The proliferation of immersive applications such as Virtual, Augmented, and Mixed Reality (VR/AR/MR) imposes stringent low-latency and reliability requirements that challenge conventional O-RAN slicing mechanisms. Existing frameworks often fail to anticipate rapid XR traffic fluctuations driven by user motion and gaze dynamics, leading to inefficient resource utilization and SLA violations. To overcome these limitations, we propose a cross-layer intelligent control framework that integrates traffic prediction and reinforcement learning-based slice orchestration across the Non-RT and Near-RT RIC. By coupling long-term foresight with short-term adaptability, the proposed design enables proactive, SLA-aware scheduling under highly dynamic conditions. We further develop a trace-driven network emulator to reproduce realistic 5G behaviors and validate system robustness. Extensive experiments demonstrate that our framework consistently achieves over 95% SLA compliance, below 2% latency violations, and up to 30% latency reduction compared with state-of-the-art baselines, confirming its effectiveness and scalability for next-generation immersive networks.

Real-time transmission of large-scale data, high computational demands, and resource limitations on edge devices pose significant challenges for intelligent sports systems. The proliferation of Internet of Things (IoT) technologies has catalyzed the rise of Smart Sport, where wearable sensors, cameras, and intelligent algorithms are integrated to revolutionize athletic training. Despite this transformation, access to professional coaching remains constrained by factors such as time, cost, and scalability. A critical limitation of existing remote coaching approaches is their inability to perform effective spatio-temporal analysis, hindering comprehensive evaluation and refinement of athletic performance. This paper introduces MRCoach, a mixed reality-based, immersive, and interactive sports coaching system that enables data-driven training without requiring in-person supervision. MRCoach reconstructs 3D volumetric avatars of both learners and expert athletes, allowing users to visualize and compare their movements side-by-side in a mixed reality environment for intuitive skill refinement. To ensure responsive and efficient feedback, we propose an adaptive semantic transmission strategy that prioritizes sport-relevant joints, thereby reducing latency and bandwidth requirements without sacrificing accuracy. Furthermore, a 3D sports analysis framework is developed to evaluate motion based on normalized joint positions, velocities, and accelerations. This framework computes real-time similarity scores and delivers actionable guidance to learners. Experimental results across four sports—tennis, soccer, basketball, and baseball—demonstrate MRCoach’s effectiveness in providing personalized, real-time training experiences. Compared to state-of-the-art baselines such as MagicStream, ExPose, and PIXIE, MRCoach achieves significantly lower end-to-end latency (79.9 ms) and higher frame rates (≥ 56 FPS), while maintaining accurate pose tracking and high avatar fidelity.

To improve logistics efficiency, smart logistics sorting is an inevitable trend in logistics development. Existing smart logistics sorting systems suffer from high construction costs or limited scalability. To solve these problems, we design a brand-new two-dimensional conveyor system called Omniveyor, which transports and sorts high-density packages within a limited space. It is assembled from multiple repetitive square conveyor modules, achieving the goal of cost-effectiveness and easy maintenance. To realize automatic sorting, we model the planning problem on Omniveyor and propose a scheduling strategy named MMPPO by reinforcement learning. Unlike traditional path planning, MMPPO assigns actions to modules rather than packages, which reduces scheduling overhead in high-throughput scenarios. Furthermore, we develop a simulation environment to inspect the effectiveness of our method, which solves an intractable package-following problem that has plagued simulation implementation in this field. Experimental results show that MMPPO outperforms baselines in terms of throughput and overall consumption at high densities. Besides, we implement a physical prototype of Omniveyor to validate its feasibility. Note to Practitioners—The motivation of the paper is to solve the sorting problems in the logistics system. Existing logistics sorting systems often have problems such as low sorting efficiency and high costs, making them unsuitable for small-sized warehouses. In this paper, we present a modular 2D desktop logistics system that is both scalable and efficient for sorting. We mathematically describe the platform package transportation process and propose an algorithm that addresses scheduling and planning problems, capable of continuous planning under pipeline input. Preliminary simulation tests indicate that our approach is feasible, and we have also built a small-scale prototype. In future research, we will further expand the scale of the platform and conduct research.

