Voice conversion using General Regression Neural Network

The objective of voice conversion system is to formulate the mapping function which can transform the source speaker characteristics to that of the target speaker. In this paper, we propose the General Regression Neural Network (GRNN) based model for voice conversion. It is a single pass learning network that makes the training procedure fast and comparatively less time consuming. The proposed system uses the shape of the vocal tract, the shape of the glottal pulse (excitation signal) and long term prosodic features to carry out the voice conversion task. In this paper, the shape of the vocal tract and the shape of source excitation of a particular speaker are represented using Line Spectral Frequencies (LSFs) and Linear Prediction (LP) residual respectively. GRNN is used to obtain the mapping function between the source and target speakers. The direct transformation of the time domain residual using Artificial Neural Network (ANN) causes phase change and generates artifacts in consecutive frames. In order to alleviate it, wavelet packet decomposed coefficients are used to characterize the excitation of the speech signal. The long term prosodic parameters namely, pitch contour (intonation) and the energy profile of the test signal are also modified in relation to that of the target (desired) speaker using the baseline method. The relative performances of the proposed model are compared to voice conversion system based on the state of the art RBF and GMM models using objective and subjective evaluation measures. The evaluation measures show that the proposed GRNN based voice conversion system performs slightly better than the state of the art models.     

Data-efficient learning of robotic clothing assistance using Bayesian Gaussian process latent variable model

ABSTRACT

Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks.     

基于双树复小波的无参考立体图像质量评价

随着3D技术的不断发展，立体图像的使用领域越来越广泛，同时人们对图像的清晰度要求越来越高，因此，立体图像的质量评价成为关注点，基于此，提出了一种基于双树复小波变换的立体图像质量评价算法。使用双树复小波变换对立体图像的左、右视图进行处理，生成纹理结构图像，且根据最小能量误差的原理，获取左右视图的视差图；对纹理结构图像和视差图提取非对称广义高斯分布模型的参数、梯度幅值、相对梯度方向方差和奇异值曲线与坐标轴的面积等特征；使用AdaBoosting BP神经网络，进行训练和预测立体图像的质量得分。在LIVE立体图像数据库上的实验结果表明，新方法预测得分与主观得分有较好的一致性，获得了比较好的实验结果。     

3D holoscopic image coding scheme using HEVC with Gaussian process regression

3D holoscopic image, also known as integral imaging, light field imaging and plenoptic imaging, can provide a natural and fatigue-free 3D visualization. However, a large amount of data is required to represent the 3D holoscopic content. Therefore, efficient coding schemes for such particular type of image are needed. In this paper, we propose a Gaussian process regression based prediction scheme to compress the 3D holoscopic image. In the proposed scheme, the coding block and its prediction supports are modeled as a Gaussian process (GP) and Gaussian process regression (GPR) is used to obtain a better prediction of the coding block. Limited searching windows in horizontal and vertical directions are used to obtain the prediction supports, and a filtration method is designed to judge the reliability of the obtained prediction supports. Moreover, in order to alleviate the high complexity caused by GPR, a sparsification method is also put forward. Experimental results demonstrate the advantage of the proposed scheme for 3D holoscopic image coding in terms of different quality metrics as well as the visual quality of the views rendered from decompressed 3D holoscopic content, compared to the HEVC intra-prediction method and several other prediction methods in this field.     

Impact of heliostat curvature on optical performance of Linear Fresnel solar concentrators

The present paper gives a numerical investigation of the effect of mirror curvature on optical performance of a Linear Fresnel Reflector solar field installed recently in Morocco. The objective is to highlight and discuss the effect of mirror curvature on the flux density distribution over the receiver and the system optical efficiency. For this purpose, a Monte Carlo-ray tracing simulation tool is developed and used to optimize the optical design taking into account the curvature degree of the heliostat field. In order to assess the accuracy of the numerical code developed and the validity of simulation results, a set of verification tests were developed and detailed within this article. Then, the optical performance of the system is evaluated as a function of mirror curvature and receiver height. The major challenge of this study is to find a trade-off between heliostat curvature and receiver height since lower and smaller receivers may reduce the system cost. It has been found that the flux distribution over the receiver and the optical efficiency of the system are relatively sensitive to the mirror curvature. We have demonstrated quantitatively how the use of curved mirrors can enhance the optical performance and reduce the required receiver size.     

