Technologies such as intelligent reflective surface (IRS)-assisted free-space optics (FSO) systems, are capable of providing connectivity with varied bandwidth through flexibly allocating resources on a planer panel. This flexibility provides new opportunities in handling inter-rack traffic that is often unevenly distributed in space and time. In this paper, we explore a new type of resource allocation scheme by taking full advantage of this flexibility. The key ideas include: (1) dynamically divide the resource on the planar panel into two planes, such that the small and large flows can be handled separately; (2) in the plane serving small flows, use multi-hop routing with variable bandwidth connections to handle skewed traffic between source-destination pairs; (3) in the plane serving large flows, use direct links with variable bandwidth to schedule unevenly distributed demand. In essence, through flexible resource allocation in and between the two planes, this scheme is able to make more efficient use of resources. We theoretically analyze the capacities of the proposed scheme, where the effectiveness conditions and performance bounds for dynamic resource adaptation are derived and the traffic scheduling with flexible bandwidth in two planes is proved to be able to achieve constant factor approximation to the ideally optimal performance. We then propose a set of lightweight algorithms with performance guarantees in each of two planes, collectively calledFlexAlloc, to realize the flexibility and adaptivity of the proposed scheme, and discuss an example of this scheme in the context of IRS-assisted FSO systems. Our simulation results show thatFlexAllocexhibits significant superiority over existing schemes for unevenly distributed traffic at low computational complexity, and can reduce the average flow completion time by 56-81%.

3D Gaze estimation is a challenging task due to two main issues. First, existing methods focus on analyzing dense features (e.g., large pixel regions), which are sensitive to local noise (e.g., light spots, blurs) and result in increased computational complexity. Second, an eyeball model can correspond multiple gaze directions, and the entangled representation between gazes and models increases the learning difficulty. To address these issues, we propose De2Gaze, a lightweight and accurate model-aware 3D gaze estimation method. In De2Gaze, we introduce two key innovations for deformable and decoupled representation learning. Specifically, first, we propose a deformable sparse attention mechanism that can adapt sparse sampling points to attention areas to avoid local noise influences. Second, we propose a spatial decoupling network with a dual-branch decoding architecture to disentangle invariant (e.g., eyeball radius, position) and variable (e.g., gaze, pupil, iris) features from the latent space. Compared to existing methods, De2Gaze requires fewer sparse features, and achieves faster convergence speed, lower computational complexity, and higher accuracy in 3D gaze estimation. Qualitative and quantitative experiments demonstrate that De2Gaze achieves state-of-the-art accuracy and high-quality semantic segmentation for 3D gaze estimation on the TEyeD dataset.

Surgeons are frequently subjected to prolonged unnatural postures, such as sustained neck flexion during surgical procedures, increasing their susceptibility to work-related musculoskeletal disorders (WMSDs). Despite the prevalence of such issues, there remains a scarcity of ergonomic solutions that effectively mitigate neck pain while permitting full operational functionality across diverse environments. In this paper, we present a novel variable-stiffness neck exoskeleton with pneumatic-driven tensile actuators for prolonged head flexion assistance. Its innovative design facilitates unrestricted head movement while providing expected neck support, thereby minimizing strain on antagonist muscle groups. Comprehensive experimental evaluations were conducted to assess the system’s performance. Transitional response times between flexible and rigid states were 0.28 s and 0.46 s, respectively. Experimental trials involving five healthy subjects demonstrated that the average muscular activity reduction of the splenius capitis and the sternocleidomastoid muscles were 38.8 ± 2.0% and 9.7 ± 2.5%, respectively. These experimental results demonstrated great potential of the exoskeleton in practical application for alleviating the physical burden during prolonged head flexion.

When optical switching is deployed in Data Center Networks (DCNs), the reconfiguration of the optical switching matrix leads to substantially longer overheads, posing a significant impact on the system performance. Despite the extensive studies on the scheduling algorithms based on demand matrix decomposition (DMD), the stateful and irregular nature of the scheduling processes hinders the development of quantitative models, thereby limiting our understanding of resource scheduling in optical switching DCNs based on DMD. In this paper, we model the DMD based resource scheduling process under a bursty and skewed traffic pattern and derive closed-form equations for the burst completion time. Our study shows that an increased reconfiguration delay will lead to an approximate linear increase in the burst completion time. Our study also demonstrates that the size of the slot and the maximum allowed duration of one match are approximately inversely proportional to the burst completion time, with diminishing marginal returns.

Intelligent reflective surface (IRS)-assisted free-space optics (FSO) systems allow allocation of different number of IRS elements between communication end-points. It thus can provide connectivity with variable bandwidth in data centers, where the traffic between top-of-rack switches is often unevenly distributed in space and time. In this paper, we propose a novel traffic scheduling technique that allows variable bandwidth in the context of IRS-assisted FSO systems in data center networks (DCNs). We formalize the problem of scheduling with variable bandwidth, explore the performance space for designing scheduling algorithms, and provide insights on how to use heuristics in practice. Then, we propose a low-complexity and performance-guaranteed parallel scheduling algorithm,pVarband, that takes advantage of variable bandwidth to avoid the time-consuming maximum weight matching and can run at a complexity of$\mathcal {O}(N\log N)$in a DCN withNracks. We theoretically demonstrate that scheduling with variable bandwidth requires at most$\mathcal {O}(\log (1/\epsilon))$reconfigurations to obtain an$\epsilon $-approximate decomposition for an arbitrary traffic matrix. Also, we provide insights for design and deployment of an IRS-assisted FSO system to achieve performance saturation. Our results show that scheduling allowing variable bandwidth can achieve almost optimal performance for unevenly distributed traffic, and also outperforms its fixed bandwidth counterpart in terms of running time and decomposition error. Our work also provides useful insights for scheduling with variable bandwidth in contexts other than IRS-assisted FSO systems.

Steerable free-space optics (FSO) communications are flexible in link reconfiguration (LR), and easy to deploy, especially when the available physical space is limited. Thus it is considered as a good complement to the wired network in data centers. In this paper, we are interested in understanding the role of FSO when used as a hotspot offloading technique in data center networks. We aim to quantify the performance of FSO-based hotspot offloading, in the presence of link impairment, failed negotiations and reconfiguration overhead. We model this steerable FSO-based traffic offloading process as a vacation queueing system with limited service discipline. A general expression for the average delay is derived, and then equations for three different hybrid automatic repeat request (HARQ) protocols are separately derived. To overcome the inherent difficulty of calculating the average delay with limited service, efficient low-complexity approximations are developed. We then show that the system delay increases approximately linearly with the traffic rate until a point, beyond which it grows rapidly. Further, we analyze the effect of various factors on the delay performance and offer insights for the design configurations to achieve the desired performance. The simulation results verify the validity of the theoretical analysis.

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a singleadaptiveDMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor.

This paper presents a seven-link dynamic walking model that is more close to human beings. We add hip actuation, upper body, flat feet and compliant ankle joints to the model. Walking sequence of the flat-foot walker has several sub-streams that form bipedal walking with dynamic series of phases, which is different with the motion of round-foot and point-foot models. We investigate the characteristics of three different walking gaits with different step lengths. Comparison of these walking gaits in walking velocity, efficiency and stability reveals the relation between step length and walking performance. Experimental results indicate that the gait which is more close to human normal walking achieves higher stability and energetic efficiency.