Search Results (524)

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as time-varying system parameters (TVSPs) of the SDOF system for the tissue–contact-sensor (TCS) stack between an artery and a sensor. This model distinguishes the effects of MA and respiration on the instant parameters of harmonics in a measured pulse signal. Accordingly, a generalized SDOF-model-based time–frequency (SDOF-TF) method is developed to obtain the instant parameters of each harmonic in a measured pulse signal. These instant parameters are utilized to reconstruct the pulse signal with MA removal and extract heart rate (HR) and respiration parameters. The method is applied to analyze seven measured pulse signals at rest under different physiological conditions using a tactile sensor and a PPG sensor. Some observed differences between these conditions are validated with the related findings in the literature. As compared to instant frequency, the instant initial phase of a harmonic extracts respiration parameters with better accuracy. Since HR variability (HRV) affects arterial pulse waveform (APW), the extracted APW with a constant HR serves better for deriving arterial indices. Full article

This paper investigates the feasibility of diagnosing abdominal aortic aneurysm (AAA) via deep learning (DL)-enabled analysis of non-invasive arterial pulse waveform signals. We generated arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals across a diverse synthetic patient cohort using a systemic arterial circulation model coupled with a viscoelastic model relating arterial BP to PVR while simulating a range of AAA severity levels. We confirmed the plausibility of the synthetic data by comparing the alterations in the simulated waveform signals due to AAA against previously reported in vivo findings. Then, we developed a convolutional neural network (CNN) with continuous property-adversarial regularization that can estimate AAA severity from brachial and tibial PVR signals. We evaluated the algorithm’s performance in comparison with an identical CNN trained on invasive arterial BP waveform signals. The DL-enabled PVR-based algorithm achieved robust AAA detection across different severity thresholds with area under the ROC curve values >0.89, and showed reasonable accuracy in severity estimation, though slightly lower than its invasive BP counterpart (MAE: 12.6% vs. 10.3%). These findings suggest that DL-enabled analysis of PVR waveform signals offers a non-invasive and cost-effective approach for AAA diagnosis, potentially enabling accessible screening through operator-agnostic and point-of-care technologies. Full article

Power electronic devices for AC/DC and AC/AC conversion are, nowadays, widely distributed in electrified transportation and industrial applications, which can determine significant deviation in supply voltage waveform from the AC sinusoidal and promote insulation extrinsic aging mechanisms as partial discharges (PDs). PDs are one of the most harmful processes as they are able to cause accelerated extrinsic aging of electrical insulation systems and are the cause of premature failure in electrical asset components. PD phenomenology under pulse width modulated (PWM) voltage waveforms has been dealt with in recent years, also through some IEC/IEEE standards, but less work has been performed on PD harmfulness under AC distorted waveforms containing voltage harmonics and notches. On the other hand, these voltage waveforms can often be present in electrical assets containing conventional loads and power electronics loads/drives, such as for ships or industrial installations. The purpose of this paper is to provide a contribution to this lack of knowledge, focusing on PD sensing and phenomenology. It has been shown that PD patterns can change considerably with respect to those known under sinusoidal AC when harmonic voltages and/or notches are present in the supply waveform. This can impact PD typology identification, which is based on features related to PD pattern-based physics. The adaptation of identification AI algorithms used for AC sinusoidal voltage as well as distorted AC waveforms is discussed in this paper, showing that effective identification of the type of defects generating PD, and thus of their harmfulness, can still be achieved. Full article

In high-voltage direct-current (HVDC) transmission and large-scale power-system operation, DC-bias effects can drive current-transformer (CT) cores into premature saturation, distorting the secondary current and seriously jeopardizing the reliability of protective relaying and metering. To address the limited fitting capability and robustness of conventional compensation approaches in the presence of nonlinear distortion, this paper proposes a convolutional neural network (CNN)-based inversion method for CT saturation current. First, a simulation model is built on the PSCAD/EMTDC platform to generate a dataset of saturated, distorted currents covering DC components from −50 A to +50 A. Then, a CNN with a three-layer one-dimensional convolutional architecture is designed; leveraging local convolutions and parameter sharing, it extracts features from current sequences and reconstructs the true primary current. Simulation results show that the proposed method accurately recovers the primary-current waveform under mild-to-severe saturation, with errors within 2%, and exhibits strong adaptability and robustness with respect to both the polarity and magnitude of the DC component. These findings verify the effectiveness of CNNs for CT-saturation compensation. Full article

