Translation gain is a key Redirected Walking (RDW) technique in Virtual Reality (VR) that enables users to navigate virtual environments (VEs) larger than the available physical space. The technique was originally developed to scale users' walking distance in the VE and is typically applied continuously, regardless of the user's motion state. We introduce Gait-Synced Translation Gain (GSTG), a novel approach that adapts translation gain by synchronizing it with the user's gait cycle. GSTG leverages the single-limb support phase of walking-when users are less stable and thus less sensitive to external disturbances-to apply higher levels of gain. This approach allows greater manipulation while preserving natural walking sensations and avoiding additional cybersickness. A user study comparing GSTG with continuous translation gain demonstrates significant improvements in perceived naturalness and comfort. Our results highlight the potential of gait-synchronized gain to enhance immersion, offering new possibilities for more realistic and comfortable VR locomotion.

ABSTRACT This paper investigates a multi‐UAV cargo delivery scenario, where each UAV picks up goods from one location and delivers them to another destination while maintaining connectivity with the ground cellular network. Optimizing task assignment and UAV trajectory design to minimize completion time under the constraints is a significant challenge. To address this, the approach is structured into two principal phases. First, Dijkstra's algorithm is utilized to derive the shortest paths between points while ensuring communication connectivity meets specific quality constraints. Second, these paths are integrated with a novel hybrid optimization algorithm fusing a genetic algorithm and an ant colony algorithm to solve the coupled task assignment and route planning problem subject to communication and payload limitations. The hybrid approach efficiently balances exploration and exploitation, leading to superior task allocation and route planning. Numerical results show that our proposed method is effective in balancing task allocation and reducing overall completion time by comparing it with other integrated optimization techniques.

Redirected Walking (RDW) is an important intermediary layer in virtual reality (VR) interaction systems. It addresses the issues of spatial restrictions in VR exploration by imperceptibly remapping the virtual environment’s movement to the physical environment, enhancing the user’s immersive experience. In VR, jumping is also a noteworthy motion besides walking. However, existing redirected walking (RDW) algorithms typically focus on reducing collisions between users and obstacles during walking but overlook the safety when users perform significant actions such as jumping. This oversight can pose serious risks to users during VR exploration, especially when there are physical obstacles or boundaries near the virtual locations that require user jumping. We propose SafeRDW, the first RDW algorithm that takes the user’s jumping safety into consideration. The proposed method considers both walking and jumping actions in the virtual environment, reducing physical resets and redirecting users to safer locations when a jump is required in the virtual space, ensuring user safety. Simulation experiments and user study results both show that our method not only reduces the number of resets, but also significantly ensures user safety when they reach the jumping points in the virtual scene.

Virtual Reality (VR) offers an immersive 3D digital environment, but enabling natural walking sensations without the constraints of physical space remains a technological challenge. Previous VR locomotion methods, including game controller, teleportation, treadmills, walking-in-place, and redirected walking (RDW), have made strides towards overcoming this challenge. However, these methods also face limitations such as possible unnaturalness, additional hardware requirements, or motion sickness risks. This paper introduces "Spatial Contraction (SC)", an innovative VR locomotion method inspired by the phenomenon of Lorentz contraction in Special Relativity. Similar to the Lorentz contraction, our SC contracts the virtual space along the user's velocity direction in response to velocity variation. The virtual space contracts more when the user's speed is high, whereas minimal or no contraction happens at low speeds. We provide a virtual space transformation method for spatial contraction and optimize the user experience in smoothness and stability. Through SC, VR users can effectively traverse a longer virtual distance with a shorter physical walking. Different from locomotion gains, the spatial contraction effect is observable by the user and aligns with their intentions, so there is no inconsistency between the user's proprioception and visual perception. SC is a general locomotion method that has no special requirements for VR scenes. The experimental results of our live user studies in various virtual scenarios demonstrate that SC has a significant effect in reducing both the number of resets and the physical walking distance users need to cover. Furthermore, experiments have also demonstrated that SC has the potential for integration with existing locomotion techniques such as RDW.

