A possible solution to the problem of aggregating heterogeneous fields in the all-sciences case relies on the normalization of the raw citations received by all publications. In this paper, we study an alternative solution that does not require any citation normalization. Provided one uses sizeand scale-independent indicators, the citation impact of any research unit can be calculated as the av...

In this paper, we propose several compensation approaches to alleviate the effect of additive noise on speech features for speech recognition. These approaches are simple yet efficient noise reduction techniques that use online constructed pseudo stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transforms for noisecorrupted speech features to ...

Most compensation methods to improve the robustness of speech recognition systems in noisy environments such as spectral subtraction, CMN, and MVN, rely on the fact that noise and speech spectra are independent. However, the use of limited window in signal processing may introduce a cross-term between them, which deteriorates the speech recognition accuracy. To tackle this problem, we introduce...

MicroRNA (miRNA) regulate gene expression through posttranscriptional mRNA degradation or suppression of translation. Many (pre)analytical issues remain to be resolved for miRNA screening with TaqMan Low Density Arrays (TLDA) in plasma samples, such as optimal RNA isolation, preamplification and data normalization. We optimized the TLDA protocol using three RNA isolation protocols and preamplif...

We propose a method for estimating the parameters of SPLICElike transformations from individual utterances so that this type of transformation can be used to normalize acoustic feature vectors for speech recognition on an utterance-by-utterance basis in a similar manner to cepstral mean normalization. We report results on an in-house French language multi-speaker database collected while deploy...

Previous approaches for modulation spectrum equalization were evaluated only for the Aurora 2 small vocabulary task. We further apply these approaches on the Aurora 4 large vocabulary task. In the spectral histogram equalization (SHE) approach, we equalize the histogram of the modulation spectrum for each utterance to a reference histogram obtained from clean training data. In the magnitude rat...

In this paper we present a comparison of the two dominant image preprocessing techniques for palmprint recognition, namely, histogram equalization and mean-variance normalization. We show that both techniques pursue a similar goal and that the difference in recognition efficiency stems from the fact that not all assumptions underlying the mean-variance normalization approach are always met. We ...

This paper introduces a delta cepstrum normalization (DCN) technique for speaker verification under noisy conditions. Cepstral feature normalization techniques are widely used to mitigate spectral variations caused by various types of noise; however, little attention has been paid to normalizing delta features. A DCN technique that normalizes not only base features but also delta-features was r...

We propose a method for estimating the parameters of SPLICElike transformations from individual utterances so that this type of transformation can be used to normalize acoustic feature vectors for speech recognition on an utterance-by-utterance basis in a similar manner to cepstral mean normalization. We report results on an in-house French language multi-speaker database collected while deploy...

PURPOSE To test the effect of a novel signal normalization method for reducing systematic optical coherence tomography (OCT) measurement differences among multiple spectral-domain (SD) OCT devices. METHODS A total of 109 eyes from 59 subjects were scanned with two SD-OCT devices (Cirrus and RTVue) at the same visit. Optical coherence tomography image data were normalized to match their signal...