Improvement in mechanical properties of recycled GF prepreg with CNT reinforced composites using a spray coating method

Dispersion and shape of nanoparticles, as well as interfacial conditions, add significantly to difficulties in composite manufacture. In the work reported here, an innovative method of recycling composites using out-of-date prepreg was investigated in which the carbon nanotube (CNT) on the prepreg was optimally coated. Nanocomposites utilizing the out-of-date prepreg were coated with CNT and fabricated by a sheet molding method. CNT nanofillers were observed to be uniformly dispersed on epoxy prepreg by spray coating. The mechanical and interfacial properties of these CNT coated nanocomposites were improved over those of more conventionally manufactured carbon fiber/epoxy composites. The CNT nanofillers were embedded at the epoxy and fiber interface, as a result of etching of the epoxy prepreg surface by a CNT dispersion solution which enhanced interfacial reactivity.     

Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016