As an important technique for synthetic aperture radar (SAR) 3D imaging, tomographic SAR (TomoSAR) effectively resolves urban layover problems and retrieves target elevations. With the increasing availability of multi-baseline observations from Chinese commercial SAR systems such as Fucheng-1, efficient baseline configuration has become an important issue in practical TomoSAR processing. Therefore, this paper introduces the minimum redundancy array (MRA) concept to construct a baseline optimization strategy, aiming to extract a small optimal baseline subset for high-quality TomoSAR 3D reconstruction. Combined with compressive sensing (CS) algorithms, an experiment was conducted the new campus of Northwestern Polytechnical University(NPU), where 15 baselines were finally and optimally selected from 51 original images for 3D inversion. Results show that the proposal reduces data redundancy and computational costs by approximately 70% while effectively maintaining the structural integrity and elevation accuracy of 3D point clouds. The results demonstrate the feasibility of efficient urban TomoSAR reconstruction based on Fucheng-1 data, and provide a useful reference for baseline configuration and engineering processing of Chinese commercial SAR datasets.

This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices. To this end, we introduce a Mixture-of-Experts strategy for local state estimation, with several specialized decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-Softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder, and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pre-trained, scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones), and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance.

In signal processing, the data collected from sensing devices is often a noisy linear superposition of multiple components, and the estimation of components of interest constitutes a crucial pre-processing step. In this work, we develop a Bayesian framework for signal component decomposition, which combines Gibbs sampling with plug-and-play (PnP) diffusion priors to draw component samples from the posterior distribution. Unlike many existing methods, our framework supports incorporating model-driven and data-driven prior knowledge into the diffusion prior in a unified manner. Moreover, the proposed posterior sampler allows component priors to be learned separately and flexibly combined without retraining. Under suitable assumptions, the proposed DiG sampler provably produces samples from the posterior distribution. We also show that DiG can be interpreted as an extension of a class of recently proposed diffusion-based samplers, and that, for suitable classes of sensing operators, DiG better exploits the structure of the measurement model. Numerical experiments demonstrate the superior performance of our method over existing approaches.

Traditional live-line work on distribution power lines relies on manual labor, characterized by high risk, high labor intensity, and high training costs; thus, developing distribution live-line working robots is an essential path to replacing manual operations. However, the working environment is highly unstructured and dynamic, with challenges such as complex outdoor lighting and unpredictable wind conditions. Existing robots primarily use either master-slave teleoperation or fully autonomous modes, which impose heavy mental burdens on operators, are highly susceptible to misoperation, and exhibit limited environmental adaptability. To address these issues, this study presents a teleoperated distribution live-line working robot based on human-robot shared control. The final manipulator trajectory is obtained by a linearly weighted combination of two reference trajectories separately provided by the human operator and the robot's automatic planning module. This approach reduces the robot's dependency on the operator, alleviates operator mental burden, improves efficiency, and prevents abrupt misoperations by the human operator. Finally, the paper presents the system design and verifies its effectiveness through application in real-world live-line tapping operations.

Quantitative speed-of-sound (SoS) and attenuation of tissues are closely related to pathology; however, conventional B-mode images are limited to qualitative visualization. Existing ultrasound full-waveform inversion (FWI) methods for quantitative SoS reconstruction are primarily developed under double-sided or ring-shaped arrays, which limits their applicability to widely adopted routine clinical acquisitions. In this work, we develop a frequency-domain, total variation (TV)-regularized FWI framework tailored for single-sided linear ultrasound arrays, which enables quantitative reconstruction of SoS maps using standard clinical probes. To address the severe ill-posedness and computational challenges in this setup, efficient forward modeling, fast gradient evaluation, ADMM-based optimization, and multi-GPU parallelization are integrated into the inversion framework. Numerical experiments in a thyroid cyst imaging scenario demonstrate that the proposed method reconstructs the SoS of both simple (fluid-filled) and solid cysts with improved visual and quantitative performance compared to conventional FWI. Additional 2D and 3D simulations across different target and array apertures further elucidate the capabilities and limitations of single-sided ultrasound FWI.

