Sorry, you need to enable JavaScript to visit this website.

In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise.Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF).

A sensor network wishes to transmit information to a fusion center to allow it to detect a public hypothesis, but at the same time prevent it from inferring a private hypothesis. We propose a multilayer sensor network structure, where each sensor first applies a nonlinear fusion function on the information it receives from sensors in a previous layer, and then a linear weighting matrix to distort the information it sends to sensors in the next layer.

In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class,the classification accuracy is guaranteed by a constraint that the ratio
of variances of signals in two different classes is lower bounded.Then, a sparse spatial filter is achieved by minimizing the l1-norm of filter coefficients and channels of EEG signals can be further optimized.

Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction.

Information from different bio--signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio--signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech--based features. Three different problems are addressed: classification of PD patients vs.