Modelling non-linearities in a MEMS square wave oscillator

The modelling of the non-linear behaviour of MEMS oscillators is of interest to understand the effects of non-linearities on start-up, limit cycle behaviour and performance metrics such as output frequency and phase noise. This paper proposes an approach to integrate the non-linear modelling of the resonator, transducer and sustaining amplifier in a single numerical modelling environment so that their combined effects may be investigated simultaneously. The paper validates the proposed electrical model of the resonator through open-loop frequency response measurements on an electrically addressed flexural silicon MEMS resonator driven to large motional amplitudes. A square wave oscillator is constructed by embedding the same resonator as the primary frequency determining element. Measurements of output power and output frequency of the square wave oscillator as a function of resonator bias and driving voltage are consistent with model predictions ensuring that the model captures the essential non-linear behaviour of the resonator and the sustaining amplifier in a single mathematical equation. © 2012 IEEE.

Low-rank optimization for distance matrix completion

This paper addresses the problem of low-rank distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data embedding space is possibly unknown but small compared to the number of considered data points. The focus is on high-dimensional problems. We recast the considered problem into an optimization problem over the set of low-rank positive semidefinite matrices and propose two efficient algorithms for low-rank distance matrix completion. In addition, we propose a strategy to determine the dimension of the embedding space. The resulting algorithms scale to high-dimensional problems and monotonically converge to a global solution of the problem. Finally, numerical experiments illustrate the good performance of the proposed algorithms on benchmarks. © 2011 IEEE.

Tests of an adaptive QM/MM calculation on free energy profiles of chemical reactions in solution.

We present reaction free energy calculations using the adaptive buffered force mixing quantum mechanics/molecular mechanics (bf-QM/MM) method. The bf-QM/MM method combines nonadaptive electrostatic embedding QM/MM calculations with extended and reduced QM regions to calculate accurate forces on all atoms, which can be used in free energy calculation methods that require only the forces and not the energy. We calculate the free energy profiles of two reactions in aqueous solution: the nucleophilic substitution reaction of methyl chloride with a chloride anion and the deprotonation reaction of the tyrosine side chain. We validate the bf-QM/MM method against a full QM simulation, and show that it correctly reproduces both geometrical properties and free energy profiles of the QM model, while the electrostatic embedding QM/MM method using a static QM region comprising only the solute is unable to do so. The bf-QM/MM method is not explicitly dependent on the details of the QM and MM methods, so long as it is possible to compute QM forces in a small region and MM forces in the rest of the system, as in a conventional QM/MM calculation. It is simple, with only a few parameters needed to control the QM calculation sizes, and allows (but does not require) a varying and adapting QM region which is necessary for simulating solutions.

Nanoscale mode selector in silicon waveguide for on chip nanofocusing applications.

We demonstrate a nanoscale mode selector supporting the propagation of the first antisymmetric mode of a silicon waveguide. The mode selector is based on embedding a short section of PhC into the waveguide. On the basis of the difference in k-vector distribution between orthogonal waveguide modes, the PhC can be designed to have a band gap for the fundamental mode, while allowing the transmission of the first antisymmetric mode. The device was tested by directly measuring the modal content before and after the PhC section using a near field scanning optical microscope. Extinction ratio was estimated to be approximately 23 dB. Finally, we provide numerical simulations demonstrating strong coupling of the antisymmetric mode to metallic nanotips. On the basis of the results, we believe that the mode selector may become an important building block in the realization of on chip nanofocusing devices.

Design and modeling of an integrated device for acoustic resonance spectroscopy

This paper investigates the design and modelling of an integrated device for acoustic resonance spectroscopy (ARS). Miniaturisation of such platforms can be achieved using MEMS technology thereby enabling scaling of device dimensions to investigate smaller specimens while simultaneously operating at higher frequencies. We propose an integrated device where the transducers are mounted in close proximity with the specimen to be analysed (e.g. by integrating ultrasound transducers within a microfluidic channel). A finite element (FE) model and a simplified analytical model have been constructed to predict the acoustic response of a sample embedded in such a device configuration. A FE simulation is performed in COMSOL by embedding the piezoelectric transducers in representative fluid media. Resonant frequencies associated with the measurement can be extracted from this data. The response of various media modelled through FEA matches with analytical predictions for a range of biological media. A variety of biological media may be identified by using the measured resonant frequencies as a signature of relevant physical characteristics. The paper establishes the modelling basis of an integrated acoustic resonant spectrometer that is then applied to examine the impact of geometrical scaling on system resolution. © 2013 IEEE.

