Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n...

This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs). To achieve faster parsing, our proposal employs a pruning technique to reduce unnecessary edges in the search space. The key is to repetitively conduct Viterbi inside and outside parsing, while gradually expanding the search space to efficiently compute heuristic bounds used for pruni...

Our main objective in this thesis is to study the effect of narrow band interference on OFDM systems operating in the 2.4 Ghz ISM band and identify ways to improve upon existing techniques to deal with them. We first consider how narrow band signals interfere with OFDM systems. Various noise variance estimation and signal to noise ratio estimation techniques for OFDM systems are then discussed....

A new insertion/deletion correction scheme is presented for standard convolutional codes that makes use of multiple parallel-interconnected Viterbi decoders. Whenever an insertion or deletion error occurs, the connections between different Viterbi decoders ensure that decoding will proceed from the decoder that is in synchronization. In this way, a larger Viterbi decoder is created that can cor...

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the Baum, Viterbi, and Baum–Welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss–Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that th...

DBNs (Dynamic Bayesian Networks) [1] are powerful tool in modeling time-series data, and have been used in speech recognition recently [2,3,4]. The “decoding” task in speech recognition means to find the viterbi path [5](in graphical model community, “viterbi path” has the same meaning as MPE “Most Probable Explanation”) for a given acoustic observations. In this paper we describe a new algorit...

In this paper we extend an estimation technique based on Viterbi algorithm for discrete-time chaotic signals immersed in noise. The proposed modification allows for orbits generated by maps with nonuniform invariant density to be estimated. This modified Viterbi algorithm is used in two digital modulation schemes: the Modified Maximum Likelihood Chaos Shift Keying using one and two maps. Both h...

In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with a deterministic but unknown spectral amplitude. The Bernoulli variables are modeled in turn by first order Markov processes with transition probabilities learned from a training database. We consi...

In this paper, which is an extended version of our work at LVA/ICA 2010 [1], the problem of Inter Symbol Interface (ISI) Sparse channel estimation and equalization will be investigated. We firstly propose an adaptive method based on the idea of Least Mean Square (LMS) algorithm and the concept of smoothed l0 (SL0) norm presented in [2] for estimation of sparse ISI channels. Afterwards, a new no...

In this paper a probabilistic tagger for molecular biology related abstracts is presented and evaluated. The system consists of three modules: a rule based molecular-biology names detector, an unknown words handler, and a Hidden Markov model based tagger which are used to annotate the corpus with an extended set of grammatical and molecular biology tags. The complete system has been evaluated u...