C pm MPPAC for manufacturing quality control applied to the precision voltage reference process

The multiprocess performance analysis chart (MPPAC) based on the process capability index C_pm, called C_pm MPPAC, is developed for analysing the manufacturing quality of a group of processes in a multiple process environment. The C_pm MPPAC conveys critical information of multiple processes regarding the departure of the process and process variability from one single chart. Existing research works on MPPAC are restricted to obtaining quality information from one single sample of each process ignoring sampling errors. The information provided from existing MPPAC charts, therefore, is unreliable and misleading and results in incorrect decisions. In this paper, we consider the natural estimator of C_pm based on multiple samples. Based on the natural estimator of C_pm, we consider the sampling errors by providing an explicit formula with the Matlab program to obtain the estimation accuracy of the C_pm. We tabulate the sampling accuracy of C_pm for sample size determination so that the engineers/practitioners can use it for their in-plant applications. An example of multiple precision voltage reference (PVR) processes is presented to illustrate the applicability of C_pm MPPAC for manufacturing quality control.     

微机械谐振器的快速测试方法

为了缩短微机械谐振器的测量时间,提高测量效率.提出了一种针对压阻检测的微谐振器的频率特性测试新方法,该方法利用欧姆鉴相器,积分器和微处理器实现对微弱信号的有效检测.欧姆鉴相器以检测电阻为鉴相器,利用欧姆定律实现参考信号和被测信号的乘法鉴相运算;通过积分器实现对鉴相结果的积分运算,抑制干扰和噪声;微处理器用于对积分数据的...     

一种基于高阶统计信息的信噪比估计改进算法

针对多进制正交幅度调制(Multiple quadrature amplitude modulation,MQAM)信号在低信噪比条件下估计精度不高的问题,提出了一种基于综合利用高阶统计信息的信噪比估计改进算法。根据所选高阶统计量最高阶数的不同,建立了3种信噪比与多种高阶统计量运算式之间的线性关系,利用全回归线性分析方法将3种线性关系转化为3种全回归模型,并求解模型系数。该算法充分利用了多种高阶统计量的有用信息,提高了信噪比估计精度。MQAM的仿真结果表明:在低信噪比条件下,该算法减小了信噪比估计误差,其估计性能明显优于传统的其他算法,且3种模型估计性能依次增加,可依据不同的信噪比要求对3种模型进行选取。     

基于APR—SVM的音频分类方法

音频分类在多媒体应用中十分广泛,主要有时域分析和频域分析方法。文中提出了一种基于自适应间距比（APR）算法和支持向量机（svrd）算法的音频分类方法,先用APR算法区分语音与非语音;对于非语音,再通过SVM进行音频分类。APR算法是比较PR参数和阈值来区分语音和非语音,它和信噪比密切相关;而将非语音分成四组：音乐,汽车,会议,雨声,提取特征因子。实验结果表明：文中设计的分类器的精度达到93．75％以上,能很好地把各类型音频分开。     

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obvi-ously, in order to address this problem, the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs. However, it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spec-trums are employed as the input of multi-source information fusion model. And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spec-trum can be obtained by performing a final decision-making method. Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators. Even under low SNR conditions, promising DOA estimation performance can be achieved.     

基于SPW的流星余迹极低信噪比通信系统建模与仿真*

SPW具有丰富的通信库和DSP库,且具有灵活的自定义模块能力,是一种复杂通信系统建模和仿真工具.流星余迹通信是一种特殊的通信方式,在通信领域中具有特殊的重要地位.介绍了流星余迹极低信噪比通信系统原理的基础,给出了基于SPW的流星余迹极低信噪比通信系统仿真模型及仿真结果.     

高斯信道下针对LoRa调制的信噪比估计改进算法

信噪比是LoRa自适应网络中实现发射参数准确自适应调节的重要标准,为了增加LoRa调制下信噪比估计的准确性和稳定性,在基于谱分析的信噪比估计算法的基础上,根据LoRa调制解调的特点,提出了一种针对LoRa调制的信噪比估计改进算法。在高斯信道下,利用LoRa解调频谱峰值代表接收信号、其他谱线与噪声具有相关性的特点,定义了信噪比估计参数r,并通过实验确定了不同SF值时信噪比估计参数r与信噪比之间的经验公式,从而准确估计信噪比。仿真结果表明,在最好的情况下改进算法能够准确估计的信噪比提高了-5 dB,以2 dB为标准改进算法的均方根误差能够达到时的信噪比提升了-14 dB,因此与基于谱分析的信噪比估计算法相比,改进算法具有更高的准确性和稳定性。     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

Consistent estimation of the memory parameter for nonlinear time series

Abstract. For linear processes, semiparametric estimation of the memory parameter, based on the log‐periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξ_t and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite‐sample performance of the estimator is investigated in a small Monte Carlo study.