Search Results (1,156)

Voltage source converters (VSCs), together with their inner current and outer power/voltage control loops, are fundamental building blocks in the modern, converter-dominated power systems, particularly within high-voltage DC (HVDC) frameworks. Selecting effective control methods for VSCs is essential to ensure the stability, power quality, and dynamic performance of HVDC grids. This paper seeks to advance the current body of research by delivering an in-depth, consistent, unified framework and systematic examination of VSC control architectures within HVDC networks. It thoroughly explores various control strategies for VSCs interfacing with HVDC grids, such as grid-following and grid-forming strategies, with particular emphasis on both stationary ($\alpha \beta$) and synchronous (dq) reference frames. Moreover, the paper provides a comprehensive analysis of the theoretical underpinnings and decoupled control strategies, like the feedforward decoupling of the d- and q-axis currents in the dq frame and the inherently decoupled structure of the αβ frame. Additionally, advanced filtering techniques, including Moving Average Filter (MAF), Cascaded Delayed Signal Cancellation (DSC), and LCL filters, are analyzed. In addition, harmonic mitigation strategies, like parallel/multiple resonant (PR) terms in the αβ frame and cascaded notch filters in the dq frame, are presented. Furthermore, precise power control approaches and synchronization methods are discussed in detail. Also, this paper presents a detailed comparison of the performance characteristics of phase-locked loop (PLL) and frequency-locked loop (FLL) in response to grid frequency variations. Moreover, this paper proposes circuit representations and VSC models in both synchronous and stationary reference frames. The simulation results corroborate the theoretical insights discussed in the paper under various operational conditions, including initial responses, grid disturbances, three-phase-to-ground temporary fault scenarios, harmonic distortions, and load imbalances, in terms of overshoot, settling time, active- and reactive-power fluctuation reduction, voltage unbalance factor, total harmonic distortion, and post-fault convergence time, all evaluated in accordance with the limits defined in EN-50160. This comprehensive comparison of the presented control strategies facilitates researchers in identifying the most appropriate controller depending on their specific application requirements. Full article

Recent studies have proposed improving Laser Speckle Contrast Imaging (LSCI) by using polarimetric filtering to isolate multiply scattered photons from moving red blood cells (RBCs), an approach referred to as Laser Speckle Orthogonal Contrast Imaging (LSOCI). This reliance on multiple scattering enables the development of a calibration method based on a moving reference sample, chosen to generate dynamic circular Gaussian speckle fields that replicate the statistical properties of RBC scattering in both intensity and the distribution of polarization states. Assuming that multiply scattered photons from both RBCs and the reference sample exhibit a homogeneous distribution of polarization states over the Poincaré sphere, the proposed calibration links in vivo speckle contrast reduction bijectively to an equivalent speed of the reference sample. We demonstrate that this equivalent-velocity metric yields consistent in vivo measurements across distinct instruments despite the use of different laser spectral widths, thereby providing a standardized and transferable means to quantify microcirculatory activity. Full article

To address the challenge of hand intrusion detection in frozen tuna cutting operations where operators wear thermal-insulating gloves, this study proposes a hand detection method based on dual-domain background modeling with absolute accuracy constraints. To tackle issues arising from low-resolution infrared arrays, such as defective pixels, random noise, and complex low-temperature backgrounds, a data preprocessing pipeline integrating defective pixel correction, exponential moving average (EMA), and median filtering is developed. A dual-domain background suppression (DDBS) strategy, combining spatial-domain and temporal-domain models with sensor absolute accuracy constraints, is employed to extract hand foregrounds under complex thermal conditions. Temperature thresholding, connected-component analysis, and hole-filling are further applied to effectively separate hands from frozen tuna. An experimental platform incorporating a Raspberry Pi 4B and an MLX90640 sensor was constructed, and a dataset comprising 1173 infrared frames was collected for validation purposes. Experimental results demonstrate that the proposed method achieves an accuracy of 94.12%, precision of 91.69%, recall of 97.55%, and F1-score of 94.53% for hand intrusion detection, with an average processing time of approximately 1.84 ms per frame. This provides a cost-effective, real-time solution for hand safety monitoring in frozen food processing operations. Full article

Raw sentiment extracted from Chinese financial news is noisy and difficult to use directly for market prediction. This study proposes a mathematical filtering framework that converts noisy Chinese financial news sentiment into reliable quantitative signals for financial market prediction. Three daily sentiment measures were constructed from Chinese financial news: sentiment mean, sentiment dispersion, and polarity imbalance. Seven filtering methods were applied to each measure, including exponential smoothing, autoregressive filtering, ARIMA filtering, moving average smoothing, discrete wavelet transform, Savitzky–Golay filtering, and Kalman filtering. The seven filtered outputs were averaged to produce an ensemble-smoothed sentiment signal. Support vector machines and neural networks were then used to compare the predictive performance of raw and filtered signals for stock index log returns and realized volatility. Filtering reduced the standard deviation of sentiment mean by 48%, sentiment dispersion by 55%, and polarity imbalance by 50%, while mean levels remained stable. Filtered sentiment consistently outperformed raw sentiment across all model configurations. The improvement was larger for realized volatility than for returns: the best support vector machine reduced volatility prediction error by 16.9% and return prediction error by 5.8%. A moderate neural network with 20 hidden neurons achieved optimal performance for both outcomes. Mathematical filtering extracts stable and informative sentiment signals from Chinese financial news. Filtered sentiment is more useful than raw sentiment for predicting market volatility, and the improvement holds across multiple machine learning models. Full article

To protect human health and the environment, it is necessary to reduce the number of solid particles and harmful gases in the air or to minimize such pollution. Filtration and separation devices are intended for various industrial operations to capture pollutants from various technological processes. In the introduction, this article points out the use of cyclone filters in individual operations, names the most frequently occurring elements of pollution, and suggests the most suitable method of separation. In paint shops, grinding shops, welding workplaces, machining lines, and when handling powder materials, particles with very different properties are created. An important advantage of using cyclone filters is not only their simple construction but also their usability at high temperatures and pressures. Furthermore, this article highlights that cyclones are easy to maintain, typically contain no moving parts, are simple to manufacture, and are cost-effective, particularly as pre-filtration devices. Their efficiency generally ranges from 50% to 99% and is strongly influenced by design and operating parameters, especially cyclone geometry, which affects pressure drop, flow structure, cut diameter, and fractional collection efficiency. The article also summarizes that various modifications of the inlet, vortex finder, outlet pipe, and cyclone body have been proposed to enhance separation performance, particularly for smaller particles. Nevertheless, due to the centrifugal and inertial nature of cyclone separation, fine and submicrometric particulate matter remains difficult to remove using cyclones alone. Fabric filters are also analyzed as a possible solution, but high loading by coarse particles may cause clogging, increased pressure drop, and higher maintenance costs. In the end, the combination of a cyclone with an electrostatic precipitator is presented as a staged separation approach, enabling efficient removal of both coarse particles and fine particulate matter from the gas stream. Full article

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

Understanding how climate variability is reflected in streamflow is essential for reservoir management and hydropower planning. This study investigated how temporal scale influences climate–streamflow relationships, persistence characteristics, and predictability in two Brazilian reservoirs: Três Marias (São Francisco Basin) and Serra da Mesa (Tocantins Basin). Monthly streamflow and climate-index records (Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Antarctic Oscillation (AAO)) from 1979–2020 were analyzed using a 12-month moving average (MA12) filter to emphasize low-frequency variability. Temporal filtering strengthened climate–streamflow relationships, particularly for PDO and AAO, revealing signals that were less apparent in the original monthly series. Lagged-correlation analyses identified contrasting persistence structures between the reservoirs. Três Marias exhibited multi-year persistence timescales (22–27 months), whereas Serra da Mesa showed shorter and more heterogeneous response timescales, ranging from an immediate PDO response to approximately 14–19 months for ENSO and AAO. Forecasting experiments using benchmark models (Persistence and Linear Regression) and deep learning architectures (LSTM and TCN) showed limited predictive skill on the raw monthly series but substantially improved performance after temporal filtering. For the MA12-filtered series, the benchmark models achieved the highest performance in both reservoirs ($R^2\approx 0.95$ in Três Marias and $R^2\approx 0.93$ in Serra da Mesa). Overall, the results indicate that temporal scale strongly influences the detectability of climate signals, the persistence of streamflow variability, and the predictability of reservoir inflows. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

