Search Results (365)

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Hydraulic fracturing is a crucial technology for developing unconventional oil and gas resources. However, conventional geophysical methods struggle to efficiently and accurately image proppant-connected channels created by hydraulic fracturing. The borehole-to-surface electromagnetic imaging method (BSEM) overcomes this limitation by utilizing a controlled cased well source. Placing the source close to the target reservoir and deploying multi-component receivers on the surface enable high-precision lateral monitoring, providing an effective approach for dynamic monitoring of hydraulic fracturing operations. This study focuses on key aspects of forward modeling for BSEM. A three-dimensional finite-volume method based on the Yee grid was used to simulate the borehole-to-surface electromagnetic system incorporating metal casings, validating the method of simulating metal casing using multiple line sources. The simulation of the observation system and the frequency-domain electromagnetic monitoring simulation based on actual well data confirm BSEM’s high sensitivity for monitoring deep subsurface formations. Critically, well casing exerts a substantial influence on surface electromagnetic responses, while the electromagnetic contribution from line sources emulating perforation zones necessitates explicit incorporation within data processing workflows. Full article

Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after spontaneous blinking, and indexes neural processes as the brain evaluates new visual information appearing after eye re-opening. Prior studies have reported BRO utility as both a clinical and non-clinical biomarker of cognition, but no study has demonstrated BRO measurement using textile-based EEG devices that facilitate user comfort for real-world applications. Methods: We investigated BRO measurement using a four-channel EEG system with textile electrodes by extracting BRO responses using existing, publicly available EEG data (n = 9). We compared BRO effects derived from textile-based electrodes with those from standard dry Ag/Ag-Cl electrodes collected at the same locations (i.e., Fp1, Fp2, F7, F8) and using the same EEG amplifier. Results: Results showed that BRO effects measured using textile electrodes exhibited similar features in both time and frequency domains compared to dry Ag/Ag-Cl electrodes. Data from both technologies also showed similar performance in artifact removal and signal capture. Conclusions: These findings provide the first demonstration of successful BRO signal capture using four-channel EEG with textile electrodes, providing compelling evidence toward the development of a comfortable and user-friendly EEG technology that uses the simple activity of blinking for objective brain function assessment in a variety of settings. Full article

This study presents a comprehensive mathematical model of a semiconductor structure based on vanadium dioxide (VO₂), specifically in its conductive phase. The model was developed using the finite element method (FEM), enabling detailed simulation of the formation of a conductive channel under the influence of low-frequency alternating voltage (50 Hz). The VO₂ structure under investigation exhibits pronounced electric field concentration at the surface, where the field strength reaches approximately 5 × 10⁴ V/m, while maintaining a more uniform distribution of around 2 × 10⁴ V/m within the bulk of the material. The simulation results were validated experimentally using a test circuit. Minor deviations—no greater than 8%—were observed between the simulated and measured current values, attributed to magnetic core saturation and modeling assumptions. A distinctive feature of the model is its ability to incorporate the nonlinear dependencies of VO₂’s electrical properties on frequency. Analytical expressions were derived for the magnetic permeability and resistivity of VO₂, demonstrating excellent agreement with experimental data. The coefficients of determination (R²) for the frequency dependence of magnetic permeability and resistance were found to be 0.9976 and 0.9999, respectively. The current version of the model focuses exclusively on the conductive phase and does not include the thermally induced metal–insulator phase transition characteristic of VO₂. The study confirms that VO₂-based structures exhibit high responsiveness and nonlinear switching behavior, making them suitable for applications in electronic surge protection, current limiting, and switching elements. The developed model provides a reliable and physically grounded tool for the design and optimization components based on VO₂ in power electronics and protective circuitry. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

