The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyographic (sEMG) signals. The system analyzes sEMG signals to understand muscle function and neuromuscular control, employing convolutional neural networks (CNNs) for pattern recognition. The electrical signals analyzed on healthy and unhealthy subjects are acquired using a meticulously developed integrated circuit system featuring biopotential acquisition electrodes. The signals captured in the database are extracted, classified, and interpreted by the application of CNNs with the aim of identifying patterns indicative of neuromuscular problems. By leveraging advanced learning techniques, the proposed method addresses the non-stationary nature of sEMG recordings and mitigates cross-talk effects commonly observed in electrical interference patterns captured by surface sensors. The integration of an AI algorithm with the signal acquisition device enhances the qualitative outcomes by eliminating redundant information. CNNs reveals their effectiveness in accurately deciphering complex data patterns from sEMG signals, identifying subjects with neuromuscular problems with high precision. This paper contributes to the landscape of biomedical research, advocating for the integration of advanced computational techniques to unravel complex physiological phenomena and enhance the utility of sEMG signal analysis.

The evolution of electronic systems has significantly improved the monitoring of electrical consumption, ensuring precise measurement of energy assets. This paper proposes a three-step approach to improving the energy system management of an industrial plant. The first phase involves the identification of hot surfaces in electrical panels using non-destructive instrumentation for thermographic measurements. The second phase develops an electronic system consisting of current sensors and a temperature control system, a control unit made with Raspberry Pi integrated with Arduino, and a data transmission system using a 4G LTE Internet device capable of measuring the electrical current used by a machine in the plant. The electronic system measures current absorption (CA) by the lines of the electrical switchboards. The final phase focuses on the implementation of deep learning method based on the integration of an LSTM model for the forecasting of hourly CA and a U-Net model for supporting the classification and detection of anomalies identified during thermographic assessment. The results demonstrated substantial improvements in the accuracy of power consumption classification and the potential to optimise power distribution and reduce consumption accordingly. This classification results in reduced energy consumption and improved process quality.

Recently, the incidence of, and interest in, respiratory diseases has been amplified by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and other respiratory diseases with a high prevalence. Most of these diseases require mechanical ventilation for homecare and clinical therapy. Herein, we propose a portable and non-invasive mechanical fan (NIV) for home and clinical applications. The NIV’s core is a turbine for airflow generation, which can provide and monitor a positive two-level pressure of up to approximately 500 lpm at 50 cmH₂O according to the inspiration/expiration phase. After calibration, the proposed NIV can precisely set the airflow with a pressure between 4 cmH₂O and 20 cmH₂O, providing a versatile device that can be used for continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP). The airflow is generated by a turbine monitored using a mass flow sensor. The whole NIV is monitored with a 16 MHz clock microcontroller. An analog-to-digital converter is used as the input for analog signals, while a digital-to-analog converter is used to drive the turbine. I2C protocol signals are used to manage the display. Moreover, a Wi-Fi system is interfaced for the transmission/reception of clinical and technical information via a smartphone, achieving a remote-controlled NIV.

Computed tomography (CT), magnetic resonance imaging (MRI), and radiography expose patients to electromagnetic fields (EMFs) and ionizing radiation. As an alternative, Electrical Impedance Tomography (EIT) offers a less EMF-influenced method for imaging by measuring superficial skin currents to provide a map of the body’s conductivity. EIT allows for functional monitoring of anatomical regions using low electromagnetic fields and minimal exposure times. This paper investigates the application of EIT for the morphological and functional assessment of tissues. Using the Finite Element Method (FEM) (Comsol 5.2), both two-dimensional and three-dimensional models and simulations of physiological and pathological tissues were developed to replicate EIT operations. The primary objective is to detect carcinoma by analysing the electrical impedance response to externally applied excitations. An eight-electrode tomograph was utilised for this purpose, specifically targeting epithelial tissue. The study allowed the characterisation of tomographs of any size and, therefore, the possibility to verify both their geometric profile and the ideal value of the excitation current to be delivered per second of the type of tissue to be analysed. Simulations were conducted to observe electrical impedance variations within a homogeneously modelled tissue and a carcinoma characterized by regular geometry. The outcomes demonstrated the potential of EIT as a viable technique for carcinoma detection, emphasizing its utility in medical diagnostics with reduced EMF exposure.

High-intensity, low-frequency magnetic fields (MFs) have been widely used in the treatment of diseases and in drug delivery, even though they could induce structural changes in pharmacological molecules. Morphological changes in ketoprofen and KiOil were investigated through Fourier-transform infrared spectroscopy (FT-IR). Unsupervised principal component analysis was carried out for data clustering. Clinical validation on 22 patients with lower back pain was managed using diamagnetic therapy plus topical ketoprofen or KiOil. The Numerical Rating Scale (NRS) and Short-Form Health Survey 36 (SF-36) were used to evaluate clinical and functional response. Ketoprofen showed clear clustering among samples exposed to MF (4000–650 cm⁻¹), and in the narrow frequency band (1675–1475 cm⁻¹), results evidenced structural changes which involved other excipients than ketoprofen. KiOil has evidenced structural modifications in the subcomponents of the formulation. Clinical treatment with ketoprofen showed an average NRS of 7.77 ± 2.25 before and an average NRS of 2.45 ± 2.38 after MF treatment. There was a statistically significant reduction in NRS (p = 0.003) and in SF-36 (p < 0.005). Patients treated with KiOil showed an average NRS of 7.59 ± 2.49 before treatment and an average NRS of 1.90 ± 2.26 after treatment (p < 0.005). SF-36 showed statistical significance for all items except limitations due to emotional problems. A high-intensity pulsed magnetic field is an adjunct to topical treatment in patients with localized pain, and the effect of MF does not evidence significant effects on the molecules.

Single-photon avalanche diodes (SPADs) belong to a family of avalanche photodiodes (APDs) with single-photon detection capability that operate above the breakdown voltage (i.e., Geiger mode). Design and technology constraints, such as dark current, photon detection probability, and power dissipation, impose inherent device limitations on avalanche photodiodes. Moreover, after the detection of a photon, SPADs require dead time for avalanche quenching and recharge before they can detect another photon. The reduction in dead time results in higher efficiency for photon detection in high-frequency applications. In this work, an electronic interface, based on the pole-zero compensation technique for reducing dead time, was investigated. A nanosecond pulse generator was designed and fabricated to generate pulses of comparable voltage to an avalanche transistor. The quenching time constant (τ_q) is not affected by the compensation capacitance variation, while an increase of about 30% in the τ_q is related to the properties of the specific op-amp used in the design. Conversely, the recovery time was observed to be strongly influenced by the compensation capacitance. Reductions in the recovery time, from 927.3 ns down to 57.6 ns and 9.8 ns, were observed when varying the compensation capacitance in the range of 5–0.1 pF. The experimental results from an SPAD combined with an electronic interface based on an avalanche transistor are in strong accordance, providing similar output pulses to those of an illuminated SPAD.