In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.

X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression.

Cooperative jamming task allocation for UAV swarms must jointly consider radar priority, the effectiveness of different jamming modes, and heterogeneous resource limits. This paper establishes a radar threat assessment model from radar operating parameters and evaluates each candidate jamming assignment in the time, frequency, and power domains. Based on these evaluations, a binary integer programming model is formulated to jointly determine radar selection, UAV assignment, and jamming mode under coverage, capacity, and mode availability constraints. To solve the model, an improved multi-population genetic algorithm (IMPGA) is developed using crossover on radar task blocks, mutation at the task level, stochastic feasibility repair, and cooperative evolution among multiple subpopulations. Experimental comparisons are conducted under identical function evaluation budgets, and each representative scenario is evaluated through 100 independent Monte Carlo runs. The results show that the IMPGA reliably obtains exact or near-optimal solutions and provides improved solution quality and consistency, particularly as the problem scale and resource coupling increase. The scalability experiments further demonstrate that the algorithm maintains small optimality gaps in larger instances. Ablation results confirm that the radar task operators and stochastic repair make important contributions to the final solution quality and convergence process.

Distributed navigation interference is a critical technique for degrading the performance of multi-element adaptive nulling antennas, where optimal spatial deployment of interference sources is pivotal to maximizing overall interference effectiveness. However, interference signal propagation is constrained by transmit power, path loss, and terrain occlusion, rendering optimal cooperative source deployment in complex terrain a core challenge in navigation spectrum analysis and interference management. To address this gap, this paper proposes DDL-NSGA-II, an improved NSGA-II algorithm for distributed navigation interference source deployment. By integrating the free-space propagation model with reference-plane-based viewshed analysis, the effective coverage of interference signals in 3D terrain is estimated, and a bi-objective model is constructed with multi-weight coverage rate and effective dispersion as optimization objectives. Three well-established mechanisms are adapted and integrated into standard NSGA-II for this specific problem: differential evolution mutation with binomial crossover, stage-wise dynamic deduplication, and external archive-assisted local search refinement. The contribution lies in their problem-specific integration and adaptation to integer-encoded multi-source deployment, which significantly enhances global exploration and convergence accuracy. Simulation results show that DDL-NSGA-II delivers high-coverage deployment schemes with favorable dispersion under varying terrain resolutions, source quantities, and single-station interference ranges. It outperforms three baseline algorithms in convergence, Pareto front coverage, and solution uniformity, providing robust algorithmic support for intelligent multi-source cooperative deployment in complex terrain.

As a foundation for numerical solvers in computational electromagnetics, particularly for multiphysics and electromagnetic compatibility applications, Jefimenko’s equations offer a generalized solution to Maxwell’s equations, enabling the direct computation of electromagnetic fields from time-dependent source distributions without the boundary-condition artifacts inherent to grid-based methods. However, the numerical integration of these equations is computationally intensive, typically scaling as $\mathcal{O}\left(N_{\mathrm{s}}{N}_{\mathrm{o}}\right)$ for $N_{\mathrm{s}}$ source points and $N_{\mathrm{o}}$ observation points. In this paper, we present JefiFast, a highly optimized graphics processing unit (GPU) implementation that significantly outperforms the state-of-the-art JefiGPU algorithm. We identify that previous implementations are strictly memory-bound due to inefficient global memory transactions and a lack of data reuse. JefiFast addresses these bottlenecks through four key optimizations: (i) a packed memory layout (PML) using an array-of-structures approach to ensure coalesced memory access for source densities and their derivatives; (ii) geometry-aware shared memory tiling strategies that maximize L2 (level-2) cache hit rates and on-chip data reuse; (iii) pre-computation of time derivatives to minimize redundant arithmetic operations; and (iv) a robust observation domain decomposition strategy that enables linear scaling across multiple GPUs. Benchmarks demonstrate that JefiFast achieves speedups ranging from $4.08$ times (for ${30}^3$ grids on a single NVIDIA V100 graphic processor) to $84.51$ times (for ${50}^3$ grids on 4 NVIDIA V100 processors) compared to the baseline. Notably, for a ${50}^3$ grid on a single GPU, JefiFast reduces execution time from about 51 min to just about 2.6 min ($19.54$ times speedup). These performance advances make high-resolution relativistic heavy-ion collision simulations feasible in near real-time.

In the field of Prognostics and Health Management (PHM), deep learning methods have shown excellent remaining useful life (RUL) prediction ability because of their powerful data fitting ability. Such methods require a large amount of failure history to train, but for industrial assets, their life-cycle data are often insufficient, and effective repairs or replacements are frequently carried out before the failure occurs. On the one hand, these suspension history data cannot be directly used by traditional deep learning methods, resulting in the certain loss of degradation information. On the other hand, the internal structure of industrial assets and the types of features are complex and diverse. Traditional methods use Euclidean space to represent relevant data, ignoring the correlation between features. Therefore, we develop a semi-supervised adversarial graph prediction (SAGP) framework that combines adversarial learning, graph neural networks, and semi-supervised training, using both failure and suspension histories for RUL prediction. Based on graph neural networks, SAGP can effectively obtain the degradation information in the failure and suspension history by representing the degradation data in the form of graphs and setting multi-objective functions. Through semi-supervised learning, SAGP looks for failure and suspension histories similar to the current state and generates simulated data through adversarial training to improve the robustness of the model and finally achieve RUL prediction. Tool, engine, and bearing datasets are used to verify the accuracy and reliability of the SAGP model.