Au@Si-n: Growth behavior, stability and electronic structure

The configurations, stability, and electronic structure of AuSin (n = 1-16) clusters have been investigated within the framework of the density functional theory at the B3PW91/LanL2DZ and PW91/DNP levels. The results show that the Au atom begins to occupy the interior site for cages as small as Si-11 and for Si-12 the Au atom completely falls into the interior site forming Au@Si-12 cage. A relatively large embedding energy and small HOMO-LUMO gap are also found for this Au@Si-12 structure indicating enhanced chemical activity and good electronic transfer properties. All these make Au@Si-12 attractive for cluster-assembled materials.

Optical characteristics of InAs quantum dots on GaAs matrix by using various InGaAs structures

The effects of various InGaAs layers on the structural and optical properties of InAs self-assembled quantum dots (QDs) grown by molecular-beam epitaxy ( MBE) were investigated. The emission wavelength of 1317 nm was obtained by embedding InAs QDs in InGAs/GgAs quantum well. The temperature-dependent and timed-resolved photoluminescence (TDPL and TRPL) were used to study the dynamic characteristics of carriers. InGaAs cap layer may improve the quality of quantum dots for the strain relaxation around QDs, which results in a stronger PL intensity and an increase of PL peak lifetime up to 170 K. We found that InGaAs buffer layer may reduce the PL peak lifetime of InAs QDs, which is due to the buffer layer accelerating the carrier migration. The results also show that InGaAs cap layer can increase the temperature point when, the thermal reemission and nonradiative recombination contribute significantly to the carrier dynamics.

Phase character of the electron transport through a double quantum dot in a mesoscopic ring

We theoretically study the electron transport through a double quantum dot (QD) in the Coulomb blockade regime and reveal the phase character of the transport by embedding the double QD in a mesoscopic Aharonov-Bohm ring. It is shown that coherent transport through the double QD is preserved in spite of intradot and interdot Coulomb interactions.

Local histogram based geometric invariant image watermarking

Compared with other existing methods, the feature point-based image watermarking schemes can resist to global geometric attacks and local geometric attacks, especially cropping and random bending attacks (RBAs), by binding watermark synchronization with salient image characteristics. However, the watermark detection rate remains low in the current feature point-based watermarking schemes. The main reason is that both of feature point extraction and watermark embedding are more or less related to the pixel position, which is seriously distorted by the interpolation error and the shift problem during geometric attacks. In view of these facts, this paper proposes a geometrically robust image watermarking scheme based on local histogram. Our scheme mainly consists of three components: (1) feature points extraction and local circular regions (LCRs) construction are conducted by using Harris-Laplace detector; (2) a mechanism of grapy theoretical clustering-based feature selection is used to choose a set of non-overlapped LCRs, then geometrically invariant LCRs are completely formed through dominant orientation normalization; and (3) the histogram and mean statistically independent of the pixel position are calculated over the selected LCRs and utilized to embed watermarks. Experimental results demonstrate that the proposed scheme can provide sufficient robustness against geometric attacks as well as common image processing operations. (C) 2010 Elsevier B.V. All rights reserved.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Room temperature ionic liquid doped DNA network immobilized horseradish peroxidase biosensor for amperometric determination of hydrogen peroxide

A novel electrochemical H2O2 biosensor was constructed by embedding horseradish peroxide (HRP) in a 1-butyl-3-methylimidazolium tetrafluoroborate doped DNA network casting on a gold electrode. The HRP entrapped in the composite system displayed good electrocatalytic response to the reduction of H2O2. The composite system could provide both a biocompatible microenvironment for enzymes to keep their good bioactivity and an effective pathway of electron transfer between the redox center of enzymes, H2O2 and the electrode surface. Voltammetric and time-based amperometric techniques were applied to characterize the properties of the biosensor. The effects of pH and potential on the amperometric response to H2O2 were studied. The biosensor can achieve 95% of the steady-state current within 2 s response to H2O2. The detection limit of the biosensor was 3.5 mu M, and linear range was from 0.01 to 7.4 mM. Moreover, the biosensor exhibited good sensitivity and stability. The film can also be readily used as an immobilization matrix to entrap other enzymes to prepare other similar biosensors.

