Search Results (87)

Talking Head Generation (THG) is a rapidly advancing field at the intersection of computer vision, deep learning, and speech synthesis, enabling the creation of animated human-like heads that can produce speech and express emotions with high visual realism. The core objective of THG systems is to synthesize coherent and natural audio–visual outputs by modeling the intricate relationship between speech signals, facial dynamics, and emotional cues. These systems find widespread applications in virtual assistants, interactive avatars, video dubbing for multilingual content, educational technologies, and immersive virtual and augmented reality environments. Moreover, the development of THG has significant implications for accessibility technologies, cultural preservation, and remote healthcare interfaces. This survey paper presents a comprehensive and systematic overview of the technological landscape of Talking Head Generation. We begin by outlining the foundational methodologies that underpin the synthesis process, including generative adversarial networks (GANs), motion-aware recurrent architectures, and attention-based models. A taxonomy is introduced to organize the diverse approaches based on the nature of input modalities and generation goals. We further examine the contributions of various domains such as computer vision, speech processing, and human–robot interaction, each of which plays a critical role in advancing the capabilities of THG systems. The paper also provides a detailed review of datasets used for training and evaluating THG models, highlighting their coverage, structure, and relevance. In parallel, we analyze widely adopted evaluation metrics, categorized by their focus on image quality, motion accuracy, synchronization, and semantic fidelity. Operating parameters such as latency, frame rate, resolution, and real-time capability are also discussed to assess deployment feasibility. Special emphasis is placed on the integration of generative artificial intelligence (GenAI), which has significantly enhanced the adaptability and realism of talking head systems through more powerful and generalizable learning frameworks. Full article

Markerless motion capture (MMC) provides a non-invasive alternative for motion analysis; however, its validity at the standard frame rate of 25 Hz commonly used in broadcast and surveillance applications remains to be established. This study evaluated the performance of a 25 Hz multi-camera MMC workflow using consumer-grade cameras for capturing basketball-specific movements. Three highly trained male athletes completed seven tasks, including sprinting and simulated sport-specific skills, while being synchronously recorded by six MMC cameras (DJI Action 5 Pro, 25 fps) and a 10-camera Vicon system (25 Hz). Kinematic data were processed using an RTMDet–RTMPose pipeline and low-pass filtered at 6 Hz. Waveform validity was assessed using Pearson’s correlation coefficient (r) and the root mean square error (RMSE). The displacement magnitudes of 12 joints showed excellent agreement (r = 0.916–0.994; median nRMSE = 0.54–1.32%), indicating robust trajectory reconstruction. In contrast, agreement decreased for derivative variables: velocity (r = 0.583–0.867) and acceleration (r = 0.232–0.677) were highly sensitive to the low sampling rate and numerical differentiation. Although a 25 Hz configuration is insufficient for high-precision impact analysis, it provides acceptable accuracy for macroscopic displacement tracking and external-load quantification in resource-constrained training environments. Future optimization should prioritize temporal synchronization to improve the reliability of derivative variables. Full article

Weed management during the seedling stage is a critical component of soybean production. Efficient weed control can significantly improve crop yield and crop quality. However, conventional spraying techniques exhibit low pesticide utilization and contribute to environmental pollution. To address these challenges, this study proposes a deep learning-based precision target spraying method. A lightweight YOLOv5-MobileNetv3-SE model was developed by replacing the backbone feature extraction network and incorporating an attention mechanism. Field images of weeds were collected to construct a dedicated dataset, and the detection performance of the model was evaluated. Furthermore, a grid-based matching spraying algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control delay, and spray deposition delay, was analyzed and measured, and a time-delay compensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. The weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km·h⁻¹. Compared with conventional continuous spraying, the proposed method achieved pesticide reduction rates of 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practical approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article

This study proposed a large-target-surface and high-frame-rate display method using multiple Digital Micromirror Devices (DMDs) for high-resolution, high-frame-rate aerospace applications. DMDs offer high frame rates and contrast ratios, but their surface size is constrained. By employing Pulse Width Modulation (PWM) with synchronization signals for grayscale modulation and synchronizing multiple DMDs, this method achieved a target surface four times larger than a single DMD at 400 Hz frame rate, with synchronization errors below 10 ns. This enhances simulation efficiency and provides an effective infrared scene simulation solution. Full article

