in this paper, a new topology is proposed for three-phase to single-phase matrix converters, where more voltage levels are produced in the output in comparison with the conventional topologies. in addition, a new control method based on minimum error between the generated and the desired output voltages is proposed for three-phase to single-phase matrix converters. in the proposed control metho...

This paper computationally obtains optimal bounded-weight, binary, error-correcting codes for a variety of distance bounds and dimensions. We compare the sizes of our codes to the sizes of optimal constant-weight, binary, error-correcting codes, and evaluate the di erences.

In this paper we use a Minimum Classification Error (MCE) training paradigm to build a speaker identification system. The training is optimized at the string level for a text-dependent speaker identification task. Experiments performed on a small set speaker identification task show that MCE training can reduce closed-set identification errors by up to 20-25% over a baseline system trained usin...

The stability behaviour of the iterative decoding (ID) of Turbo-Codes and its innuence on the overall performance are discussed. Several criteria to characterize the ID are proposed and the most reliable ones are determined. These criteria are used to partition the transnitted blocks into stable and unstable ones. For unstable blocks the decoding step is determined which most likely produces a ...

Adaptive filtering has traditionally been developed based on the minimum mean square error (MMSE) principle and has found ever-increasing applications in communications. The paper develops adaptive filtering based on an alternative minimum bit error rate (MBER) criterion for communication applications. It is shown that the MBER filtering exploits the non-Gaussian distribution of filter output e...

We derive a general lower bound on the minimum-error probability for ambiguous discrimination between arbitrary m’s mixed quantum states with given prior probabilities. When m = 2, this bound is precisely the well-known Helstrom limit. Also, we give a general lower bound on the minimum-error probability for discriminating quantum operations. In particular, we show necessary and sufficient condi...

Ahstract: In this paper we discuss the thresholding methods [1] due to Kittler and Illingworth. Two methods arc suggested in [1]. namely, 1) minimizing a criterion function and 2) iterative search of the minimum error threshold. We have tested the two methods. The first one is found to be robust even in the case of weak bimodality in the histogram. The second one is faster than the first, but a...

Recent studies suggest that the minimum error entropy (MEE) criterion can outperform the traditional mean square error criterion in supervised machine learning, especially in nonlinear and non-Gaussian situations. In practice, however, one has to estimate the error entropy from the samples since in general the analytical evaluation of error entropy is not possible. By the Parzen windowing appro...

We describe an open-source implementation of minimum error rate training (MERT) for statistical machine translation (SMT). This was implemented within the Moses toolkit, although it is essentially standsalone, with the aim of replacing the existing implementation with a cleaner, more flexible design, in order to facilitate further research in weight optimisation. A description of the design is ...

We construct a distribution-free Bayes optimal classifier called the Minimum Error Minimax Probability Machine (MEMPM) in a worst-case setting, i.e., under all possible choices of class-conditional densities with a given mean and covariance matrix. By assuming no specific distributions for the data, our model is thus distinguished from traditional Bayes optimal approaches, where an assumption o...