A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.

The purpose of this paper is to introduce a new structure called primal. Primal is the dual structure of grill. Like ideal, the dual of filter, this new structure also generates a new topology named primal topology. We introduce a new operator using primal, which satisfies Kuratowski closure axioms. Mainly, we prove that primal topology is finer than the topology of a primal topological space. Also, we provide the structure of the base of primal topology and prove other fundamental results related to this new structure. Furthermore, we not only discuss some of this new structure’s properties but also enrich it with many examples.

Photos taken in unconstrained tourist environments often present challenges for accurate 3D scene reconstruction due to variable appearances and transient occlusions, which can introduce artifacts in novel view synthesis. Recently, in-the-wild 3D scene reconstruction has been achieved realistic rendering with Neural Radiance Fields (NeRFs). With the advancement of 3D Gaussian Splatting (3DGS), some methods also attempt to reconstruct 3D scenes from unconstrained photo collections and achieve real-time rendering. However, the rapid convergence of 3DGS is misaligned with the slower convergence of neural network-based appearance encoder and transient mask predictor, hindering the reconstruction efficiency. To address this, we propose a novel sky-aware framework for scene reconstruction from unconstrained photo collection using 3DGS. Firstly, we observe that the learnable per-image transient mask predictor in previous work is unnecessary. By introducing a simple yet efficient greedy supervision strategy, we directly utilize the pseudo mask generated by a pretrained semantic segmentation network as the transient mask, thereby achieving more efficient and higher quality in-the-wild 3D scene reconstruction. Secondly, we find that separately estimating appearance embeddings for the sky and building significantly improves reconstruction efficiency and accuracy. We analyze the underlying reasons and introduce a neural sky module to generate diverse skies from latent sky embeddings extract from unconstrained images. Finally, we propose a mutual distillation learning strategy to constrain sky and building appearance embeddings within the same latent space, further enhancing reconstruction efficiency and quality. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing methods in novel view and appearance synthesis, offering superior rendering quality with faster convergence and rendering speed.

The uterus is the most important organ in the female reproductive system. Its shape plays a critical role in fertility and pregnancy outcomes. Advances in medical imaging, such as 3D ultrasound, have significantly improved the exploration of the female genital tract, thereby enhancing gynecological healthcare. Despite well-documented data for organs like the liver and heart, large-scale studies on the uterus are lacking. Existing classifications, such as VCUAM and ESHRE/ESGE, provide different definitions for normal uterine shapes but are not based on real-world measurements. Moreover, the lack of comprehensive datasets significantly hinders research in this area. Our research, part of the larger NURSE study, aims to fill this gap by establishing the shape of a normal uterus using real-world 3D vaginal ultrasound scans. This will facilitate research into uterine shape abnormalities associated with infertility and recurrent miscarriages. We developed an automated system for the segmentation and alignment of uterine shapes from 3D ultrasound data, which consists of two steps: automatic segmentation of the uteri in 3D ultrasound scans using deep learning techniques, and alignment of the resulting shapes with standard geometrical approaches, enabling the extraction of the normal shape for future analysis. The system was trained and validated on a comprehensive dataset of 3D ultrasound images from multiple medical centers. Its performance was evaluated by comparing the automated results with manual annotations provided by expert clinicians. The presented approach demonstrated high accuracy in segmenting and aligning uterine shapes from 3D ultrasound data. The segmentation achieved an average Dice similarity coefficient (DSC) of 0.90. Our method for aligning uterine shapes showed minimal translation and rotation errors compared to traditional methods, with the preliminary average shape exhibiting characteristics consistent with expert findings of a normal uterus. We have presented an approach to automatically segment and align uterine shapes from 3D ultrasound data. We trained a deep learning nnU-Net model that achieved high accuracy and proposed an alignment method using a combination of standard geometrical techniques. Additionally, we have created a publicly available dataset of 3D transvaginal ultrasound volumes with manual annotations of uterine cavities to support further research and development in this field. The dataset and the trained models are available at https://github.com/UL-FRI-LGM/UterUS.

Cybersecurity research demands continuous monitoring of the dynamic threat landscape to detect novel attacks. Researchers and security professionals often deploy honeypot networks to intercept and examine real attack data. However, due to the volume and variety of the collected data, it is very challenging for security analysts to investigate the attacks, compare their characteristics and infer their potential connections. To this end, we propose a novel graph-based cyberattack model for storing, analyzing, and visualizing honeynet-captured attacks as the main contribution of our work. Our model enables attack graph analysis and presents the attack data analogous to the Cyber Kill Chain framework to enable intuitive visualizations. We construct the attack graph by decomposing the intercepted attacks into a set of unique entities (represented as nodes) and actions (represented as edges) and merge them into a global attack graph. We develop a user-centric, interactive attack analysis and visualization tool that leverages the proposed model to aid the heuristic cyberattack investigation. We describe the design and technical implementation of the developed model and visual-interactive tool in detail. Finally, we demonstrate the developed tools and validate the model in an analysis of real-world attack data captured on our own distributed honeypot platform. We use the attack model and (sub)graph visualizations to depict attack topologies, identify recurring attackers, and quantify detected malware types. We also leverage graph data science algorithms to uncover and rank malware distribution networks, reveal hidden links between the attackers, and cluster the attack entities to identify potential botnets.