2010
Authors
Gomes, EF; Bras, LMR; Ribeiro, MMM;
Publication
International Journal of Imaging
Abstract
In this paper we present an algorithm that identifies circular drops of different sizes in monochromatic digitized frames of a liquid-liquid chemical process. These image frames were obtained at our Laboratory, using a non-intrusive process, with a digital video camera, a microscope, and an illumination setup from a dispersion of toluene in water within a transparent mixing vessel. Here we describe in detail the two-phase approach used for the automatic identification of the drops in images of the chemical process, which employs a Hough transform. Empirical evaluation on an independent set of images shows promising results for the automatic classification of the drops. Copyright © 2010 by IJI (CESER Publications).
2010
Authors
Novoa, I; Gallego, J; Ferreira, PG; Mendez, R;
Publication
NATURE CELL BIOLOGY
Abstract
Meiotic and early-embryonic cell divisions in vertebrates take place in the absence of transcription and rely on the translational regulation of stored maternal messenger RNAs. Most of these mRNAs are regulated by the cytoplasmic-polyadenylation-element-binding protein (CPEB), which mediates translational activation and repression through cytoplasmic changes in their poly(A) tail length. It was unknown whether translational regulation by cytoplasmic polyadenylation and CPEB can also regulate mRNAs at specific points of mitotic cell-cycle divisions. Here we show that CPEB-mediated post-transcriptional regulation by phase-specific changes in poly(A) tail length is required for cell proliferation and specifically for entry into M phase in mitotically dividing cells. This translational control is mediated by two members of the CPEB family of proteins, CPEB1 and CPEB4. We conclude that regulation of poly(A) tail length is not only required to compensate for the lack of transcription in specialized cell divisions but also acts as a general mechanism to control mitosis.
2010
Authors
Barczy, M; Ispany, M; Pap, G; Scotto, M; Silva, ME;
Publication
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Abstract
We consider integer-valued autoregressive models of order one contaminated with innovational outliers. Assuming that the time points of the outliers are known but their sizes are unknown, we prove that Conditional Least Squares (CLS) estimators of the offspring and innovation means are strongly consistent. In contrast, CLS estimators of the outliers' sizes are not strongly consistent. We also prove that the joint CLS estimator of the offspring and innovation means is asymptotically normal. Conditionally on the values of the process at time points preceding the outliers' occurrences, the joint CLS estimator of the sizes of the outliers is asymptotically normal.
2009
Authors
de Sousa, RJT;
Publication
BIOSIGNALS 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIO-INSPIRED SYSTEMS AND SIGNAL PROCESSING
Abstract
In this paper, an accurate method that estimates the HNR from sustained vowels based on harmonic structure modeling is proposed. Basically, the proposed algorithm creates an accurate harmonic structure where each harmonic is parameterized by frequency, magnitude and phase. The harmonic structure is then synthesized and assumed as the harmonic component of the speech signal. The noise component can be estimated by subtracting the harmonic component from the speech signal. The proposed algorithm was compared to others HNR extraction algorithms based on spectral, cepstral and time domain methods, and using different performance measures.
2009
Authors
Almeida, R; Reis, LP; Jorge, AM;
Publication
Actas da 4a Conferencia Iberica de Sistemas e Tecnologias de Informacao, CISTI 2009
Abstract
2009
Authors
Mendes Moreira, J; Jorge, AM; Soares, C; de Sousa, JF;
Publication
MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION
Abstract
Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.
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