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Many sensing gesture recognition systems based on Wi-Fi signals are introduced because of the commercial off-the-shelf Wi-Fi devices without any need for additional equipment. In this paper, a deep learning-based sign language recognition system is proposed. Wi-Fi CSI amplitude and phase information is used as input to the proposed model. The proposed model uses three types of deep learning: CNN, LSTM, and ABLSTM with a complete study of the impact of optimizers, the use of amplitude and phase of CSI, and preprocessing phase. Accuracy, F-score, Precision, and recall are used as performance metrics to evaluate the proposed model. The proposed model achieves 99.855%, 99.674%, 99.734%, and 93.84% average recognition accuracy for the lab, home, lab + home, and 5 different users in a lab environment, respectively. Experimental results show that the proposed model can effectively detect sign gestures in complex environments compared with some deep learning recognition models.

This paper introducss a new technique for designing a feedback cantroller of Hach nunber in a w i n d tunnel involving a tine delay. A new alternative representation is f i r s t devsloped, which transforms the infinite d h s i o n a l linear time-delay systw into a finite dimensional linear generalized state space system. This new approach

This paper focuses on falling of the elderly people which is considered as one of the most critical issue that can face them in their life. To deal with such issue, we propose a new approach named a Spatio-temporal Residual AutoEncoder (SRAE) model. This model is an unsupervised fall detector based on utilizing the deep learning technique to detect falls of the elderly people. Our proposed model uses autoencoder based on convolutional neural network, convolutional long short term memory (ConvLSTM) network, and residual connections to extract spatial and temporal features of videos captured from thermal cameras. The reconstruction error of an autoencoder is used to detect falls recorded in such thermal videos. Furthermore, SRAE model is tested on the publicly available thermal dataset where thermal images conserve the privacy of the elderly under observation which is a very important issue. The obtained results show that the our proposed model detects falls with high receiver operating characteristic area under curve (ROC AUC) (97%) ,and precision recall area under curve (PR AUC) (93%) compared to denoising autoencoder (DAE), convolutional autoencoder (CAE), and convolutional long short term memory autoencoder (CLSTMAE) introduced in the literature.

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