Noncovalent functionalization of multiwalled carbon nanotubes: Application in hybrid nanostructures

We developed a reproducible, noncovalent strategy to functionalize multiwalled carbon nanotubes (MWNTs) via embedding nanotubes in polysiloxane shells. (3-Aminopropyl)triethoxysilane molecules adsorbed to the nanotube surfaces via hydrophobic interactions are polymerized simply by acid catalysis and form a thin polysiloxane layer. On the basis of the embedded MWNTs, negatively charged gold nanoparticles are anchored to the nanotube surfaces via electrostatic interactions between the protonated amino groups and the gold nanoparticles. Furthermore, these gold nanoparticles can further grow and magnify along the nanotubes through heating in HAuCl4 aqueous solution at 100 degrees C; as a result these nanoparticles are joined to form continuous gold nanowires with MWNTS acting as templates.

Study of Multiple Films of Copper Phthalocyanine Embedded SnO2 Ultrafine Particles

Multiple films of copper phthalocyanine derivative embedded SnO2 ultrafine particles were studied, The results indicated that there is interaction between CuPc and SnO2, and structure of CuPc is destroyed to some extent. Gas sensitivity measurements show that conductance of LB films after embedding increases about one order of magnitude, stability of gas-sensing also increases.

基于切空间的局部嵌入映射近邻选择

针对欧式距离下局部嵌入映射近邻点选择的缺陷,从其定义出发,引入切空间距离,改进了近邻点的选择方法,从而能够更好的满足LLE对于局部线性的要求。论文用剩余方差测试其性能,通过对S-curve数据和Swiss-roll数据的仿真可以看到,基于切空间距离的方法能够更好的表示数据的输入/输出映射质量。

基于局部线性嵌入映射的非线性传感器故障诊断研究

当今社会，随着系统复杂度越来越高以及对系统可靠性、安全性要求的日益提高，故障诊断越来越受到人们的重视，在这其中，非线性系统由于自身的特点，一直是故障诊断中的一个热点和难点。本文针对这个问题，对非线性系统传感器故障诊断的若干关键技术进行了深入研究，旨在建立一种相对通用的非线性系统传感器故障诊断方法。本论文研究的主要内容包括系统数据故障特征提取、故障检测、故障识别等。对于故障数据的特征提取问题，在深入分析现有各种算法的基础上，结合非线性传感器数据的特点，将现有的局部线性嵌入映射算法（LLE）引入到故障诊断中来。LLE算法克服了传统线性方法如主元分析（PCA）法在非线性系统上应用的局限，同时也具有以下几个突出的优点：1、能够很好的表达数据的内在流形结构，保留数据特征，这点在故障诊断中有重要的意义，可以比较好的保留原有数据的故障特征；2、不存在局部最优解；3、算法本身参数的选择很少，适合于工程应用。特征提取的研究为后续的故障检测与故障诊断打下基础。同时对原有的LLE算法本身存在的一些缺陷做了一定的改进，完善了其在故障诊断中的应用。LLE算法假设每个数据点及其近邻是局部线性的，因此近邻的选择就不能改变数据的固有流形。从这个概念出发，引入切空间距离代替传统的欧式空间距离，使近邻点的选择更加符合局部线性的要求，从而能更好的提高数据的输入/输出映射质量。针对LLE算法中内在维数难以估计的问题，在算法中应用分形中相关维的计算方法，用线性拟和方法改进了G-P算法，实现了线性区域的自动选择，提高了内在维数估计的精度。最后，通过核函数估计的方法，建立一个数据投影模型，可以实现实时数据到特征空间的直接投影，避免重复的计算权重矩阵，减少了计算复杂度。特征提取之后，针对故障检测问题，提出了一种基于空间分布的故障检测方法，通过将系统数据与高维空间分布相结合，把同一状态下的系统数据近似成为同一分布下的样本采样，检测实时数据是否服从系统正常状态下的分布，从而完成数据的故障检测。同时，结合LLE算法中的核函数投影方法，实现了数据在投影的同时完成故障判别，减少了计算复杂度。在故障检测的基础上，提出了基于投影寻踪的方法进行故障识别。对原有的投影寻踪的应用方法做了一定的修改，通过对最优投影向量的模式匹配完成故障的识别。首先建立各种故障状态下与正常数据之间的最优投影向量的数据库，然后将实时数据的最优投影向量与历史故障进行模式匹配，从而完成故障识别。为了提高投影向量寻找的速度与精度，采用粒子群优化算法，这也可以同时避免算法陷入局部极值点。最后，在总结全文的基础上，讨论了基于局部线性嵌入映射算法在理论及应用上有待于进一步研究的若干问题。