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.

In this work we introduce a novel and viable model for the measurement of ethanol concentration in water. The concept relies on the optical high spectral sensitivity of plasmonic devices. The ethanol in water mixture shows a linear ratio with the refractive index and the concentration percentage on a selected range of 0 to 45%. The proposed model is based on the refractive index linear relationships of the ethanol concentration and the plasmonic resonance wavelength. Therefore enabling the indirect measurement of the ethanol concentration through the plasmonic resonance wavelength.

Based on a V-shaped microactuator with a pair of beams, modifications were made to the length and width of a microactuator to observe the effects. A theoretical approach and numerical characterization of the modified microactuator were performed. Its performance was compared to a similar microactuator with equal beam widths, and a V-shaped microactuator. The proposed microactuator, fed at 2 V, compared to the V-shaped actuator, showed a 370.48% increase in force, but a 29.8% decrease in displacement. The equivalent von Mises stress level increased (until 74.2 MPa), but was below the silicon ultimate stress. When the modified microactuator was applied to the proposed microgripper, compared to the case using a V-shaped actuator, the displacement between the jaws increased from 0.85 µm to 4.85 µm, the force from 42.11 mN to 73.61 mN, and the natural frequency from 11.36 kHz to 37.99 kHz; although the temperature increased, on average, from 42 °C up to 73 °C, it is not a critical value for many microobjects. The maximum equivalent von Mises stress was equal to 68.65 MPa. Therefore, it has been demonstrated that the new modified microactuator with damping elements is useful for the proposed microgripper of novel geometry, while a reduced area is maintained.

This article solves the problem of detecting water leaks with a minimum size of down to $1\mathrm{mm}$ in diameter. Two new mathematical tools are used to solve this problem: the first one is the ${\mathcal{T}}_e$ cross-spectral density and the second is ${\mathcal{T}}_e$ 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 $150\mathrm{dB}$ 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 $1\mathrm{mm}$ with a signal-to-noise ratio of $2.82\mathrm{dB}$ and flow rate of $33.7\mathrm{mL}/\mathrm{s}$, and the second one has a diameter of $4\mathrm{mm}$ with a signal-to-noise ratio of $9.73\mathrm{dB}$ with a flow rate of $125.0\mathrm{mL}/\mathrm{s}$. The results reported in this paper show that both the ${\mathcal{T}}_e$ cross-spectral density and ${\mathcal{T}}_e$ coherence are higher than those reported in scientific literature.

The implementation of embedded systems for myoelectric signal classification is key to the development of prosthetic and other real-time assistive technologies. This work presents a hand gesture classification system based on an artificial neural network (ANN) deployed on an ESP32 microcontroller. In contrast to approaches that require multiple acquisition channels or complex configurations, the proposed system uses a single surface electromyography (sEMG) channel, reducing cost and complexity. Data were collected from eight participants performing eight hand and wrist gestures. The system achieved classification accuracies of up to 93.05% for six gestures, including rest. Furthermore, the ESP32 implementation demonstrated inference times compatible with real-time operation, achieving an average inference time of 0.17 ms. When using a majority voting strategy, classification accuracy increased to 99%, confirming the feasibility of implementing efficient and accurate gesture recognition on low-cost embedded hardware. These results support its integration into portable and autonomous systems for prosthetic control and other interactive applications.

The nonlinear conductivity of 2D electron gas in graphene and 3D electron gas in the narrow-gap n-InSb semiconductor has been simulated in terahertz (THz) range by various methods including the direct quantum approach, the quasi-classical kinetics, and the quasi-relativistic hydrodynamics. These methods yield the same results under the electron temperatures ≤100 K when the kinetic electron effective mass used in the nonlinear hydrodynamics is equal to the effective mass in n-InSb and it is equal to some nonzero mass that depends on 2D electron concentration in the graphene. The linear resonant dependencies of the complex electron conductivity are simulated both from the kinetic theory and from the hydrodynamic one. The kinetic dependencies of the resonant conductivity on frequency coincide with ones obtained from the hydrodynamic theory under realistic electron concentrations and collision frequencies. Thus, the nonlinear hydrodynamic equations are valid to describe the nonlinear dynamics of the electron gas in graphene and in n-InSb. The nonlinear hydrodynamics has been applied for simulations of nonlinear propagation of THz electromagnetic waves through the multilayer structures dielectric - graphene or n-InSb - dielectric placed in a bias magnetic field. The simplest three-layer structures demonstrate the sharp nonlinear switching of the transparency of THz waves and the bistability under relatively low values of amplitudes of the incident electromagnetic wave.