Search Results (3,184)

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

Video denoising in extremely low-light surveillance scenarios is a challenging task in computer vision, as it suffers from harsh noise and insufficient signal to reconstruct fine details. The denoising algorithm for these scenarios encounters challenges such as the lack of ground truth, and the noise distribution in the real world is far more complex than in a normal scene. Consequently, recent state-of-the-art (SOTA) methods like VRT and Turtle for video denoising perform poorly in this low-light environment. Additionally, some methods rely on raw video data, which is difficult to obtain from surveillance systems. In this paper, a denoising method is proposed based on the trilateral filter, which aims to denoise real-world low-light surveillance videos. Our trilateral filter is a weighted filter, allocating reasonable weights to different inputs to produce an appropriate output. Our idea is inspired by an experimental finding: noise on stationary objects can be easily suppressed by averaging adjacent frames. This led us to believe that if we can track moving objects accurately and filter along their trajectories, the noise may be effectively removed. Our proposed method involves four main steps. First, coarse motion vectors are obtained by bilateral search. Second, an amplitude-phase filter is used to judge and correct erroneous vectors. Third, these vectors are refined by a full search in a small area for greater accuracy. Finally, the trilateral filter is applied along the trajectory to denoise the noisy frame. Extensive experiments have demonstrated that our method achieves superior performance in terms of visual effects and quantitative tests. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent “black-box” nature of DL decision-making, which complicates physician scrutiny and accountability. Eye tracking (ET) technology offers a transformative solution by capturing radiologists’ gaze patterns to generate supervisory signals. These signals enhance DL models through two key mechanisms: providing weak supervision to improve feature recognition and diagnostic accuracy, particularly when labeled data are scarce, and enabling direct comparison between machine and human attention to bridge interpretability gaps and build clinician trust. This approach also extends effectively to multimodal learning models (MLMs) and vision–language models (VLMs), supporting the alignment of machine reasoning with clinical expertise by grounding visual observations in diagnostic context, refining attention mechanisms, and validating complex decision pathways. Conducted in accordance with the PRISMA statement and registered in PROSPERO (ID: CRD42024569630), this review synthesizes state-of-the-art strategies for ET-DL integration. We further propose a unified framework in which ET innovatively serves as a data efficiency optimizer, a model interpretability validator, and a multimodal alignment supervisor. This framework paves the way for clinician-centered AI systems that prioritize verifiable reasoning, seamless workflow integration, and intelligible performance, thereby addressing key implementation barriers and outlining a path for future clinical deployment. Full article

Shear wave elasticity imaging, an ultrasound-based method for imaging tissue elasticity, has been widely accepted in both preclinical studies and clinical practices for diagnosing various diseases. Currently, shear wave elasticity imaging is primarily implemented using a single-transducer approach, in which the same ultrasound transducer is used for both generating and recording shear waves in target tissue. This technical implementation well served the need for imaging bulk tissues in various cases. However, the limited bandwidth of the ultrasound transducer is a great obstacle to extending the application of shear wave elasticity imaging to cases where higher spatial resolution and/or stronger tissue stimulation are needed. To address this challenge, we proposed a dual-transducer approach in which two ultrasound transducers perform shear wave generation and tracking, each optimized for its respective task. The feasibility of the proposed method is demonstrated and verified in a phantom study. In this pioneering work, the strength of the dual-transducer approach is shown by its performance in shear wave tracking at various frequencies. This performance is evaluated by four measures: signal-to-noise ratio, contrast-to-noise ratio, spatial resolution, and precision in quantitative measurement. The experimental results demonstrate the superior elasticity imaging capabilities of the dual-transducer approach compared to the conventional single-transducer approach, offering a reliable strategy for further development of this imaging method for specific applications. Full article

