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Cyber threats are continually evolving and becoming increasingly complex, affecting various industries. Healthcare institutions are the second most targeted industry, preceded by manufacturing. The industry is on the lookout for a reliable cybersecurity system. This research analyzed the feasibility and reality of implementing a Zero Trust Architecture (ZTA) framework within a large healthcare enterprise with a workforce within the range of 45 k to 50 k personnel. It utilizes a baseline concept centered on the widely used Perimeter-Based Security Model (PBSM) in production environments. The focus is on assessing the feasibility of transitioning from a PBSM to a ZTA framework and specifically aims to assess the effects of such a transition on security, control, cost-effectiveness, supportability, risk, operational aspects, and the extent to which ZTA is applicable across different applications. Company X was used as a case study and provided data for analysis in support engagements and host traffic telemetry values. Findings indicated that a PBSM remains effective in providing defense measures for an organization mainly when a significant financial incentive is involved. On the other hand, ZTA offers a more secure environment with a notable reduction in risk, albeit at an additional cost and with added support variables.

Previous camera calibration methods often use a checkerboard to capture images and estimate the camera parameters from the correspondences between images and the checkerboard. The corner points in the checkerboard images are used as useful features for correspondence matching. Therefore, it is essential to precisely find the corner points in the checkerboard images. In many previous works, the corner points are extracted assuming that the checkerboard images are not distorted by its lens. Instead, image blurring and Gaussian noise on the images are usually considered, but other cases are not dealt with. However, the captured checkerboard images are often corrupted by lens distortions and compression artifacts, which leads to performance degradation of corner point extraction. Moreover, the corner points are extracted individually in the previous methods without considering their geometric relations. To better handle the corner point extraction problem under lens distortions, in our corner point extraction optimization, the distorted locations of the pixels on checkerboard images are corrected with the camera parameters, and the structural constraints for checkerboard image grids are then applied under the line-to-line mapping. Also, to robustly find the blurred edges between corner points due to JPEG compression, an edge surface model is newly proposed that models the transitions with over- and under-shoots around the blurred edges. Extensive experimental results show that our method significantly outperforms the state-of-the-art method with average 88.3% and 54.3% reduction in RMSE for corner point reprojection and camera parameter estimation, respectively under compression and lens distortions for synthetic and real data.

The representation of voxel colors has become crucial because voxelized 3D data are used in a variety of applications. In particular, texture atlases are widely used for representing voxel colors. The texture atlas is typically utilized for encoding voxel colors using image/video encoders, so it is important to design a texture atlas while maintaining the connectivity between voxels. In this paper, we present a texture atlas based on a surface scanning scheme. To this end, we represent a 3D object as slices, and texture strips are obtained by scanning voxels along the surface for each slice. The texture strips represent the voxel colors while preserving the connectivity of the voxels. Besides, because the texture strips are highly correlated, we construct a texture atlas by aligning texture strips. We also propose a partitioning method for a 3D object to solve the self-occlusion problem that occurs in the alignment process. As an application of the proposed texture atlases, we utilize it for encoding voxel colors and propose encoding strategies. Experimental results indicate that the proposed texture atlases outperform the previous methods. To the best of our knowledge, this is the first attempt to use a surface scanning scheme for a texture atlas.

A visual hull is an approximation of a three-dimensional (3-D) object generated by the shape-from-silhouettes (SfS) technique. Because a visual hull is calculated from silhouettes, a visual hull can be represented by silhouette images, encoded by a small number of bits. However, it is challenging to represent the concave regions of a visual hull. In this paper, we model voxel data with a set of visual hulls, thereby handling the concave regions of voxel data. To accomplish this, silhouettes are generated from input voxel data using virtual cameras, and a visual hull is computed by SfS using the silhouettes. To handle the concave regions, we calculate the residuals of visual hulls, and the residuals are represented by visual hulls again. This process is repeated until all concave regions are processed, and a hierarchical data structure, i.e., a visual hull tree, is generated. Because the visual hull tree is constituted from a set of visual hulls, it can represent the details of the voxel data even in the root node. Also, because a set of visual hulls can be represented by silhouettes, a visual hull tree has a small number of bits. From the experiments, we compare our method to the octree-based representation, and our method demonstrates good encoding performance.

The strain tolerance and promising optoelectronic properties of 2D materials can be leveraged to design functional optical sensing devices. Here, the authors provide a demonstration of arrays of independently addressable photodetectors constructed from graphene and MoS2 engineered in 3D Kirigami geometries.