Objective: Given the side effects and reduced efficacy of conventional local anesthetics in inflammatory conditions, there is a compelling need for complementary alternative medicine (CAM), particularly those based on phytochemicals. While a previous study showed that in vivo local injection of (-)-epigallocatechin-3-gallate (EGCG) into the peripheral receptive field suppresses the excitability of rat trigeminal ganglion (TG) neurons in the absence of inflammation, the acute effects of EGCG in vivo, especially on TG neurons under inflammatory conditions, are still unknown. We aimed to determine if acute local EGCG administration into inflamed tissue effectively attenuates the excitability of nociceptive TG neurons evoked by mechanical stimulation. Methods: The escape reflex threshold was measured to assess hyperalgesia caused by complete Freund’s adjuvant (CFA)-induced inflammation. To assess neuronal activity, extracellular single-unit recordings were performed on TG neurons in anesthetized CFA-inflamed rats in response to orofacial mechanical stimulation. Results: The mechanical escape threshold was significantly lower in CFA-inflamed rats compared to before CFA injection. EGCG (1–10 mM) reversibly and dose-dependently inhibited the mean firing frequency of TG neurons evoked by both non-noxious and noxious mechanical stimuli (p < 0.05). For comparison, 1% lidocaine (37 mM), a local anesthetic, also caused reversible inhibition of the mean firing frequency in inflamed TG neurons responding to mechanical stimuli. Importantly, 10 mM EGCG produced a significantly greater magnitude of inhibition on TG neuronal discharge frequency than 1% lidocaine (noxious, lidocaine vs. EGCG, 19.7 ± 3.3% vs. 42.3 ± 3.4%, p < 0.05). Conclusions: Local injection of EGCG into inflamed tissue effectively suppresses the excitability of nociceptive primary sensory TG neurons, as indicated by these findings. Significantly, locally administered EGCG exerted a more potent local analgesic action compared to conventional voltage-gated sodium channel blockers. This heightened efficacy originates from EGCG’s ability to inhibit both generator potentials and action potentials directly at nociceptive primary nerve terminals. As a result, EGCG stands out as a compelling candidate for novel analgesic development, holding particular relevance for CAM strategies. Full article

The aging of dry meat enhances its flavor and tenderness; however, continuous internal quality monitoring throughout the aging process is challenging. We developed and validated a novel electromagnetic response measurement system for meat aging that enables continuous bioimpedance monitoring under stable, optimal temperature/humidity conditions. The system comprises a temperature-controlled dry aging fridge and a newly designed puncture-type semi-rigid coaxial probe, allowing for minimally invasive internal measurements over a broad frequency range. The probe achieved stable measurements across 10 kHz to 10 MHz, and its small diameter (1.25 mm) enabled almost non-destructive internal sensing. Beef and pork samples were monitored over 14 days via multi-channel bioimpedance measurements. After an initial stabilization period, bioimpedance steadily decreased throughout aging. This decline reflected progressive increases in tissue conductivity as cell membranes broke down and intracellular fluids leaked out. High-frequency measurements (e.g., around 10 MHz) were more sensitive to environmental disturbances. Periodic defrost cycles in the chamber caused temporary impedance dips at these frequencies, highlighting the influence of short-term temperature/humidity fluctuations. The system enables long-term continuous measurement without removing samples from the fridge, thus maintaining aging conditions during monitoring. Overall, the system enables the stable, long-term, and multi-channel electromagnetic monitoring of meat quality under optimal aging conditions—a capability not achieved in previous studies. This new method offers a minimally invasive, frequency-resolved approach for assessing meat quality evolution during aging. This advance demonstrates a new approach for tracking meat quality changes during dry aging. Full article

A complex study was performed of the disturbances in geophysics parameters that were observed during a short-term period of earthquake preparation. On 4 March 2024, an $M6.1$ earthquake (N 42.93, E 76.966) occurred with the epicenter 12.2 km apart from the complex of geophysical monitoring. Preparation of the earthquake we detected in real time, 8 days prior to the main shock, when a characteristic cove-like decrease appeared in the gamma-ray flux measured 100 m below the surface of the ground, which observation indicated an approaching earthquake with high probability. Besides the gamma-ray flux, anomalies connected with the earthquake preparation were studied in the variation of the Earth’s natural pulsed electromagnetic field (ENPEMF) at very low frequencies (VLF) $f=7.5$ kHz and $f=10.0$ kHz and at ultra-low frequency (ULF) in the range of 0.001–20 Hz, as well as in the shift of Doppler frequency (DFS) of the ionospheric signal. A drop detected in DFS agrees well with the decrease in gamma radiation background. A sequence of disturbance appearance was revealed, first in the variations of ENPEMF in the VLF band and of the subsurface gamma-ray flux, both of which reflect the activation dynamic of tectonic processes in the lithosphere, and next in the variation of DFS. Two types of earthquake-connected effects may be responsible for the transmission of the perturbation from the lithosphere into the ionosphere: the ionizing gamma-ray flux and the ULF/VLF emission, as direct radiation from the nearby earthquake source. In the article, we emphasize the role of medium ionization in the propagation of seismogenic effects as a channel for realizing the lithosphere–ionosphere coupling. Full article

