Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Evaluating and Improving Internet Load Balancing with Large-Scale Latency Measurements

Load balancing is used in the Internet to distribute load across resources at different levels, from global load balancing that distributes client requests across servers at the Internet level to path-level load balancing that balances traffic across load-balanced paths. These load balancing algorithms generally work under certain assumptions on performance similarity. Specifically, global load balancing divides the Internet address space into client aggregations and assumes that clients in the same aggregation have similar performance to the same server; load-balanced paths are generally selected for load balancing as if they have similar performance. However, as performance similarity is typically achieved with similarity in path properties, e.g., topology and hop count, which do not necessarily lead to similar performance, performance between clients in the same aggregation and between load-balanced paths could differ significantly. This dissertation evaluates and improves global and path-level load balancing in terms of performance similarity. We achieve this with large-scale latency measurements, which not only allow us to systematically identify and evaluate the performance issues of Internet load balancing at scale, but also enable us to develop data-driven approaches to improve the performance. Specifically, this dissertation consists of three parts. First, we study the issues of existing client aggregations for global load balancing and then design AP-atoms, a data-driven client aggregation learned from passive large-scale latency measurements. Second, we show that the latency imbalance between load-balanced paths, previously deemed insignificant, is now both significant and prevalent. We present Flipr, a network prober that actively collects large-scale latency measurements to characterize the latency imbalance issue. Lastly, we design another network prober, Congi, that can detect congestion at scale and use Congi to study the congestion imbalance problem at scale. For both latency and congestion imbalance, we demonstrate that they could greatly affect the performance of various applications.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168012/1/yibo_1.pd

Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Shrinkage Based Particle Filters for Tracking in Wireless Sensor Networks with Correlated Sparse Measurements

This thesis focuses on the development of mobile tracking approaches in wireless sensor networks (WSNs) with correlated and sparse measurements. In wireless networks, devices have the ability to transfer information over the network nodes via wireless signals. The strength of a wireless signal at a receiver is referred as the received signal strength (RSS) and many wireless technologies such as Wi-Fi, ZigBee, the Global Positioning Systems (GPS), and other Satellite systems provide the RSS measurements for signal transmission. Due to the availability of RSS measurements, various tracking approaches in WSNs were developed based on the RSS measurements. Unfortunately, the feasibility of tracking using the RSS measurements is highly dependent on the connectivity of the wireless signals. The existing connectivity may be intermittently disrupted due to the low-battery status on the sensor node or temporarily sensor malfunction. In ad-hoc networks, the number of observation of the RSS measurements rapidly changing due to the movements of network nodes and mobile user. As a result, the tracking algorithms have limited data to perform state inference and this prevents accurate tracking. Furthermore, consecutive RSS measurements obtained from nearby sensor nodes exhibit spatio-temporal correlation, which provides extra information to be exploited. Exploiting the statistical information on the measurements noise covariance matrix increases the tracking accuracy. When the number of observations is relatively large, estimating the measurement noise covariance matrix is feasible. However, when they are relatively small, the covariance matrix estimation becomes ill-conditioned and non-invertible. In situations where the RSS measurements are corrupted by outliers, state inference can be misleading. Outliers can come from the sudden environmental disturbances, temporary sensor failures or even from the intrinsic noise of the sensor device. The outliers existence should be considered accordingly to avoid false and poor estimates. This thesis proposes first a shrinkage-based particle filter for mobile tracking in WSNs. It estimates the correlation in the RSS measurement using the shrinkage estimator. The shrinkage estimator overcomes the problems of ill-conditioned and non-invertibility of the measurement noise covariance matrix. The estimated covariance matrix is then applied to the particle filter. Secondly, it develops a robust shrinkage based particle filter for the problem of outliers in the RSS measurements. The proposed algorithm provides a non-parametric shrinkage estimate and represents a multiple model particle filter. The performances of both proposed filters are demonstrated over challenging scenarios for mobile tracking