End-point detection in potentiometric titration by continuous wavelet transform

The aim of this work was construction of the new wavelet function and verification that a continuous wavelet transform with a specially defined dedicated mother wavelet is a useful tool for precise detection of end-point in a potentiometric titration. The proposed algorithm does not require any initial information about the nature or the type of analyte and/or the shape of the titration curve. The signal imperfection, as well as random noise or spikes has no influence on the operation of the procedure.The optimization of the new algorithm was done using simulated curves and next experimental data were considered. In the case of well-shaped and noise-free titration data, the proposed method gives the same accuracy and precision as commonly used algorithms. But, in the case of noisy or badly shaped curves, the presented approach works good (relative error mainly below 2% and coefficients of variability below 5%) while traditional procedures fail. Therefore, the proposed algorithm may be useful in interpretation of the experimental data and also in automation of the typical titration analysis, specially in the case when random noise interfere with analytical signal.     

A wavelet-based methodology for monitoring thermoelastic structures

A wavelet-based methodology for detecting critical events in displacement and temperature histories, obtained by monitoring in-situ thermoelastic structures, is proposed. It is based on the wavelet analysis of thermoelastic potentials furnishing also the possibility to detect separately cases in which sudden jumps occur in the displacement history from those in which they occur on temperature history.     

关于James-Stein估计的小样本性质

首先给出了Jam es-S te in估计优于岭估计的充分条件,随后在P itm an准则下给出了Jam es-S te in估计优于最小二乘估计的简短证明.     

广义双剪应力准则角隅模型

本文将广义双剪应力准则变换为π平面和静水应力轴的柱坐标方程,然后将其角隅光滑化,得到了新的光滑、外凸的广义双剪应力准则角隅模型,研究结果表明,近年来作为光滑化Mohr-Coulomb理论提出的各种角隅模型,实际上并不是光滑化的Mohr-Coulomb理论,而是光滑化的广义双剪应力理论。     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

Data fusion in multivariate calibration transfer

We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.     

Acoustic detection and classification of river boats

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.     

Real-time optical reconstruction of the diffused 3D object using phase information calculated by Continuous Wavelet Transform

We propose the real time optical reconstruction of a three-dimensional (3D) object from a digitally recorded hologram. Phase distribution of the recorded hologram is numerically calculated by 1-dimensional Continuous Wavelet Transform (1D-CWT) for digital reconstruction with phase only information. Also, the phase distribution of 1D-CWT transferred to the spatial light modulator (SLM) is used to obtain optically reconstructed image. It is observed that an efficient real-time analysis can be achieved, if phase of 1D-CWT is used. In the same time, optically reconstructed 3D objects obtained by only phase information are about three times brighter than bleached hologram intensity's. So the minimum power loss can be obtained. Numerical and experimental results are presented.     

A new wavelet transform for reliability-guided phase unwrapping of optical fringe patterns

A new daughter wavelet definition for reliability-guided phase unwrapping of the optical fringe pattern is introduced. The new daughter wavelet definition is given by normalized mother wavelet with a new factor including a Gaussian function. The modulus of the wavelet transform coefficients, obtained by using daughter wavelet under this new daughter wavelet definition, includes not only modulation information but also local frequency information of the deformed fringe pattern. Therefore, it can be treated as a good parameter which represents the reliability of the reconstructed phase data. Mathematical demonstration of this parameter is given. Both the computer simulation and experiment reveal the validity of the proposed method.     

超宽带聚合物调制器的优化设计

设计了一种以聚合物作为材料的低损耗、宽带宽的Mach—Zehnder光波导调制器。分析了调制器的脊波导的模式特性,设计了脊波导的结构,并使用BPM软件模拟了脊形波导的光场分布;通过对光场分布的分析,优化了脊形波导的宽度Wg,脊高6,芯层高度H。同时对聚合物调制器的电极进行了优化,包括电极宽度W和电极间距D,使得调制器有较小的导体损耗以及较好的阻抗匹配。并结合了脊波导的结构参数和电极的优化参数,给出了优化结果,它能够使微波的有效折射率与光波的有效折射率达到匹配,从而使带宽达到177GHz,导体损耗为0．2569dB／cm·GHz1／2。