We analyze a secure dual-hop mixed radio frequency-free space optical (RF-FSO) downlink simultaneous wireless information and power transfer system. The FSO link and all RF links experience Gamma-Gamma, independent, and identical Nakagami-m fading, respectively. We analyze the effects of atmospheric turbulence, pointing error, detection technology, path loss, and energy harvesting on secrecy performance. Signalto-noise ratios at both the legitimate and illegitimate receivers are not independent since they are both simultaneously influenced by the FSO link. We derive the closed-form expression of the secrecy outage probability (SOP) as well as the asymptotic result for SOP when signal-to-noise ratios at relay and legitimate destinations tend to infinity. Monte-Carlo simulations are performed to verify the accuracy of our analysis. The results show that the secrecy diversity order (SDO) depends on the fading parameter of the relay-destination link and the number of the destination's antennas. In addition, the SDO also depends on the fading parameters, the pointing error parameter, and the detection type of the FSO link.

Sparse bundle adjustment is widely used in many computer vision applications. In this paper, we propose a method for performing bundle adjustments using the L1norm. After linearizing the mapping function in bundle adjustment on its first order, the kernel step is to compute the L1norm equations. Considering the sparsity of the Jacobian matrix in linearizing, we find two practical methods to solve the L1norm equations. The first one is an interior-point method, which transfer the original problem to a problem of solving a sequence of L2norm equations, and the second one is a decomposition method which uses the differentiability of linear programs and represents the optimal updating of parameters of 3D points by the updating variables of camera parameters. The experiments show that the method performs better for both synthetically generated and real data sets in the presence of outliers or Laplacian noise compared with the L2norm bundle adjustment, and the method is efficient among the state of the art L1minimization methods.

BACKGROUND: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability. RESULTS: We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature. CONCLUSIONS: Evaluation of a gene's contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.