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Low intensity pulsed ultrasound (LIPUS) has been widely used in clinic for the treatment of repairing pseudarthrosis, bone fractures and of healing in various soft tissues. Some reports indicated that LIPUS accelerated peripheral nerve regeneration including Schwann cells (SCs) and injured nerves. But little is known about its appropriate intensities on autograft nerves. This study was to investigate which intensity of LIPUS improved the regeneration of gold standard postsurgical nerves in experimental rat model. Sprague-Dawley rats were made into 10 mm right side sciatic nerve reversed autologous nerve transplantation and randomly treated with 250 mW/cm2, 500 mW/cm2 or 750 mW/cm2 LIPUS for 2–12 weeks after operation. Functional and pathological results showed that LIPUS of 250 mW/cm2 significantly induced faster rate of axonal regeneration. This suggested that autograft nerve regeneration was improved.

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Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Urban areas have been focused recently on the remote sensing applications since their function closely relates to the distribution of built-up areas, where reflectivity or scattering characteristics are the same or similar. Traditional pixel-based methods cannot discriminate the types of urban built-up areas very well. This paper investigates a deep learning-based classification method for remote sensing images, particularly for high spatial resolution remote sensing (HSRRS) images with various changes and multiscene classes. Specifically, to help develop the corresponding classification methods in urban built-up areas, we consider four deep neural networks (DNNs): 1) convolutional neural network (CNN); 2) capsule networks (CapsNet); 3) same model with a different training rounding based on CNN (SMDTR-CNN); and 4) same model with different training rounding based on CapsNet (SMDTR-CapsNet). The performances of the proposed methods are evaluated in terms of overall accuracy, kappa coefficient, precision, and confusion matrix. The results revealed that SMDTR-CNN obtained the best overall accuracy (95.0%) and kappa coefficient (0.944) while also improving the precision of parking lot and resident samples by 1% and 4%, respectively.INDEX TERMS Deep learning, convolution neural network, urban built-up area, capsule network, model ensemble, high resolution remote sensing classification.