This study investigates the use of Autonomous Sensing Infrastructure and Connected and Autonomous Vehicles (CAV) technologies to support infrastructure-to-vehicle (I2V) and infrastructure-to-everything (I2X) communications, including the alerting of drivers and pedestrians. It describes research findings in the following CAV functionalities: (1) Intersection-based object detection and tracking; (2) Basic Safety Message (BSM) generation and transmission; and (3) In-Vehicle BSM receipt and display, including handheld (smartphone) application BSM receipt and user presentation. The study summarizes the various software and hardware components used to create the I2V and I2X prototype solutions, which include open-source and commercial software as well as industry-standard transportation infrastructure hardware, e.g., Signal Controllers. Results from in-lab testing demonstrate effective object detection (e.g., pedestrians, bicycles) based on sample traffic camera video feeds as well as successful BSM message generation and receipt using the leveraged software and hardware components. The I2V and I2X solutions created as part of this research are scheduled to be deployed in a real-world intersection in coordination with state and local transportation agencies. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Model predictive control (MPC) has been proven effective in terms of cooperative control for wind turbines (WTs). Previous work was limited to segmented linearization at a specific operating point, which significantly affected the robustness of the MPC performance. Moreover, due to nonlinearity, frequent control switching would result in the instability and fluctuation of the closed-loop control system. To address these issues, this paper proposes a novel cooperative control strategy considering fatigue load suppression for wind turbines, which is named soft switch multiple model predictive control (SSMMPC). Firstly, based on the gap metric, a model bank is constructed to divide the nonlinear WT model into several linear segments. Then, the multiple MPC is designed in a wide range of operating points. To settle the control signal oscillation problem, a soft-switching rule based on the triangular–trapezoidal hybrid membership function is proposed during controller selection. Several simulations are performed to verify the effectiveness and flexibility of SSMMPC in the partial-load region and full-load region. The results confirm that the proposed SSMMPC exhibits excellent performance in both reference operating point tracking and fatigue load mitigation, especially for the main shaft torque and tower bending load. Full article

Soft skills such as communication and collaboration are vital in both professional and educational settings, yet difficult to train and assess objectively. Traditional role-playing scenarios rely heavily on subjective trainer evaluations—either in real time, where subtle behaviors are missed, or through time-intensive post hoc analysis. Virtual reality (VR) offers a scalable alternative by immersing trainees in controlled, interactive scenarios while simultaneously capturing fine-grained behavioral signals. This study investigates how task design in VR shapes non-verbal and paraverbal behaviors during dyadic collaboration. We compared two puzzle tasks: Task 1, which provided shared visual access and dynamic gesturing, and Task 2, which required verbal coordination through separation and turn-taking. From multimodal tracking data, we extracted features including gaze behaviors (eye contact, joint attention), hand gestures, facial expressions, and speech activity, and compared them across tasks. A clustering analysis explored whether o not tasks could be differentiated by their behavioral profiles. Results showed that Task 2, the more constrained condition, led participants to focus more visually on their own workspaces, suggesting that interaction difficulty can reduce partner-directed attention. Gestures were more frequent in shared-visual tasks, while speech became longer and more structured when turn-taking was enforced. Joint attention increased when participants relied on verbal descriptions rather than on a visible shared reference. These findings highlight how VR can elicit distinct soft skill behaviors through scenario design, enabling data-driven analysis of collaboration. This work contributes to scalable assessment frameworks with applications in training, adaptive agents, and human-AI collaboration. Full article

Against the backdrop of increasing demands for the safety and longevity of the bridge infrastructure, this review synthesizes the recent advances in structural health monitoring (SHM) sensing systems. Carbon nanotube (CNT), piezoelectric, RFID, wireless, fiber optic, and computer-vision-based sensing are thoroughly explored and elucidated in the existing literature survey that distills their working principles, documented deployments, and anticipated research directions. CNT sensors detect minute resistance variations for strain and crack surveillance; piezoelectric devices transduce mechanical stimuli into high-resolution electrical signals; RFID tags combine location tracking with modular sensing and wireless data relay; and wireless sensing technology integrates sensor nodes with microprocessors and communication modules, which can facilitate efficient data processing and autonomous management. Fiber optic sensing technology, known for precision and interference resistance, is ideal for high-precision monitoring under strong electromagnetic interference conditions, and vision-based systems emulate human perception to extract geometric descriptors via image analytics. The comparative analysis reveals complementary strengths that guide practitioners in selecting optimal sensor suites for specific bridge conditions. The findings underscore the transformative role of these technologies in enhancing SHM reliability and suggest that synergistic integration with robotics and emerging materials will further advance future resilient monitoring frameworks. Full article

