparison of candidate models regardless of whether there are missing data or not. What is more, it is very simple to compute as it is just a by-product of Monte Carlo EM algorithm when the estimations of parameters are obtained. A simulation study and a real example are used to illustrate the proposed methodologies.     

Information methods for model selection in linear mixed effects models with application to HCV data

In this paper, we derive a small sample Akaike information criterion, based on the maximized loglikelihood, and a small sample information criterion based on the maximized restricted loglikelihood in the linear mixed effects model when the covariance matrix of the random effects is known. Small sample corrected information criteria are proposed for a special case of linear mixed effects models, the balanced random-coefficient model, without assuming the random coefficients covariance matrix to be known. A simulation study comparing the derived criteria and several others for model selection in the linear mixed effects models is presented. We illustrate the behavior of the studied information criteria on real data from a study of subjects coinfected with HIV and Hepatitis C virus. Robustness of the criteria, in terms of the error distributed as a mixture of normal distributions, is also studied. Special attention is given to the behavior of the conditional AIC by Vaida and Blanchard (2005). Among the studied criteria, GIC performs best, while cAIC exhibits poor performance. Because of its inferior performance, as demonstrated in this work, we do not recommend its use for model selection in linear mixed effects models.     

基于AIC＋的新型工业局域网络及应用

本文介绍了ＡＩＣ＋（ＡｄｖａｎｃｅｄＩｎｔｅｒｆａｃｅＣｏｎｖｅｒｔｅｒ）的功能、接口及工作模式等，具体说明利用ＡＩＣ＋连接而成的ＰＬＣ工业局域网──“ＤＨ－４８５网”的结构，其特点是接口简单、组态方便、可靠性高，最大传输距离可达２４３８ｍ。     

基于DSPs DMA和McBSP接口的信号采集

本文针对测控技术及自动化仪器仪表对实时数据采集和处理技术的发展要求,设计了基于DSP的DMA和McBSP技术的数据采集系统.文中分析了TMS320VC5409 DSP的DMA和McBSP的技术性能、接口方式和初始化编程,以及系统的设计过程.当CPU时钟频率为100MHz时,DMA单通道能达到的16位最高传输率为25M/s.实践证明该系统不仅提高了DSP的运行效率,而且有利于数字系统性能的全面提升.     

基于AIC准则和时窗能量比的电缆局部放电在线检测与定位

在利用多高频电流传感器进行电缆局部放电在线检测与定位时,针对局放信号初至时刻拾取精度不高影响定位精度的问题,提出一种基于AIC(Akaike''s Information Criterion)准则和时窗能量比的局放故障在线检测与精确定位方法.首先利用时窗能量比检测出局部放电发生的时窗,然后求取确定时窗的局部AIC特征曲线,并基于AIC准则精确拾取局放信号初至时刻.最后,运用到达时间法对局放源进行定位.仿真结果表明,该方法定位精度高,抗噪声干扰能力强,在-2 dB的噪声环境下可实现99.85％的定位准确率,具备工程实用价值.     

含置信区间的ARIMA与GARCH混合式电价预测方法研究

通过对已有的各种电价预测方法的深入研究,提出一种带置信区间的混合式电价预测方法.比较了自相关函数和偏自相关函数判断平稳性及残差方差图定阶法,认为游程检验判断电价序列平稳性方法和AIC准则模型定阶法可避免预测过程中的主观因素,提高预测精度.通过引入电价误差序列异方差判断,结合ARIMA和GARCH,提出含置信区间的电价预测方法,极大地克服了单点预测缺点,增加算法的灵活性,使得市场竞价者可以根据自己对电价预测精度的期望选择电价波动的范围.采用PJM市场数据,验证了算法的有效性.     

音频编解码器TLV320AIC23与CPLD接口设计

介绍了音频编解码器TLV320AIC23芯片的功能,相关寄存器的设置和采样率的选取,通过分析提出了用CPLD代替DSP,进行TLV320AIC23与CPLD的接口设计,实现数字语音设备学生机终端的A/D及D/A转换。