We propose a novel framework for real-time acquisition and reconstruction of temporally-varying 3D phenomena with high quality. The core of our framework is a deep neural network, with an encoder that directly maps to the structured illumination during acquisition, a decoder that predicts a 1D density distribution from single-pixel measurements under the optimized lighting, and an aggregation module that combines the predicted densities for each camera into a single volume. It enables the automatic and joint optimization of physical acquisition and computational reconstruction, and is flexible to adapt to different hardware configurations. The effectiveness of our framework is demonstrated on a lightweight setup with an off-the-shelf projector and one or multiple cameras, achieving a performance of 40 volumes per second at a spatial resolution of 1283. We compare favorably with state-of-the-art techniques in real and synthetic experiments, and evaluate the impact of various factors over our pipeline.

Network slicing has been widely recognized as one of the flagship use cases for Open Radio Access Network (O-RAN), enabling the provisioning of isolated network services over a shared physical infrastructure. Each slice is characterized by a set of distinct service level agreements (SLAs) tailored to meet the needs of various industries and applications. At the same time, industry-critical applications often require strict adherence to the SLA even in the worst-case scenarios. However, existing network slicing strategies merely incorporate SLA violations as penalties within the reward function, thus failing to consistently ensure perpetual SLA compliance. To address these challenges, this paper introduces PSASlicing, an intelligent resource allocation system designed for RAN slice management across the access network. More specifically, PSASlicing introduces a new reinforcement learning algorithm for maximizing resource utilization while perpetually guaranteeing the diverse SLA requirements across slices. Furthermore, PSASlicing also incorporates a trace-driven network emulator that effectively replicates the dynamic behavior of cellular networks by integrating a transition model with real-world data from an over-the-air 5G Standalone testbed. A comprehensive experimental evaluation showcases that PSASlicing achieves an average resource savings of approximately 24.0% when compared to the state-of-the-art, while guaranteeing no SLA violations.

A key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to capture human low limbs information, and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.

In medical procedures such as surgery and diagnosis, it is crucial to provide doctors and nurses with up-to-date patient information. In this paper, we propose Health-MR, a portable Mixed-Reality (MR) system that helps medical staff monitor patient conditions. Health-MR consists of three components: (1) Patient Identification Recognition using face detection, (2) Medical Cloud Database for patient information retrieval, and (3) Non-invasive Heart Rate Measurement via image processing and Fast Fourier Transform (FFT). Our evaluation demonstrates that Health-MR significantly reduces the time needed to query patient information and provides remote, accurate, and real-time heart rate monitoring.

Uncertainty modeling and reasoning in intelligent systems are crucial for effective decision-making, such as complex evidence theory (CET) being particularly promising in dynamic information processing. Within CET, the complex basic belief assignment (CBBA) can model uncertainty accurately, while the complex rule of combination can effectively reason uncertainty with multiple sources of information, reaching a consensus. However, determining CBBA, as the key component of CET, remains an open issue. To mitigate this issue, we propose a novel method for generating CBBA using high-level features extracted from Box–Cox transformation and discrete Fourier transform (DFT). Specifically, our method deploys complex Gaussian fuzzy number (CGFN) to generate CBBA, which provides a more accurate representation of information. The proposed method is applied to pattern classification tasks through a multisource information fusion algorithm, and it is compared with several well-known methods to demonstrate its effectiveness. Experimental results indicate that our proposed CGFN-based method outperforms existing methods, by achieving the highest average classification rate in multisource information fusion for pattern classification tasks. We found the Box–Cox transformation contributes significantly to CGFN by formatting data in a normal distribution, and DFT can effectively extract high-level features. Our method offers a practical approach for generating CBBA in CET, precisely representing uncertainty and enhancing decision-making in uncertain scenarios.