The Normalized Least-Mean-Squares (NLMS) algorithm commonly used in echo cancelers suffers from a number of limitations due to the existing sparsity in the echo path. Although some sparsity-aware algorithms have been proposed, the high computational complexity greatly increases the their difficulty of implementation. To reduce the complexity and improve the performance in echo cancelers, we introduce a Coefficients-Switched (CS) processing into the NLMS algorithm and propose the CS-NLMS algorithm and the Mean-square-deviation (MSD) analysis to evaluate its effectiveness. Simulations of the MSD were performed with different system parameters. The performance of the proposed algorithm is verified by comparison with the simulation results of the previously proposed algorithms. The superiority of the proposed algorithm in terms of computational complexity is also discussed.

The deep convolutional neural network (CNN) has been extensively applied to polarimetric synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent on numerous labeled samples for revealing and modeling the characteristics of different targets, thus remaining a challenge in maintaining high accuracy in limited sample cases. To address this issue, a deep reinforcement learning (RL)-based PolSAR image classification framework, named deep Q-fully CNN (DQFCN), is proposed in this article. In this framework, two ways are adopted to boost the classification performance while reducing training samples. On the information utilization hand, the spatial neighboring information and polarimetric decomposition information of PolSAR data are both extracted to enrich the feature representation of the sample. Meanwhile, the 3-D CNN architecture is adopted to learn the spatial-polarimetric jointed characteristics simultaneously. On the model learning hand, two RL learning strategies are employed to promote classification performance. The first one is learning from scratch, which does not use any label information as prior knowledge but learns from its self-generated experience. Learning from pretraining is the second strategy in which the networks are sequentially trained from labeled samples and experience data to reduce the time cost. As far as we know, it is the first time that an RL-based fully CNN has been proposed for PolSAR image classification. Experiments on three benchmark datasets prove the effectiveness of the proposed framework, suggesting that the two adopted strategies achieve boosted performance in all experiments, particularly in a limited sample size.

Stereo matching is a challenging problem, and high-accuracy stereo matching is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and 3-D reconstruction. Therefore, we present a novel image-guided stereo matching algorithm, which employs the efficient combined matching cost and multistep disparity refinement, to improve the accuracy of existing local stereo matching algorithms. Different from all the other methods, we introduce a guidance image for the whole algorithm. This filter-based guidance image is generated by extracting the enhanced information from the raw stereo image. The combined matching cost consists of the novel double-RGB gradient, the improved lightweight census transform, and the image color. This cost measurement is robust against image noise and textureless regions in computing the matching cost. Furthermore, a new systemic multistep refinement process, which includes outlier classification, four-direction propagation, leftmost propagation, and an exponential step filter, is proposed to remove the outliers in the raw disparity map. Experiments on the Middlebury benchmark demonstrate our algorithm's superior performance that it ranks first among the 158 submitted algorithms. Moreover, the proposed method is also robust on the 30 Middlebury data sets and the real-world Karlsruhe Institute of Technology and Toyota Technological Institute benchmark.

ABSTRACT Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we study how to deploy new access points (AP) to achieve improved accuracy for WiFi- based indoor localization systems. Existing mechanisms in this aspect are typically simulation based and further they do not consider how to use pre-existing APs in target environment for achieving high localization performance. To overcome these issues, in this paper, we propose a measurement-based AP deployment mechanism (MAPD). MAPD takes advantage of those pre-existing APs to identify candidate positions with poor localization accuracy for deploying new APs. We then collect the fingerprints for all possible AP deployment layouts via over- deployment of APs, one at each candidate position. Finally, we present a greedy search algorithm to identify m positions out of the n candidate positions (mn) while minimizing the location error. Experimental results demonstrate that the localization errors can be largely reduced: Mean error distance can be reduced by 0.56 meter (26%) and 0.17 meter (10%) as compared with the case without deploying new APs and previous work, respectively; Moreover, the maximum location error can be reduced by 1.53 meter (27%) and 0.51 meter (11%), respectively.

