The growing adoption of social virtual reality (VR) platforms underscores the importance of safeguarding personal VR space to maintain user privacy and security. Teleportation, a prevalent instantaneous locomotion method in VR, facilitates user engagement but can also inadvertently intrude upon personal VR space, thereby raising privacy concerns. This paper introduces three innovative negotiated teleportation techniques designed to secure user-to-user teleportation and protect personal space privacy, all under a unified small-group development framework. We have designed and evaluated three types of negotiated teleportation techniques: Sector technique for directional control, Distance technique for minimum social distance control, and Area technique for defining circular permissible teleportation areas. These techniques foster a collaborative approach to selecting teleportation points that respect personal space. To evaluate the efficacy of these techniques, we conducted a user study with 20 participants who performed social tasks within a virtual campus environment. The findings demonstrate that our techniques significantly enhance privacy protection and alleviate anxiety associated with unwanted proximity in social VR.

Fast Fourier Transform (FFT) is one of the most widely used algorithms in digital signal processing (DSP). Many Internet of Things (IoT) devices include dedicated FFT hardware accelerators at the price of both extra hardware resources and power consumption. Limited by constrained resources for IoT applications at the edge, it is favorable as well as crucial to reduce such overhead as much as possible without sacrificing FFT performance. Meanwhile, open-source RISC-V microprocessors have become serious contenders to ARM or x86 alternatives, especially in IoT applications. Recent studies have shown that Application-specific Instruction-set Processors (ASIP) can achieve considerable acceleration performance with low overhead. This paper proposes a fine-grained RISC-V Instruction Set Architecture (ISA) extension scheme for efficient FFT operations with 12 new instructions, using four 16-bit multipliers and adders as computing units to minimize overhead. Our extended instructions are deeply coupled with the CPU pipeline, thus significantly reducing the hardware overhead of decoding and executing logic, and theoretically can accelerate the calculation of FFT with an arbitrary number of points. We demonstrate the proposed scheme on a Xilinx PYNQ-Z2 FPGA using the open-source RISC-V NutShell soft IP core, with data loading from either specially designed vector registers (V-mode) or RAM off-the-core (R-mode). The evaluation shows the proposed FFT acceleration scheme achieves a performance gain of 118 times in V-mode and 6.5 times in R-mode respectively, with only 16% power consumption required as compared to the vanilla NutShell RISC-V microprocessor.

The recent popularity of augmented reality (AR) devices has enabled players to participate in interactive narratives through virtual events and characters populated in a real-world environment, where different actions may lead to different story branches. In this paper, we propose a novel approach to adapt narratives to real spaces for AR experiences. Our optimization-based approach automatically assigns contextually compatible locations to story events, synthesizing a navigation graph to guide players through different story branches while considering their walking experiences. We validated the effectiveness of our approach for adapting AR narratives to different scenes through experiments and user studies.

We present a novel interactive augmented reality (AR) storytelling approach guided by indoor scene semantics. Our approach automatically populates virtual contents in real-world environments to deliver AR stories, which match both the story plots and scene semantics. During the storytelling process, a player can participate as a character in the story. Meanwhile, the behaviors of the virtual characters and the placement of the virtual items adapt to the player's actions. An input raw story is represented as a sequence of events, which contain high-level descriptions of the characters' states, and is converted into a graph representation with automatically supplemented low-level spatial details. Our hierarchical story sampling approach samples realistic character behaviors that fit the story contexts through optimizations; and an animator, which estimates and prioritizes the player's actions, animates the virtual characters to tell the story in AR. Through experiments and a user study, we validated the effectiveness of our approach for AR storytelling in different environments.

Construction industry has the largest number of preventable fatal injuries, providing effective safety training practices can play a significant role in reducing the number of fatalities. Building on recent advancements in virtual reality-based training, we devised a novel approach to synthesize construction safety training scenarios to train users on how to proficiently inspect the potential hazards on construction sites in virtual reality. Given the training specifications such as individual training preferences and target training time, we synthesize personalized VR training scenarios through an optimization approach. We validated our approach by conducting user studies where users went through our personalized guidance VR training, free exploration VR training, or slides training. Results suggest that personalized guidance VR training approach can more effectively improve users' construction hazard inspection skills.

In this paper, we introduce an interactive background music synthesis algorithm guided by visual content. We leverage a cascading strategy to synthesize background music in two stages: Scene Visual Analysis and Background Music Synthesis. First, seeking a deep learning-based solution, we leverage neural networks to analyze the sentiment of the input scene. Second, real-time background music is synthesized by optimizing a cost function that guides the selection and transition of music clips to maximize the emotion consistency between visual and auditory criteria, and music continuity. In our experiments, we demonstrate the proposed approach can synthesize dynamic background music for different types of scenarios. We also conducted quantitative and qualitative analysis on the synthesized results of multiple example scenes to validate the efficacy of our approach.

In this paper, we propose an optimization-based approach for automatically generating virtual scenarios for wheelchair training in virtual reality. To generate a virtual training scenario, our approach automatically generates a realistic furniture layout for a scene as well as a training path that the user needs to go through by controlling a simulated wheelchair. The training properties of the path, namely, its desired length, the extent of rotation, and narrowness, are optimized so as to deliver the desired training effects. We conducted an evaluation to validate the efficacy of the proposed virtual reality training approach. Users showed improvement in wheelchair control skills in terms of proficiency and precision after receiving the proposed virtual reality training.

We propose a novel approach for generating paths with desired exertion properties, which can be used for delivering highly realistic and immersive virtual reality applications that help users achieve exertion goals. Given a terrain as input, our optimization-based approach automatically generates feasible paths on the terrain which users can bike to perform body training in virtual reality. The approach considers exertion properties such as the total work and the perceived level of path difficulty in generating the paths. To verify our approach, we applied it to generate paths on a variety of terrains with different exertion targets and constraints. To conduct our user studies, we built an exercise bike whose force feedback was controlled by the elevation angle of the generated path over the terrain. Our user study results showed that users found exercising with our generated paths in virtual reality more enjoyable compared to traditional exercising approaches. Their energy expenditure in biking the generated paths also matched with the specified targets, validating the efficacy of our approach.

The effort to extend cellular technologies to unlicensed spectrum has been gaining high momentum. Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study throughput optimal LBT transmission strategy for load based equipment (LBE). We find that the optimal rule is a pure threshold policy: The LBE should stop listening and transmit once the channel quality exceeds an optimized threshold. We also reveal the optimal set of LBT parameters that are compliant with regulatory requirements. Our results shed light on how the regulatory LBT requirements can affect the transmission strategies of radio equipment in unlicensed spectrum.