Millimeter-Wave Lange Coupler Design and Its Application in the Monolithic Image Rejection Mixer Design

This paper reports on the design of a Ka-band monolithic Lange coupler and its application in the monolithic fourth-harmonic image rejection mixer. Detailed design and analysis using Ansoft-HFSS simulator have been carried out. The simulated results of the Lange Coupler show the insert loss is better than −3.64 dB; the amplitude balance is less than 0.55 dB and the phase balance is less than 0.65° from the 90° phase difference over the 30 to 40 GHz frequency range. The Lange Coupler is employed in a monolithic image rejection mixer that is fabricated by a commercial 0.18-μm pseudomorphic high electron-mobility transistor (pHEMT) process. The chip size is 1.4 mm × 1.9 mm. The image rejection ratio (IMR) is from 15 to 34 dB in the RF frequency range of 30 to 40 GHz.     

Blazing of smooth gratings

The diffraction efficiency of smooth weak gratings can depend significantly on the profile of the gratings as, in the diffraction process there exists wave coupling. By adjusting the grating profile it is possible to obtain enhancement or suppression of the diffraction efficiency. Possible applications include improvements in grating devices, grating profile reconstruction, and precise control of the slightest periodical disturbances. Some numerical examples are given.     

Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the power originating from the integrated electrical control circuit in state preparation with the help of machine learning, where the state preparation is assumed to be perfect in initial security proofs. Specifically, we describe a possible power model and show a complete attack based on a support vector regression (SVR) algorithm. The simulation results show that the secret key information decreases with the increase of the accuracy of the attack, especially in a situation with less excess noise. In particular, Eve does not have to intrude into the transmitter chip (Alice), and may perform a similar attack in practical chip-based discrete-variable quantum key distribution (DVQKD) systems. To resist this attack, the electrical control circuit should be improved to randomize the corresponding power. In addition, the power can be reduced by utilizing the dynamic voltage and frequency scaling (DVFS) technology.     

“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study

We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application.     

降阶扩展卡尔曼滤波算法在船用INS/GPS组合导航系统中的应用

TNNS(真航向导航系统)由MS860接收机、INS及处理数据的PC/104架构的嵌入式工控机构成。针对TNNS推导了INS(惯性导航系统)的误差模型,提出了适合于TNNS的降阶扩展卡尔曼滤波算法组合GPS和INS。系统在东海作了三次海试,软件及滤波算法平台由C/C 编制。海上试验表明,组合滤波后,INS的位置误差由100m降低到40m以下;进行最优化滤波后的航向误差σ由原来的0.105°减小为0.034°,纵横摇的误差也大幅减小。整个海试结果表明,在TNNS中组合GPS/INS采用的降阶扩展卡尔曼滤波算法,大幅提高了系统精度和可靠性。     

组合导航系统多重衰减因子自适应估计算法比较研究

提出了多重衰减因子自适应估计卡尔曼滤波方法,用该方法对系统每个误差状态估计进行控制,提高滤波器的估计性能。仿真结果表明,新算法在系统噪声特性不准确的情况下,能够抑制卡尔曼滤波估计的发散,GPS/SINS组合导航精度比强跟踪滤波估计的精度高。这种算法推导形式简单,计算量小,适合在线运算。     

Micro-disks embedded microring for optical filter

We propose a novel ultra compact structure of micro-disks embedded microring filter (MDEMR) and several parameters which impact the performance of MDEMR are analyzed. The filter is shown to exhibit much smaller size and better spectrum than traditional microring one. The number and radius of the inner disks are found to mainly influence the resonance frequency of the micro-disks, and the valley of transmittivity. The resonance between microring and micro-disks is decrease with increasing the gap between microring and micro-disks, which also leads to the extinction ratio improved significantly. The Q factor is found to be improved from 300 to 3000 with the gap increased from 0.2 μm to 0.4 μm, while the transmission peak affected slightly.     

领结形中空表面等离子体波导的传输特性

设计了一种领结形中空表面等离子体波导.采用频域有限差分法,对这种波导所支持的基模的能流密度分布、有效折射率、传播长度和模式面积随几何结构参数和工作波长的依赖关系进行了分析.结果表明,沿纵向的能流主要分布在两个上下突起所形成的中间区域.通过调整几何参数及工作波长,可以调节模式的有效折射率、传播长度和模式面积.在工作波长确定的条件下,有效折射率随突起半径的增大呈减小趋势,而传播长度和模式面积则随着突起半径的增大呈增大趋势,四个角上的圆弧半径对波导的传输特性有微调作用,左右扇形区域的半径对波导的传输特性有较明显 关键词： 集成光学 光波导 表面等离子体波导     

Maximum integrated likelihood estimator of the interest parameter when the nuisance parameter is location or scale

The problem of estimation of an interest parameter in the presence of a nuisance parameter, which is either location or scale, is studied. Two estimators are considered: the usual maximum likelihood estimator and the estimator based on maximization of the integrated likelihood function. The estimators are compared, asymptotically, with respect to the bias and with respect to the mean squared error. The examples are given.