In this paper, we conduct a detailed study of the YouTube CDN with a view to understanding the mechanisms and policies used to determine which data centers users download video from. Our analysis is conducted using week-long datasets simultaneously collected from the edge of five networks-two university campuses and three ISP networks-located in three different countries. We employ state-of-the-art delay-based geolocation techniques to find the geographical location of YouTube servers. A unique aspect of our work is that we perform our analysis on groups of related YouTube flows. This enables us to infer key aspects of the system design that would be difficult to glean by considering individual flows in isolation. Our results reveal that while the RTT between users and data centers plays a role in the video server selection process, a variety of other factors may influence this selection including load-balancing, diurnal effects, variations across DNS servers within a network, limited availability of rarely accessed video, and the need to alleviate hot-spots that may arise due to popular video content.

Virtual reality and augmented reality (VR/AR) are evolving. The market demands and innovation efforts call for a shift in the key VR/AR technologies and engaging people virtually. Tele‐haptics with multimodal and bilateral interactions are emerging as the future of the VR/AR industry. By transmitting and receiving haptic sensations wirelessly, tele‐haptics allow human‐to‐human interactions beyond the traditional VR/AR interactions. The core technologies for tele‐haptics include multimodal tactile sensing and feedback based on highly advanced sensors and actuators. Recent developments of haptic innovations based on active materials show that active materials can significantly contribute to addressing the needs and challenges for the current and future VR/AR technologies. Thus, this paper intends to review the current status and opportunities of active material‐based haptic technology with a focus on VR/AR applications. It first provides an overview of the current VR/AR applications of active materials for haptic sensing and actuation. It then highlights the state‐of‐the‐art haptic interfaces that are relevant to the materials with an aim to provide perspectives on the role of active materials and their potential integration in haptic devices. This paper concludes with the perspective and outlook of immersive multimodal tele‐haptic interaction technologies.

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Electrovibration technology has the potential for seamless integration into ordinary smartphones and tablets to provide programmable haptic feedback. The aim of this work is to seek effective ways to improve 3D perception of visual objects rendered on an electrovibration display. Utilizing a gradient-based algorithm, we first investigated whether rendering only lateral frictional force on an electrovibration display improves 3D shape perception compared to doing the same using a force-feedback interface. We observed that although users do not naturally associate electrovibration patterns to geometrical shapes, they can map patterns to shapes with moderate accuracy if guidance or context is given. Motivated by this finding, we generalized the gradient-based rendering algorithm to estimate the surface gradient for any 3D mesh and added an edge detection algorithm to render sharp edges. Then, we evaluated the advantages of our algorithm in a user study and found that our algorithm can notably improve the performance of 3D shape recognition when visual information is limited.

With the introduction of variable friction displays, either based on ultrasonic or electrovibration technology, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic texture rendering on an electrovibration display. We first describe a motorized linear tribometer designed to collect lateral frictional forces from textured surfaces under various scanning velocities and normal forces. We then propose an inverse dynamics model of the display to describe its output-input relationship using nonlinear autoregressive neural networks with external input. Forces resulting from applying a pseudo-random binary signal to the display are used to train each network under the given experimental condition. In addition, we propose a two-step interpolation scheme to estimate actuation signals for arbitrary conditions under which no prior data have been collected. A comparison between real and virtual forces in the frequency domain shows promising results for recreating virtual textures similar to the real ones, also revealing the capabilities and limitations of the proposed method. We also conducted a human user study to compare the performance of our neural-network-based method with that of a record-and-playback method. The results showed that the similarity between the real and virtual textures generated by our approach was significantly higher.