People with vision impairment use Braille language for reading, writing, and communication. The basic structure of the Braille language consists of six dots arranged in three rows and two column cells, which are identified by visually impaired people using finger touch. However, it is difficult to memorize the pattern of dots that form the Braille characters. This research presents a novel approach for automatic Braille characters recognition. The designed approach works in two main stages. In first stage, image alignment & enhancement are performed using several image preprocessing techniques. In second stage, character recognition is performed with a proposed lightweight convolution neural network (CNN). As CNN shows promise for accurate recognition of optical characters. Therefore, we adopted several recently proposed state-of-the-art CNN networks for Braille characters’ recognition. To make the networks light and improve their recognition performance, we proposed a strategy by replacing few modules in the original CNNs with an inverted residual block (IRB) module with less computational cost. The novelty of this work lies in CNN model design and output performance. We executed the effectiveness of the designed setup through experiments on two different publicly available benchmark Braille datasets obtained from visually impaired people. On the English Braille and Chinese double-sided Braille image (DSBI) datasets, the proposed model shows a prediction accuracy of 95.2% and 98.3%, respectively. The reported test time of model is about 0.01s for English and 0.03s DSBI Braille images. In comparison to state-of-the-art, designed method is robust, effective, and capable to identify the Braille characters efficiently. In future, the functional performance of the proposed Braille recognition scheme will be tested through accessible user interfaces.

It is well known that there may be significant individual differences in physiological signal patterns for emotional responses. Emotion recognition based on electroencephalogram (EEG) signals is still a challenging task in the context of developing an individual-independent recognition method. In our paper, from the perspective of spatial topology and temporal information of brain emotional patterns in an EEG, we exploit complex networks to characterize EEG signals to effectively extract EEG information for emotion recognition. First, we exploit visibility graphs to construct complex networks from EEG signals. Then, two kinds of network entropy measures (nodal degree entropy and clustering coefficient entropy) are calculated. By applying the AUC method, the effective features are input into the SVM classifier to perform emotion recognition across subjects. The experiment results showed that, for the EEG signals of 62 channels, the features of 18 channels selected by AUC were significant (p < 0.005). For the classification of positive and negative emotions, the average recognition rate was 87.26%; for the classification of positive, negative, and neutral emotions, the average recognition rate was 68.44%. Our method improves mean accuracy by an average of 2.28% compared with other existing methods. Our results fully demonstrate that a more accurate recognition of emotional EEG signals can be achieved relative to the available relevant studies, indicating that our method can provide more generalizability in practical use.

The Covid-19 pandemic is a prevalent health concern around the world in recent times. Therefore, it is essential to screen the infected patients at the primary stage to prevent secondary infections from person to person. The reverse transcription polymerase chain reaction (RT-PCR) test is commonly performed for Covid-19 diagnosis, while it requires significant effort from health professionals. Automated Covid-19 diagnosis using chest X-ray images is one of the promising directions to screen infected patients quickly and effectively. Automatic diagnostic approaches are used with the assumption that data originating from different sources have the same feature distributions. However, the X-ray images generated in different laboratories using different devices experience style variations e.g., intensity and contrast which contradict the above assumption. The prediction performance of deep models trained on such heterogeneous images of different distributions with different noises is affected. To address this issue, we have designed an automatic end-to-end adaptive normalization-based model called style distribution transfer generative adversarial network (SD-GAN). The designed model is equipped with the generative adversarial network (GAN) and task-specific classifier to transform the style distribution of images between different datasets belonging to different race people and carried out Covid-19 detection effectively. Evaluated results on four different X-ray datasets show the superiority of the proposed model to state-of-the-art methods in terms of the visual quality of style transferred images and the accuracy of Covid-19 infected patient detection. SD-GAN is publicly available at: https://github.com/tasleem-hello/SD-GAN/tree/SD-GAN .