@conference {18218,
	title = {Gradient descent approach for secure localization in resource constrained wireless sensor networks},
	booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/03//},
	pages = {1854 - 1857},
	abstract = {Many sensor network related applications require precise knowledge of the location of constituent nodes. In these applications, it is desirable for the wireless nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most localization algorithms rely on anchor nodes whose locations are known to determine the positions of the remaining nodes. In an adversarial scenario, some of these anchor nodes could be compromised and used to transmit misleading information aimed at preventing the accurate localization of the remaining sensors. In this paper, a computationally efficient algorithm to determine the location of sensors that can resist such attacks is described. The proposed algorithm combines gradient descent with a selective pruning of inconsistent measurements to achieve good localization accuracy. Simulation results show that the proposed algorithm has performance comparable to existing schemes while requiring less computational resources.},
	keywords = {algorithms;resource, approach;localization, computational, constrained, descent, localization;gradient, methods;wireless, networks;, networks;secure, resources;gradient, sensor, wireless},
	doi = {10.1109/ICASSP.2010.5495371},
	author = {Garg,R. and Varna,A.L. and M. Wu}
}
@conference {12581,
	title = {Kernel fully constrained least squares abundance estimates},
	booktitle = {Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International},
	year = {2007},
	month = {2007/07//},
	pages = {4041 - 4044},
	abstract = {A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.},
	keywords = {abundance, algorithm;kernel, analysis;, AVIRIS, based, constrained, constraint;feature, constraint;spectral, estimates;linear, extraction;geophysical, feature, fully, image;hyperspectral, imagery;kernel, least, mixing, model;nonnegativity, processing;geophysical, processing;multidimensional, processing;spectral, signal, space;kernel, squares, techniques;image, unmixing;sum-to-one},
	doi = {10.1109/IGARSS.2007.4423736},
	author = {Broadwater, J. and Chellapa, Rama and Banerjee, A. and Burlina, P.}
}
@conference {12691,
	title = {A hybrid algorithm for subpixel detection in hyperspectral imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2004. IGARSS {\textquoteright}04. Proceedings. 2004 IEEE International},
	volume = {3},
	year = {2004},
	month = {2004/09//},
	pages = {1601 -1604 vol.3 - 1601 -1604 vol.3},
	abstract = {Numerous subpixel detection algorithms utilizing structured backgrounds have been developed over the past few years. These range from detection schemes based on spectral unmixing to generalized likelihood ratio tests. Spectral unmixing algorithms such as the Fully Constrained Least Squares (FCLS) algorithm have the advantage of physically modeling the interactions of spectral signatures based on reflectance/emittance spectroscopy. Generalized likelihood ratio tests like the Adaptive Matched Subspace Detector (AMSD) have the advantage of identifying targets that are statistically different from the background. Therefore, a hybrid detector based on both AMSD and FCLS was developed to take advantage of each detector{\textquoteright}s strengths. Results demonstrate that the hybrid detector achieved the lowest false alarm rates while also producing meaningful abundance estimates},
	keywords = {alarm, algorithm;abundance, algorithm;Fully, algorithm;hybrid, algorithm;statistical, AMSD;Adaptive, analysis;structured, approximations;maximum, backgrounds;subpixel, constrained, detection;alarm, detection;target, Detector;FCLS, detector;hyperspectral, estimation;, estimation;emittance, identification;adaptive, imagery;reflectance, least, likelihood, Matched, matching;least, processing;geophysical, rate;generalized, ratio, signal, signatures;spectral, spectra;false, spectra;spectral, squares, subspace, systems;geophysical, techniques;image, tests;hybrid, unmixing},
	doi = {10.1109/IGARSS.2004.1370633},
	author = {Broadwater, J. and Meth, R. and Chellapa, Rama}
}
@conference {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
