Walking has always been considered as the gold standard for navigation in Virtual Reality research. Though full rotation is no longer a technical challenge, physical translation is still restricted through limited tracked areas. While rotational information has been shown to be important, the benefit of the translational component is still unclear with mixed results in previous work. To address this gap, we conducted a mixed-method experiment to compare four levels of translational cues and control: none (using the trackpad of the HTC Vive controller to translate), upper-body leaning (sitting on a "NaviChair", leaning the upper-body to locomote), whole-body leaning/stepping (standing on a platform called NaviBoard, leaning the whole body or stepping one foot off the center to navigate), and full translation (physically walking). Results showed that translational cues and control had significant effects on various measures including task performance, task load, and simulator sickness. While participants performed significantly worse when they used a controller with no embodied translational cues, there was no significant difference between the NaviChair, NaviBoard, and actual walking. These results suggest that translational body-based motion cues and control from a low-cost leaning/stepping interface might provide enough sensory information for supporting spatial updating, spatial awareness, and efficient locomotion in VR, although future work will need to investigate how these results might or might not generalize to other tasks and scenarios.

Most AR and VR headsets use stereoscopic displays to show virtual objects in 3D. However, the limitations of current stereo display systems affect depth perception through conflicting depth cues, which then also affect virtual hand interaction in peri-personal space, i.e., within arm's reach. We performed a Fitts' law experiment to better understand the impact of stereo display deficiencies of AR and VR headsets on pointing at close-by targets arranged laterally or along the line of sight. According to our results, the movement direction and the corresponding change in target depth affect pointing time and throughput; subjects' movements towards/away from their head were slower and less accurate than their lateral movements (left/right). However, even though subjects moved faster in AR, we did not observe a significant difference for pointing performance between AR and VR headsets, which means that previously identified differences in depth perception between these platforms seem to have no strong effect on interaction. Our results also help 3D user interface designers understand how changes in target depth affect users' performance in different movement directions in AR and VR.

Text entry is still a challenging task in modern Virtual Reality (VR) systems. The lack of efficient text entry methods limits the applications that can be used productively in VR. Previous work has addressed this issue through virtual keyboards or showing the physical keyboard in VR. While physical keyboards afford faster text entry, they usually require a seated user and an instrumented environment. We introduce a new keyboard, worn on a hawker’s tray in front of the user, which affords a compact, simple, flexible, and efficient text entry solution for VR, without restricting physical movement. In our new video condition, we also show the keyboard only when the user is looking down at it. To evaluate our novel solution and to identify good keyboard visualizations, we ran a user study where we asked participants to enter both lowercase sentences as well as complex text while standing. The results show that text entry rates are affected negatively by simplistic keyboard visualization conditions and that our solution affords desktop text entry rates, even when standing.

Controllers are currently the typical input device for commercial Virtual Reality (VR) systems. Yet, such controllers are not as efficient as other devices, including the mouse. This motivates us to investigate devices that substantially exceed the controller’s performance, for both VR and Augmented Reality (AR) systems. We performed a user study to compare several input devices, including a mouse, controller, and a 3D pen-like device on a VR and AR pointing task. Our results show that the 3D pen significantly outperforms modern VR controllers in all evaluated measures and that it is comparable to the mouse. Participants also liked the 3D pen more than the controller. Finally, we show how 3D pen devices could be integrated into today’s VR and AR systems.

Constrained interaction and navigation methods for virtual reality reduce the complexity of the interaction. Yet, with previously presented solutions, users need to learn new interaction tools or remember different actions for changing between different interaction methods. In this paper, we propose Fluid VR, a new 3D user interface for interactive virtual environments that lets users seamlessly transition between navigation and selection. Based on the selected object's properties, Fluid VR applies specific constraints to the interaction or navigation associated with the object. This way users have a better control of their actions, without having to change tools or activate different modes of interaction.

