Search Results (4)

Remote photoplethysmography (rPPG) has attracted growing attention due to its non-contact nature. However, existing non-contact heart rate detection methods are often affected by noise from motion artifacts and changes in lighting, which can lead to a decrease in detection accuracy. To solve this problem, this paper initially employs manual extraction to precisely define the facial Region of Interest (ROI), expanding the facial area while avoiding rigid regions such as the eyes and mouth to minimize the impact of motion artifacts. Additionally, during the training phase, illumination normalization is employed on video frames with uneven lighting to mitigate noise caused by lighting fluctuations. Finally, this paper introduces a 3D convolutional neural network (CNN) method incorporating an attention mechanism for heart rate detection from facial videos. We optimize the traditional 3D-CNN to capture global features in spatiotemporal data more effectively. The SimAM attention mechanism is introduced to enable the model to precisely focus on and enhance facial ROI feature representations. Following the extraction of rPPG signals, a heart rate estimation network using a bidirectional long short-term memory (BiLSTM) model is employed to derive the heart rate from the signals. The method introduced here is experimentally validated on two publicly available datasets, UBFC-rPPG and PURE. The mean absolute errors were 0.24 bpm and 0.65 bpm, the root mean square errors were 0.63 bpm and 1.30 bpm, and the Pearson correlation coefficients reached 0.99, confirming the method’s reliability. Comparisons of predicted signals with ground truth signals further validated its accuracy. Full article

Early SOC balancing techniques primarily centered on simple hardware circuit designs. Passive balancing circuits utilize resistors to consume energy, aiming to balance the SOC among batteries; however, this approach leads to considerable energy wastage. As research progresses, active balancing circuits have garnered widespread attention. Successively, active balancing circuits utilizing capacitors, inductors, and transformers have been proposed, enhancing balancing efficiency to some extent. Nevertheless, challenges persist, including energy wastage during transfers between non-adjacent batteries and the complexity of circuit designs. In recent years, SOC balancing methods based on software algorithms have gained popularity. For instance, intelligent control algorithms are being integrated into battery management systems to optimize control strategies for SOC balancing. However, these methods may encounter issues such as high algorithmic complexity and stringent hardware requirements in practical applications. This paper proposes a fast state-of-charge (SOC) balance control strategy that incorporates a weighting factor within a modular battery energy storage system architecture. The modular distributed battery system consists of battery power modules (BPMs) connected in series, with each BPM comprising a battery cell and a bidirectional buck–boost DC-DC converter. By controlling the output voltage of each BPM, SOC balance can be achieved while ensuring stable regulation of the DC bus voltage without the need for external equalization circuits. Building on these BPMs, a sliding mode control strategy with adaptive acceleration coefficient weighting factors is designed to increase the output voltage difference of each BPM, thereby reducing the balancing time. Simulation and experimental results demonstrate that the proposed control strategy effectively increases the output voltage difference among the BPMs, facilitating SOC balance in a short time. Full article

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns. Full article

We adapted a fast Fourier transform-based Beam Propagation Method (FFT-BPM) to investigate waveguide discontinuities in plasmonic waveguides. The adaptation of the FFT-BPM to treat transverse magnetic (TM) fields requires the circumvention of two major difficulties: the mixed derivatives of the magnetic field and waveguide refractive index profile in the TM wave equation and the step-like index change at the transverse metal-dielectric boundary of the plasmonic guide and the transverse boundaries of the dielectric waveguide as well. An equivalent-index method is adopted to transform TM fields to transverse electric (TE) ones, thus enabling the benefit of the full power and simplicity of the FFT-BPM. Moreover, an appropriate smoothing function is used to approximate the step-like refractive index profile in the transverse direction. At the junction plane, we used an accurate combined spatial-spectral reflection operator to calculate the reflected field. To validate our proposed scheme, we investigated the modal propagation in a silicon waveguide terminated by air (like a laser facet in two cases: with and without a coating layer). Then we considered a subwavelength plasmonic waveguide (metal-insulator-metal MIM) butt-coupled with a dielectric waveguide, where the power transmission efficiency has been calculated and compared with other numerical methods. The comparison reveals good agreement. Full article