This paper presents the design, modeling, and hardware implementation of a 1 MHz Gallium Nitride (GaN) quadratic boost converter. Developed as a versatile experimental shield for an STM32 microcontroller development board, the proposed hardware enables direct measurement of all state variables to facilitate the experimental evaluation of advanced control algorithms. Based on a comprehensively derived state-space model, discrete-time Voltage Mode Control (VMC) is initially analyzed, highlighting the difficulties arising from the nature of the cascaded system. During large-signal operation at 1 MHz, this simple control strategy is highly vulnerable to dangerous current surges and oscillatory transients. To mitigate these instabilities, a hybrid Peak Current Mode Control (PCMC) strategy is proposed and implemented. An inner high-speed analog loop provides cycle-by-cycle current limiting, operating the power stage as a voltage-controlled current source to provide an input current limit as protection, even during inter-sample periods, where a slower digital controller remains “blind”. The shield architecture is especially useful here, since it allows the usage of built-in high-speed comparators of the microcontroller. Furthermore, this study investigates the complications arising from executing the outer digital control loop at sampling frequencies (100 kHz and 10 kHz) substantially lower than the switching frequency. Non-linear simulations reveal that at lower sampling rates, the control effort becomes too aggressive, causing the output to oscillate around a setpoint rather than stabilize. Applying a digital filter—specifically, Exponential Moving Averaging (EMA) to the controller output—is implemented to stabilize the reference signal. Both non-linear Simulink simulations and hardware experiments validate the proposed filtered PCMC architecture. Full article

Temperature monitoring is critical for lithium-ion battery (LIB) safety and performance, yet instrumenting every cell in a commercial pack remains impractical due to cost and wiring constraints. Existing sensorless methods rely on either physics-based thermal models requiring extensive parameterization or nonlinear recurrent estimators that cannot integrate sensor feedback when measurements become available. Motivated by these limitations, this paper proposes a Deep Koopman observer that enforces linear latent dynamics, enabling direct compatibility with Kalman filtering. The observer estimates surface temperature from four standard BMS signals and two exponential moving averages of squared current that capture thermal memory at distinct time scales, operating in two modes: fully sensorless for uninstrumented cells, or sensor-fused via a one-state EKF when a thermistor is available. Evaluated under strict cell-to-cell split across twelve drive cycles and five ambient temperatures, the open-loop observer achieves 17% lower error than the strongest reproduced CNN-LSTM baseline without online resistance identification or thermal-model simulation, and the EKF path delivers a further 35% reduction over the open-loop estimate. The evaluation is limited to a single cell chemistry and manufacturing batch; cross-chemistry and aging validation remain for future work. Full article

West African cuisine is among the world’s most complex and historically significant culinary traditions, shaped by diverse ecosystems, centuries of trans-regional trade, and the cultural heritage of more than three hundred distinct ethnic groups spanning the Atlantic coast and the Sahel. West African cuisine has undergone a significant cultural and culinary transformation in the American food landscape, moving from relative obscurity to mainstream visibility. This entry examines the rise of West African cuisine in the United States, with particular attention to jollof as a cultural symbol of identity, diaspora, and culinary diplomacy. Drawing on academic scholarship, food journalism, and primary cultural sources, the entry traces the historical roots of West African foodways through the transatlantic slave trade and their enduring influence on American culinary traditions. It further explores how contemporary chefs, restaurateurs, and food writers of West African descent, including Eric Adjepong, Pierre Thiam, and Kwame Onwuachi, have elevated the cuisine within American fine dining and popular culture. The entry also addresses the role of social media, particularly the viral “Jollof Wars,” in amplifying West African culinary culture globally, culminating in UNESCO’s recognition of Senegalese jollof rice as an element of intangible cultural heritage. Questions of structural barriers, authenticity, and representation are critically examined. The entry argues that while West African cuisine is experiencing unprecedented visibility in America, its mainstream acceptance remains mediated by cultural filters that risk diluting its complexity and richness. Ultimately, this entry positions West African cuisine not merely as a culinary trend but as a living expression of diasporic identity, cultural resilience, and global influence. Full article