There are a lot of techniques that help computer systems or devices identify their users in order to not only protect privacy, personal information, and sensitive data but also provide appropriate treatments, advertisements, or benefits. With passcode, password, fingerprint, or iris, people need to explicitly do some required activities such as typing their codes, showing their eyes, and putting their fingers on the scanners. Those solutions should be used in high-secure scenarios such as executing banking transactions and unlocking personal phones. In other systems such as gaming machines and collaborative frameworks, which aim to prioritize user experience and convenience, it would be better if user profile can be collected and built implicitly. Among those systems, virtual reality (VR) is a new trend, a new platform supporting not only fully immersive experience for gamers but also a collaborative environment for students, researchers, and other people. Currently, VR systems can track user physical activities via trackable devices such as HMD and VR controllers. Therefore, we aim to use virtual reality as our identification equipment. In virtual reality, we can easily simulate an invariant condition at any time so that people have larger probability to replicate their behaviors without any external affections. Therefore, we want to investigate if we could classify VR users based on their periodic interaction with virtual objects. We collect the position and direction of user's head or hands when doing a task and build a classification model based on those data using convolutional neural network approach. We have done an experiment to explore the capability of our proposed technique. The result was motivated with the highest accuracy of 90.92%. Identification in VR hence is potentially applicable. In the future, we plan to do a large-scale experiment with a larger group of participants to examine the strength of our method.

A novel number theoretic transform called Inverse Gray Robust Symmetrical Number System (IGRSNS) is proposed for error control coding in this paper. IGRSNS is obtained by modifying Robust Symmetrical Number System (RSNS) that was proposed earlier, using Inverse Gray code property. Due to ambiguities present in each residue, RSNS has a short dynamic range (DR) compared to that in other number systems. The short DR of RSNS enables it to be effectively used for error detection without the addition of any redundant modulus as in Redundant Residue Number System. Although RSNS has a large redundant range, its detection ability is not optimal due to the Gray code property associated with it. In an attempt to overcome this limitation, we have proposed Inverse Gray coding to be combined with RSNS in increasing its effectiveness in error detection. The resulting IGRSNS thus inherits properties of both RSNS and Gray code. Analysis and simulations show that IGRSNS has a near-optimal error detection ability. The potential of IGRSNS for error correction is also investigated. Further, a novel error correction algorithm using one redundant modulus is proposed. Simulations show that the proposed algorithm performs well under all cases of single bit errors.

In this paper, a preprocessing architecture for folding Analog-to-Digital converter (ADC) based on a new number system called Robust Folding Number System (RFNS) is presented. The RFNS is developed from robust symmetrical number system (RSNS) that was proposed by Pace et al. The enhanced Dynamic Range (DR) of RFNS due to the introduction of the folding bit results in higher ADC resolution. The Symmetrical Residues (SRs) that are obtained in the intermediate step during processing of analog signals are converted to equivalent residues by simple mapping. This results in a conversion architecture that is less complex compared to that proposed in.

In this paper, a new architecture to compute 1-D Discrete Wavelet Transform (DWT) based on Folding Number System (FNS) is introduced. Numbers for FNS are extracted directly from analog input without any forward converter like in the Residue Number System (RNS) based DWT. The large Dynamic Range makes the FNS DWT more efficient than those implemented using RNS systems. The proposed 1D DWT architecture makes use of the relation between the coefficients of the low-pass and high-pass decomposition filters for orthogonal wavelets families to compute the transform with minimum numbers of modular multipliers. These multipliers are based on pipelined Look-Up Tables (LUT). A 6-tap Daubechies analysis filter bank is synthesized at a structural level using VHDL. FLEX10K devices of Altera are used in the simulation of DWT using binary arithmetic, RNS and FNS to evaluate its performance. Simulations show a significant throughput improvement is achieved in the proposed FNS DWT architecture when {2k- 1.2k,2k+ 1} moduli set is used.

In an ideal Software Radio, digitization of the received signal occurs immediately after receive antenna to enable channelization to be performed in the discrete time domain. However, this vision is still far from reality due to limitations of current analog to digital converter performance. In this paper, a novel number system called the Folding Number System (FNS) is proposed for use in Software Radio. We implement sample rate conversion immediately after the receive antenna using this number system thus providing an efficient solution to the above problem. By decomposing the input analog signal into separate channels in parallel, symmetrical residues are obtained with reduced number of comparators. These residues together with the folding information are processed in parallel in the FNS domain. The FNS has the same computational complexity as that in Residue Number System but the need for the intermediate steps such conversion of the analog data into binary and subsequently into residues is absent.