信号的角度调制原理

角度调制是用调制信号去控制载波信号角度(频率或相位)变化的一种信号变换方式,可以分为频率调制(FM)和相位调制(PM)。信号经过角度调制后,频谱结构将发生变化。     

A decision rule to minimize daily capital charges in forecasting value‐at‐risk

Under the Basel II Accord, banks and other authorized deposit‐taking institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of value‐at‐risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realized losses are above the estimated risk. In this paper we analyze the profit‐maximizing problem of an ADI subject to capital requirements under the Basel II Accord as ADIs have to choose an optimal VaR reporting strategy that minimizes daily capital charges. Accordingly, we suggest a dynamic communication and forecasting strategy that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor's 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits. Copyright © 2009 John Wiley & Sons, Ltd.     

一种适用于高阶QAM载波恢复的改进型稳定性算法

本文提出了一种结合频率相位载波恢复的数字QAM接收系统,将非零均值引入到Stop—and—Go控制器中,有效地解决了稳定状态的误差偏移较大的问题。仿真结果表明系统在15dB信噪比时,采用载波恢复电路只需7500个符号周期即可捕获达到6％符号率的频偏和30。的相偏,同时其稳态相位抖动亦显著减少到0．07。     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

便携式发电机整流稳压电源设计

针对便携式发电机的特殊要求,给出了一种能够满足宽压和宽频输入的整流电源的设计方案。为了使系统获得良好的稳态和动态性能,在系统中加入了反馈控制和前馈控制相结合的复合控制。实验证明,该系统不仅能够适应宽压和宽频输入,而且在输入电压和频率大幅度变化时仍能稳定可靠地工作。     

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.     

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.     

新一代无线电平台数字射频核心技术研究与工程实践

无线电平台数字射频是提高现代无线电系统复杂电磁环境适应能力、机动能力、传输能力和综合集成能力等基础性能的关键.本文分析了射频技术的发展需求和国内外数字射频领域的研究进展,以及模拟射频核心单元——模拟射频功放主要指标相互矛盾的机理,并将数字射频概念扩展到综合数字射频,重点论述了数字射频核心技术——数字射频功放和全数字发信机的基本原理、优势和关键技术,提出了基于系统与工艺相结合的数字射频设计方法、数字射频功放的物理模型和一种全数字发信机体系结构以及我国自主可控发展数字射频的技术路线建议.本文认为在当今时代背景下,数字射频对于新一代无线电平台具有革命性和可行性;如果不能自主可控发展该项核心技术,我国又将出现一个新的受制于人的脆弱点.     

海杂波频谱的多重分形特性分析

本文主要研究海杂波频谱的多重分形特性并分析其影响因素．首先介绍了将多重分形理论引入到海杂波频谱分析中的理论基础,然后,采用X波段与S波段相参雷达实测数据验证海杂波频谱的多重分形特性,并采用多重分形去趋势起伏分析（MF-DFA）法对海杂波频谱进行多重分形特性分析、分析结果表明海杂波频谱是一种受长程相关性和概率分布共同影响的多重分形序列,并且频域多重分形参数广义Hurst指数对海杂波单元与目标单元具有一定的区分能力．此外．通过分析广义Hurst指数的影响因素还发现FFT时所采用的时间序列长度对广义Hurst指数影响相对较大,而FFT点数对其影响相对较小．     

多基站协作通信系统的频偏补偿策略

在多基站协作通信系统中,为了获得空间分集或复用增益,协作基站同时同频向多个终端发送数据,而终端同时同频收到多个具有不同频偏的信号．基站或终端不能通过预补偿或后补偿方法独立解决多基站多用户频偏补偿问题．针对这一难题,本文提出基于最大可容忍剩余频偏的收发联合补偿策略．仿真结果表明：该策略虽然不能完全消除多基站多用户频偏,但可将其控制在可容忍范围内．