Search Results (182)

Electromyography (EMG) signals have diverse applications, ranging from prosthetic hands and assistive suits to rehabilitation devices. Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. To address this issue in real-time prediction, we investigated a sliding-window normalization (SWN) technique that merges z-score normalization with sliding-window processing to align the EMG amplitude across channels and mitigate the performance degradation caused by electrode displacement. We validated SWN using experimental data from a right-arm trajectory-tracking task involving three motion classes (rest, flexion, and extension of the elbow). Offline analysis revealed that SWN mitigated accuracy degradation to −1.0% without additional data for re-training or multi-condition training, a 6.6% improvement compared with the −7.6% baseline without normalization. The advantage of SWN is that it operates with data from a single electrode position for training, which eliminates both the collection of multi-position training data and the calibration of deep learning models before practical use in EMG applications. Moreover, combining SWN with multi-position training exceeded the classification accuracy of the no-shift condition by 2.4%. Full article

Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasible. Clustering algorithms offer an effective solution by enabling the automatic classification and quantification of contamination levels. Despite the rapid development of machine learning, its application in molecular electronics remains limited. In this work, we present a methodology based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to extract representative traces from both clean and contaminated regimes, providing a scalable and objective tool to evaluate environmental contamination in molecular junction experiments. Full article

Industrial organic wastewater, with its complex composition, high biological toxicity, and recalcitrance, has become a major challenge in water pollution control. This is especially true for antibiotic-containing wastewater, such as ofloxacin wastewater, for which there is an urgent need to develop effective treatment technologies. Conventional treatment processes are insufficiently efficient, while individual advanced oxidation processes (AOPs) have drawbacks such as poor oxidation selectivity and catalyst deactivation. To address these issues, researchers have explored the coupling of different AOPs and found that such combinations can enhance the oxidation performance, achieve complementary advantages, reduce the equipment costs, and offer great development potential. An experiment was conducted to evaluate the performance of an Ozone-Electro-Fenton coupled process in treating ofloxacin industrial wastewater. The results demonstrated that under the same conditions, after four hours of treatment, the coupled process achieved a 70% reduction in the UV absorption peak of the wastewater, compared to less than 20% for individual processes, indicating a significant synergistic effect. Further optimization of the ozone aeration structure revealed that with a hole size of 0.5 mm, single-layer aeration holes, and six holes, the COD removal rate reached 96% after six hours, the ozone utilization improved to 85%, and the gas holdup stabilized at 4.6%. Under these conditions, the mixture of ozone and air bubbles formed mixed bubbles. Influenced by the electric field and electrode plate wall effects, the bubble residence time was prolonged. The bubble size was approximately 2.8 mm, the gas flow horizontal velocity was about 18.5 m/s, and after a horizontal displacement of 0.17 mm in the wastewater, the lateral velocity became zero. The ratio of the distance between the bubble center and the wall to the equivalent bubble diameter was approximately 3.45. The bubbles were subject to a strong wall effect, which extended their residence time. This not only facilitated the removal of small bubbles from the electrode plates but also enhanced the ion diffusion near the plates, thereby boosting pollutant degradation. This study shows that the Ozone-Electro-Fenton coupled process is highly effective in degrading ofloxacin industrial wastewater, offering an innovative solution for treating other antibiotic-containing wastewater. Future research will focus on further optimizing the process, improving its adaptability to complex matrix wastewater, and validating it at the pilot scale to promote its engineering application. Full article

To address the challenge of dynamic monitoring during grouting operations in coal mine fault zones under pressurized mining, this study proposes the Borehole–Tunnel Joint Resistivity Method (BTJRM). By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. Key findings include: (1) Grout diffusion reduced resistivity by 10%, aligning with electrical response patterns during fracture-filling stages; (2) 3D inversion reveals that grout propagates along the principal stress axis, forming a “Y”-shaped low-resistivity anomaly zone that penetrates the fault structural block and extends into roadway areas. The maximum planar and vertical displacements of grout reach 100 m and 40 m, respectively. Thirty days post-grouting, resistivity recovers by up to 22%, reflecting the electrical signature of grout consolidation; (3) This method enables 3D reconstruction of grout diffusion pathways, extends the time window for early warning of water-conducting channel development, and enhances pre-warning capabilities for grout migration. It provides a robust framework for real-time sealing control of fault strata, offering a novel dynamic monitoring technology for mine water inrush prevention. The technology can provide reliable grouting evaluation for mine disaster control engineering. Full article