Edema is a potential indicator of underlying pathological changes. However, its low-contrast signature is often masked in conventional B-mode imaging by strong scatterers, making reliable detection challenging. Ultrasound (US) provides a non-invasive, non-ionizing, and cost-efficient imaging option that is widely used. Conventional techniques, which rely on beamforming, often lack sufficient physical interpretability. Quantitative US (QUS) can estimate physical properties such as the speed of sound (SoS) and density by solving a physics-based inverse problem directly on the measured US wavefields, i.e., the raw per-element channel data (CD), to recover their spatial distribution. However, state-of-the-art physics-based inversion methods, including full waveform inversion (FWI) and model-based quantitative radar and US (MB-QRUS), are computationally intensive and susceptible to local minima, which constrains their clinical utility. We introduce deep unfolded FWI (DUFWI), a physics-faithful unfolded iterative inversion method that exhibits FWI-like refinement behavior while learning the update rule from data, requiring only a small number of iterations for real-time SoS reconstruction. Across both simulated datasets and hardware measurements acquired with a Verasonics US system, the DUFWI significantly outperforms classical FWI and MB-QRUS in reconstruction quality while maintaining high computational efficiency. These results demonstrate real-time edema diagnosis in both simulation and hardware experiments, with phantom-based validation using cylindrical rods, supporting practical deployment under typical US imaging setting.

Efficient route planning for overhead transmission lines is crucial for reducing construction costs, ensuring operational safety, and improving environmental adaptability. Traditional algorithms such as Dijkstra and rapidly-exploring random tree (RRT) demonstrate certain advantages in finding feasible paths or exploring complex terrain features, yet they still suffer from issues including excessive turning angles, high terrain undulation sensitivity, and suboptimal overall path smoothness. To address these limitations, this paper proposes an improved A*-based route planning method that incorporates a multi-source constraint cost function that simultaneously considers path length, terrain undulation, number of crossings through risk areas, and turning angle. Simulation results indicate that the proposed method achieves a more balanced optimization effect among multiple metrics. Comparative experiments based on four different cost function methods demonstrate that Method D (the final proposed method) provides optimal performance with a path length of 5482 m, an average terrain undulation of 1.9 m, six crossings, and the lowest turning angle of 51.8°. Further compared with Dijkstra and RRT, the improved A* method reduces the total turning angle by 28.6% and 46.5%, respectively, while maintaining a competitive path length and minimizing cross-terrain risk. Overall, the proposed method improves route smoothness and environmental adaptability while ensuring routing feasibility, which verifies its applicability for intelligent transmission line planning and offers references for engineering practice.

We study a feature-based magnetotelluric (MT) data inversion regularized by the variational autoencoder (VAE). A proper geophysical dataset is synthetically generated, composed of the possible subsurface models sampled from the a priori resistivity distribution. By training a VAE with the dataset, a priori knowledge is extracted by establishing a regularized latent variable space. The pixel-based subsurface resistivity is reparameterized by the latent variables of the pre-trained VAE. Specifically, we adopt a 1D subdomain reparameterization scheme, i.e., the 2D resistivity model is divided into multiple 1D resistivity-depth models reparameterized by one 1D VAE. In the inversion, latent variables of all the subdomains are optimized with Gauss-Newton method. Experiments show that the proposed inversion effectively enhances the accuracy and resolution of MT data inversion compared with the conventional method.

Recent advancements in deep learning-based geophysical inversion have drawn considerable attention. Most of these inversions are supervised, which requires the creation of training models that capture as much prior geological information as is available in an area of interest. However, creating such geologically informed training models is challenging because some geological knowledge is difficult to be expressed in mathematical terms. Moreover, geological prior information is not always tied to specific spatial locations. To address these challenges, a novel method based on alpha shapes, a concept from computational geometry, was proposed to generate training models that can easily integrate five key types of geological prior information, namely, specialIntscript top boundaries, specialIntscript dip angles, specialIntscript surface outcrop contacts, specialIntscript mineralization zones intersected by drillholes, and specialIntscript measured physical property values on rock samples. Three distinct scenarios were presented to demonstrate how the proposed method can be used to systematically generate geologically informed training models. It was also shown that deep generative models, such as the conditional variational autoencoder, trained on these geologically informed models, can not only output inversion results that align with prior geological knowledge but also quantify the associated uncertainties. To validate our approach, it was applied to a set of magnetic measurements collected in Qinghai Province, China, for the exploration of Cu-Mo critical mineral deposits. The resulting susceptibility models reveal a major dipping structure that is consistent with surface geology and the magnetic data. The proposed method offers a flexible and unified framework for generating geologically informed training models for deep learning-based geophysical inversions.

