This work proposes an approach for dynamic facial expression recognition to recognize emotions in controlled environments, due to its computational efficiency. The CREMA-D and RAVDESS datasets are used, from which sequences of 100 frames per video are extracted. Preprocessing for geometric features is performed using Face Mesh and facial alignment, while for deep features, face detection, facial alignment, resizing, and center cropping are applied. Geometric features are computed from the internal angles between facial landmarks, and deep features are extracted using MobileNetV2, ShuffleNetV2, and EfficientNet-B0, followed by dimensionality reduction via NCA. Both representations are concatenated and used as input to an LSTM (for CREMA-D) and a BiLSTM (for RAVDESS). The proposed method achieves UAR/WAR scores of 63.68%/63.71% on CREMA-D and 79.50%/80.21% on RAVDESS, demonstrating that the proposed approach is efficient and competitive without relying on architectures with higher computational cost.

This article proposes a computer-aided diagnosis (CAD) system for the detection and identification of respiratory diseases, such as COVID-19, pneumonia, etc., in addition to differentiating a healthy case. Starting then, the proposal is comprised of the Discrete Wavelet Transform (DWT) with the Symlet10 function for the extraction of main features, together with the Limited Contrast Adaptive Histogram (CLAHE) method for contrast enhancement. For the classification of the cases, the Support Vector Machine (SVM) was used. The results showed considerable performance with the Medium Gaussian SVM model delivering 82.4% of correctly estimated values. Improve the capacity of detection and identification based on a supervised learning algorithm without the need to use high computational performance, considering that, in most of the health systems in Mexico, there is not the necessary hardware for the installation and operation of systems with high computational demand requirements.

The development of efficient audio coding schemes allowing an increasing information security and reduction of the storage requirement, is problem that has attracted the researchers interest over the last several years. To this end several schemes have been proposed, that allows an efficient compression and encryption of digital information. In most cases the information is firstly compressed before the encryption process. Because in several situations, to achieve a real time communication process, it is desirable to compress and encrypt the audio signal now when it is captured, it would be desirable to implement coding schemes able to encrypt and compress sensitive information simultaneously. A suitable approach to achieve this goal, is to use a compressing sensing-based systems which allows a simultaneous compression and encryption of the signal to be transmitted. This paper presents an audio encoding scheme using compressive sensing techniques which firstly segments the signal to be encoded signal in segments of M frames, each one with N samples. These are then transformed into a set of M sparse frames using the Discrete Cosine Transform (DCT) and multiplied by a properly designed random matrix of size M × N. The resulting vectors are then concatenated to generate the compressed and encrypted matrix. It is then feed to a chaotic mixing scheme to further increase the security of proposed system. Evaluation results shows that the proposed system achieves and efficient and secure compression and encryption, while satisfying the extended Wyner secrecy criterion (EWS).

Drowsiness and driver distraction are considered the main causes of traffic accidents in the world. Considering this situation, this paper proposes two important modifications to our previously proposed driver drowsiness and distraction detector for real-time implementation on handheld mobile devices, such as smartphones. The first modification is due to a large variation in the capacity of mobile devices. To adapt the proposed system to a wide range of mobile devices, we present two automatic threshold calculations, which are used to differentiate driver drowsiness from normal blinking and dangerous driver distraction from normal short-term distraction. The second modification is related to the alarm during a continuous dangerous situation of the driver. We introduce a new algorithm to ensure the continuous activation of the alarm while the dangerous situation continues. These improvements perform as the general algorithm, since when it was implemented in mobile devices with low computational power, as well as in devices that do not have these limitations, the alarm activation times were not affected; On the other hand, it was possible to increase the accuracy originally given by the first system with respect to Ground Truth by almost 25% on average, resulting in alarm activations not being affected to a great extent by the natural errors that the convolutional neural networks (CNN) may cause, these improvements are shown and supported by the implementation in real time through video links provided in this work.

This paper propose a new interest point detector, denominated Atomic Harris-Affine detector, in which the Gaussian function is replaced by a 2D up(x,y) Atomic function (AF). Proposed scheme takes advantage of the desirable characteristics of AF, such as compact support and low spectral leakage. The simulation results show that proposed detector improves repeatability and convergence rate on, 15% and 20%, respectively, compared with the conventional one under several image conditions, such as illumination, viewpoints, blurring level, rotation angle, scaling factor and JPEG lossy compression rate. Also the detection accuracy of proposed detector allows a faster detection of interest points compared with the conventional one.