Various density light field image coding based on distortion minimization interpolation

In recent years, the light field (LF) as a new imaging modality has attracted wide interest. The large data volume of LF images poses great challenge to LF image coding, and the LF images captured by different devices show significant differences in angular domain. In this paper we propose a view prediction framework to handle LF image coding with various sampling density. All LF images are represented as view arrays. We first partition the views into reference view (RV) set and intermediate view (IV) set. The RVs are rearranged into a pseudo sequence and directly compressed by a video encoder. Other views are then predicted by the RVs. To exploit the four dimensional signal structure, we propose the linear approximation prior (LAP) to reveal the correlation among LF views and efficiently remove the LF data redundancy. Based on the LAP, a distortion minimization interpolation (DMI) method is used to predict IVs. To robustly handle the LF images with different sampling density, we propose an Iteratively Updating depth image based rendering (IU-DIBR) method to extend our DMI. Some auxiliary views are generated to cover the target region and then the DMI calculates reconstruction coefficients for the IVs. Different view partition patterns are also explored. Extensive experiments on different types LF images also valid the efficiency of the proposed method.     

Analytical study of surface wave multiple refraction in boundary of a scalar impedance surface with a tensor impedance surface

In this paper, the multiple refraction phenomenon is investigated on the boundary of a scalar impedance surface (SIS) and a tensor impedance surface (TIS). When a surface wave (SW) propagates on the SIS and radiates to the boundary of the TIS, the propagation direction of it is changed and the refraction phenomenon is accrued. The method that is proposed in this paper can predict the multiple refraction for the SW. Moreover, another analytical method is introduced for designing the proposed structure which the double refraction (DR) occurs at arbitrary angles on it. Using it, a sample of the structure is designed by printed circuits in 15.2GHz and the results are verified by the full‐wave simulation and measurement. The results are shown that in the structure, DR is occurred in 2° and 22° as predicted. The proposed method can provide many applications such as design of SW power dividers based on the TISs, impedance surface based waveguides, holographic antennas, and feeding of array antennas.     

机翼极限环颤振的CFD/CSD全隐式紧耦合方法

针对轴承振动实时状态监控的需要，从符号动力学角度提出了符号聚合近似与Lempel-ziv复杂度（SAX-LZC）融合的振动监控参数。首先，以Logistic映射和Duffing方程为对象，从理论角度验证了SAX-LZC对动力学结构表征的准确性，并验证了该指标的抗噪能力和计算效率；其次，将SAX-LZC指标与信息熵、样本熵、多分段Lempel-ziv复杂度等动力学参数性能进行了综合对比；最后，从实验角度对轴承早期微弱异常进行了监测，并对轴承典型故障进行了特征提取。理论研究结果表明，SAX-LZC具有动力学结构表征准确、抗噪能力好、计算高效简洁等优点，克服了常规动力学参数工程应用能力弱的问题。实验研究结果表明，SAX-LZC对早期微弱异常有准确的监测，对不同种类故障具有较好的区分度，弥补了时域和频域对轴承微弱故障表征能力不足的缺陷，是一种轴承振动实时状态监控与故障特征提取的有效参数。     

Computing approximate blocking probabilities for transparent waveband switching based WDM networks using hierarchical cross-connects

An analytical model is presented to study the dynamics of wavelength division multiplexing (WDM) networks with waveband switching (WBS). The reduced load approximation method is considered to compute approximated network blocking probabilities in WBS-based WDM networks. The analytical model considers the link blocking probability due to insufficient link capacity and an impact of the waveband granularity (G). The analytical model also considers the node blocking probability due to unavailability of a switch port at the wavelength cross connect (WXC) layer of an Hierarchical cross connect (HXC) switch node. The set of nonlinear equations is obtained with the link independence assumption and solved using repeated substitutions. The accuracy of the analytical model is examined by comparing with simulation results considering the random-fit algorithm for waveband and wavelength assignments in different network scenarios. Lightpaths are routed between source and destination (s-d) HXC switch nodes using shortest path first (SPF) routing. An impact of the switch parameter to limit the input and the output WXC switch ports of an HXC switching node is also being investigated using the analytical model as well as through simulation results.     

Replenishment policy for a purchase-to-order seller: a tradeoff between ordering cost and delay cost

We consider replenishment decisions on when and how many goods to purchase for a seller under a purchase-to-order mode where there is no inventory and the seller purchases goods to fulfil orders already placed. For each purchase, there is a constant ordering cost. For each order, delay cost will be incurred if it is not fulfilled timely. Generally, the more frequent the replenishment, the higher the ordering cost but the lower the delay cost. Consequently, there is a tradeoff between the ordering cost and the delay cost for the seller to make replenishment decisions minimising the total cost. In this paper, we study three cases of the problem and investigate both offline versions and online versions according to the seller's knowledge about information of future orders. For offline versions with perfect information, we either develop an optimal policy, or prove it is NP-hard and develop an approximation policy. For online versions without any information about future orders, from the perspective of competitive analysis we prove the lower bound of competitive ratio for any possible online policy and present a 10-competitive online policy for the general case and a 2-competitive online policy for each of two special cases.     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Process noise covariance estimation via stochastic approximation

Kalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples.     

A directional control method for interface flow considering static voltage stability

A directional control method (DCM) for power flows on a set of interface lines between two regions of power system considering static voltage stability margin is developed in this paper. A surface approximation approach is firstly used to obtain the relationship between the interface flow solution and the generation direction of generator (the portion of generation variation in each participating generator to satisfy the desired power increase on the interface and the system loss). Then, an optimization model is built to determine the optimum dispatching scheme of generators. This method not only can control the total power on the interface to satisfy the power demand but also can realize the directional control of power on each interface line based on the needs of operation. The proposed DCM is further extended to determine the optimum dispatching scheme of generators for maximizing the interface flow margin (IFM), which is the active power margin of the key transmission lines between two regions of power system constrained by static voltage stability. A modified continuation power flow (MCPF) is used to show and evaluate the impacts of the DCM on the IFM. The New England 39-bus system and the IEEE 300-bus system have been employed to verify the effectiveness of the DCM.