In this paper we propose a novel approach to estimating Multiple-Input-Multiple-Output (MIMO) channels in space-time coded systems. The channel is assumed to introduce dispersion in both the spatial and temporal dimensions. The channel estimator is obtained by applying the Maximum Likelihood principle not only over a known pilot sequence, as it is done in the classical Least-Squares approaches,...

In this contribution, both numerical and experimental investigations are performed to evaluate the impact of diffuse scattering on the characteristics of vehicular propagation channels in highway environments. The response of a vehicle-tovehicle (V2V) channel can be composed of discrete specular path components and diffuse scattering components. Simulation results in two V2V scenarios with diff...

The performance of biometric recognition systems can become limited in operating environments due to the presence of extraneous noise factors. Biometric fusion alleviates this problem by consolidating information from various biometric sources, thereby achieving a higher recognition rate. One challenge faced in fusion is how to optimally combine information originating from the different source...

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the appro...

We propose using large-scale clustering of dependency relations between verbs and multiword nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since dependency relations capture the semantics of MNs well, the MN clusters constructed by using dependency relations should serve as a good gazetteer. However, the high level of computational cost has prevented the use of cluster...

In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In parti...

Multi skill scenarios are common place in real world problems and Intelligent Tutoring System questions alike, however, system designers have often relied on ad-hoc methods for modeling the composition of multiple skills. There are two common approaches to determining the probability of correct for a multi skill question: a conjunctive approach, which assumes that all skills must be known or a ...

In this paper, a new solution to the inverse problem of iterated random function systems is presented. The solution is based on a generalized hidden Markov model formalism to model the process generated by an iterated random function system. Instead of the assumption of conditional independence of observation sequence elements given the state sequence, the new model assumes the existence of sho...

Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appealing alternative to the ExpectationMaximization algorithm (Dempster et al., 1977). Seeking to avoid search error, DA begins by globally maximizing an easy concave function and maintains a local maximum as it gradually ...

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous work, we proposed factored MLLR (FMLLR) where MLLR parameter is defined as a function of a control parameter vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationm...