Learn More
A brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP) is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the(More)
We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has been previously shown that the NHMC model enables the detection of several semantic structural features of hyperspectral signatures. However, there are some aspects of the spectral data that are not fully captured(More)
Feature design is a crucial step in many hyperspectral signal processing applications like hyperspectral signature classification and unmixing, etc. In this paper, we describe a technique for automatically designing universal features of hyperspectral signatures. Universality is considered both in terms of the application to a multitude of classification(More)
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding(More)
We propose a new spectral unmixing method using a semantic spectral representation, which is produced via nonhomogeneous hidden Markov chain (NHMC) models applied to wavelet transforms of the spectra. Previous studies have shown that the representation is robust to spectral variability in the same materials because it can automatically detect the diagnostic(More)
The high data dimensionality of hyperspectral images increases the burden on data computation, storage, and transmission; fortunately, the high redundancy in the spectral domain allows for significant dimensionality reduction. Band selection provides a simple dimensionality reduction scheme by discarding bands that are highly redundant, therefore preserving(More)
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library. Existing semisupervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most such(More)
High performance liquid chromatography (HPLC) has been widely considered as the most effective way for the separation and preparation of optically pure enantiomers. In the resolution by HPLC, the separation ability of a column strongly depends on the properties of a chiral stationary phase (CSP). Among many CSPs, the immobilized CSPs, which are becoming one(More)