@article {17591,
	title = {Efficient lookup on unstructured topologies},
	journal = {IEEE Journal on Selected Areas in Communications},
	volume = {25},
	year = {2007},
	month = {2007/01//},
	pages = {62 - 72},
	abstract = {We present LMS, a protocol for efficient lookup on unstructured networks. Our protocol uses a virtual namespace without imposing specific topologies. It is more efficient than existing lookup protocols for unstructured networks, and thus is an attractive alternative for applications in which the topology cannot be structured as a Distributed Hash Table (DHT). We present analytic bounds for the worst-case performance of LMS. Through detailed simulations (with up to 100,000 nodes), we show that the actual performance on realistic topologies is significantly better. We also show in both simulations and a complete implementation (which includes over five hundred nodes) that our protocol is inherently robust against multiple node failures and can adapt its replication strategy to optimize searches according to a specific heuristic. Moreover, the simulation demonstrates the resilience of LMS to high node turnover rates, and that it can easily adapt to orders of magnitude changes in network size. The overhead incurred by LMS is small, and its performance approaches that of DHTs on networks of similar size},
	keywords = {Computer science, DHT, distributed algorithms, Distributed computing, distributed hash table, Least squares approximation, LMS, local minima search, lookup protocol, Network topology, node failures, Peer to peer computing, Performance analysis, Protocols, replication strategy, Resilience, Robustness, table lookup, telecommunication network topology, unstructured network topology},
	isbn = {0733-8716},
	doi = {10.1109/JSAC.2007.07007},
	author = {Morselli,R. and Bhattacharjee, Bobby and Marsh,M.A. and Srinivasan, Aravind}
}
@article {19662,
	title = {MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/12//},
	pages = {1960 - 1972},
	abstract = {In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC},
	keywords = {algorithms, approximate inference, Artificial intelligence, auxiliary variable particle filter, Computational efficiency, continuous state space, downdating, Image Enhancement, Image Interpretation, Computer-Assisted, Inference algorithms, Information Storage and Retrieval, laser range scanner, laser range scanner., Least squares approximation, least squares approximations, Least squares methods, linear least squares, Markov chain Monte Carlo, Markov processes, MCMC data association, merged measurements, Monte Carlo methods, Movement, multiple merged measurements, multitarget tracking, particle filter, particle filtering (numerical methods), Particle filters, Pattern Recognition, Automated, probabilistic model, QR factorization, Radar tracking, Rao-Blackwellized, real time multitarget tracking, Reproducibility of results, Sampling methods, Sensitivity and Specificity, sensor fusion, sparse factorization updating, sparse least squares, State-space methods, Subtraction Technique, target tracking, updating},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {11957,
	title = {Optimal Computing Of Structure From Motion Using Point Correspondences In Two Frames},
	booktitle = {Proceedings of Second International Conference on Computer Vision},
	year = {1988},
	month = {1988/12/05/8},
	pages = {449 - 453},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Automation, Computer vision, Educational institutions, Gaussian noise, Image motion analysis, Laboratories, Least squares approximation, Least squares methods, Motion estimation, Optical computing},
	isbn = {0-8186-0883-8},
	doi = {10.1109/CCV.1988.590022},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