The accuracy of shallow-water multibeam bathymetry is critically dependent on precise heave correction. However, sensor limitations often lead to incomplete correction, leaving periodic along-track stripe noises (heave residuals) that distort seabed morphology. Traditional filtering methods suppress this noise at the expense of genuine topographic detail. This paper proposes an innovative Data Feedback Correction (DFC) method that corrects the error at its source. DFC establishes a closed-loop framework: it diagnoses the residual’s dominant frequency from central beam data, extracts the residual signal via targeted filtering, and feeds it back as a compensation term into the original sensor heave sequence. This drives a recomputation of the geometric positioning, achieving source-level correction. In a field case, DFC demonstrated targeted, high-fidelity performance. Across all 34 survey lines, DFC achieved an average spectral attenuation of 1.85 dB (range: 1.0–3.7 dB) in the dominant residual band and reduced the RMSE of overlap discrepancies from 0.0923 m to 0.0773 m (a 16.25% improvement). Independent validation using 94,999 control line intersections further demonstrates a 14.31% RMSE improvement relative to an uncorrected control line reference, confirming that the correction improves both internal consistency and external accuracy, significantly enhancing internal consistency. Compared to moving average and wavelet denoising, DFC achieved comparable quantitative improvement while effectively suppressing visual stripes and features that are consistent with the original data, avoiding the over-smoothing or residual noise of traditional methods. This study confirms that closed-loop feedback of data residuals can fundamentally address spectrally aliased stripe noise, shifting the paradigm from “masking noise” to “correcting the source.” The method enhances data consistency in the tested scenario without sacrificing topographic authenticity, providing a promising new tool that warrants further validation across diverse survey conditions. Full article

With the development of the low-altitude economy, low-altitude intelligent agents such as delivery robots, courier drones, and outdoor cleaning robots are gradually moving towards widespread application. One of the core challenges faced by such systems is localization and mapping in complex scenarios characterized by satellite signal denial and unknown environmental prior information. To address this requirement, this paper proposes ESKF-g2o-SLAM, a stereo visual-inertial SLAM system that integrates an ESKF (Error-State Kalman Filter)-based visual-inertial odometry front-end with an ORB-feature-based g2o graph optimization back-end in a cascaded, loosely coupled manner. The proposed method was evaluated on 11 sequences of the EuRoC dataset and compared with state-of-the-art approaches including ORB-SLAM2 (stereo), MSCKF-VIO, OKVIS, and VINS-Fusion (stereo). Ablation studies show marginal improvements on selected sequences and suggest potential robustness advantages under more challenging visual conditions. Experimental results show that our method achieves competitive accuracy in terms of both Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), exhibiting good robustness and stability. Full article

Carbon/inorganic hybrid composites have evolved from filler-reinforced materials into design platforms for coupled electromagnetic, thermal, sensing, environmental, protective and energy-related functions. Their distinctive value lies in the possibility of combining a conductive, polarizable or porous carbon phase with an inorganic phase that contributes dielectric, magnetic, catalytic, ionic, thermally conductive or barrier behavior. This review examines carbon/inorganic hybrid multifunctional composites from the viewpoint of structure–property relationships, with emphasis on interfacial design, percolation, anisotropy, hierarchical architecture, processing and metrology. Selected graphitic composite studies are discussed as case studies for broadband dielectric spectroscopy, microwave shielding, high-frequency contact metrology, thermal diffusivity analysis and impedance-monitored graphene filters; these case studies are integrated with the broader international literature on CNT and graphene polymer composites, MXene films and foams, graphene/metal oxide photocatalysts, boron nitride/carbon thermal networks, biochar–graphene adsorbents, smart coatings, sensors, supercapacitors and water remediation systems. The central argument is that credible multifunctionality requires more than measuring several properties on the same material. It requires simultaneous or service-relevant co-optimization under constraints of thickness, density, processability, aging, humidity, corrosive media, regeneration, toxicity, economic feasibility and scalable fabrication. The review concludes with design rules and reporting recommendations intended to help move the field from impressive property demonstrations toward application-ready hybrid material systems. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article