Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique.

In magnetic resonance imaging (MRI), low-resolution (LR) images often hamper clinical diagnosis and research due to constraints in imaging conditions and technology limitations. Recent studies in super-resolution (SR) reconstruction of multi-contrast MRI have shown promise by leveraging the complementary information from different MRI contrasts. However, existing multi-contrast MRI SR techniques face several challenges: 1) a lack of pre-alignment precision can result in distorted reconstructions; 2) prevailing transformer network structures, with their smaller windows (e.g., 8 × 8), struggle to effectively capture long-range dependencies and lack the ability to interact between different windows; and 3) current methods are limited to fixed integer scaling (e.g., 2 ×, 3 ×, 4 ×), which limits flexibility and increases complexity in training and storage. To address these challenges, we propose a novel arbitrary-scale SR network for multi-contrast MRI. Specifically, our approach compensates for spatial misalignment between modalities through deformable registration module and employs permuted cross-attention transformer in MR images. In addition, we introduce a ref-scale ensemble implicit attention module that better integrates high-frequency information from reference images and enables arbitrary-scale upsampling. Extensive experiments on two publicly available MRI datasets validate the superiority of our method in multi-contrast MRI SR, demonstrating its significant potential in clinical applications.

Visual cryptography scheme (VCS) serves as an effective tool in image security. Size-invariant VCS (SI-VCS) can solve the pixel expansion problem in traditional VCS. On the other hand, it is anticipated that the contrast of the recovered image in SI-VCS should be as high as possible. The investigation of contrast optimization for SI-VCS is carried out in this article. We develop an approach to optimize the contrast by stacking t ( k ≤ t ≤ n ) shadows in (k, n) -SI-VCS. Generally, a contrast-maximizing problem is linked with a (k, n) -SI-VCS, where the contrast by t shadows is considered as an objective function. An ideal contrast by t shadows can be produced by addressing this problem using linear programming. However, there exist (n-k+1) different contrasts in a (k, n) scheme. An optimization-based design is further introduced to provide multiple optimal contrasts. These (n-k+1) different contrasts are regarded as objective functions and it is transformed into a multi-contrast-maximizing problem. The ideal point method and lexicographic method are adopted to address this problem. Additionally, if the Boolean XOR operation is used for secret recovery, a technique is also provided to offer multiple maximum contrasts. The effectiveness of the proposed schemes is verified by extensive experiments. Comparisons illustrate significant advancement on contrast is provided.

This paper describes an algorithm for determining if an event occurs persistently within an interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. This solution is geared towards discovering human routines by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. After describing the problem and the proposed solution, in this paper we demonstrate using testbed data and simulations that this approach uncovers components of routines by identifying which events are parts of the same routine through their temporal properties.

This poster abstract introduces the problem of macroscopic sensing composition, where a sensor capable to detect complex events is synthesized dynamically by a collection of simpler sensors using a data-driven approach. Our solution is geared towards discovering the structure of human activities by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. The goal is to identify routines from their components by leveraging the fact that the components have the same temporal persistence as the routines themselves. To this end we have devised an algorithm for determining if an event occurs consistently within a time interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. Our first results using testbed data and simulations indicate that this approach can uncover components of routines by identifying which events are parts of the same routine through their temporal properties.

Determining node positions is essential for many next-generation network functionalities. Previous localization algorithms lack correctness guarantees or require network density higher than required for unique localizability. In this paper, we describe a class of algorithms for fine-grained localization called Sweeps. Sweeps correctly finitely localizes all nodes in bilateration networks. Sweeps also handles angle measurements and noisy measurements. We demonstrate the practicality of our algorithm through extensive simulations on a large number of networks, upon which it consistently localizes one-thousand-node networks of average degree less than five in less than two minutes on a consumer PC.