光接收机调谐前端的分析

为了设计高性能的谐振式光接收机,应用场效应管模型和多种调谐网络的拓扑形式,提出了一个统一的光接收机调谐前端分析方法。推导出了对多种调谐网络通用的计算公式,用以计算谐振频率、传输函数和等效输入噪声电流密度,便于应用计算机优化光接收机的电路拓扑形式。使用该方法,编制计算机辅助程序模拟了各种不同型式的电感谐振式光接收机的特性,对于光接收机前端设计具有指导作用。     

平均速率受限条件下OFDM系统联合自适应分配策略

提出一种满足不同用户速率要求的OFDM系统的联合子载波、比特和功率分配算法,以达到进一步减小发射功率的目的。首先在用户平均速率受限的条件下进行子载波分配,然后对分给每个用户的子载波进行速率注水,进行比特和功率分配,使系统在满足用户传输速率要求的同时传输功率最小。仿真结果表明,该算法功率分配部分具有较低的复杂度,相同性能要求下所需最小传输功率比其他算法小。     

线调频小波变换及其应用

线调频小波变换是一种新的线性时频分析方法.在介绍线调频小波变换的基础上,比较了短时傅里叶变换、连续小波变换、线调频小波变换在瞬时频率计算方面的优缺点.最后给出了在石油勘探开发中利用线调频小波变换对沉积旋回信号进行分析的一些初步应用.     

阶梯型宽带双频全向单极天线

文章介绍了一种新型宽带双频全向单极天线。在正方形单极天线贴片的基础上寄生一块矩形贴片的方法达到双频工作的效果,且在每个谐振点都具有宽带特性;同时,为了克服单极天线在高频段却很难达到全向或者接近全向辐射模式的缺点,采用一种简单的阶梯型结构,在高频段提供良好的全向辐射特性。     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.     

基于双稳阱内随机共振的OFDM信号检测方法研究

为了解决OFDM(orthogonal frequency division multiplexing)传输系统中接收端对微弱OFDM信号的检测问题,本文将双稳阱内随机共振系统与OFDM信号增强和解调过程相结合,推导了多载波信号激励下双稳阱内随机共振系统从零状态变化到势阱状态所需时间的解析表达式,分析了暂态响应导致的多载波信号在一个符号内的能量损失问题;推导了系统稳态输出方程,分析了稳态过程对子载波已调信号叠加直流偏置分量的问题;探讨了在不同系统参数和噪声强度下阱内随机共振对OFDM信号解调性能的影响。仿真结果表明：该方法可有效实现信号与噪声阱内协同作用,提高OFDM信号的检测性能。     

具有预防维修策略的两个不同型部件组成的冷贮备系统的可靠性分析

研究了具有预防维修策略的两个不同型部件组成的冷贮备系统.所谓预防维修策略是指当工作部件的工作时间达到指定时间T系统尚未故障时则立即对工作部件进行预防维修,经预防维修,部件恢复如新.利用补充变量的方法,求出了系统的可用度和故障频度的Laplace变换.     

燃料电池内阻在线测试系统研究

内阻是反应燃料电池性能的重要指标,设计燃料电池内阻在线测试系统具有重要意义.但燃料电池内阻具有明显的非线性和时变特性,一般难以精确测量.为了解决这个问题,本文在对燃料电池内阻进行分析的基础上,结合电化学阻抗谱测试思想,提出了一种改进的燃料电池欧姆内阻在线测试法--交流变频调幅测量法,并基于高性能数字信号处理器设计了测试系统硬件电路和软件,得到了较好的测量效果.     

Optimal sampling frequency for volatility forecast models for the Indian stock markets

This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non‐normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd.     

HLA高分辨等位基因多态性及单倍体与广西汉族终末期肾病易感性的相关性分析

为研究广西汉族终末期肾病(End Stage Renal Disease, ESRD)与人类白细胞抗原(Human Leukocyte Antigen, HLA)等位基因及单倍体的关联性,本研究采用聚合酶链反应-序列特异性寡核苷酸(Polymerase Chain Reaction-Sequence Specific Oligonucleotides, PCR-SSO)技术对广西汉族578例终末期肾病患者(ESRD组)进行HLA-A、B和DRB1基因分型,HLA等位基因频率用直接计数法来计算,采用Arlequin 3.5.2.2软件计算单倍型频率,并与1 644例广西汉族健康造血干细胞捐献者(对照组)比较。结果显示,ESRD组HLA-A*11:01、A*02:01、B*13:01、DRB1*12:02、DRB1*04:05、DRB1*14:54和DRB1*11:01的基因频率大于对照组(P<0.05),HLA-A*11:02、A*30:01、A*32:01、B*13:02、B*07:02、DRB1*07:01和DRB1*13:02的基因频率小于对照组(P<0.05)。ESR...