Image reconstruction plays an important role in modern electronic information systems and equipment and has attracted widespread intense attention in academic and industrial fields.Restoring delicate image particulars using traditional handcrafted prior image reconstruction methods is often difficult because of the inferior prior characterization abilities of these methods.In this article, an efficient image reconstruction model, named DGMR, is proposed based on deep Gaussian mixture learning.In particular, a channel attention mechanism is designed for image spatial correlation exploitation.Both the external and internal information are explored using deep Gaussian mixture.A residual Swin transformer module is constructed to learn Gaussian mixture priors, including the images ' means and variances; in contrast, existing methods calculate only the mean and ignore the variance.Moreover, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization.Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the current advanced comparison approaches.

High-resolution image reconstruction from partial observations plays an important role in modern electronic information systems and has attracted widespread intense attention in both the academic and industrial fields. It is often difficult for the traditional handcrafted prior image reconstruction methods to recover delicate image details because of the inferior prior characterization abilities of these methods; this is especially true in the presence of complex electromagnetic perturbations in real-world environment scenes. Therefore, in this paper, an efficient image high-resolution reconstruction and perturbation defense model, named DGMR, is proposed based on deep Gaussian mixture learning. In particular, a simultaneous maximum a posterior (MAP) reconstruction-defense framework is designed based on the learned deep Gaussian mixture prior. Moreover, a channel attention mechanism is designed for image spatial correlation exploitation. Both the external and internal information are explored using a deep Gaussian mixture. A deep residual Swin transformer module is constructed to further characterize the image and learn Gaussian mixture priors, including both the image means and variances; in contrast, existing methods calculate only the image means but ignore the variances. Furthermore, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization. Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the existing state-of-the-art systems.

Ultra high-speed target recognition in complex electromagnetic environments is a critical and fundamental machine perception issue. It is difficult to ensure privacy protection and intelligent confrontation with centralized training in many practical situations. In this paper, an efficient ultra high-speed target recognition approach, named InVision, for robot systems is proposed using deep trustworthy federated learning (DTFL). InVision is accurate, safe, fast, and robust. Particularly, geometric component transformer (GCT) is presented to significantly promote neural element's complex representation description ability. And an ambiguity-aware cooperative learning (AACL) scheme is developed to relieve the noisy label problem. Moreover, decentralized federated training (DFT) is designed to mitigate the intractable privacy protection problem, to efficiently search for similarities and reduce the representation redundancy. Furthermore, to promote the running speed of the system in real-world environments, a lightweight deep architecture, called Mobile-XB, is developed. Extensive quantitative and qualitative experiments are carried out, and the results demonstrate that InVision greatly outperforms the outstanding comparison methods, establishing efficient connections and extraction, and providing security guarantees.

Weak feature enhancement is an important and intractable issue in machine vision fields. In this paper, an effective complex-scene inverse synthetic aperture radar (ISAR) image feature enhancement method is proposed based on spatial neighborhood sparse constraint hybrid clustering oversegmentation (SNSC-HCOS). A hybrid superpixel concept is first developed considering the consistency of pixel labels and the discrimination of perturbations. The scattering characteristic similarity, spatial proximity, neighborhood information, sparsity bias, and overall image features are simultaneously explored. The SNSC-HCOS algorithm is designed based on the hybrid superpixel concept, data reconstruction, and fuzzy c-means (FCM) clustering. Both sparsity constraints and local spatial information constraints are embedded into the oversegmentation framework, improving the perturbation defense capability and clustering robustness. To correct misclassified pixel membership and improve system efficiency, median membership filtering and area merging strategies are further applied. Extensive qualitative and quantitative experiments are conducted on a real-scene dataset, demonstrating that SNSC-HCOS greatly outperforms the outstanding comparison systems. The proposed system has good segmentation performance, can generate more pure superpixels with regular shapes and uniform sizes, and has perturbation defense and feature enhancement capabilities.

The location of the equipment reserve of the general information system determines the cost and timeliness of logistics transportation, and the factors that have the greatest impact on the location of the warehouse are often the distance of logistics transportation and the cost of transportation. Layout designers often want to obtain the optimal reserve warehouse layout scheme to achieve economic efficiency optimization. The paper uses immunooptimization methods to generate initial schemes from alternative warehouse locations and continuously iteratively optimizes to obtain the best layout scheme. Through case calculation and analysis, the reliability and effectiveness of the immunooptimization algorithm in dealing with the location problem of the combat readiness reserve warehouse of the general information system equipment are verified.