智能宽带信号检测系统

为了能够准确的测量信号的参数,设计了一种宽带交直流信号检测系统,该系统的控制芯片采用的是STC12C5A32S2单片机.首先,信号采集电路采集到的信号经交直流测量电路进行处理;然后,通过分压和自动增益放大电路等处理后,在频率和相位检测电路中测量信号的频率和相位;最后,在液晶显示器上显示待测信号的幅度、相位和频率等信息.在实验中,测量了频率为0-20KHz的不同幅度信号,实验结果表明,在低频情况下测量误差可以忽略,在高频信号测量时的测量误差小于1%.     

Periodic event‐triggered observation and control for nonlinear Lipschitz systems using impulsive observers

In this paper, we investigate the observation and stabilization problems for a class of nonlinear Lipschitz systems, subject to network constraints, and partial state knowledge. In order to address these problems, an impulsive observer is designed, making use of the event‐triggered technique in order to diminish the network communications. Sufficient conditions are given to ensure a milder version of the separation principle for these systems, controlled via an event‐triggered controller. The proposed observer ensures practical state estimation, while the corresponding dynamic controller ensures practical stabilization. The sampling and the data transmission are carried out asynchronously. The dynamic controller is tested in simulation on a flexible joint. Copyright © 2017 John Wiley & Sons, Ltd.     

空心互感式位移传感器

本文介绍一种空心互感式位移传感器,其工作原理是采用开关方式,以恒流源双向向空心互感的原边线圈充放电,采样副边线圈在稳定放电期间的窄脉冲高度来测量位移量,量程可达20mm,相对精度为2%。论述了互感值的测量,分别设计了轴向和径向两个位移的试验及捕获采样数据的电路,从数据分析证明该方法可以测量位移,减少了空心线圈中的干扰对测量的影响,轴向位移还可以直接测量电介质材料的厚度。     

一种改进的基于FFT的信号插值算法

在传统运用FFT进行信号插值运算的基础上,提出了一种提高插值精度的改进算法.通过子序列重叠和裁剪,舍弃重建序列边缘误差较大的样点,再将相对准确的样点进行重组,从而大幅提高插值精度.实验结果表明:与Prasad等算法相比,在计算量增加3.1％的情况下,不同子序列长度对应的归一化均方误差平均下降至原来的1/19;在计算量增加2倍的情况下,不同子序列长度对应的归一化均方误差平均下降至原来的1/75.     

图像映射技术在公共座椅元素设计中的应用

简要介绍了Image Map（图像映射）技术在工业设计中的运用方法,将该技术运用于产品设计元素提取中,使设计过程科学严谨。提取了数量众多的公共座椅实例作为研究对象,利用Image Map（图像映射图）技术进行全面分析,以提取较有说服力的设计元素。     

Stabilizing composite control for a class of linear systems modeled by singularly perturbed Ito differential equations

In this paper, the problem of robust H_∞ control is investigated for sampled-data systems with probabilistic sampling. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. For the simplicity of technical development, only two different sampling periods are considered whose occurrence probabilities are given constants and satisfy Bernoulli distribution, which can be further extended to the case with multiple stochastic sampling periods. By applying an input delay approach, the probabilistic sampling system is transformed into a continuous time-delay system with stochastic parameters in the system matrices. By linear matrix inequality (LMI) approach, sufficient conditions are obtained, which guarantee the robust mean-square exponential stability of the system with an H_∞ performance. Moreover, an H_∞ controller design procedure is then proposed. An illustrative example is included to demonstrate the effectiveness of the proposed techniques.     

Monte Carlo simulations of the HP model (the "Ising model" of protein folding)

Using Wang–Landau sampling with suitable Monte Carlo trial moves (pull moves and bond-rebridging moves combined) we have determined the density of states and thermodynamic properties for a short sequence of the HP protein model. For free chains these proteins are known to first undergo a collapse “transition” to a globule state followed by a second “transition” into a native state. When placed in the proximity of an attractive surface, there is a competition between surface adsorption and folding that leads to an intriguing sequence of “transitions”. These transitions depend upon the relative interaction strengths and are largely inaccessible to “standard” Monte Carlo methods.     

Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence

Chlorophyll fluorescence is related to photosynthesis and can serve as a remote sensing proxy for estimating photosynthetic energy conversion and carbon uptake. Recent advances in sensor technology allow remote measurements of the sun-induced chlorophyll fluorescence signal (Fs) at leaf and canopy scale. The commonly used Fraunhofer Line Depth (FLD) principle exploits spectrally narrow atmospheric oxygen absorption bands and relates Fs to the difference of the absorption feature depth of a fluorescensing and a non-fluorescensing surface. However, due to the nature of these narrow bands, Fs retrieval results depend not only on vegetation species type or environmental conditions, but also on instrument technology and processing algorithms. Thus, an evaluation of all influencing factors and their separate quantification is required to further improve Fs retrieval and to allow a reproducible interpretation of Fs signals.Here we present a modeling study that isolates and quantifies the impacts of sensor characteristics, such as spectral sampling interval (SSI), spectral resolution (SR), signal to noise ratio (SNR), and spectral shift (SS) on the accuracy of Fs measurements in the oxygen A band centered at 760 nm (O₂-A). Modeled high resolution radiance spectra associated with known Fs were spectrally resampled, taking into consideration the various sensor properties. Fs was retrieved using the three most common FLD retrieval methods, namely the original FLD method (sFLD), the modified FLD (3FLD) and the improved FLD (iFLD). The analysis investigates parameter ranges, which are representative for field and airborne instruments currently used in Fs research (e.g., ASD FieldSpec, OceanOptics HR, AirFLEX, AISA, APEX, CASI, and MERIS).Our results show that the most important parameter affecting the retrieval accuracy is SNR, SR accounts for ≤ 40% of the error, the SSI for ≤ 12%, and SS for ≤ 7% of the error. A trade-off study revealed that high SR can partly compensate for low SNR. There is a strong interrelation between all parameters and the impact of specific parameters can compensate or amplify the influence of others. Hence, the combination of all parameters must be considered by the evaluation of sensors and their potential for Fs retrieval. In general, the standard FLD method strongly overestimates Fs, while 3FLD and iFLD provide a more accurate estimation of Fs. We conclude that technical sensor specifications and the retrieval methods cause a significant variability in retrieved Fs signals. Results are intended to be one relevant component of the total uncertainty budget of Fs retrieval and have to be considered in the interpretation of retrieved Fs signals.     

Importance sampling for volumetric illumination of flames

Physically based rendering of scenes with volumetric illumination of flames remains a challenging problem due to the complexity of their heterogeneous radiative properties. Current bidirectional importance sampling strategies have been focusing on emissive light sources without anisotropic extinction. In this paper, we present an efficient importance sampling method for volumetric light sources with anisotropic extinction. According to the radiative properties of flames, we separate the computation of anisotropic extinction from the evaluation of illumination inside flames and utilize cluster-based hierarchies to rapidly estimate them. To exploit the coherence of radiative voxels, we also propose a new similarity metric to aggregate voxels into clusters. For each pixel to be shaded, we use these clusters to rapidly approximate the importance function of voxels, and draw final illumination samples from clusters. Our results show that this approach substantially reduces the variance of images when rendering scenes with flames.     

Using capture-recapture data and hybrid Monte Carlo sampling to estimate an animal population affected by an environmental catastrophe

We propose a dynamic model for the evolution of an open animal population that is subject to an environmental catastrophe. The model incorporates a capture-recapture experiment often conducted for studying wildlife population, and enables inferences on the population size and possible effect of the catastrophe. A Bayesian approach is used to model unobserved quantities in the problem as latent variables and Markov chain Monte Carlo (MCMC) is used for posterior computation. Because the particular interrelationship between observed and latent variables negates the feasibility of standard MCMC methods, we propose a hybrid Monte Carlo approach that integrates a Gibbs sampler with the strategies of sequential importance sampling (SIS) and acceptance-rejection (AR) sampling for model estimation. We develop results on how to construct effective proposal densities for the SIS scheme. The approach is illustrated through a simulation study, and is applied to data from a mountain pygmy possum (Burramys Parvus) population that was affected by a bushfire.