This study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4–0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition–modeling–reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer–LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R² compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification. Full article

High-speed data acquisition systems based on field-programmable gate arrays (FPGAs) often face synchronization challenges when interfacing with commercial analog-to-digital converters (ADCs), particularly under constrained hardware routing conditions and vendor-specific clocking assumptions. This work presents a vendor-independent FPGA–ADC synchronization architecture that enables reliable and repeatable high-speed data acquisition without relying on clock-capable input resources. Clock and frame signals are internally reconstructed and phase-aligned within the FPGA using mixed-mode clock management (MMCM) and input serializer/deserializer (ISERDES) resources, enabling time-sequential phase observation without the need for parallel snapshot or delay-line structures. Rather than targeting absolute metrological limits, the proposed approach emphasizes a reproducible and transparent data acquisition methodology applicable across heterogeneous FPGA–ADC platforms, in which clock synchronization is treated as a system-level design parameter affecting digital interface timing integrity and data reproducibility. Experimental validation using a custom Kintex-7 (XC7K325T) FPGA and an AFE7225 ADC demonstrates stable synchronization at sampling rates of up to 125 MS/s, with frequency-offset tolerance determined by the phase-tracking capability of the internal MMCM-based alignment loop. Consistent signal acquisition is achieved over the 100 kHz–20 MHz frequency range. The measured interface level timing uncertainty remains below 10 ps RMS, confirming robust clock and frame alignment. Meanwhile, the observed signal-to-noise ratio (SNR) performance, exceeding 80 dB, reflects the phase–noise-limited measurement quality of the system. The proposed architecture provides a cost-effective, scalable, and reproducible solution for experimental and research-oriented FPGA-based data acquisition systems operating under practical hardware constraints. Full article

Aquaculture monitoring increasingly relies on computer vision to evaluate fish behavior and welfare under farming conditions. This dataset was collected in a commercial recirculating aquaculture system (RAS) integrated with hydroponics in Queretaro, Mexico, to support the development of robust visual models for Nile tilapia (Oreochromis niloticus). More than ten hours of underwater recordings were curated into 31 clips of 30 s each, a duration selected to balance representativeness of fish activity with a manageable size for annotation and training. Videos were captured using commercial action cameras at multiple resolutions (1920 × 1080 to 5312 × 4648 px), frame rates (24–60 fps), depths, and lighting configurations, reproducing real-world challenges such as turbidity, suspended solids, and variable illumination. For each recording, physicochemical parameters were measured, including temperature, pH, dissolved oxygen and turbidity, and are provided in a structured CSV file. In addition to the raw videos, the dataset includes 3520 extracted frames annotated using a polygon-based JSON format, enabling direct use for training object detection and behavior recognition models. This dual resource of unprocessed clips and annotated images enhances reproducibility, benchmarking, and comparative studies. By combining synchronized environmental data with annotated underwater imagery, the dataset contributes a non-invasive and versatile resource for advancing aquaculture monitoring through computer vision. Full article

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures. Full article

Current harmonics in six-phase permanent magnet synchronous motors (PMSMs) arise from inherent asymmetries caused by manufacturing tolerances and nonlinear characteristics in the inverter output. Additionally, magnetic saturation and slight imbalances in the windings introduce flux linkage asymmetries, resulting in both fundamental current imbalance and low-order harmonics. Although these imbalances are minor and do not indicate fault conditions, they can cause uneven copper loss and eventually reduce the overall service life of the motor. This paper proposes a harmonic suppression strategy for mitigating imbalance current harmonics in non-ideal six-phase PMSMs. The method integrates back-electromotive force harmonic feedforward compensation (BEMF-HFC) with harmonic synchronous reference frame current control (HSRF-CC). An imbalance flux linkage harmonic model is developed in simulations to replicate the measured imbalance phase currents and to validate the effectiveness of the proposed strategy. The experimental setup is built using a microcontroller from Texas Instruments (TI), which generates six-phase complementary PWM signals for the power stage and receives feedback signals including phase currents, DC bus voltage, and rotor position. Rotor position is acquired through a 12-pole resolver and a 12-bit resolver-to-digital converter (RDC). The six-phase PMSM used in the tests is specified with 12 poles, a rated DC bus voltage of 600 V, a rated current of 200 Arms, and a rated rotor speed of 1200 rpm. Compared with conventional harmonic suppression strategies that do not target imbalance current harmonics, the proposed method achieves a better current balance and lower total harmonic distortion (THD). At 1200 rpm, the magnitude deviation of the fundamental, third, and fifth current harmonics is reduced from 8.61%, 2.88%, and 2.94% to 1.19%, 1.02%, and 0.5%, respectively. Full article