Calibration of power equipment has become an essential task in modern power systems. This paper proposes a distributed remote calibration prototype based on a cloud–edge–end architecture by integrating intelligent sensing, Internet of Things (IoT) communication, and edge computing technologies. The prototype employs a high-precision frequency-to-voltage conversion module leveraging satellite signals to address traceability and value transmission challenges in remote calibration, thereby ensuring reliability and stability throughout the process. Additionally, an environmental monitoring module tracks parameters such as temperature, humidity, and electromagnetic interference. Combined with video surveillance and optical character recognition (OCR), this enables intelligent, end-to-end recording and automated data extraction during calibration. Furthermore, a cloud-edge task scheduling algorithm is implemented to offload computational tasks to edge nodes, maximizing resource utilization within the cloud–edge collaborative system and enhancing service quality. The proposed prototype extends existing cloud–edge collaboration frameworks by incorporating calibration instruments and sensing devices into the network, thereby improving the intelligence and accuracy of remote calibration across multiple layers. Furthermore, this approach facilitates synchronized communication and calibration operations across symmetrically deployed remote facilities and reference devices, providing solid technical support to ensure that measurement equipment meets the required precision and performance criteria. Full article

Intra-fractional respiratory management represents one of the greatest challenges of modern cancer radiotherapy (RT), as significant breathing-induced lesion motion might affect target coverage and organs at risk (OARs) sparing, jeopardizing oncological and toxicity outcomes. The detrimental effects on dosage of uncompensated organ motion are exacerbated in RT with charged particles (e.g., protons and carbon ions), due to their higher ballistic selectivity. The simplest strategies to counteract this phenomenon are the use of larger treatment margins and reductions in or control of respiration (e.g., by means of compression belts, breath hold). Gating and tracking, which synchronize beam delivery with the respiratory signal, also represent widely adopted solutions. When tracking the tumor itself or surrogates, invasive procedures (e.g., marker implantation), an unnecessary imaging dose (e.g., in X-ray-based fluoroscopy), or expensive equipment (e.g., magnetic resonance imaging, MRI) is usually required. When chest and abdomen excursions are measured to infer internal tumor displacement, the additional devices needed to perform this task, such as pressure sensors or surface cameras, present inherent limitations that can impair the procedure itself. In this context, radars have intrigued the radiation oncology community, being inexpensive, non-invasive, contactless, and insensitive to obstacles. Even if real-world clinical implementation is still lagging behind, there is a growing body of research unraveling the potential of these devices in this field. The purpose of this narrative review is to provide an overview of the studies that have delved into the potential of radar-based technologies for motion-adaptive photon and particle RT applications. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

Indoor wireless networks face increasing challenges in maintaining stable coverage and performance, particularly with the widespread use of high-frequency Wi-Fi and growing demands from smart home devices. Traditional methods to improve signal quality, such as adding access points, often fall short in dynamic environments where user movement and physical obstructions affect signal behavior. In this work, we propose a system that leverages existing Internet of Things (IoT) devices to perform real-time user localization and network adaptation using fine-grained Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) measurements. We deploy multiple ESP-32 microcontroller-based receivers in fixed positions to capture wireless signal characteristics and process them through a pipeline that includes filtering, segmentation, and feature extraction. Using supervised machine learning, we accurately predict the user’s location within a defined indoor grid. Our system achieves over $82\%$ accuracy in a realistic laboratory setting and shows improved performance when excluding redundant sensors. The results demonstrate the potential of communication-based sensing to enhance both user tracking and wireless connectivity without requiring additional infrastructure. Full article