Equivalent refractive index of MQW waveguides

In this paper, we have proposed some new numerical and semi-analytical methods for developing an equivalent three-layer model of an MQW waveguide. The waveguiding properties like effective index, field distribution, and fractional power within the core of the waveguide of these equivalent structures are compared with those of previously reported equivalent methods. These results are also compared with the results obtained from the exact multilayer analysis of the MQW waveguide. The waveguiding properties are accurately predicted by the semi-analytical method using variational analysis, and the computational effort is significantly reduced. The use of the three-layer equivalent is illustrated in obtaining an estimation of the waveguide losses and is used to study the effect of nonlinearity     

Fibular shortening in poliomyelitis

We have determined the nucleotide and encoded amino acid sequences of the capsid, membrane precursor, membrane, envelope, and nonstructural NS1 protein genes of a dengue-2 virus (D2-04) isolated from a patient in Hainan, China. The sequenced region contains a gene organization similar to that of other flaviviruses. The overall amino acid sequence similarity between D2-04 and other dengue-2 viruses is greater than 92%, whereas that between D2-04 and members of the other dengue serotypes is about 65%.     

Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest neighbor

The Journal of Supercomputing - In this article, the authors suggested a rotation-invariant local binary pattern-based weighted generalized closest neighbor (RILBP-WGCN) method for HSI...     

A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

Neural Computing and Applications - This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat...     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.