BACKGROUND Since myocardial blood flow changes dynamically after reperfusion and since both hyperemia and impairment in microvascular function exist within the acutely reperfused bed, we sought to investigate the role of myocardial contrast echocardiography (MCE) in (1) defining the temporal variability in perfusion patterns after reflow and relating these to microsphere-derived blood flow; (2)...

Multi-car elevator (MCE for short) systems have been receiving increasing attention these days. An MCE has two or more cars in one shaft. It is difficult to control MCEs by using algorithms developed for single-car elevator systems, because MCEs need to avoid collision of the cars of the same shaft. In this paper, we propose an algorithm to control MCE systems, and show the effectiveness of our...

BACKGROUND Distinguishing ischemic from nonischemic origin in patients presenting with acute heart failure (AHF) not resulting from acute myocardial infarction has both therapeutic and prognostic implications. The aim of the study was to assess whether myocardial contrast echocardiography (MCE) can identify underlying coronary artery disease (CAD) as the cause of AHF. METHODS AND RESULTS Fift...

This paper evaluates the application of the Minimum Classification Error (MCE) training to online-handwritten text recognition based on Hidden Markov Models. We describe an allograph-based, character level MCE training aimed at minimizing the character error rate while enabling flexibility in writing style. Experiments on a writer-independent discrete character recognition task covering all alp...

Discriminative training for hidden Markov models (HMMs) has been a central theme in speech recognition research for many years. One most popular technique is minimum classification error (MCE) training, with the objective function closely related to the empirical error rate and with the optimization method based traditionally on gradient descent. In this paper, we provide a new look at the MCE ...

We propose a deterministic annealing (DA) algorithm to design classifiers based on continuous observation hidden Markov models. The algorithm belongs to the class of minimum classification error (MCE) techniques that are known to outperform maximum likelihood (ML) design. Most MCE methods smooth the piecewise constant classification error cost to facilitate the use of local descent optimization...

INTRODUCTION AND OBJECTIVES It has been suggested that abnormal perfusion as derived from cardiovascular magnetic resonance imaging (CMR) is a transient dysfunction of microcirculation after myocardial infarction (MI) with TIMI 3 flow. We hypothesized that defects of myocardial perfusion may persist during the following months. METHODS Forty-seven patients with MI and sustained TIMI 3 flow un...

In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates the necessity of artificial constraint...

Minimum Classification Error (MCE) training is difficult to apply to language modeling due to inherent scarcity of training data (N-best lists). However, a whole-sentence exponential language model is particularly suitable for MCE training, because it can use a relatively small number of powerful features to capture global sentential phenomena. We review the model, discuss feature induction, fi...

This paper presents a prototype of a simulation model based on cellular automata (CA), and multicriteria evaluation (MCE) and integrated with GIS. Speci® cally, a method, analytical hierarchy process (AHP), of MCE is used here to derive behaviour-oriented rules of transition in CA. A t̀ight’ integration strategy is adopted, which means that the modules of MCE and CA are written in the C programm...