Conceptual Framework for On-site Digital Interpretation Developments in Cultural Heritage Sites

On-site heritage interpretation plays a vital role in cultural heritage sites in conveying the significance and multiple heritage values to the visitors. In an era where the world is transforming with innovative digital applications, the heritage sites are also being integrated with digital interpretation techniques to deliver a better interpretation and new dimensional experience to the visitors. Though multiple digital solutions are available, not all the techniques are appropriate, applicable and feasible to every site. Besides, neither proper worldwide principles nor framework has been exerted for these digital heritage interpretation developments. Therefore, this study is focused on building a generic conceptual framework to select the most appropriate digital interpretation technique(s) that fit the context of the heritage site, giving special reference to the six Cultural World Heritage Sites of Sri Lanka. The relevant qualitative and quantitative data were gathered via in-depth interviews, field observation, literature survey and a visitor survey questionnaire. The main themes and sub-themes derived through the thematic analysis were adopted as the theoretical framework for the research to analyze the collected data of the six Cultural World Heritage Sites and the selected digital techniques. Based on the results, the study recommends appropriate digital techniques for each Cultural World Heritage Sites of the country. Further as aimed, the study presents a conceptual framework for on-site digital interpretation developments for cultural heritage sites by categorizing the 24 criteria derived for data analysis under five phases namely ‘Prepare’, ‘Assess’, ‘Design’, ‘Implement’ and ‘Sustain’. DOI: http://doi.org/10.31357/fhss/vjhss.v07i01.0

C-LOG: A Chamfer Distance based method for localisation in occupancy grid-maps

In this paper, the problem of localising a robot within a known two-dimensional environment is formulated as one of minimising the Chamfer Distance between the corresponding occupancy grid map and information gathered from a sensor such as a laser range finder. It is shown that this nonlinear optimisation problem can be solved efficiently and that the resulting localisation algorithm has a number of attractive characteristics when compared with the conventional particle filter based solution for robot localisation in occupancy grids. The proposed algorithm is able to perform well even when robot odometry is unavailable, insensitive to noise models and does not critically depend on any tuning parameters. Experimental results based on a number of public domain datasets as well as data collected by the authors are used to demonstrate the effectiveness of the proposed algorithm. © 2013 IEEE

Is Androgen Excess Masked in Alopecia Areata Patients: A Retrospective Data Analysis of 1,587 Patients

Studies on the pathophysiology and comorbidities associated with alopecia areata (AA) are limited. The purpose of this study was to determine the prevalence of androgen excess in AA and its subtypes, in relation to demographics and comorbidities. Medical records of 1,587 Patchy AA, AT, AU, and ophiasis patients seen in the Department of Dermatology at the Cleveland Clinic Foundation in Ohio between 2005 and 2015 were reviewed. Out of this cohort, 226 patients met the inclusion criteria. There is evidence that patients with AA had significantly greater prevalence of polycystic ovary syndrome (PCOS) than the general population (p\u3c0.001). Androgen excess was identified in 42.5% (n=96) of the 226 patients with AA or any subtype (p\u3c0.001). The androgen excess group was significantly more likely to present with irregular menses, hirsutism, adult acne, PCOS, and/or ovarian cysts (p\u3c0.001). This study was limited by being retrospective. Our study demonstrated that AA is associated with androgen excess

An extended Kalman filter for localisation in occupancy grid maps

© 2015 IEEE. The main contribution of this paper is an extended Kalman filter (EKF) based framework for mobile robot localisation in occupancy grid maps (OGMs), when the initial location is approximately known. We propose that the observation equation be formulated using the unsigned distance transform based Chamfer Distance (CD) that corresponds to a laser scan placed within the OGM, as a constraint. This formulation provides an alternative to the ray-casting model, which generally limited localisation in OGMs to Particle Filter (PF) based frameworks that can efficiently deal with observation models that are not analytic. Usage of an EKF is attractive due to its computational efficiency, especially as it can be applied to modern day field robots with limited on-board computing power. Furthermore, well-developed tools for dealing with potential outliers in the observations or changes to the motion model, exists in the EKF framework. The effectiveness of the proposed algorithm is demonstrated using a number of simulation and real life examples, including one in a dynamic environment populated with people

Adaptive Placement for Mobile Sensors in Spatial Prediction under Locational Errors

© 2016 IEEE. This paper addresses the problem of driving robotic sensors for an energy-constrained mobile wireless network in efficiently monitoring and predicting spatial phenomena, under data locational errors. The paper first discusses how errors of mobile sensor locations affect estimating and predicting the spatial physical processes, given that spatial field to be monitored is modeled by a Gaussian process. It then proposes an optimality criterion for designing optimal sampling paths for the mobile robotic sensors given the localization uncertainties. Although the optimization problem is optimally intractable, it can be resolved by a polynomial approximation algorithm, which is proved to be practically feasible in an energy-constrained mobile sensor network. More importantly, near-optimal solutions of this navigation problem are guaranteed by a lower bound within 1-(1/e) of the optimum. The performance of the proposed approach is evaluated on simulated and real-world data sets, where impact of sensor location errors on the results is demonstrated by comparing the results with those obtained by using noise-less data locations

A Monocular Indoor Localiser Based on an Extended Kalman Filter and Edge Images from a Convolutional Neural Network

© 2018 IEEE. The main contribution of this paper is an extended Kalman filter (EKF)based algorithm for estimating the 6 DOF pose of a camera using monocular images of an indoor environment. In contrast to popular visual simultaneous localisation and mapping algorithms, the technique proposed relies on a pre-built map represented as an unsigned distance function of the ground plane edges. Images from the camera are processed using a Convolutional Neural Network (CNN)to extract a ground plane edge image. Pixels that belong to these edges are used in the observation equation of the EKF to estimate the camera location. Use of the CNN makes it possible to extract ground plane edges under significant changes to scene illumination. The EKF framework lends itself to use of a suitable motion model, fusing information from any other sensors such as wheel encoders or inertial measurement units, if available, and rejecting spurious observations. A series of experiments are presented to demonstrate the effectiveness of the proposed technique

Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena

This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expected-value function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on real-world datasets have verified the superiority of the proposed approach. © 2013 IEEE