Abstract

Abstract
Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter.

Abstract
To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes.

Abstract
Hereditary Amyloidosis associated with variant Transthyretin (ATTRv Amyloidosis) is a progressive and highly disabling neurological disorder that affects gait. Quantitative motion analysis is useful for assessing motor function, including gait, in diseases affecting movement. A single markerless RGB-D camera enables 3D full-body motion capture in a less expensive and intrusive, and more portable way than multi-camera marker-based systems. In this study, we examine whether a gait analysis system based on an RGB-D camera can be used to detect significant changes in the gait of ATTRv amyloidosis patients over time, when compared with a 12-camera system. We acquired 3D data provided by both systems from six ATTRv amyloidosis patients, while performing a simple gait task, once (T0) and 18 months later (T1). A direct comparison of systems has already been conducted. In this work, however, for each patient, we investigated if the RGB-D camera system detects statistically significant differences between the two different acquisitions in a similar way to the reference system, and whether it is reliable to use during patients' follow-up. The obtained results show that the differences detected between T0 and T1 for both systems follow the same tendency for 65% of the spatiotemporal gait parameters, and for 38% of the kinematic parameters (38%). The most reliable parameters were: stride duration/length, gait speed (and its variability), and arm/foot swing velocity, all with an almost perfect strength of agreement.

Abstract
Bloch Surface Waves (BSW) consist of electromagnetic modes generated at the interface between a photonic crystal and an isotropic dielectric. This type of surface mode displays sharp resonances and high sensitivity to external refractive index variations, and thus appears to be an ideal candidate for usage in optical sensors. Nevertheless, design and optimization of photonic crystals is not a trivial task and constitutes an ongoing field of research. The sensitivity of BSW in both refractometric and adsorption sensing is calculated analytically using first-order perturbation theory for TE modes, allowing the understanding of how several physical parameters of the photonic crystal influence the sensitivity. Preliminary experimental results are presented, which aim to use the analytical calculations to allow for both refractometric and adsorption sensing in a single photonic crystal structure. © Published under licence by IOP Publishing Ltd.

Abstract
The Anterior Nucleus of Thalamus (ANT) Deep Brain Stimulation (DBS) has long been touted as the most effective DBS-target for interrupting seizures in focal refractory epilepsy patients. The ANT is primarily involved in cognitive tasks but has extensive reciprocal connections with motor-related regions, suggesting that it is also involved in motor-cognitive tasks. In this work, we aimed to assess the involvement of the ANT during voluntary upper limbs movements. For this purpose, we analyzed Local Field Potentials (LFPs) signals recorded during a movement protocol from one of the first epilepsy patients implanted with a Percept™ PC system, who performed a 5-day period of simultaneous video electroencephalography (vEEG) and Percept PC-LFPs recordings. We estimated time-frequency maps and performed event-related desynchronization (ERD) or synchronization (ERS) analysis and we found that synchronizations found in left hemisphere 7-17 Hz map corresponded to maximum hand rotations. Positive peaks on the ERD/ERS curve occurred at a similar frequency of the hand movements (1.09-0.99Hz against 1.27-0.90{Hz}. These results suggested that the ANT may be involved in the execution of automatisms. Moreover, we found that ERD/ERS appeared approximately 2 seconds before the movement onset, as it was found on the EEG of healthy subjects performing the same protocol.