In this paper, a super-resolution reconstruction algorithm based on Projection onto Convex Sets (POCS) and wavelets transform is proposed. A high resolution image, after wavelet transform, can be decomposed into two parts: approximate subband and detail subbands. Under some special conditions, the blurred low-resolution images can be thought as the wavelets transform approximate subbands of a high resolution image. Based on the above relationship, we can construct a series of convex sets and then apply the POCS method to recovering high resolution image based on the convex sets. After finite iterative computation, the desired high solution image can be obtained. The experimental results show that the algorithm has good performance in super-resolution reconstruction when the magnification is large enough.

Resilience has currently attracted increasing interest in the optimization of water distribution systems (WDSs). Most research mainly focuses on optimal design problems. However, the system operation has not been investigated adequately regarding resilience. Therefore, we proposed an integral format of the demand-weighted modified resilience index (IMRI), which can capture the overall resilient performance throughout the operational period. This indicator was incorporated into the multi-objective operation optimization model as one of the objectives. Two benchmark networks were considered as case studies. The resulting Pareto fronts show a clear competing relationship between cost and resilience. Operating conditions in pumps, reservoirs and tanks at each regulation step were characterized by methods of resilience decomposition, which proved valuable intuitively for resilience regulation. A framework for explicit resilience assessment was also developed to examine directly the overall performance in statistics about those optimal solutions obtained. Explicit resilience results show that the IMRI can effectively quantify the resilience of system operation in the temporal dimension. Furthermore, scheduling more pumps, higher trigger-on levels of tanks and a wider range of trigger-level control could yield a more resilient solution to the operation of WDSs.

This paper proposes a method based on the first spectroface and singular value decomposition (SVD) to deal with face recognition with one training image per person. To acquire more information from the single training sample, the first order spectroface method is applied to obtain spectroface representation of facial image, then the spectroface representation is projected onto a uniform eigen-space that is obtained from SVD of standard spectroface image and the resultant coefficient vector is used as the feature of the facial image for recognition. The L1 distance classifier is adopted in recognition. Two standard databases from Yale University and Olivetti Research Laboratory are selected to evaluate the recognition accuracy of the proposed method. Experimental results show the effectiveness of the presented method.

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