The existing results show the applicability of the Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, it may give rise to unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using a small and noisy data set. To overcome this corruptive phenomenon. To overcome this corruptive phenomenon, in this paper, a robust Hammerstein-Wiener model identification method is developed for highly nonlinear systems when using a small and noisy data set, where two parsimonious parametrization models with fewer parameters are used, and an iteration method is then used to retrieve the true system parameters from the parametrization models. Such modification can improve the parameter estimation performance in terms of accuracy and variance compared with the over-parametrization model based identification methods. All the above-mentioned developments are analyzed with variance analysis, along with a simulation example to confirm the effectiveness.

Depth-Image-Based-Rendering (DIBR) procedure aims to create virtual viewpoint using the adjacent viewpoints and the corresponding depth maps for free-viewpoint video applications. However, this processing inevitably introduces visual artifacts which differ from those of natural images, mainly including holes, stretching, and blurring. To accurately evaluate the quality of 3D-synthesized images, a robust blind metric is developed by analysis of Local and Global Statistical Properties (LGSP). On the one hand, for local features, since the DIBR processing tends to damage the amount of information at the disoccluded regions in 3D-synthesized images, we extract the local two-dimensional entropy features from the spatial domain and log-Gabor domain guided by the corresponding saliency map to measure local information variation. Moreover, local statistical features are extracted from fractal dimension maps to capture local texture variations due to significant local texture degradation engendered by the DIBR procedure. On the other hand, for global features, to measure the DIBR-induced geometric distortion, steerable pyramid decomposition is utilized to extract its cross-scale and cross-orientation joint statistical features. Meanwhile, log-energies features are extracted from multi-scale and multi-orientation wavelet subbands to characterize its global blurring. Finally, the combined local and global quality-aware features are utilized to train the quality prediction model using regression function to establish the mapping between visual features and subjective scores. Extensive experiments over three public 3D-synthesized image benchmark databases show that our metric outpaces the newly-developed 3D-synthesized image metrics and the popular 2D image metrics.

Objective. Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage. Approach. To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P ² CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training. Main results. Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P ² CSL in cross-subject EEG classification in comparison with SOTA methods. Significance. Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.