Volumetric video technology enables an immersive and interactive experience for viewers. However, the point cloud frames composing the video are prohibitively large, imposing significant demands on storage and transmission. The existing inter-frame compression methods perform motion estimation based on a fixed block size, ignoring the inherent motion characteristics of the volumetric content and failing to fully utilize the inter-frame redundancy information. To overcome this limitation, we introduce MP-VVC, a patch-adaptive volumetric video compression framework based on volumetric content and motion analysis. Specifically, MP-VVC involves a two-stage segmentation of volumetric frames, body-part segmentation and patch segmentation. Subsequently, we employ the iterative closest point algorithm to determine rigid movements of body parts and patches. Furthermore, we also take into account the volumetric content of non-rigid movements and compress it. Experiments show that MP-VVC reduces the bitrate by up to$7.58 \times$compared with two baselines, with at most$3.63 \times$improvement in decoding time compared with MP3DG.

The rapid development of large language models (LLMs) has prompted researchers to explore the potential applications, especially for human interaction scenarios.Among these, facilitating meaningful and efficient communication stands out as a critical area, which is especially relevant in elder care. Meanwhile The increasing ratio of elder population highlights the urgent need for personalized care for elders. In this paper, we propose a multi-LLM approach to combine the strengths of multiple LLMs, where LLMs generate complementary answers to the same question, and the final answer is selected based on the combined relevance scores. To assist the answer selection, we provide a comprehensive evaluation of the current state-of-the-art LLMs on elderly care, incorporating real-world feedback from 30 elderly participants on various types of questions. We summarize LLMs’ strengths and weaknesses facing distinct elderly care scenarios, and highlight potential challenges and opportunities for future research.

Visible light communication (VLC) has become a powerful and complementary technology to radio frequency (RF) communication thanks to its unrestricted bandwidth resources and high transmission rates. However, current VLC applications are limited to the short-ranged and interference-free scenarios because of the attenuation characteristics of light and the interference of ambient light. In order to extend VLC to various IoT applications, this paper tries to prolong the communication range of VLC systems based on M-frequency shift keying (MFSK), where $M$ denotes the modulation index. Specifically, 1) we modulate the luminous intensity of the LEDs to form sine waves instead of square waves and tune the sine wave frequency to transmit data. Compared to light intensity modulation, it can effectively resist the ambient light interference. Meanwhile, 2) we propose a demodulation method based on gradient thresholding to increase its fault tolerance in decoding. In addition, 3) we built a real VLC platform and conducted comprehensive experiments to verify the communication distance and reliability of the low-power VLC systems (with 1W/3W LEDs). The experimental results show that the proposed work successfully prolongs the communication distance to 5.8 meters with 1 W LED and to 10 meters with 3 W LED, extending the VLC range by nearly $\mathbf{2. 5 x}$ compared to existing work.

At present, some achievements have been made in the research of plant leaf classification such as the introduction of artificial intelligence algorithm. But there are still some problems. First, the existing achievements do not consider the subjective perception mechanism and role of human visual system in leaf classification data labels. Second, the implementation of the deep learning algorithm completely depends on the computing power level of the high-cost machine hardware and the large-scale image database. Finally, these research results rarely consider the noise pollution of leaf image samples. In order to solve the above problems, the paper fully considered the subjective perception principle and characteristics of human vision system (HVS), and proposed a lightweight classification method of noisy plant leaves (LCM-NPLs) based on visual domain perception. First, the most suitable HVS front-end perception characteristics were applied to the physical visual processing of leaves. Then the plant leaves were denoised through the information processing mechanism of HVS back-end. The visual effect of regular and orderly plant leaves is obtained. Finally, the classification is realized by principal component analysis (PCA) and third-order nearest neighbor algorithm. The results of ablation contrast experiments show that the classification accuracy of the method in this paper is 82.50% for plant leaves in the presence of serious noise interference with PSNR of 10.2421, more than 90% for plant leaves with general noise pollution transmission with PSNR of more than 15.3759, and 98.33% for plant leaves of light pollution with PSNR of 20.5659. The proposed method has achieved very good results. The proposed method can not only accurately classify plant leaves in different growth periods, but also maintain a high classification accuracy rate in the presence of serious noise interference.

Abstract The paper discusses the GOLD project (Grid‐based Information Models to Support the Rapid Innovation of New High Value‐Added Chemicals) whose principal aim is to carry out research and development into enabling technologies to support the formation, operation and termination of virtual organizations. The paper discusses the outcome of this research, which is the GOLD Middleware infrastructure. The infrastructure has been implemented in the form of a set of Middleware components, which address issues such as trust, security, contract monitoring and enforcement, information management and coordination. We discuss all these issues in turn and more importantly we demonstrate how current WS standards can be used to implement these issues. In addition, the paper follows a top down approach starting with a brief outline on the architectural elements derived during the requirements engineering phase and demonstrates how these elements were mapped onto actual services that were implemented according to service‐oriented architecture principles and related technologies. Copyright © 2008 John Wiley & Sons, Ltd.