A Neural Network-Based Random Access Protocol for Crowded Massive MIMO Systems.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage.

Low-Cost Joystick for Pediatric Respiratory Exercises.

The use of body signals for health care applications has become ubiquitous in the last decade. One utilization of such measurements is the monitoring of respiratory flow for physiotherapy assistance. This application is based on relative flow measures which can rely on inexpensive sensors. Based on that, we present a low-cost electronic device that detects blows and suctions with a pressure sensor and emulates a keyboard for interfacing with computers. This joystick allows children to control free internet games by associating blows and suctions with different intensities to keyboard actions. Also, the intensity can be calibrated according to the user's pulmonary capacities. This feature is adequate for gradual respiratory physiotherapy and can be customized for each patient. In order to verify the operation of the proposed device, practical tests were performed with three online free games, where the joystick functionality was assessed with different therapeutic configurations.

Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders.

This work describes a novel algorithm to identify laryngeal pathologies, by the digital analysis of the voice. It is based on Daubechies' discrete wavelet transform (DWT-db), linear prediction coefficients (LPC), and least squares support vector machines (LS-SVM). Wavelets with different support-sizes and three LS-SVM kernels are compared. Particularly, the proposed approach, implemented with modest computer requirements, leads to an adequate larynx pathology classifier to identify nodules in vocal folds. It presents over 90% of classification accuracy and has a low order of computational complexity in relation to the speech signal's length.