This study explores the vibrational behavior of Thermoformed Magneto-Piezoelectrets (TMPs), multifunctional materials consisting of thermoformed piezoelectrets with open tubular channels integrated with an additional magnetic layer. The inverse piezoelectric effect was characterized using laser vibrometry analysis, measuring the mechanical response of TMPs subjected to electrical excitation over a frequency range of 0–20 kHz. Vibrational analysis was conducted at 144 spatial points, enabling the construction of detailed three-dimensional (3D) maps of the vibration operational modes and the spatial distribution of the piezoelectric coefficient ($d_{33}$). The results demonstrated significant frequency-dependent behavior, with open channels exhibiting pronounced resonance peaks, whereas valleys displayed smoother and more uniform responses due to enhanced damping effects. The observed heterogeneity in vibrational behavior is attributed to structural variations, material composition, and anisotropic coupling between the piezoelectric and magnetic properties. The findings presented in this research provide a comprehensive understanding of the development and utilization of TMPs, offering parameters for enhancing their application and supporting new discoveries in studies related to the fabrication of novel thermoformed piezoelectric sensors. Full article

Gastrointestinal (GI) tract diseases are among the most common diseases in the world, resulting in more than 8 million deaths. The majority of these deaths occur due to cancer or tumors. Early detection of these tumors can greatly lower the mortality rate. In this work, an implantable multiple-input-multiple-output (MIMO) antenna sensor is constructed for GI tract devices to detect the tumor. The implantable MIMO antenna sensor has two embedded antennas, each operating at 915 MHz. Both elements of the system are placed 0.6 mm apart from each other (edge-to-edge). The volume consumed by this design is measured to be 7 × 7 × 0.25 = 12.25 mm³. It occupies a very small volume due to miniaturization achieved using meandered resonating structures and a high-permittivity substrate. It maintains stable radiation performance (gain = −26.2 dBi at resonance). The antenna units are decoupled by maintaining a proper gap between them and adding a slot on the bottom side. An isolation level greater than 28.7 dB is achieved using these approaches. Since the MIMO system utilizes two antenna elements, its effectiveness is verified using MIMO parameters. At SNR = 20 dB, the channel capacity reaches 8.75 bps/Hz. The proposed antenna ensures high channel capacity and enables seamless communication while simultaneously acting as a sensor to monitor internal changes in the observed region. The frequency response change with variations in the permittivity of human tissue, enabling its sensing capability. Moreover, the antenna sensor maintains stable radiation and S-parameter performance throughout the sensing mechanism. Thus, the proposed solution is suitable for biomedical implants requiring both high-data-rate communication and sensing. Full article

With the increasing complexity of the electromagnetic environment, radar waveform design needs to break through the limitation of traditional single-function architectures, prompting the emergence of integrated radar waveforms. Currently, the mainstream integrated signals are achieved through conventional waveform synthesis or time/frequency division multiplexing. However, the former suffers from limited design flexibility and is confined to single scenario applications, while the latter has interference between different sub-channels, which will limit the performance of multi-function radar. Aiming at the above problems, this paper proposes a waveform optimization method for a detection and cover integrated signal with high Doppler tolerance. By constructing a joint optimization model, the sidelobe characteristics of the signal’s autoambiguity function and the output response of the non-cooperative matched filter were incorporated into the unified objective function framework. The gradient descent algorithm is used to solve the model, and the optimized waveform with low sidelobe characteristics and multiple false target interference abilities is obtained. When the optimized waveform generates multiple false targets to cover our radar position, its peak sidelobe level (PSL) drops below −23 dB, and most of the sidelobe levels in the range-Doppler interval of interest drop below −40 dB. Finally, the proposed integrated waveform undergoes hardware-in-the-loop experiments, experimentally validating its performance and the effectiveness of the proposed method. Full article