Ultrasonic applications such as non-destructive testing, biomedical imaging or range measurements are currently based on piezoelectric bulk transducers. Yet, these kinds of transducers with their mm to cm dimensions are rather impractical in fields in which both frequencies in the kHz region and small-feature sizes are required. This fact mainly relates to the inverse relationship between the resonance frequency constant and the transducers’ dimension, yielding a higher frequency and attenuation with a decreasing size. Piezoelectric micromachined ultrasonic transducers (PMUTs), in comparison, incorporate a small-scale µm design while preserving the operating frequency in the desired kHz range. This contribution presents the detailed manufacturing of such a PMUT with a multiple ring electrode‑based structure to additionally adjust the sound pressure fields. The PMUT will be characterized by its deflection in air along with the characterization of the piezoelectric material lead zirconate titanate (PZT) itself. The measurements showed a maximum polarization of 21.8 µC/cm² at 50 kV/cm, the PMUT’s displacement of 30.50 nm/V in air when all electrodes are driven, and an adjustable deflection via different electrode excitations without the need for additional hardware. The ring design also offered the possibility to emit two distinct frequencies simultaneously. These results demonstrate the potential of the designs for small-feature-size applications as they are in high demand for implantable devices, miniaturized ultrasonic-based communication or drug delivery. Full article

The quantum efficiency $(QE)$ or gain $(G)$ of a photoconductive device is most commonly given in the literature as a ratio of carrier lifetime to transit time, allowing for a value much greater than unity. In this work, by assuming primary photoconductivity, we reexamine the photoconductive theory for the device with an intrinsic (undoped) semiconductor, with nearly zero equilibrium carrier densities. Analytic gain formula is obtained for arbitrary drift and diffusion parameters under a bias voltage and by neglecting the polarization effect due to the relative displacement in the electron and hole distributions. We find that the lifetime/transit-time ratio formula is only valid in the limit of weak field and no diffusion. Numerical simulations are performed to examine the polarization effect, confirming that it does not change the qualitative conclusions. We discuss the distinction between two $QE$ definitions used in the literature: accumulative $QE$ $\left({QE}_{acc}\right)$, considering the contributions of the flow of all photocarriers, regardless of whether they reach the electrode; and apparent $QE$ (${QE}_{app})$, measuring the photocurrent at the electrode. In general, ${QE}_{acc}>{QE}_{app}$, due to an inhomogeneous photocurrent in the channel; however, both approach the same unity limit for strong drift. We find that ${QE}_{acc} \ne { QE}_{app}$ is a deficiency of the commonly adopted constant-carrier-lifetime approximation in the recombination terms. Full article

The well-known Poisson–Nernst–Planck model is a classical approach usedto describe ion transport in liquids. Extended versions of this model account for thegeneration–recombination of ions at equilibrium. In this paper, we investigate the influenceof the generation–recombination term on dielectric relaxation in an electrolytic cell shapedlike a slab, bounded by two parallel blocking electrodes. We show that in the adiabaticlimit—which holds when the reaction time is much longer than the dielectric relaxationtime—the electric current in the external circuit does not follow a simple relaxation mechanism.Instead, it is characterized by two distinct relaxation times: a short relaxationtime associated with dielectric relaxation and a longer relaxation time related to the iondissociation–association process. Conversely, this information could be used to assess thepresence and/or significance of the generation–recombination effect in an electrolytic cell. Full article

Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human–Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving $93.36$% average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm’s efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications. Full article

Cardiovascular diseases represent one of the major problems faced by modern society. In addition to reducing people’s quality of life, bringing high costs to the health system, and causing losses in economic productivity, they are the leading cause of death in the world. Early diagnosis and treatment are the best actions to minimize the damage and costs caused by these diseases. For this, developing techniques and technologies that have higher accuracy in the analysis of electrocardiogram (ECG) signals is necessary. Early diagnosis benefits from relevant ECG interpretation. Then, it can contribute to reducing healthcare costs by replacing interventionist responses with preventive actions. This work presents a method and system for heart rate estimation using Linear Prediction Coefficients (LPCs) centered on an ESP32 microprocessor module and an AD8232 ECG signal conditioning module. The proposal was validated with a Tektronix AFG1022 function generator that produces ECG signals and obtained measurements with accuracy above 98.87%, showing performance similar to studies presented in the literature. Also, the LPC algorithm showed good performance in rejecting low-frequency noise caused by some common artifacts, such as body movement and electrode displacement. Full article

