Explore the vast range of titles available.

This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion detection system (IDS) is expected to adapt and secure the computing infrastructures from the ever-changing sophisticated threat landscape. Many deep learning approaches have recently been proposed; however, these techniques face significant challenges in identifying all types of attacks, especially rare attacks due to network traffic imbalances and the lack of a sufficient number of abnormal traffic samples for model training. To overcome these shortcomings and improve detection performance, this paper presents an unsupervised deep learning approach for intrusion detection. Unlike the existing IDS model that extracts features and trains a classifier in two separate stages, a single-stage IDS approach that integrates a one-dimensional convolutional autoencoder (1D CAE) and a one-class support vector machine (OCSVM) as a classifier into a joint optimization framework is introduced in this paper for the first time. Using only the normal traffic samples, the approach simultaneously optimizes the 1D CAE for compact feature representation and the OCSVM for classification by defining a unified objective function combining reconstruction error with classification error. Thus, the generated compact feature representation has not only reconstruction ability but also discriminative ability for classification. An in-depth ablation analysis validates the design decisions and provides further insight of the proposed approach. An extensive set of experiments on two benchmark intrusion datasets, NSL-KDD and UNSW-NB15, demonstrates the generalization ability of the proposed model for unseen attacks and confirms it as a competitive approach over the recent state-of-the-art intrusion detection baselines. Overall, the obtained results emphasize that the proposed approach has potential to serve as a baseline for building an effective IDS.

Wireless communication technologies such as fifth generation mobile networks (5G) will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies to entire industries to support Internet of things (IOT) technologies. Compared to existing mobile communication techniques, 5G has more varied applications and its corresponding system design is more complicated. The resurgence of artificial intelligence (AI) techniques offers an alternative option that is possibly superior to traditional ideas and performance. Typical and potential research directions related to the promising contributions that can be achieved through AI must be identified, evaluated, and investigated. To this end, this study provides an overview that first combs through several promising research directions in AI for 5G technologies based on an understanding of the key technologies in 5G. In addition, the study focuses on providing design paradigms including 5G network optimization, optimal resource allocation, 5G physical layer unified acceleration, end-to-end physical layer joint optimization, and so on.

Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Image stitching is a computational technique designed to align and seamlessly fuse multiple overlapping images into a single panoramic image with an extended field of view. It plays a critical role in diverse domains, including mobile photography, autonomous navigation, and visual perception systems. However, most conventional image stitching pipelines implicitly assume that the input images have been pre-corrected for geometric distortions, particularly radial distortion inherent to wide-angle and fisheye lenses. This assumption often fails in practice, as many consumer-grade cameras lack built-in correction or calibration support. Consequently, applying standard image stitching methods to the uncorrected imagery frequently degrades feature correspondence reliability and introduces visible geometric misalignments and seam discontinuities in the final panorama. To overcome these limitations, this paper introduces a task-driven joint iterative optimization framework for image stitching that unifies unsupervised radial distortion correction, distortion-aware feature registration, and seam-aware blending within a single cohesive optimization objective. Specifically, lens distortion parameters are explicitly modeled as learnable variables and embedded into both the geometric registration and seam optimization sub-problems. An efficient closed-loop optimization strategy is then employed to jointly refine distortion parameters, homography estimates, and optimal seam paths in an alternating, mutually reinforcing manner. Implementation-wise, we first propose a calibration-free initial radial distortion estimation method which leverages intrinsic image gradients and epipolar consistency to provide physically plausible initialization for subsequent optimization. During iteration, distortion parameters are progressively refined by integrating robust geometric constraints derived from current feature matches (via RANSAC-based consensus filtering) with photometric consistency cues. Extensive experiments on multiple public benchmarks featuring pronounced radial distortion demonstrate that our method achieves superior stitching fidelity using metrics including PSNR and SSIM. It also confirms enhanced feature matching stability, which outperforms both distortion-agnostic approaches and two-stage pipelines that decouple distortion correction from registration. Furthermore, comprehensive ablation studies quantitatively and qualitatively validate the functional necessity and synergistic contribution of each core module, confirming the design rationale and effectiveness of the proposed joint optimization architecture.

Degraded underwater images decrease the accuracy of underwater object detection. Existing research uses image enhancement methods to improve the visual quality of images, which may not be beneficial in underwater image detection and lead to serious degradation in detector performance. To alleviate this problem, we proposed a bidirectional guided method for underwater object detection, referred to as BG-YOLO. In the proposed method, a network is organized by constructing an image enhancement branch and an object detection branch in a parallel manner. The image enhancement branch consists of a cascade of an image enhancement subnet and object detection subnet. The object detection branch only consists of a detection subnet. A feature-guided module connects the shallow convolution layers of the two branches. When training the image enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task. The shallow feature map of the trained image enhancement branch is output to the feature-guided module, constraining the optimization of the object detection branch through consistency loss and prompting the object detection branch to learn more detailed information about the objects. This enhances the detection performance. During the detection tasks, only the object detection branch is reserved so that no additional computational cost is introduced. Extensive experiments demonstrate that the proposed method significantly improves the detection performance of the YOLOv5s object detection network (the mAP is increased by up to 2.9%) and maintains the same inference speed as YOLOv5s (132 fps).

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

With the rapid development of intelligent services, communication objectives are shifting from humans to multi-agent (MA) systems. This transition necessitates new communication paradigms capable of supporting real-time perception, decision-making, and collaboration among agents. Semantic communication (SeC) focuses on the efficient transmission and accurate understanding of information “meaning” and is well-suited to meet the needs of Mas, such as collaborative perception, reasoning, and decision-making. However, the transmission of semantic information is still constrained by dynamic environments and the diversity of MA tasks. To address these challenges, this work proposes a COmparative learning Joint Optimal (COJO) SeC framework. This work makes three main contributions: first, it jointly optimizes the image reconstruction and classification functions designed for multi-task semantic objectives under different channel conditions, thereby improving the overall task performance of the system; second, based on input image features, compression ratio, task requirements, and channel conditions, an enhanced further compressor is designed, which obtains a training-based mask to significantly reduce the volume of transmitted data; finally, to prevent the loss of key semantic information in multi-task scenarios under channel constraints, it designs a task-driven end-to-end semantic communication training scheme.

Building energy optimization faces challenges in simultaneously addressing spatial design and energy performance prediction. This paper proposes a novel framework integrating generative adversarial networks (GANs) with physics-based simulation engines to achieve joint optimization of building energy consumption prediction and spatial configuration. The framework establishes bidirectional coupling between conditional GAN architectures and EnergyPlus thermodynamic calculations, embedding physical constraints directly into the generative process through a physics-informed discriminator. Experimental validation across office, residential, and educational building typologies demonstrates superior performance, achieving 6.8% mean absolute percentage error in energy prediction and 23.7% average energy consumption reduction compared to code-compliant baselines. The model generates well-distributed Pareto fronts containing 42–56 non-dominated solutions, outperforming conventional sequential optimization approaches by 8–12 percentage points while maintaining computational efficiency. This hybrid methodology advances sustainable building design by transcending limitations inherent to purely data-driven or physics-only approaches, providing a scalable solution for intelligent architectural optimization.

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module’s performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.