We propose a posterior sampling algorithm for the problem of estimating multiple independent source signals from their noisy superposition. The proposed algorithm is a combination of Gibbs sampling method and plug-and-play (PnP) diffusion priors. Unlike most existing diffusion-model-based approaches for signal separation, our method allows source priors to be learned separately and flexibly combined without retraining. Moreover, under the assumption of perfect diffusion model training, the proposed method provably produces samples from the posterior distribution. Experiments on the task of heartbeat extraction from mixtures with synthetic motion artifacts demonstrate the superior performance of our method over existing approaches.

In the power industry maintenance, the capability of live working robots to detect and operate with power components in real time is paramount. This paper proposes a cascaded detection framework for real-time detection of live working operation points, named YOLO-GDCNN. The framework consists of two parts. The first part is the proposed Lightweight YOLOv5 (Li-YOLOv5), which is composed of Stem, ShuffleBlock and SimSPPF, enhancing the network's generalisation ability while reducing the computational load. The second part proposes a Generative Dense Convolutional Neural Network (GDCNN), which improves the detection accuracy of the lightweight network through dense modules. Through the series of the above modules, the framework successfully combines detection accuracy and real-time performance. Finally, we design experiments in simulated laboratory environments and real-world live working scenarios. The experimental results demonstrate that the proposed YOLO-GDCNN is adept at real-time and accurate detection of live working object types and their operating point, demonstrating its suitability for live working robots.

In this study, we investigate feature-based 2.5-D marine controlled source electromagnetic (mCSEM) data inversion using generative priors. The 2.5-D modeling using the finite difference method (FDM) is adopted to compute the response of horizontal electric dipole (HED) excitation. Rather than using a neural network to approximate the entire inverse mapping in a closed box manner, we adopt a plug-and-play strategy in which a variational autoencoder (VAE) is used solely to learn prior information on conductivity distributions. During the inversion process, the conductivity model is iteratively updated using the Gauss-Newton method, while the model space is constrained by projections onto the learned VAE decoder. This framework preserves explicit control over data misfit (DM) and enables flexible adaptation to different survey configurations. Numerical and field experiments demonstrate that the proposed approach effectively incorporates prior information, improves reconstruction accuracy, and exhibits good generalization performance.

Magnetotelluric (MT) data inversion reconstructs the subsurface resistivity structure using measured natural electromagnetic (EM) fields. Due to the attenuation of low-frequency EM waves in geologic conductors, MT inversion has lower sensitivity and resolution for subsurface structures compared with seismic reflection imaging. Seismic reflection provides rich, detailed subsurface information that complements EM data, such as the spatial locations of geologic boundaries and fine-scale geophysical attributes. This work develops a super-resolution MT inversion approach incorporating the seismic texture constraint. We develop a multiresolution model parameterization scheme to represent the unknown model with the coarse-scale resistivity background and fine-scale resistivity details. A modified, pretrained VGG19 neural network (NN) serves as an effective texture extraction operator. This inversion method requires no NN training and simultaneously optimizes MT data misfit, texture discrepancy, and various regularization penalties using the adaptive moment estimation optimizer. Experiments show that our inversion can reduce or at least maintain the data misfit and effectively improve MT inversion resolution to a seismic-like level. This approach has promising application potential in the oil industry for highlighting quasi-layered subsurface structures.

This study demonstrates a microwave system for noninvasive cerebral perfusion monitoring. A miniaturized ultra-wideband antenna (relative bandwidth = 103%) was designed specifically for this propose. The system's performance was experimental validated on animal models and human subjects. In particular, the cardiac-cyclecorrelated perfusion signal was observed among all subjects. These results establish microwave technology as a promising alternative for continuous bedside cerebrovascular assessment.

