Detection of Background Water Leaks Using a High-Resolution Dyadic Transform

This article solves the problem of detecting water leaks with a minimum size of down to 1 mm in diameter. Two new mathematical tools are used to solve this problem: the first one is the Te cross-spectral density and the second is Te coherence. These mathematical tools provide the possibility of discriminating spurious frequency components, making use of the property of multi-sensitivity. This advantage makes it possible to maximize the sensitivity of the frequency spectrum. The wavelet function used was Daubechies 45, because it provides an attenuation of 150 dB in the rejection band. The tools were validated with two scenarios. For the first scenario, a synthetic signal was analyzed. In the second scenario, two types of background leakage were analyzed: the first one has a diameter of 1 mm with a signal-to-noise ratio of 2.82 dB and flow rate of 33.7 mL/s, and the second one has a diameter of 4 mm with a signal-to-noise ratio of 9.73 dB with a flow rate of 125.0 mL/s. The results reported in this paper show that both the Te cross-spectral density and Te coherence are higher than those reported in scientific literature

Selection of the Best Set of Features for sEMG-Based Hand Gesture Recognition Applying a CNN Architecture

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

Image set preparation: A platform to prepare a myoelectric signal to train a CNN

Derived from the good performance in the classification of surface Electromyography signals using CNN for its application in prosthetics, rehabilitation, and medicine, we present a platform that, from a surface Electromyography, performs the necessary digital processing to generate an image database to train a Convolutional Neural Network. This platform requires inputting the protocol parameters under which the myoelectric signal was acquired. In addition, it allows selection among four groups of Time-Domain features and four types of images that have shown good performance (above 90%) in the current literature. The platform generates images in separate folders for each movement according to the selected parameters. This work offers a valuable tool in classification using surface Electromyography and Convolutional Neural Networks, enabling more efficient customization and optimization of training processes