Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

Solving Large Scale Instances of the Distribution Design Problem Using Data Mining

In this paper we approach the solution of large instances of the distribution design problem. The traditional approaches do not consider that the instance size can significantly reduce the efficiency of the solution process. We propose a new approach that includes compression methods to transform the original instance into a new one using data mining techniques. The goal of the transformation is to condense the operation access pattern of the original instance to reduce the amount of resources needed to solve the original instance, without significantly reducing the quality of its solution. In order to validate the approach, we tested it proposing two instance compression methods on a new model of the replicated version of the distribution design problem that incorporates generalized database objects. The experimental results show that our approach permits to reduce the computational resources needed for solving large instances by at least 65%, without significantly reducing the quality of its solution. Given the encouraging results, at the moment we are working on the design and implementation of efficient instance compression methods using other data mining techniques

Novel efficient genome-wide SNP panels for the conservation of the highly endangered Iberian lynx

Background: The Iberian lynx (Lynx pardinus) has been acknowledged as the most endangered felid species in the world. An intense contraction and fragmentation during the twentieth century left less than 100 individuals split in two isolated and genetically eroded populations by 2002. Genetic monitoring and management so far have been based on 36 STRs, but their limited variability and the more complex situation of current populations demand more efficient molecular markers. The recent characterization of the Iberian lynx genome identified more than 1.6 million SNPs, of which 1536 were selected and genotyped in an extended Iberian lynx sample. Methods: We validated 1492 SNPs and analysed their heterozygosity, Hardy-Weinberg equilibrium, and linkage disequilibrium. We then selected a panel of 343 minimally linked autosomal SNPs from which we extracted subsets optimized for four different typical tasks in conservation applications: individual identification, parentage assignment, relatedness estimation, and admixture classification, and compared their power to currently used STR panels. Results: We ascribed 21 SNPs to chromosome X based on their segregation patterns, and identified one additional marker that showed significant differentiation between sexes. For all applications considered, panels of autosomal SNPs showed higher power than the currently used STR set with only a very modest increase in the number of markers. Conclusions: These novel panels of highly informative genome-wide SNPs provide more powerful, efficient, and flexible tools for the genetic management and non-invasive monitoring of Iberian lynx populations. This example highlights an important outcome of whole-genome studies in genetically threatened species