In recent years, the rapid advancement of intelligent robotics technology has led to the widespread integration of robots across various industries. Within the power sector, the live operation of substation switchgear presents significant safety risks and demands specialized technical expertise. To enhance operational safety and efficiency, the development of autonomous robotic systems has become imperative. This study focuses on the design and development of an autonomous switchgear operation robot, specifically tailored to meet the operational demands of substation switchgear. The research introduces an innovative “one system, three tasks” autonomous operation framework. At the system level, the robot incorporates an automated tool-switching mechanism that enables efficient transitions between multiple operation tools. At the task level, the study proposes a novel multi-task perception algorithm for autonomous switchgear operations, which integrates target detection, posture regression, and force point determination into a unified end-to-end deep learning network. This comprehensive approach facilitates precise autonomous manipulation of various switchgear components, including buttons, knobs, and handcarts. Experimental evaluations demonstrate that the developed embodied intelligent system, characterized by its seamless software-hardware integration, exhibits stable and efficient performance, effectively meeting the operational requirements for autonomous substation switchgear operations.

This study employs a temperature field inversion method to obtain the oil film temperature distribution. A test rig integrating real-time bearing temperature measurement capabilities was first established. Subsequently, thermal images of the bearing outer wall were captured using an infrared thermal imager. Finally, an iterative coupled solving algorithm was applied to inversely compute the temperature fields of both inner and outer bearing walls under varying rotational speeds (r/min) and applied loads (N), revealing the temperature distribution patterns and evolution trends under different operating conditions. Experimental results demonstrate that the inversion-based computational method represents a novel approach for acquiring oil film temperatures during sliding bearing operation.

Under complex sea condition, the radar echoes from corner reflector arrays exhibit scattering characteristics highly similar to those of actual ships, posing challenges to maritime target recognition. Traditional single-feature-based methods exhibit limitations in discriminating such highly similar targets. To address this issue, this paper proposes a dual-channel gated attention fusion network (DGAF-Net). The model extracts short-time Fourier transform (STFT) features and statistical features simultaneously from the fully polarimetric high-resolution range profiles (HRRP). By using a gated attention mechanism, the network adaptively fuses these features to capture subtle differences in scattering mechanisms. The experimental results demonstrate that the proposed DGAF-Net improves recognition accuracy under complex sea condition compared to single-feature methods, validating its effectiveness and robustness.

We propose a control method that integrates adaptive fuzzy sliding-mode control (AF-SMC) with a fixed-time disturbance observer (FTDO) to address modeling errors, external disturbances, and input saturation in ship path tracking. The designed adaptive fuzzy system dynamically adjusts the SMC gain to enhance adaptability to parameter variations and modeling errors. Furthermore, the proposed method enables rapid estimation of the total uncertainty term by incorporating an FTDO, ensuring fixed-time estimation and feedforward compensation of the total matched uncertainty without requiring prior knowledge of the disturbance bound. Lyapunov stability analysis was employed to verify the bounded stability of the closed-loop system. Simulation results indicate that the proposed method provides high control accuracy and robustness.

This paper presents an efficient inversion method for microwave stroke imaging by learning the low-rank approximation of the descent directions in the supervised descent method (SDM). Our approach significantly reduces the trainable parameters in SDM without sacrificing imaging accuracy. Experiments on 2D stroke models show that the proposed method maintains high reconstruction accuracy with over 90% parameter reduction.

The automatic door-opening operation is critical for many tasks. Yet, the relevant systems mostly depend on precise visual location, and the inevitable errors may cause fatal harm to the robotic system. Furthermore, no force-control techniques have been specifically designed for them. This research addresses these issues by proposing a hybrid complementary control strategy. Firstly, a two-stage active visual positioning method from coarse to fine was introduced to locate the door handle efficiently. Secondly, a strengthened force control method was proposed, and the force errors and countermeasures in the door-opening process were systematically analyzed. Thirdly, a versatile door-opening system was designed and implemented. Real-world trials were carried out to verify the effectiveness. As the results revealed, with comparative small contacting force and torque, the entire process from recognition to successfully door-opening operation can be completed in 12 seconds at the fastest. In addition, compared with several state-of-the-art door opening methods, the proposed strategy was faster, with less interactive force and torque, demonstrating the superior performance of the proposed method in complex robotic operations.