To meet the demand for energy-efficient and high-performance computing in resource-limited sensor edge applications, this paper presents a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM). The scheme exploits the analog multi-level resistance characteristics of memristors to enable parallel multi-bit processing, overcoming the constraints of traditional binary computing and significantly improving storage density and computational efficiency. Furthermore, by employing dynamic adjustment of the mapping between input signals and reference voltages, the circuit supports dynamic switching between exact and approximate CAM modes, substantially enhancing functional flexibility. Experimental results demonstrate that the 32 × 36 memristor array based on a TiN/TiOx/HfO₂/TiN structure exhibits eight stable and distinguishable resistance states with excellent retention characteristics. In large-scale array simulations, the minimum voltage separation between state-representing waveforms exceeds 6.5 mV, ensuring reliable discrimination by the readout circuit. This work provides an efficient and scalable hardware solution for intelligent edge computing in next-generation sensor networks, particularly suitable for real-time biometric recognition, distributed sensor data fusion, and lightweight artificial intelligence inference, effectively reducing system dependence on cloud communication and overall power consumption. Full article

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER). Full article

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz and supports 16-bit digitization with scalable sampling rates up to 30 kS/s. To enhance signal quality, the ASIC includes an adjustable digital high-pass filter and a fast-settling function for rapid recovery from stimulation artifacts. SoC also incorporates on-chip electrode-impedance measurements as a built-in safety feature by reusing the recording channels. The stimulation subsystem generates current-controlled monopolar biphasic pulses with a high compliance voltage of ±6 V using standard low-voltage (1.8 V/3.3 V) CMOS devices. Each of the 32 stimulation channels provides arbitrary 9-bit programmable waveforms and dual current modes (4 μA/bit and 8 μA/bit), supporting both fine-resolution microstimulation and high-current applications such as spinal-cord and deep-brain stimulation. On-chip charge-balancing switches in each channel further ensure safe and reliable stimulation delivery. The SoC supports digital communication via a standard SPI with both 3.3 V CMOS and low-voltage differential signaling options and integrates all required analog references and low-dropout regulators. The prototype was fabricated in a standard 180 nm CMOS process, occupying 31.92 mm² (equivalently, 0.2 mm² per recording-and-stimulation channel), and was fully validated through benchtop measurements and in vitro experiments. Full article

Rogowski coils are increasingly being used in electricity metering systems. However, owing to their operating principle, they require an additional active integrating circuit to produce an output voltage or current that is directly proportional to the input current. A signal conditioner has the most significant impact on the overall conversion accuracy of the combined transducer. In this paper, a new measurement system and testing procedure utilizing a digital power meter and arbitrary waveform generator are proposed. This approach enables the characterization of the conversion accuracy of both types of active integrators: voltage-to-voltage and voltage-to-current converters. The conversion error for distorted input voltage harmonics and additional phase shift across a range of frequencies are determined. Instead of using the actual signal from the Rogowski coil during testing —which would be challenging owing to the required high RMS value of the distorted current for its input and difficulties in accurately measuring the RMS values of harmonics and their phase angles in relation to the output voltage or current of the tested converter—an arbitrary waveform generator is used. The input voltage to the active integrating circuit replicates the output voltage of the Rogowski coil: as the harmonic order increases, its RMS voltage rises proportionally. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types. Full article

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article

Pulsatile blood flow generates complex wall shear stress (WSS) patterns at the carotid bifurcation, which critically regulate endothelial function and structure. While physiological pulsatile WSS (PWSS) is essential for maintaining vascular health, low oscillatory WSS (OWSS) near the carotid sinus is closely associated with endothelial dysfunction, atherosclerotic plaque formation, and stenosis. Reproducing these hemodynamic conditions in vitro is therefore crucial for investigating endothelial mechanobiology and elucidating the pathogenesis of atherosclerosis. Although microfluidic technologies have emerged as promising platforms for simulating either pulsatile or oscillatory WSS, a system capable of simultaneously replicating both characteristic waveforms—as found in vivo at the carotid bifurcation—remains undeveloped. In this study, we designed a variable cross-section microfluidic channel using Computational Fluid Dynamics (CFD) simulations. Numerical results demonstrate that the optimized channel accurately reproduces low OWSS at a stepped section emulating the carotid sinus, alongside high PWSS in a downstream uniform section. Vortex formation induced by the step structure is identified as key to generating low OWSS, influenced by step height, channel width ratio, and input flow rate. This work provides a novel and robust methodology for designing microfluidic systems that mimic complex hemodynamic microenvironments, facilitating future studies on the interplay between distinct WSS patterns and endothelial dysfunction. Full article

The reliability of the generator circuit breaker (GCB) switching coil affects the safe and stable operation of the power system, in which the faults of abnormal voltage, poor contact, and mechanical jamming of the switching coil can easily lead to the refusal of the circuit breaker, which threatens the safety of the power grid. In order to study the fault characteristics of the GCB switching coil, this paper combines multi-physical field simulation and experimental testing, establishes the electromagnetic field simulation model of the switching coil, and analyzes the characteristics of current waveforms under typical faults such as voltage abnormality, poor contact, and core jamming. Through simulation and testing to verify the mechanism of current waveform distortion under different fault states, demonstrated the change rule of characteristic parameters when the fault occurs, and provided a basis for the diagnosis of the operation status of the switching coil based on current waveform. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article