Spectral performance of an airborne dispersive pushbroom imaging spectrometer cannot be assumed to be stable over a whole flight season given the environmental stresses present during flight. Spectral performance monitoring during flight is commonly accomplished by looking at selected absorption features present in the Sun, atmosphere, or ground, and their stability. The assessment of instrumen...

Since launch, both Terra and Aqua MODIS instruments have continued to operate and make measurements of the earth’s top of atmospheric (TOA) radiances and reflectance. MODIS collects data in 36 spectral bands covering wavelengths from 0.41 to 14.4 m. These spectral bands and detectors are located on four focal plane assemblies (FPAs). MODIS on-board calibrators (OBC) include a spectro-radiometr...

Cochlear implants provide a limited number of electrodes, each of which represents a channel of spectral information. Studies have shown that implant recipients are not receiving all of the information from the channels presented to their implant. The present paper provides a quantitative framework for evaluating how many spectral channels of information are necessary for speech recognition. Sp...

A linear pseudo-inverse method for unsupervised illuminant recovery from natural scenes is presented. The algorithm, which uses a digital RGB camera, selects the naturally occurring bright areas (not necessarily the white ones) in natural images and converts the RGB digital counts directly into the spectral power distribution of the illuminants using a learning-based spectral procedure. Computa...

This paper compares the performance of various spectral shift estimators for use in spectral elastography, namely, the normalized cross-correlation (NCC), sum squared difference (SSD) and sum absolute difference (SAD). Simulation and experimental results demonstrate that the spectral SSD-based elastographic method exhibits no marked difference in performance compared to the more computationally...

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The re...

Spectral Clustering has become quite popular over the last few years and several new algorithms have been published. In this paper, we compare several of the best-known algorithms from the point of view of clustering quality over artificial and real datasets. We implement many variations of the existing spectral algorithms and compare their performance to see which features are more important. ...

In this paper, interpolation of linear predictive coding (LPC) parameters in terms of the following representations is investigated: linear prediction coefficient representation, reflection coefficient representation, log-arearatio representation, arc-sine reflection coefficient representation, cepstral coefficient representation, line spectral frequency representation, autocorrelation coeffici...