FootSLAM meets adaptive thresholding

The is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCalibration of the zero-velocity detection threshold is an essential prerequisite for zero-velocity-aided inertial navigation. However, the literature is lacking a self-contained calibration method, suitable for large-scale use in unprepared environments without map information or pre-deployed infrastructure. In this paper, the calibration of the zero-velocity detection threshold is formulated as a maximum likelihood problem. The likelihood function is approximated using estimation quantities readily available from the FootSLAM algorithm. Thus, we obtain a method for adaptive thresholding that does not require map information, measurements from supplementary sensors, or user input. Experimental evaluations are conducted using data with different gait speeds, sensor placements, and walking trajectories. The proposed calibration method is shown to outperform fixed-threshold zero-velocity detectors and a benchmark using a speed-based threshold classifier.National Institute of Standards and Technology (NIST

Map-aided navigation for emergency searches

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordReal-time positioning of emergency personnel has been an active research topic for many years. However, studies on how to improve navigation accuracy by using prior information on the idiosyncratic motion characteristics of firefighters are scarce. This paper presents an algorithm for generating pseudo observations of position and orientation based on standard search patterns used by firefighters. The iterative closest point algorithm is used to compare walking trajectories estimated from inertial odometry with search patterns generated from digital maps. The resulting fitting errors are then used to integrate the pseudo observations into a map-aided navigation filter. Specifically, we present a sequential Monte Carlo solution where the pattern comparison is used to both update particle weights and create new particle samples. Experimental results involving professional firefighters demonstrate that the proposed pseudo observations can achieve a stable localization error of about one meter, and offer increased robustness in the presence of map errors

3D Object reconstruction from a single depth view with adversarial learning

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets show that the proposed 3D-RecGAN significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects. Our code and data are available at: https://github.com/Yang7879/3D-RecGAN

Robust occupancy inference with commodity WiFi

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. In this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity WiFi infrastructure. Depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. We evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. Experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness

DeepTIO: a deep thermal-inertial odometry with visual hallucination

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordVisual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model

Mammography screening: views from women and primary care physicians in Crete

Background: Breast cancer is the most commonly diagnosed cancer among women and a leading cause of death from cancer in women in Europe. Although breast cancer incidence is on the rise worldwide, breast cancer mortality over the past 25 years has been stable or decreasing in some countries and a fall in breast cancer mortality rates in most European countries in the 1990s was reported by several studies, in contrast, in Greece have not reported these favourable trends. In Greece, the age-standardised incidence and mortality rate for breast cancer per 100.000 in 2006 was 81,8 and 21,7 and although it is lower than most other countries in Europe, the fall in breast cancer mortality that observed has not been as great as in other European countries. There is no national strategy for screening in this country. This study reports on the use of mammography among middleaged women in rural Crete and investigates barriers to mammography screening encountered by women and their primary care physicians. Methods: Design: Semi-structured individual interviews. Setting and participants: Thirty women between 45–65 years of age, with a mean age of 54,6 years, and standard deviation 6,8 from rural areas of Crete and 28 qualified primary care physicians, with a mean age of 44,7 years and standard deviation 7,0 serving this rural population. Main outcome measure: Qualitative thematic analysis. Results: Most women identified several reasons for not using mammography. These included poor knowledge of the benefits and indications for mammography screening, fear of pain during the procedure, fear of a serious diagnosis, embarrassment, stress while anticipating the results, cost and lack of physician recommendation. Physicians identified difficulties in scheduling an appointment as one reason women did not use mammography and both women and physicians identified distance from the screening site, transportation problems and the absence of symptoms as reasons for non-use. Conclusion: Women are inhibited from participating in mammography screening in rural Crete. The provision of more accessible screening services may improve this. However physician recommendation is important in overcoming women's inhibitions. Primary care physicians serving rural areas need to be aware of barriers preventing women from attending mammography screening and provide women with information and advice in a sensitive way so women can make informed decisions regarding breast caner screening

An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal

Abstract Behavioural events that are important for understanding sociobiology and movement ecology are often rare, transient and localised, but can occur at spatially distant sites e.g. territorial incursions and co‐locating individuals. Existing animal tracking technologies, capable of detecting such events, are limited by one or more of: battery life; data resolution; location accuracy; data security; ability to co‐locate individuals both spatially and temporally. Technology that at least partly resolves these limitations would be advantageous. European badgers (Meles meles L.), present a challenging test‐bed, with extra‐group paternity (apparent from genotyping) contradicting established views on rigid group territoriality with little social‐group mixing. In a proof of concept study we assess the utility of a fully automated active‐radio‐frequency‐identification (aRFID) system combining badger‐borne aRFID‐tags with static, wirelessly‐networked, aRFID‐detector base‐stations to record badger co‐locations at setts (burrows) and near notional border latrines. We summarise the time badgers spent co‐locating within and between social‐groups, applying network analysis to provide evidence of co‐location based community structure, at both these scales. The aRFID system co‐located animals within 31.5 m (adjustable) of base‐stations. Efficient radio transmission between aRFIDs and base‐stations enables a 20 g tag to last for 2–5 years (depending on transmission interval). Data security was high (data stored off tag), with remote access capability. Badgers spent most co‐location time with members of their own social‐groups at setts; remaining co‐location time was divided evenly between intra‐ and inter‐social‐group co‐locations near latrines and inter‐social‐group co‐locations at setts. Network analysis showed that 20–100% of tracked badgers engaged in inter‐social‐group mixing per week, with evidence of trans‐border super‐groups, that is, badgers frequently transgressed notional territorial borders. aRFID occupies a distinct niche amongst established tracking technologies. We validated the utility of aRFID to identify co‐locations, social‐structure and inter‐group mixing within a wild badger population, leading us to refute the conventional view that badgers (social‐groups) are territorial and to question management strategies, for controlling bovine TB, based on this model. Ultimately aRFID proved a versatile system capable of identifying social‐structure at the landscape scale, operating for years and suitable for use with a range of species. EPSRC WILDSENSING projec

Efficient Data Propagation in Traffic-Monitoring Vehicular Networks.

Road congestion and traffic-related pollution have a large negative social and economic impact on several economies worldwide. We believe that investment in the monitoring, distribution, and processing of traffic information should enable better strategic planning and encourage better use of public transport, both of which would help cut pollution and congestion. This paper investigates the problem of efficiently collecting and disseminating traffic information in an urban setting. We formulate the traffic data acquisition problem and explore solutions in the mobile sensor network domain while considering realistic application requirements. By leveraging existing infrastructure such as traveling vehicles in the city, we propose traffic data dissemination schemes that operate on both the routing and the application layer; our schemes are frugal in the use of the wireless medium, rendering our system interoperable with the proliferation of competing applications. We introduce the following two routing algorithms for vehicular networks that aim at minimizing communication and, at the same time, adhering to a delay threshold set by the application: 1) delay-bounded greedy forwarding and 2) delay-bounded minimum-cost forwarding. We propose a framework that jointly optimizes the two key processes associated with monitoring traffic, i.e., data acquisition and data delivery, and provide a thorough experimental evaluation based on realistic vehicular traces on a real city map. © 2006 IEEE