This study demonstrates, for the first time, the formation of a hemiester of carbonic acid on self-assembled monolayers using voltammetric techniques and redox probes. A gold electrode (GE) was modified with 2-mercaptoethanol (ME) through self-assembly. With this modified electrode (GE-ME), a well-defined peak was observed by differential pulse voltammetry (DPV) for the negatively charged redox probe, ferricyanide/ferrocyanide, [Fe(CN)₆]³⁻/⁴⁻, in sodium acetate as an electrolyte adjusted to pH 8.2. In the presence of dissolved CO₂ in equilibrium with bicarbonate, there is a decrease in the ferrocyanide peak current with time (~30% in 60 min), attributed to the formation of hemiester 2-mercapto ethyl carbonate at the GE-ME/solution interface. Similarly, dissolved CO₂ and bicarbonate also affect the electrochemical impedance measurements by increasing resistance to the charge transfer process with time (elevation of Rct values), compatible with the formation of the hemiester. The addition of barium salt led to the displacement of the equilibrium towards BaCO₃ precipitation and consequent dissociation of the hemiester, attested by the recovery of the initial ferricyanide DPV signal. With the positively charged redox probe [Ru(NH₃)₆]²⁺, no decrease in the DPV peak was observed during the formation of the hemiester by reaction with bicarbonate. The repulsion of [Fe(CN)₆]³⁻, but not of [Ru(NH3)₆]²⁺, suggests that the formed species is the negatively charged 2-mercapto-ethyl carbonate, i.e., the hemiester with a dissociated proton. Due to the lack of a voltammetric signal from the hemiester itself, the formation of a self-assembled layer of thio-alcohol followed by the gradual formation of the corresponding carbonic acid hemiester allowed us to reach an elegant way of electrochemically demonstrating the formation of these species. Full article

Monitoring of immune factors, including interferon-gamma (IFN-γ), holds great importance for understanding immune responses and disease diagnosis. Wearable sensors enable continuous and non-invasive detection of immune markers in sweat, drawing significant attention to their potential in real-time health monitoring and personalized medicine. Among these, electrochemical sensors are particularly advantageous, due to their excellent signal responsiveness, cost-effectiveness, miniaturization, and broad applicability, making them ideal for constructing wearable sweat sensors. In this study, we present a flexible and sensitive wearable platform for the detection of IFN-γ, utilizing a DNA hydrogel with favorable loading performance and sample collection capability, and the application of mobile software achieves immediate data analysis and processing. This platform integrates three-dimensional DNA hydrogel functionalized with IFN-γ-specific aptamers for precise target recognition and efficient sweat collection. Signal amplification is achieved through target-triggered catalytic hairpin assembly (CHA), with DNA hairpins remarkably enhancing sensitivity. Ferrocene-labeled reporting strands immobilized on a screen-printed carbon electrode are displayed via CHA-mediated strand displacement, leading to a measurable reduction in electrical signals. These changes are transmitted to a custom-developed mobile application via a portable electrochemical workstation for real-time data analysis and recording. This wearable sensor platform combines the specificity of DNA aptamers, advanced signal amplification, and the convenience of mobile data processing. It offers a high-sensitivity approach to detecting low-abundance targets in sweat, paving the way for new applications in point-of-care diagnostics and wearable health monitoring. Full article

Piezoelectric materials (PZTs) enjoy extensive applications in the field of electromechanical sensors due to their sensitive response to external electric fields. The limited piezoelectric response for single-layer piezoceramic pellets drives the use of multilayered technology to increase the electric displacement of a single piezo device. As is well known, Ag is commonly used as a metal for electrodes in devices based on traditional PZTs, which always densify at a high temperature above 1100 °C, resulting in Ag migration. Here, a high-performance samarium-ion-doped Sm_0.01Pb_0.99(Zr_0.54Ti_0.46)O₃ ceramic was selected as parent materials to develop a new Ag-cofired ceramic matrix with a sintering temperature of 920 °C by glass flux. The ceramic composition with 2.0 wt% glass addition exhibits the excellent performance of piezoelectric d₃₃~492 pC/N, planar electromechanical coupling coefficient k_p~50.1%, mechanical quality factor Q_m~68.71, and Curie temperature T_c~356 °C, respectively. The cyclic stability of d₃₃ was measured below 6.6% at 30 kV/cm, which indicates that the piezoceramic has good temperature stability and fatigue resistance. Therefore, this study provides a novel high-performance piezoelectric system to meet the cofired requirement for multilayered piezoelectric devices. Full article

The present work presents an innovative marine sensing robotics device based on piezoelectric cantilever-integrated micro-electro-mechanical systems (MEMSs) modeled on fish lateral lines. The device comprises 12 cantilevers of different shapes and sizes in a cross-shaped configuration, embedded between molybdenum (Mo) electrodes in a piezoelectric thin film (PbTiO₃, GaPO₄). It has the advantage of a directional response due to the unique design of the circular cantilevers. In COMSOL software 5.5, we designed, modeled, and simulated a piezoelectric device based on a comparative study of these piezoelectric materials. Simulations were performed on cantilever microstructures ranging in length from 100 µm to 500 µm. These materials perform best when lead titanate (PbTiO₃) is used. A maximum voltage of 4.9 mV was obtained with the PbTiO₃-material cantilever with a displacement of 37 µm. A laser Doppler vibrometer was used to measure the resonance frequency mode and displacement. Our simulations and experiments were in good agreement. Its performance and compactness allow us to envision its employment in underwater acoustics for monitoring marine cetaceans and ultrasound communications. In conclusion, MEMS piezoelectric transducers can be used as hydrophones to sense underwater acoustic pulses. Full article

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. Full article

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals. Full article