Sentence examples for multimodal fusion of from inspiring English sources

To accomplish this, we intend to propose an adaptive learning architecture for the artificial neural network to learn the multimodal fusion of speech features.

Therefore, this modality-specific normalization seems appropriate for multimodal fusion of EEG and MEG.

However, there is a possibility that the hopes for multimodal fusion of fMRI and MEG/EEG may be misplaced.

The difference between the DR of multimodal fusion imaging F-Choline-PET/MRI and that of CeCT was statistically significant (P = 0.016), while, comparing mMRI and F-Choline-PET/CT with CeCT, it only tended to be significant (P = 0.093 for both modalities).

The fourth approach is to employ the methodology of multimodal fusion [34 40] to build highlight extraction/classification systems, which may bridge the gap between the extracted low-level features and the semantics of the data.

Developing such a generative model is the ultimate goal of multimodal fusion.

Only preliminary studies [ 11, 12] are available on the simultaneous acquisition of PET and MRI with integrated PET/MRI scanners on PC, and, to date, only one paper has addressed the issue of multimodal fusion PET/MRI imaging (with the two modalities acquired at separate times with different scanners) in the assessment of 17 patients with primary prostate cancer [ 17].

Considering all skeletal metastases, including those beyond the field-of-view (FOV) of mMRI and multimodal fusion imaging F-Choline-PET/MRI, F-Fluoride-PET/CT provided the highest DR (detecting all 9 patients affected by bone metastases), which was found to be significantly different from that of CeCT (P = 0.034).

Kryszczuk et al. proposed a method of performing multimodal fusion using face and speech data, combining signal quality measures and reliability estimates [17].

Gamma-band activity (30 Hz or higher) associated with multimodal fusion is enhanced early after onset of congruent auditory-visual speech stimuli (30 120 ms) [90].

This is because multimodal fusion provides multiple constraints on the estimation of unknown quantities generating data.