SABMIS: sparse approximation based blind multi-image steganography scheme.

We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner.

Probabilistically sampled and spectrally clustered plant species using phenotypic characteristics.

Phenotypic characteristics of a plant species refers to its physical properties as cataloged by plant biologists at different research centers around the world. Clustering species based upon their phenotypic characteristics is used to obtain diverse sets of parents that are useful in their breeding programs. The Hierarchical Clustering (HC) algorithm is the current standard in clustering of phenotypic data.

Vector Quantized Spectral Clustering Applied to Whole Genome Sequences of Plants.

We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of spectral clustering (SC) and vector quantization (VQ) sampling for grouping genome sequences of plants. The inspiration here is to use SC for its accuracy and VQ to make the algorithm computationally cheap (the complexity of SC is cubic in terms of the input size). Although the combination of SC and VQ is not new, the novelty of our work is in developing the crucial similarity matrix in SC as well as use of -medoids in VQ, both adapted for the plant genome data.