Data-driven resilience analysis of power grids

With the integration of renewable energy resources, the inertia of power systems significantly reduces, thereby making the system sensitive to operational disturbances. A disturbance-based method is presented herein to estimate inertia, uncovering the influence of renewables on system-resilient operations. The Gaussian process regression method is then used to predict the power system trajectory after disturbance. Extensive tests demonstrate the data-driven method mathematically estimates the inertia of the system as well as predicts the dynamics operations of power grids subject to disturbances. Numerical results also offer insights into the enhancement of system resilience by strategically designing the inertia of power systems.     

A numerical approach for a comparative study of laser drilling process under single and repetitive pulse

An axisymmetric model is developed to study laser drilling process under a single pulse as well as repetitive laser pulse. The laser pulse irradiated on the surface of the workpiece is volumetric and Gaussian in nature. The laser irradiated surface is subjected to convective‐radiative boundary condition while rest of the surfaces are insulated. Finite volume method is used to discretize the domain under consideration. The resulting algebraic equations are solved with the help of the tridiagonal matrix algorithm to find temperature distribution throughout the domain. The enthalpy‐porosity method is used to track the solid‐liquid interface generated during the laser melting process. Convective heat transfer occurs inside the generated melt pool. The current model is first used to validate the results of the existing literature and as the results agreed well, further studies are made to find out the advantages of using repetitive laser pulse over single pulse laser source for laser drilling process for the same laser energy and total heating duration. Vaporization has been avoided during the process and metal removal occurs through melting only. The present numerical model can provide some insight for practical laser drilling process.     

基于高斯混合模型和AdaBoost的夜间车辆检测

针对夜间车辆检测精度相对不高的问题，提出通过构建车头灯对空间几何关系的高斯混合模型（GMM）和采用逆投影车辆样本的AdaBoost分类器准确检测夜间车辆的方法。首先，在交通场景中根据车头灯对的空间位置关系设置逆投影面，通过图像预处理粗定位车灯区域；其次，在逆投影图像下利用车头灯对的空间几何关系构建车灯对的高斯混合模型，初步匹配车头灯对；最后，采用逆投影车辆样本，利用AdaBoost分类器进一步准确检测车辆。实验在3个交通场景的检测结果表明，与原始图像下的AdaBoost方法相比，所提方法的检测率提高了1.93%，漏检率降低了17.83%，误检率降低了27.61%；与D-S （Dempster-Shafer）证据理论方法相比，检测率提高了2.03%，漏检率降低了7.58%，误检率降低了47.51%。所提方法提高了相对检测精度，减少了地面反光和影子等的干扰，满足交通场景中夜间车辆检测的可靠性和准确性的要求。     

Molecular p doping of 4,4′-Bis(N-carbazolyl)-1,1′-biphenyl and n doping of 1,3,5-tris(N-phenylbenzimidazol-2-yl)benzene

This report details p-type doping of 4,4′-Bis(N-carbazolyl)-1,1′-biphenyl (CBP) with tetrafluorotetracyanoquinodimethane (F4-TCNQ) and n-type doping of 1,3,5-tris(N-phenylbenzimidazol-2-yl)benzene (TPBi) with decamethylcobaltocene (DMC). Both CBP and TPBi are widely used as charge transport layers in organic light-emitting diodes (OLEDs). We confirm efficient p-type F4-TCNQ doping of CBP by observing, as a function of increasing dopant concentration, a shift of the Fermi level (E_F) toward the highest occupied molecular orbital (HOMO) in ultraviolet photoelectron spectroscopy (UPS) scans, a larger amount of fluorine in the film, a transition of the core C 1s electron levels to lower binding energies in X-ray photoelectron spectroscopy (XPS) measurements, and an increase of the current in vertical metal/organic semiconductor/metal devices, which in the context of the Gaussian Disorder Model, can be attributed to an injection barrier lowering and a charge carrier concentration enhancement. In the case of doping of TPBi with DMC, as the dopant concentration increases, a shift of E_F away from the HOMO in UPS and a transition of the core C 1s electron levels to higher binding energies in XPS are observed, both of which are indicative of n-type doping. The presence of inherent degradation of TPBi films during UPS measurements is also established. These findings may pave the way for development of higher performance OLEDs based on doped transport layers.     

An optimal control approach to the optical flow problem

The optical flow problem is reduced to an optimal control problem governed by a linear parabolic equation having the unknown velocity field (the optical flow) as drift term. This model is derived from a new assumption, that is, the brightness intensity is conserved on a moving pattern driven by a Gaussian stochastic process. The optimality conditions are deduced by a passage to the limit technique in an approximating optimal control problem introduced for a regularization purpose. Finally, the controller uniqueness is addressed.