Application of multiple-wireless to a visual localisation system for emergency services

Abstract—In this paper we discuss the application of multiplewireless technology to a practical context-enhanced service system called ViewNet. ViewNet develops technologies to support enhanced coordination and cooperation between operation teams in the emergency services and the police. Distributed localisation of users and mapping of environments implemented over a secure wireless network enables teams of operatives to search and map an incident area rapidly and in full coordination with each other and with a control centre. Sensing is based on fusing absolute positioning systems (UWB and GPS) with relative localisation and mapping from on-body or handheld vision and inertial sensors. This paper focuses on the case for multiple-wireless capabilities in such a system and the benefits it can provide. We describe our work of developing a software API to support both WLAN and TETRA in ViewNet. It also provides a basis for incorporating future wireless technologies into ViewNet. I

Evocative computing – creating meaningful lasting experiences in connecting with the past

We present an approach – evocative computing – that demonstrates how ‘at hand’ technologies can be ‘picked up’ and used by people to create meaningful and lasting experiences, through connecting and interacting with the past. The approach is instantiated here through a suite of interactive technologies configured for an indoor-outdoor setting that enables groups to explore, discover and research the history and background of a public cemetery. We report on a two-part study where different groups visited the cemetery and interacted with the digital tools and resources. During their activities serendipitous uses of the technology led to connections being made between personal memo-ries and ongoing activities. Furthermore, these experiences were found to be long-lasting; a follow-up study, one year later, showed them to be highly memorable, and in some cases leading participants to take up new directions in their work. We discuss the value of evocative computing for enriching user experiences and engagement with heritage practices

Statistical Approaches for Initial Access in mmWave 5G Systems

mmWave communication systems overcome high attenuation by using multiple antennas at both the transmitter and the receiver to perform beamforming. Upon entrance of a user equipment (UE) into a cell a scanning procedure must be performed by the base station in order to find the UE, in what is known as initial access (IA) procedure. In this paper we start from the observation that UEs are more likely to enter from some directions than from others, as they typically move along streets, while other movements are impossible due to the presence of obstacles. Moreover, users are entering with a given time statistics, for example described by inter-arrival times. In this context we propose scanning strategies for IA that take into account the entrance statistics. In particular, we propose two approaches: a memory-less random illumination (MLRI) algorithm and a statistic and memory-based illumination (SMBI) algorithm. The MLRI algorithm scans a random sector in each slot, based on the statistics of sector entrance, without memory. The SMBI algorithm instead scans sectors in a deterministic sequence selected according to the statistics of sector entrance and time of entrance, and taking into account the fact that the user has not yet been discovered (thus including memory). We assess the performance of the proposed methods in terms of average discovery time

Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.Comment: 6 pages, 4 figure