Development of cryogenically-cooled low noise amplifier for mobile base station receivers

A new circuit model for designing and manufacturing an S-band low noise amplifier(LNA) with the software,Advanced Design System(ADS),is introduced in this paper.The proposed model involves shunted impedance at the grid to achieve a stable LNA without measuring the S-parameters of transistors at low temperatures.The LNA was measured over the operation band of 2.2-2.3 GHz,which has input and output standing wave ratios below 1.2.The noise figure of the manufactured LNA was about 0.2 dB and the gain was above 22 dB,which indicated that our LNA worked well at 77 K.     

用于统计语音合成的大尺度压缩HMM的方法

统计语音合成使用隐Markov模型(HMM)作为声学特征的统计模型。提出了一种利用声学模型空间距离进行HMM的大尺度压缩的量化方法,通过对矢量量化码本进行的优选迭代步骤,减小压缩后的声道谱模型与原模型之间的声学距离,使通过量化模型合成的语音更加接近未量化模型。主观和客观测试结果显示:使用该方法进行声道谱模型的压缩,在压缩至原模型大小的0.06左右时,仍有约90%的评价得分认为合成语音的质量没有明显下降。     

一种改进的Jacobi正交多项式的BP神经网络算法

 根据多项式理论,构造一种以Jacobi正交多项式作为隐层神经元激励函数的BP(back-propagation)神经网络模型.针对该网络,提出一种改进算法即隐层神经元数可快速确定的权值直接确定算法.首先介绍正交基函数和Jacobi多项式的定义,以及BP神经网络的基本原理.然后进行网络隐层数设计及其隐神经元数的确定,且设置各层连接权值、给出改进算法的步骤.最后,将其与传统矩阵迭代法和Levenberg-Marquardt训练算法进行比较.计算机实验结果表明,该算法具有比传统的BP迭代法更快的计算速度,并且能够达到更高的工作精度.     

从图形“实体”到几何“存在”——对笛卡尔解析几何的哲学反思

笛卡尔的解析几何与欧几里得的《几何原本》在点、线和空间等基本概念上有较大差异。相对于《几何原本》:解析几何中的点可相互区分,且依赖于与其它点的关系而存在;曲线的存在依赖坐标空间整体,同时是点的运动轨迹,与物理运动的可能路径相关;坐标系通过量化空间,提供了对于空间的完整构造。从图形实体到几何存在,是笛卡尔对于数学实在和存在方式所作的区分。     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

基于HMM的多维数据下扶贫对象状态预测

针对扶贫领域中贫困、脱贫和返贫状态预测不准确,影响状态变迁的关键因素难以识别的问题,从扶贫基础数据和多个行业数据中提取8个关键特征和22个观测状态,构建观察状态和隐含状态关联关系,建立扶贫对象状态预测隐马尔可夫模型(hidden markov model,HMM)。以某深度贫困县连续3年的数据为样本,进行参数训练、测试实验和结果验证,结果表明该方法对返贫、贫困和脱贫状态有较强的预测能力,误差率较低,且能准确识别出影响返贫的关键要素。该方法对指导精准扶贫工作具有非常重要的实际意义。     

2．4-6．0GHz超宽带低噪声放大器的设计

采用标准0．5μmGaAsPHEMT工艺设计了工作频段在2．4—6GHz可应用于无线局域网（WLAN）和超宽带（UWB）接收机的超宽带低噪声放大器。从宽带电路的选择、高频电路设计的器件选择和电路结构的选择等方面讨论了如何进行超宽带低噪声放大器的设计。结果表明,通过合适的电路结构和器件参数选择,可以采用0．5μmGaAsPHEMT工艺制备满足超宽带系统要求的低噪声放大器。在UWB3．1～5．15GHz低频带内,该LNA增益20．8～21．6dB,噪声系数低于0．9～1．1dB,输入输出驻波比均小于一10dB。在2．4～3GHz频带（涵盖802．11b／g的使用范围）内,该LNA增益20．8～21．5dB,噪声系数低于2dB,输入输出驻波比均小于-10dB。在频带5．2—6GHz,该LNA的噪声系数增大到1．332dB。增益则从21．4dB下降到19．7dB。电路的工作电压为3．3V。     

轨检车检测数据处理分析软件关键业务研究

从业务模型的构建和关键业务分析两个方面对检测数据里程转换和波形图对比两项关键业务进行研究,构建了检测数据里程转换模型,进行了波形图绘制和波形图联动方法的研究,并对检测数据里程转换和波形图对比这两项关键业务的业务需求及业务流程图进行了分析与绘制,最终列出了两项关键业务的功能实现情况。研究轨检车检测数据处理分析软件的关键业务,从而更高效地运用软件分析与处理轨检车数据,为保证地铁线路的安全运营奠定基础。     

工笔人物画创作中的意象造形

现代工笔人物画造型大多采用西方素描再现方法,而忽视了我国传统绘画的意象表现,本文从绘画理论和美学思想方面入手,探讨了我国工笔人物传统绘画重意轻形的美学理念,我们只有对传统文化深入了解,才能真正做到对我国绘画艺术的创新和发展。     

Forecasting Ability of a Periodic Component Extracted from Large‐Cap Index Time Series

We develop a method to extract periodic variations in a time series that are hidden in large non‐periodic and stochastic variations. This method relies on folding the time series many times and allows direct visualization of a hidden periodic component without resorting to any fitting procedure. Applying this method to several large‐cap stock time series in Europe, Japan and the USA yields a component with periodicity of 1 year. Out‐of‐sample tests on these large‐cap time series indicate that this periodic component is able to forecast long‐term (decade) behavior for large‐cap time series. Copyright © 2016 John Wiley & Sons, Ltd.