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal Multiple Access (NOMA) with MF-TDMA, employing Successive Interference Cancelation (SIC) for multi-user signal separation. A bi-directional adaptive carrier synchronization method and optimized burst structure are introduced, which collectively reduce synchronization overhead by over 40% compared to MF-TDMA. Simulations demonstrate a dramatically improved frame error rate of 0.0005% at 4 dB SNR—30 times lower than the 0.016% achieved by MF-TDMA—and a transmission efficiency of 92–97%, significantly outperforming conventional MF-TDMA. These results validate the proposed method’s substantial gains in capacity and efficiency for next-generation satellite systems. Full article

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article

Introduction: The health emergency caused by the pandemic led to severe health issues in populations across many countries worldwide, including widespread morbidity and significant mortality. Nevertheless, several countries succeeded in keeping infection rates remarkably low before the approval of vaccines and the initiation of vaccinations in early 2021. We aim to identify the success factors of health policies in managing the impact of the health emergency across a selection of countries, focusing on how they protected their populations. Our study presents outcomes of sustainable health policy measures, along with health and social system challenges, and economic responses during the global health emergency. We sometimes found it difficult to define what counted as a success factor in some countries. Method: Our study draws upon a selection of reports and documents published by various ministries and economic, social, and health authorities, which we collected online. We structured our study into three phases to frame and contextualize the impact of health policy measures and countermeasures as follows: (i) observations and content analysis; (ii) empirical support through illustrative examples; and (iii) development of a health emergency toolkit of assessment. The documents were not always easy to compare because they differed in format and detail. Results: Our study outlines ten success factors for sustainable health policy measures and countermeasures: (i) preparedness; (ii) control; (iii) precaution; (iv) proactive decision-making; (v) synchronization; (vi) adequate legislation; (vii) goal fulfillment; (viii) digital health technology; (ix) empirical evidence; (x) ethical and moral virtues. Sometimes we struggled to separate what was ethical guidance from what was simply practical advice. Conclusion: We argue that the relevance of the health emergency toolkit of assessment outlined in our study demonstrates clearly that the success factors related to sustainable health policy measures and countermeasures can be applied and adapted to the societal conditions of individual countries. These factors may form a foundation for the development of a health emergency toolkit of assessment for future health emergencies. We also maintain that these factors may serve as a platform for establishing sustainable plans across health, social, and economic domains, with clear guidelines for implementation, management, and control. It is our hope that future health systems will make use of these findings before the next crisis emerges. Full article

The application of fractional calculus in power electronics modeling provides an innovative method for improving inverter performance. This paper presents a three-phase inverter topology with fractional-order LC filter characteristics, analyzes its frequency response, and develops mathematical models in both stationary and rotating reference frames. Based on these models, a dual closed-loop decoupling control strategy for voltage and current is designed to enhance system stability and dynamic performance. In the power control loop, fractional-order virtual synchronous generator control (FOVSG) is employed. Observations show that increasing the fractional-order of the rotor leads to a higher transient frequency variation rate. To address this, an adaptive rotational inertia control scheme is integrated into the FOVSG structure (ADJ-FOVSG), enabling real-time adjustment of inertia to suppress transient frequency fluctuations. Experimental results demonstrate that when the reference active power changes, ADJ-FOVSG effectively suppresses power overshoot. Compared to traditional VSG, ADJ-FOVSG reduces the power regulation time by approximately 34.5% and decreases the peak frequency deviation by approximately 37.2%. Compared to the adaptive rotational inertia control in traditional VSG, ADJ-FOVSG improves regulation time by about 24% and reduces peak frequency deviation by roughly 24.4%. Full article

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters. Full article