Showing papers by "Emmanuel Dubois published in 2021"

HoloBar: Rapid Command Execution for Head-Worn AR Exploiting Around the Field-of-View Interaction

Abstract: Inefficient menu interfaces lead to system and application commands being tedious to execute in Immersive Environments. HoloBar is a novel approach to ease the interaction with multi-level menus in immersive environments: with HoloBar, the hierarchical menu splits between the field of view (FoV) of the Head Mounted Display and the smartphone (SP). Command execution is based on around-the-FoV interaction with the SP, and touch input on the SP display. The HoloBar offers a unique combination of features, namely rapid mid-air activation, implicit selection of top-level items and preview of second-level items on the SP, ensuring rapid access to commands. In a first study we validate its activation method, which consists in bringing the SP within an activation distance from the FoV. In a second study, we compare the HoloBar to two alternatives, including the standard HoloLens menu. Results show that the HoloBar shortens each step of a multi-level menu interaction (menu activation, top-level item selection, second-level item selection and validation), with a high success rate. A follow-up study confirms that these results remain valid when compared with the two validation mechanisms of HoloLens (Air-Tap and clicker).

A Predictive Performance Model for Immersive Interactions in Mixed Reality

Abstract: The design of immersive interaction for mixed reality based on head-mounted displays (HMDs), hereafter referred to as Mixed Reality (MR), is still a tedious task which can hinder the advent of such devices. Indeed, the effects of the interface design on task performance are difficult to anticipate during the design phase: the spatial layout of virtual objects and the interaction techniques used to select those objects can have an impact on task completion time. Besides, testing such interfaces with users in controlled experiments requires considerable time and efforts. To overcome this problem, predictive models, such as the Keystroke-Level Model (KLM), can be used to predict the time required to complete an interactive task at an early stage of the design process. However, so far these models have not been properly extended to address the specific interaction techniques of MR environments. In this paper we propose an extension of the KLM model to interaction performed in MR. First, we propose new operators and experimentally determine the unit times for each of them with a HoloLens v1. Then, we perform experiments based on realistic interaction scenarios to consolidate our model. These experiments confirm the validity of our extension of KLM to predict interaction time in mixed reality environments..

Mitigation of substrate coupling effects in RF switch by localized substrate removal using laser processing

Abstract: With the evolution of radio frequency (RF)/microwave technology, there is a demand for circuits which are able to meet highly challenging RF frontend specifications. Silicon-on-insulator (SOI) technology is one of the leading platforms for upcoming wireless generation. The degradation of performance due to substrate coupling is a key problem to address for telecommunication circuits, especially for the high throw count switches in RF frontends. In this context, a novel technique for local substrate removal is developed to fabricate membranes of mm-sized RF switch which allows for total etching of silicon handler. RF characterization of membranes reveal a superior linearity performance with lowering of 2 nd harmonic by 17.7 dB and improvement in insertion losses by 0.38 dB in comparison with High-Resistivity SOI substrates. This improvement leads to a significant increase in frontend efficiency. These results demonstrate a new route for optimization of circuit performance using post-fabrication substrate processing techniques.