Inside Magazine, March 2013

Iowa Department of Transportation Newsletter. INSIDE Magazine is developed to help keep all Iowa DOT employees informed about critical issues affecting them, recognize DOT employees for their excellent service and share interesting aspects in the lives of our co-workers

Population distribution by selected road network elements - comparison of centroids, geocoded addresses, built-up areas and total areas on the example of Slovak communes

Two research objectives can be identified in the presented paper. The first one was the development of a point layer, which would abstract from the position of a central point depending on the shape of the territory of the respective spatial unit (commune), and would express the position of a commune as regards the location of the point in the area of the commune built-up area. For such purpose, a geocoding algorithm from Google was used, for which it was possible to prepare a final dot map layer without any terrain layout, as the geocoding algorithm processes only simple text addresses of the relevant spatial units. Such an obtained dot layer was compared with the layer of centroids and the achieved differences were visualised. Another objective was to compare different methods of population distribution interpretation from the selected road network elements at the commune level. Point layers in the form of centroids and geocodes were compared with the spatial population distribution on the basis of the total area and built-up area of a commune. It is more suitable to use geocodes as the holder of statistical information in comparison with commune centroids, in particular in the areas with marked vertical division of the terrain. In assessing population distribution, the obtained values are much closer to the expression of the identical indicator calculated for the built-up area of a commune that we consider most accurate, which is also documented by the average percentage deviations between particular interpretations of population distribution.

216 Jewish Hospital of St. Louis

https://digitalcommons.wustl.edu/bjc_216/1091/thumbnail.jp

ADVANCED TECHNOLOGIES FOR EFFICIENT TRANSPORTATION CONSTRUCTION INSPECTION

Collecting load tickets is an example of an antiquated practice that puts inspectors in harm’s way either adjacent to traffic, in close proximity to moving or backing equipment, or at times requires climbing onto trucks to reach tickets. Technology exists to collect this information electronically allowing for safer, efficient inspection methods. Departments of Transportation are charged with inspecting an increasing work load with a diminishing number of inspection staff. Recently, doing more with less has led to the prioritization of inspection activities and resulted in less collection of data and visual inspection on projects. Technology advancements are available to improve data collection and provide for more efficient inspection. Using GPS and GIS technology tied into electronic scale report-out systems, a fleet tracking system traces haul routes, reports travel time and tonnage, and even assists contractors with equipment matching and balancing. Data from this system coupled with other technologies remote monitoring of temperature, intelligent compaction, and network enabled cameras provide an opportunity to enhance inspection and increase construction inspection productivity all the while enriching detail of project records. The contribution of this paper is to provide a framework in which to combine these technologies into a multi-faceted, enhanced inspection approach

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

An effective RGB color selection for complex 3D object structure in scene graph systems

The goal of this project is to develop a complete, fully detailed 3D interactive model of the human body and systems in the human body, and allow the user to interacts in 3D with all the elements of that system, to teach students about human anatomy. Some organs, which contain a lot of details about a particular anatomy, need to be accurately and fully described in minute detail, such as the brain, lungs, liver and heart. These organs are need have all the detailed descriptions of the medical information needed to learn how to do surgery on them, and should allow the user to add careful and precise marking to indicate the operative landmarks on the surgery location. Adding so many different items of information is challenging when the area to which the information needs to be attached is very detailed and overlaps with all kinds of other medical information related to the area. Existing methods to tag areas was not allowing us sufficient locations to attach the information to. Our solution combines a variety of tagging methods, which use the marking method by selecting the RGB color area that is drawn in the texture, on the complex 3D object structure. Then, it relies on those RGB color codes to tag IDs and create relational tables that store the related information about the specific areas of the anatomy. With this method of marking, it is possible to use the entire set of color values (R, G, B) to identify a set of anatomic regions, and this also makes it possible to define multiple overlapping regions

Approaching the Symbol Grounding Problem with Probabilistic Graphical Models

In order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our specific approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.U.S. Army Research Laboratory. Collaborative Technology Alliance Program (Cooperative Agreement W911NF-10-2-0016)United States. Office of Naval Research (MURI N00014-07-1-0749