Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Using tracked mobile sensors to make maps of environmental effects

We present a study the results of a study of environmental carbon monoxide pollution that has uses a set of tracked, mobile pollution sensors. The motivating concept is that we will be able to map pollution and other properties of the real world a fine scale if we can deploy a large set of sensors with members of the general public who would carry them as they go about their normal everyday activities. To prove the viability of this concept we have to demonstrate that data gathered in an ad-hoc manner is reliable enough in order to allow us to build interesting geo-temporal maps. We present a trial using a small number of global positioning system-tracked CO sensors. From analysis of raw GPS logs we find some well-known spatial and temporal properties of CO. Further, by processing the GPS logs we can find fine-grained variations in pollution readings such as when crossing roads. We then discuss the space of possibilities that may be enabled by tracking sensors around the urban environment – both in getting at personal experience of properties of the environment and in making summative maps to predict future conditions. Although we present a study of CO, the techniques will be applicable to other environmental properties such as radio signal strength, noise, weather and so on

Development and testing of a portable GNSS network solution using the magellan propark3

With increasing development and expansion of Continuously Operating Reference Station (CORS)networks, globally and at home such as Sydnet, Global Navigation Satellite System users have greater options of utilizing reference station networks to receive correction data and undertake Real Time surveys without the need of supplying their own base station. A large majority of GNSS built today are equipped with built in mobile technology which utilize bidirectional communication including Internet based cellular connections. With increasing coverage of wireless internet, users will be able to utilize this technology in more places than they could ever before. The ProMark3 RTK GNSS receiver transmits correction data via a conventional 0.5 watt UHF radio. This allows a working range of up to 1.5km in open areas and 0.3 – 0.7km in urban built up areas. Unidirectional communication such as UHF also has its limitations including line of sight requirements, transmitter power, broadcasting antenna height limitations, reliability of the link and governmental restrictions such as licensing and operational limitations. Alternative options for receiving correction data are made available within the ProMark3’s onboard software, which include Networked Transmit of RTCM via Internet Protocol (NTRIP) and Direct Internet Protocol (DIP). These methods can extend that working range to 10km which is the recommended limit for RTK surveying. Built in wireless technology is not present in the ProMark3 however the user can still connect using a separate web enabled phone with Bluetooth technology. The advantage with this option is that the phone can be still used whilst you work, giving you even greater flexibility. This research project will explore the performance of the ProMark3 using Direct IP. Two different portable base reference stations to broadcast corrections will be designed. They include an office based and field based system. A rigorous testing regime will be conducted to explore the achievable range using Direct IP, the repeatability of position on an established baseline and the time taken to achieve a fixed solution at certain distances. The final part of this project will discuss the application of the technology to the surveying industry, particularly issues of reliability, cost and quality control. The use of CORS as an alternative to receiving correction data is improving work turn around time and field efficiencies, improving security as only one GPS is being utilized and offering survey firms the chance to experiment with this technology without a large expense upfront. The concept of a portable Direct IP station will allow users to operate privately run reference station networks from the office or the field. The benefit of a portable base station is that you can disassemble the base quickly and take it anywhere you decide to work. This will allow the operator and other users the chance to access data in areas not serviced by CORS and create opportunities for surveyors wanting to experiment with this alternate technology. The future may see an increasing amount of private CORS setup operating within existing government run networks, offering users even greater choice to access spatial data

Qualitative and quantitative determination of water in airborne particulate matter

Abstract. This paper describes the optimization and validation of a new simple method for the quantitative determination of water in atmospheric particulate matter (PM). The analyses are performed by using a coulometric Karl-Fisher system equipped with a controlled heating device; different water contributions are separated by the application of an optimized thermal ramp (three heating steps: 50–120 °C, 120–180 °C, 180–250 °C). The analytical performance of the method was verified by using standard materials containing 5.55% and 1% by weight of water. The recovery was greater than 95%; the detection limit was about 20 μg. The method was then applied to NIST Reference Materials (NIST1649a, urban particulate matter) and to real PM10 samples collected in different geographical areas. In all cases the repeatability was satisfactory (10–15%). When analyzing the Reference Material, the separation of four different types of water was obtained. In real PM10 samples the amount of water and its thermal profile differed as a function of the chemical composition of the dust. Mass percentages of 3–4% of water were obtained in most samples, but values up to about 15% were reached in areas where the chemical composition of PM is dominated by secondary inorganic ions and organic matter. High percentages of water were also observed in areas where PM is characterized by the presence of desert dust. A possible identification of the quality of water released from the samples was tried by applying the method to some hygroscopic compounds that are likely contained in PM (pure SiO2, Al2O3, ammonium salts, carbohydrates and dicarboxylic acids) and by comparing the results with those obtained from field samples

