Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition

Recognizing human faces in various lighting conditions is quite a difficult problem. The problem becomes more difficult when face images are taken in extremely high dynamic range scenes. Most of the automatic face recognition systems assume that images are taken under well-controlled illumination. The face segmentation as well as recognition becomes much simpler under such a constrained condition. However, illumination control is not feasible when a surveillance system is installed in any location at will. Without compensating for uneven illumination, it is impossible to get a satisfactory recognition rate. In this paper, we propose an integrated system that first compensates uneven illumination through local contrast enhancement. Then the enhanced images are fed into a robust face recognition system which adaptively selects the most important features among all candidate features and performs classification by support vector machines (SVMs). The dimension of feature space as well as the selected types of features is customized for each hyperplane. Three face image databases, namely Yale, Yale Group B, and Extended Yale Group B, are used to evaluate performance. The experimental result shows that the proposed recognition system give superior results compared to recently published literatures.     

A practical design of content protection for H.264/AVC compressed videos by selective encryption and fingerprinting

Digital Rights Management (DRM) of videos is an important issue nowadays. Considering that H.264/AVC videos will be widely used in various applications, we propose a practical design, which combines the methodologies of selective encryption and fingerprinting, for effective DRM of H.264/AVC streaming videos. A selective encryption scheme is first presented to scramble the video content by encrypting a small amount of data in the compressed bit-stream. The scrambled video is H.264-compliant to reduce the complexity of decoder since it can still be played without triggering errors in the decoding process. A fingerprinting scheme is then introduced to provide further protection. We extract a reasonable amount of data from the video and embed the watermark acting as the fingerprint of the video recipient. To acquire the high-quality video for viewing, the user has to decrypt the video obtained from a video server and then combine it with the watermarked data provided from a user information server. The resulting viewable video is thus fingerprinted to deter the user from illegally redistributing the content. Experimental results will demonstrate the feasibility of the proposed approach.     

The bending strip method for isogeometric analysis of Kirchhoff–Love shell structures comprised of multiple patches

In this paper we present an isogeometric formulation for rotation-free thin shell analysis of structures comprised of multiple patches. The structural patches are C¹- or higher-order continuous in the interior, and are joined with C⁰-continuity. The Kirchhoff–Love shell theory that relies on higher-order continuity of the basis functions is employed in the patch interior as presented in Kiendl et al. [36]. For the treatment of patch boundaries, a method is developed in which strips of fictitious material with unidirectional bending stiffness and zero membrane stiffness are added at patch interfaces. The direction of bending stiffness is chosen to be transverse to the patch interface. This choice leads to an approximate satisfaction of the appropriate kinematic constraints at patch interfaces without introducing additional stiffness to the shell structure. The attractive features of the method include simplicity of implementation and direct applicability to complex, multi-patch shell structures. The good performance of the bending strip method is demonstrated on a set of benchmark examples. Application to a wind turbine rotor subjected to realistic wind loads is also shown. Extension of the bending strip approach to the coupling of solids and shells is proposed and demonstrated numerically.     

Integrating independent component analysis and support vector machine for multivariate process monitoring

This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA–PCA and PCA–SVM.     

Improved results for scheduling batched parallel jobs by using a generalized analysis framework

We present two improved results for scheduling batched parallel jobs on multiprocessors with mean response time as the performance metric. These results are obtained by using a generalized analysis framework where the response time of the jobs is expressed in two contributing factors that directly impact a scheduler’s competitive ratio. Specifically, we show that the scheduler IGDEQ is 3-competitive against the optimal while AGDEQ is 5.24-competitive. These results improve the known competitive ratios of 4 and 10, obtained by Deng et al. and by He et al., respectively. For the common case where no fractional allotments are allowed, we show that slightly larger competitive ratios can be obtained by augmenting the schedulers with the round-robin strategy.     

Computational and mutagenesis studies of the streptavidin native dimer interface

Wt streptavidin forms a domain swapped tetramer consisting of two native dimers. The role of tetramerization has been studied previously and is known to contribute to biotin binding by allowing the exchange of W120 between adjacent subunits. However, the role of dimer formation in streptavidin folding and function has been largely overlooked to date, although native dimers are necessary for tetramer formation and thus for high affinity biotin binding. To understand how the side chain interactions at the dimer interface stabilize the subunit association, we studied the structural and functional consequences of introducing interfacial mutations by a combination of molecular dynamics (MD) simulation and biochemical characterization. In particular, we introduced rational mutations at the dimer interface to engineer new side chain interactions and measured the stability and function of the resulting mutants. We focused on two residues that form a "knob" and a "hole" pair, G74 and T76, since steric complementarity plays an important role at these positions. We introduced mutations that would change the polarity and side chain packing to test if the interface can be rationally redesigned. Both energy calculation and geometric parameterization were used to interpret the simulated structures and predict how the mutations affect the dimer stability. In this regard, obtaining precise energy estimates was difficult because the simulated structures have large stochastic variations and some mutants did not reach an equilibrium by the end of the simulation. In contrast, comparing the wt and mutants to one another and parameterizing the simulation using a geometric parameter, i.e. the degree of solvation of the buried interface, resulted in a testable prediction regarding which mutations would result in a stable dimer. We present experimental data (denaturation and binding measurements) to show that an intuitive parameter based on physical reasoning can be useful for characterizing simulations that are difficult to analyze quantitatively.     

A gradient-based adaptive error diffusion method with edge enhancement

This paper presents a gradient-based adaptive error diffusion method with edge enhancement effect while preserving the smoothness effect. The proposed method not only can modulate the threshold in halftoning dynamically but also can determine the weights of the error diffusion filter adaptively to diffuse the quantization error to neighboring pixels properly. Under six testing images, experimental results demonstrate that our proposed new error diffusion method has a good compromise between the edge enhancement effect in the halftone image and the image quality effect in the corresponding inverse halftone image when compared to the methods by Floyd–Steinberg, Eschbach and Knox, Hwang et al., Li, and Feng et al., but it has some execution-time degradation.     

A weighted segment-based caching algorithm for video streaming objects over heterogeneous networking environments

Traditional caching technology is not applicable to cache video streaming objects over heterogeneous networking environments. The popularity of mobile devices in the heterogeneous networking environments make the access of Internet content become a common phenomenon. To support different mobile devices in the heterogeneity networking environments, a transcoding proxy is used to transcode different versions of the streaming videos according to clients’ requests. In this paper, we propose a weighted caching replace strategy for video streaming objects over heterogeneous networking environments. A new caching algorithm with static weight transcoding graph and dynamic caching relation tree is introduced. The proposed algorithm is compared with LRU, LFU, CP and PF cache algorithms in three parts: hit ratio, byte hit ratio, and average transmission delay. Experimental results show that the proposed algorithm outperforms than traditional LRU, LFU, CP and PF cache algorithms.     

Using improved grey forecasting models to forecast the output of opto-electronics industry

Numerous forecasting models have been developed. Each has its own conditions of application. However, it has always been an important research objective to improve prediction accuracy with a small amount of data. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting models still have some potential problems that need to be improved. Therefore, this study proposed an improved transformed grey model based on a genetic algorithm (ITGM(1,1)), and used the output of the opto-electronics industry in Taiwan from 1990 to 2008 as an example for verification. Three grey forecasting models, GM(1,1), rolling GM(1,1), and the transformed GM(1,1), were chosen for the purpose of comparison with ITGM(1,1) by mean absolute percent error and root mean square percent error. The results show that ITGM(1,1) is more accurate than the other three models in both in-sample and out-of-sample forecasting performance, and can greatly improve the accuracy of short-term forecasts.