Low duty cycled scheduling can largely prolong the lifetime of a wireless sensor network (WSN) but also brings longer end-to-end (E2E) delivery delay. In this paper, our design objective is to pursue the near minimum E2E energy consumption subject to a desired success ratio that the E2E delay is below a delay bound. Accordingly, we design a Markov decision process based geographic routing protocol such that each relay node currently holding a packet makes localized forwarding decision on continuing waiting or transmitting immediately to the so far best forwarder candidate based only on local network state information. Simulation results show that the designed protocol can achieve expected success ratio subject to given delay bound and also high energy use efficiency.

This article proposes a destination-driven on-demand multicast routing protocol (D-ODMRP) to improve the multicast forwarding efficiency in mobile ad hoc networks (MANETs). In D-ODMRP, the path from the multicast source to a multicast destination tends to use those paths passing through another multicast destination. If such multiple paths are available, the one leading to the least extra cost is preferred. This destination-driven strategy is introduced into the on-demand construction process of a multicast forwarding structure in a popular multicast protocol ODMRP. Simulation results show that D-ODMRP can significantly improve the forwarding efficiency as compared with ODMRP. Moreover, the destination-driven strategy can also be introduced into other existing multicast routing protocols for MANETs.

Abstract In‐network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy‐efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd.

In a wireless sensor network with mobile sinks, the maintenance of efficient data delivery structure often brings a large amount of control overhead, which may offset the benefit of introducing mobile sinks. In this paper, we propose a multi-stage data routing protocol (called MLRP) to address this problem. To reduce the protocol overhead, MLRP integrates layered Voronoi scoping and dynamic anchor selection. Such design is expected to greatly reduce the diffusion scope of sink movement updates and thus reduce the frequency at which the data delivery structure is refreshed. Simulation results show that MLRP can effectively reduce the protocol overhead while ensuring high packet delivery ratio as compared with existing work.

In this paper, we propose a data-driven routing protocol (called DDRP) for wireless sensor networks with mobile sinks (mWSNs). The design objective of DDRP is to effectively reduce the protocol overhead for data gathering in such networks. DDRP exploits the broadcast feature of wireless transmissions for sensor nodes for (gratuitous) route learning. To achieve this goal, each data packet carries an additional option recording the known distance from the sender of the packet to the destined mobile sink. The overhearing of such a data packet will gratuitously provide listeners a route to mobile sink. This is the so-called data-driven nature of DDRP. Continuous such route-learning among neighboring nodes will provide route information for more and more sensor nodes in the network. We present the detailed design of the DDRP protocol. Simulation results show that DDRP has much lower protocol overhead as compared with existing work while ensuring high packet delivery ratio.

Wireless sensor networks with mobile sinks (mWSN) have attracted a lot of attention recently. In an mWSN, each mobile sink can move freely and unpredictably. In this paper, we design two efficient data gathering protocols for mWSNs. The first protocol (called AVRP) adopts Voronoi scoping plus dynamic anchor selection to handle the sink mobility issue. In the second protocol (called TRAIL), the trail of mobile sink is used for guiding packet forwarding as sinks move in the network. In TRAIL, to forward a data packet, integration of trail-based forwarding and random walk is used. Specifically, when no fresh trail of any sink is known, random walk is used; once a sensor on a fresh sink trail is reached, data packet will be forwarded along the trail. TRAIL is simple to implement and has small protocol overhead. Simulation results show the designed protocols have high performance and further AVRP is suitable for mWSNs with heavy traffic while TRAIL is suitable for mWSNs with light traffic.

The convergence of mobile technologies and Internet protocols makes "all-IP network" be the actual trend of development for future telecommunication system. In such a system, both wired and wireless parts are built on a common network platform based on TCP/IP protocol. However, TCP/IP protocol headers are relatively large and need to be compressed to save radio channel bandwidth (the most expensive and scarce resource of the whole wireless system), especially for some services whose pay load size is small. In this paper, we propose and analyze a channel state dependent robust TCP/IP header compression algorithm (CSDROHC) for 3G wireless networks. Through adjusting the dimension of variable sliding window (VSW) of W-LSB encoding in header compressor with the accurate estimation of wireless channel state, CSDROHC can achieve the good balance of compression ratio and error-resistant robustness for its adaptive use in wireless links. Simulation results are presented to demonstrate the effectiveness of CSDROHC over wireless link.