The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.

A phase mask represented as the superposition of the four Zernike polynomials associated with primary coma and the trefoil aberrations is presented. The restoration process of the intermediate image is performed by means of the use of the quasi-point source concept. Numerical results of the extended depth of field are presented and compared with other methods of wave front coding applied on a microscope.

Industrial machines regularly work at their limits causing excessive long-term vibrations that deteriorate their movement, stability, and precision. In this sense, reference profiles are able to reduce the detrimental vibration effects by manipulating the machine motion dynamics using predefined movement trajectories. Most of the approaches for lessening damages due to long-term machine vibrations are based on polynomial functions with high computational complexity and high resources demand. Hence, in this work, an innovative acceleration outline based on a Gaussian function is proposed for machine motion trajectories. The introduced strategy simplifies the position-profile estimation that works as reference for restraining the machine movements through the parameters that define its motion dynamics; thus, a smooth and continuous jerk contour is produced, which reduces vibrations and improves the machine stability. Exhaustive computer-based and real-time experimental comparisons of the introduced scheme produces a significantly lower maximum jerk value than any of the others. The assessment of the presented approach was performed utilizing the software Matlab (R2020a) on a PC with an Intel Core i7-6500U microprocessor at 2.5 GHz, with 16 GB in RAM and a 64-bit operating system.

Proportional–integral–derivative controllers are applied for solving a wide range of problems in industrial processes. They are preferred over computational techniques because of their implementation simplicity, low cost, and robustness against noise. In this paper, a novel iterative feedback tuning method is proposed using fuzzy logic, where the design parameters proposed by the user are the desired plant overshoot and settling time. In contrast to classical methods, the proposed technique does not require a precise and complex mathematical model for tuning the proportional–integral–derivative controllers through the plant, nor does it need an expert who knows the precise behavior of the system as in methods based on computational techniques. Furthermore, unlike iterative feedback tuning methods that use cost functions and require several experiments to perform the iterations, this proposal uses fuzzy logic to update the controller parameters, which facilitates its implementation in programmable hardware. The proposed method can be easily implemented in software considering three main stages: pre-processing, fuzzy logic system, and post-processing. The simulation and experimental results demonstrate the effectiveness of the proposed method for proportional–integral–derivative controller tuning; moreover, according to the performed comparison, the proposed method provides a trade-off between performance and robustness in comparison with other tuning techniques.

This paper presents a novel non-invasive methodology to study the cutaneous wound healing process in rabbits. Spectral images were used to establish a quantitative evaluation of the healing process time-line dynamics. Measurements were conducted by image analysis of the wounds of an experimental population with two different systems: an acousto-optical acquiring/processing system permitted the experimental quantitative study of different stages naturally occurring during the healing process time-line. Honey and saline solution application were used in a subgroup of the population under study as examples of treatments that could be employed to purportedly accelerate cutaneous tissue restoration, and their efficacy was evaluated. The methodology described on this paper allows for a non-invasive and effective way to quantitatively study/monitor the wound healing processes in terms of spectral changes of the wound area. Finally, it was found that this methodology allows quantitative parametrization of measurements to be used as accurate testing grounds for new wound healing assisting clinical treatments.