The main focus of this paper is a rigorous development and validation of a novel canonical correlation featureselection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum meansquare-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave-and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors.
The implementation of a load balancing policy on a continuous basis in a delay-limited distributed computing environment may not only drain the computational resources of each computational element (CE), hut can also lead to an unnecessary exchange of loads between the CEs. This degrades the system performance, measured by the overall completion time of the total tasks in the system. Thus, for a given distribution of the load among the CEs, there has to he an optimal number and distribution of discrete balancing instants. This paper focuses on fxing the number of balancing instants and optimizing the completion time over the strength of load balancing, which is controlled by the so-called gain parameter, and the time when the balancing is executed. First, the case when the load balancing is implemented at a single instant per node is presented. Then, a strategy is considered where a second load balancing instant is allowed for each node. The simulations show that both strategies outperform the continuous balancing policy. Moreover, with the double load-balancing strategy the overall completion time is further reduced in comparison tn the single load balancing case. It is also seen that the optimal choice of the gain parameter depends on the delay and this dependence becomes mom signifeant as the delays increase. This interplay between the strength of load balancing and the magnitude delay has a direct effect on the performance of the policy and on the sensitirity to the selection of the balancing instants.
Quantum-dot infrared photodetectors (QDIPs) are emerging as a promising technology for midwave-and longwave-infrared remote sensing and spectral imaging. One of the key advantages that QDIPs offer is their bias-dependent spectral response, which is brought about by the asymmetric bandstructure of the dot-in-a-well (DWELL) configuration. Photocurrents of a single QDIP, driven by different operational biases can, therefore, be viewed as outputs of different bands. It has been shown that this property, combined with post-processing strategies (applied to the outputs of a single sensor operated at different biases), can be used to perform adaptive spectral tuning and matched filtering. However, unlike traditional sensors, bands of a QDIP exhibit significant spectral overlap, an attribute that calls for the development of novel methods for feature selection. Additionally, the presence of detector noise further complicates such feature selection. In this paper, the theoretical foundations for discriminant analysis, based on spectrally adaptive feature selection, are developed and applied to data obtained from QDIP sensors in the presence of noise. The approach is based on a generalized canonicalcorrelation-analysis framework that is used in conjunction with an optimization criterion for the selection of feature subspaces. The criterion ranks the best linear combinations of the overlapping bands, providing minimal energy norm (a generalized Euclidean norm) between the centers of classes and their respective reconstructions in the space spanned by sensor bands. Experiments using ASTER-based synthetic QDIP data are used to illustrate the performance of rock-type Bayesian classification according to the proposed feature-selection method.
Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction 3 of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters.A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.4
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