The myoelectric interfaces are being used in rehabilitation technology, assistance and as an input device. This review focuses on an insightful analysis of the data acquisition system of EMG signals from these interfaces. According to applications reported in research articles of the last five years, the properties of the sensors, the number of channels, the pre-processing of the EMG signal, as well as the software and hardware used were identified. This analysis was performed for the following applications: monitoring of muscular activation for rehabilitation, muscle activation plans, and identification of possible pathologies, exoskeletons, electric of wheelchairs, prosthetics control, myoelectric bracelets, handwriting recognition and silent speech recognition. The results presented in this review become a guide of recommendations for the myoelectric signal processing according to the application of the interface. The main developments, degrees of research and open challenges are also presented in this direction.

In this work, the authors present the evaluation of energy detection (ED) based on the Welch's periodogram for spectrum sensing applied to cognitive radio networks. The authors analyse the impact of the number of points in the discrete Fourier transform and the number of averaged periodograms for power density spectrum estimation on the performance of ED. The authors identify that the inclusion of these parameters in the distribution of the test statistic used to detect the presence of primary users, improves the probability of detection. However, in the presence of noise uncertainty, the improvement on the probability of detection will come at the expense of an increased probability of false alarm. With the approach considered in this work is possible to increment the probability of detection for a given and low signal‐to‐noise ratio, without increasing the number of samples collected from primary signal. However, to maintain a constant probability of false alarm, accurate techniques for noise variance estimation are needed, because detection‐threshold value is highly dependent on the noise power present at each sensing interval.

In this work, the evaluation of a spectrum sensing strategy based on the Frequency Domain Entropy applied to cognitive radio networks is presented. Entropy estimation is performed using Bartlett periodogram. A tradeoff between variance and the spectral resolution for Bartlett periodogram is figured out. This tradeoff affects the probability of detection and false alarm of the spectrum sensing strategy under environments with low signal-to-noise ratio and noise uncertainty. The Entropy detector is optimal when the product of the number of segments and the number of points used is equal to the number of available samples of the received signal.

For its simplicity and ease of implementation, Energy detection (ED) is attractive for spectrum sensing in cognitive radio (CR) systems. In ED, the energy captured is compared against a fixed detection threshold, which is estimated as a function of the noise power. Uncertainties caused by the imperfect knowledge of the noise power, referred to as noise uncertainty, leads to a reduced performance of the ED, particularly at low SNR levels. In this work, we introduce a novel approach that aims to overcome the SNR-wall problem of the ED caused by the noise uncertainty effects. This proposal relies on the cooperation of multiple receivers for adapting, at each sensing period, the detection threshold to the noise power present at the time of spectrum sensing process is occurring. The preliminary evaluation show that when the detection threshold is adapted to the current noise conditions, the ED observes a significant improvement on the probability of detection under the presence of noise uncertainty, compared to the non-adaptive threshold ED. Furthermore, with our proposal, the ED performs better than the well-known Eigenvalue-based Detection (EBD) and the Generalized Likelihood Ratio Test-based Detection (GLRD) for low to moderate sensing time and number of cooperating receivers.