Power-split hybrid electric vehicles (power-split-HEVs) exhibit significant engine torque fluctuations due to their mechanical coupling with the driveline, leading to pronounced torsional vibration issues in the drive shaft. This study investigates an active torsional vibration suppression strategy based on drive motor control. First, a dynamic model of the power-split-HEV driveline is established, and its intrinsic characteristics are analyzed. Subsequently, an engine excitation torque model is developed to identify the dominant response orders, while a vehicle dynamics model is constructed to elucidate the torsional vibration mechanisms in both hybrid and pure electric driving modes. Next, a torsional vibration feedback control framework is proposed, utilizing the electric motor as a secondary-channel torque disturbance compensator. Furthermore, a novel frequency-decoupled dual-loop control framework is proposed, with rigorous derivation of the sufficient conditions for decoupling. Based on this framework, two distinct vibration suppression algorithms are developed for the secondary-loop controller, each tailored for specific operational modes. Finally, the proposed algorithms are validated through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate a torque fluctuation suppression ratio of up to 72.2%, confirming that the active suppression algorithm effectively mitigates driveline torsional vibration induced by engine harmonic torque disturbances. Full article

The miniaturization of sensors and non-contact measurement techniques is currently at the forefront of smart grid development. This paper proposes a miniature voltage sensor whose size is significantly reduced while maintaining large bandwidth and high linearity. To minimize the impact of environmental factors on measurement accuracy, a differential structure is utilized to optimize the sensor. The sensor is designed with a dual-channel measurement mode for both high-frequency and power-frequency signals, addressing issues of signal refraction and reflection due to impedance mismatch. COMSOL Multiphysics 6.2 is employed to simulate the sensor’s structural design and placement. Moreover, the experimental analysis of key parameters, such as parallel resistance and capacitance, identifies the optimal parameter combination for low-voltage distribution lines and cables of 10 kV and below. Experiments show that the voltage sensor’s bandwidth ranges from 30 Hz–200 kHz when measured through a frequency response analyzer. Finally, through the measurement carried out on the overhead line and cable, we evaluate the linearity of the sensor according to the experimental data. Specifically, the nonlinear errors of the voltage measurement for the overhead line and cable are 0.62% and 0.57%, respectively. Full article

Pluvial flooding in South Jakarta poses significant economic disruptions, requiring efficient mitigation strategies. This study focuses on optimizing mobile pump deployment as a non-structural flood control measure. Despite the use of mobile pumps in flood response, there is limited research on their systematic optimization for pluvial flood mitigation. This study presents a transferable framework for deploying mobile pumps to mitigate pluvial flood risks in urban areas, demonstrated through a case study in South Jakarta, Indonesia. The findings indicate that flood depths of 75 cm have a 20–50% probability of occurrence, and rainfall in South Jakarta follows a distinct hourly distribution, with 56.6% of the rainfall occurring in the first hour and 43.4% in the second. Radar imagery from the BMKG is used here as the main tool for real-time rainfall detection. The optimization framework considers channel capacity, flood frequency, impact severity, accessibility, and operational protocols. Among 29 flood-prone locations analyzed, 8 of them require mobile pump intervention. Seven locations benefit from integration with weather prediction tools and SCADA systems, while three require dedicated operational procedures (SOPs). Simulation results indicate that placing mobile pumps near the upstream section of the flooded area yields the most effective flood reduction. A minimum pump capacity of 0.5 m³/s is recommended for optimal performance. This study demonstrates that strategic mobile pump deployment, coupled with predictive tools, significantly reduces pluvial flood risks in South Jakarta and offers a transferable framework for other urban areas. 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