Battery-free Wireless Sensor Networks (BF-WSNs) have emerged as an essential part of Internet of Things (IoT) systems. Although two-dimensional (2D) BF-WSNs have been researched, three-dimensional (3D) ones are more indicative of real-world applications. Achieving$k$-coverage in this scenario is a greater challenge and has yet to be investigated. This paper presents an optimization problem in heterogeneous 3D BF-WSNs to maximize$k$-coverage quality while considering the recharging and sampling rates. We prove the problem is NP-Hard and decouple it into two subproblems. The first optimizes$k$-coverage quality for each time slot without energy constraints. The second ensures that nodes scheduled for operation in each time slot can be immediately replenished with sufficient energy. We prove the near-optimal and optimal solutions to the two subproblems composes the near-optimal solution to the original problem. Following the development of Distributed Iterative Grouping (DIG) algorithm and Adaptive Sampling (AS) method to address two subproblems each, we propose a sleep scheduling algorithm to integrate them and solve the original problem. Simulation results verify the effectiveness and efficiency of the proposed algorithm.

Cellular patterns, from planar ornaments to architectural surfaces and mechanical metamaterials, blend aesthetics with functionality. Homogeneous patterns like isohedral tilings offer simplicity and symmetry but lack flexibility, particularly for heterogeneous designs. They cannot smoothly interpolate between tilings or adapt to double-curved surfaces without distortion. Voronoi diagrams provide a more adaptable patterning solution. They can be generalized to star-shaped metrics, enabling diverse cell shapes and continuous grading by interpolating metric parameters. Martínez et al. [2019] explored this idea in 2D using a rasterization-based algorithm to create compelling patterns. However, this discrete approach precludes gradient-based optimization, limiting control over pattern quality. We introduce a novel, closed-form, fully differentiable formulation for Voronoi diagrams with piecewise linear star-shaped metrics, enabling optimization of site positions and metric parameters to meet aesthetic and functional goals. It naturally extends to arbitrary dimensions, including curved 3D surfaces. For improved on-surface patterning, we propose a per-sector parameterization of star-shaped metrics, ensuring uniform cell shapes in non-regular neighborhoods. We demonstrate our approach by generating diverse patterns, from homogeneous to continuously graded designs, with applications in decorative surfaces and metamaterials.

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code, models and data can be found in https://reself.github.io/MovieChat.

Abstract Analysis of the pathology image is important to diagnose cancer of lung, breast and stomach. Segmenting the nucleus is a key step for quantitative analysis, and has significance to the pathology researches and computer aided diagnosis systems. The inconsistency of colour, fuzzy boundary of nucleus and overlapping of cells are the universally acknowledged challenges. To solve these problems, the difference between the inside and outside of nucleus is enhanced by obtaining the distribution of the haematoxylin based on Lambert–Beer's law and the optical characteristics of stains. An inferior encoder, which is supervised by the inferior decoder, is proposed to extract the deep features of the distribution of stains. And these features are fed into the primary encoder to improve the accuracy of segmentation. To relieve the problem that some nuclei are segmented as background because the deep feature is inapparent, dynamic convolution is introduced into the encoders. The experiments show that the proposed model can segment the nucleus in the pathological images more precisely than the compared models. The Dice similarity coefficient (DSC) and panoptic quality (PQ) are 0.810 and 0.512, respectively.

We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple LEDs are then aggregated to produce a final depth map. For appearance, learned light patterns are cast through a transparent mask to efficiently probe angularly-varying reflectance. Per-point BRDF parameters are differentiably optimized with respect to corresponding measurements, and stored in texture maps as the final reflectance. We establish a differentiable pipeline for the joint capture to automatically optimize both the mask and light patterns towards optimal acquisition quality. The effectiveness of our light is demonstrated with a wide variety of physical objects. Our results compare favorably with state-of-the-art techniques.

Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.

Abstract PUF (Physical unclonable function) is a new hardware security primitive, and the research on PUFs is one of the emerging research focuses. For PUF-based mutual authentication protocols, a method to abstract the security properties of hardware by using logic of events is proposed, and the application aspects of logic of events are extended to protocols based on hardware security. With the interaction of PUF-based mutual authentication protocol formally described by logic of events, the basic sequences are constructed and the strong authentication property in protocol interaction process is verified. Based on the logic of events, the freshness of nonces is defined, and the persist rule is proposed according to the concept of freshness, which ensures the consistency of the protocol state and behavior predicate in the proof process, and reduces the complexity and redundancy in the protocol analysis process. Under reasonable assumptions, the security of the protocol is proven, and the fact that logic of events applies to PUF-based mutual authentication protocols is shown.