Mobile Cell Data Structure Quality Improvement For User Positioning Purposes

In wireless telephony networks, each cell is a geographical coverage area which can distribute frequency among cellular networks for different specific mobile network regions. Good cell and bad cell are used in the cellular network to identify the proper user position in a certain geographical area. The good cell is identified by assuming a maximum distance between latitude and longitude of two cell points with reasonable shape in a particular geographical area. The bad cell is identified while the cell shapes are become as irregular shape. However, mobile location accuracy is important for good cells data. Some cell data are not precise in shape to become good cells. Moreover, locations of handset are dependent for the accuracy of cell data shape. Most of the cases mobile operators are facing problem for the positioning purposes due to inaccuracy of the shape of cell data. The proper position accuracy of user is not visualized due to inaccuracy of cell data shape. The proposed system identifies the bad cell and repairs as good cell using visualize tool. An XML data file contains cell data information with longitude and latitude. A data base has been created to store the longitude and latitude of cell data in a standard format using PHP code. The visualize tool identify bad cell and good cell from the database. Furthermore, the tool converts the bad cell into good cell. Moreover, the tool can able to repair the cells which are not converted as good cell shape. The system can able to help to improve quality of user position accuracy for GSM and CDMA mobile operator

Towards high-accuracy augmented reality GIS for architecture and geo-engineering

L’architecture et la géo-ingénierie sont des domaines où les professionnels doivent prendre des décisions critiques. Ceux-ci requièrent des outils de haute précision pour les assister dans leurs tâches quotidiennes. La Réalité Augmentée (RA) présente un excellent potentiel pour ces professionnels en leur permettant de faciliter l’association des plans 2D/3D représentatifs des ouvrages sur lesquels ils doivent intervenir, avec leur perception de ces ouvrages dans la réalité. Les outils de visualisation s’appuyant sur la RA permettent d’effectuer ce recalage entre modélisation spatiale et réalité dans le champ de vue de l’usager. Cependant, ces systèmes de RA nécessitent des solutions de positionnement en temps réel de très haute précision. Ce n’est pas chose facile, spécialement dans les environnements urbains ou sur les sites de construction. Ce projet propose donc d’investiguer les principaux défis que présente un système de RA haute précision basé sur les panoramas omnidirectionels.Architecture and geo-engineering are application domains where professionals need to take critical decisions. These professionals require high-precision tools to assist them in their daily decision taking process. Augmented Reality (AR) shows great potential to allow easier association between the abstract 2D drawings and 3D models representing infrastructure under reviewing and the actual perception of these objects in the reality. The different visualization tools based on AR allow to overlay the virtual models and the reality in the field of view of the user. However, the architecture and geo-engineering context requires high-accuracy and real-time positioning from these AR systems. This is not a trivial task, especially in urban environments or on construction sites where the surroundings may be crowded and highly dynamic. This project investigates the accuracy requirements of mobile AR GIS as well as the main challenges to address when tackling high-accuracy AR based on omnidirectional panoramas

Disparate View Matching

Matching of disparate views has gained significance in computer vision due to its role in many novel application areas. Being able to match images of the same scene captured during day and night, between a historic and contemporary picture of a scene, and between aerial and ground-level views of a building facade all enable novel applications ranging from loop-closure detection for structure-from-motion and re-photography to geo-localization of a street-level image using reference imagery captured from the air. The goal of this work is to develop novel features and methods that address matching problems where direct appearance-based correspondences are either difficult to obtain or infeasible because of the lack of appearance similarity altogether. To address these problems, we propose methods that span the appearance-geometry spectrum in terms of both the use of these cues as well as the ability of each method to handle variations in appearance and geometry. First, we consider the problem of geo-localization of a query street-level image using a reference database of building facades captured from a bird\u27s eye view. To address this wide-baseline facade matching problem, a novel scale-selective self-similarity feature that avoids direct comparison of appearance between disparate facade images is presented. Next, to address image matching problems with more extreme appearance variation, a novel representation for matchable images expressed in terms of the eigen-functions of the joint graph of the two images is presented. This representation is used to derive features that are persistent across wide variations in appearance. Next, the problem setting of matching between a street-level image and a digital elevation map (DEM) is considered. Given the limited appearance information available in this scenario, the matching approach has to rely more significantly on geometric cues. Therefore, a purely geometric method to establish correspondences between building corners in the DEM and the visible corners in the query image is presented. Finally, to generalize this problem setting we address the problem of establishing correspondences between 3D and 2D point clouds using geometric means alone. A novel framework for incorporating purely geometric constraints into a higher-order graph matching framework is presented with specific formulations for the three-point calibrated absolute camera pose problem (P3P), two-point upright camera pose problem (Up2p) and the three-plus-one relative camera pose problem