Zero-Shot Multi-View Indoor Localization via Graph Location Networks

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets available at https://github.com/coldmanck/zero-shot-indoor-localization-releas

A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty

Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. When a detailed digital representation of the region is to be created, domain experts are required to make best-guess decisions about each object. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by military, simulation, earth sciences and gaming communities. Many of these applications are increasingly using digitally synthesized visuals and require detailed digital 3D representations to be generated quickly after acquiring the necessary initial data. Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the semantic inferencing system and is extended with a data extraction framework. This framework enables the extraction of implicit property information using data and image analysis techniques. The data extraction framework supports extraction of spatial relationship values and attribution of uncertainties to inferred details. Uncertainty is recorded per property and used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services. Previous semantics based research in this domain has concentrated more on improving represented details through the addition of artifacts like lights, signage, crosswalks, etc. Previous attempts regarding uncertainty in assertions use a modified reasoner expressivity and calculus. Our work differs in that separating formal knowledge from data processing allows fusion of different heterogeneous data sources which share the same context. Imprecision is modeled through uncertainty on assertions without defining a new expressivity as long as KB explanation services are available for the used expressivity. We also believe that in our use case, this simplifies uncertainty calculations. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our method’s components, discuss advantages and limitations, and show sample results for the transportation domain

Design and implementation of application-specific medium access control protocol for scalable smart home embedded systems

Thesis (M.S.) University of Alaska Fairbanks, 2016By incorporating electrical devices, appliances and house features in a system that is controlled and monitored either remotely or on-site, smart home technologies have recently gained an increasing popularity. There are several smart home systems already available, ranging from simple on-site home monitoring to self-learning and Wi-Fi enabled systems. However, current systems do not fully make use of recent technological advancement and synergy among a variable number of sensors for improved data collection. For a synergistic system to be provident it needs to be modular and scalable to match exact user needs (type of applications and adequate number of sensors for each application). With an increased number of sensors intelligently placed to optimize the data collection, a wireless network is indispensable for a flexible and inexpensive installation. Such a network requires an efficient medium access control protocol to sustain a reliable system, provide flexibility in design and to achieve lower power consumption. This thesis brings to light practical ways to improve current smart home systems. As the main contribution of this work, we introduce a novel application-specific medium access control protocol able to support suggested improvements. In addition, a smart home prototype system is implemented to evaluate the protocol performance and prove concepts of recommended advances. This thesis covers the design of the proposed novel medium access protocol and the software/hardware implementation of the prototype system focusing on the monitoring and data analysis side, while providing inputs for the control side of the system. The smart home system prototype is Wi-Fi and Web connected, designed and implemented to emphasize system usability and energy efficiency

Integrative Sonic Urbanism: Artist-Led Strategies for Urban Sound Design in the Contemporary City

This doctoral research advances the fields of urban sound design and acoustic planning, presenting new ways of exploring the interrelationship between individual and collective sonic experience, the dynamic potential of the urban sound environment and the complex evolution of the contemporary cityscape. It links urban sound art practices with larger urban design processes, revealing how sound contributes to the production of urban space. The research progresses by crafting a dynamic, integrative methodology that activates contrasting sonic perspectives to critically reassess the role of sound in the public realm. As it discloses this methodology, the research navigates the tension between new modes of urban sound design guided by critical artistic practice and more conventional strategies rooted in the paradigm of environmental noise. Efforts to address urban sonic conditions through quantifiable metrics are contextualised within a wider transition in which urban form is increasingly influenced by data capture, analysis and governance. Within this transition, the critical potential of sound as an active component of urban space is obscured by remedial strategies established to improve what are construed as unfavourable conditions. This research analyses the relationship between these remedial strategies, the emergence of the ISO soundscape standard and the concepts of urban ambiances, urban atmospheres and acoustic territories. It postulates that these centralising conceptual models can serve to limit as well as to advance the critical potential of this field, pursuing instead a more tactical, performative and pluralistic methodology. The articulation of this methodology is substantiated through the exposition of three major public artworks developed by the author, including: Continuous Drift (2015–), a permanent sound installation in a public urban square; The Manual for Acoustic Planning and Urban Sound Design (2013–2020), an artist placement exploring the role of the acoustic planner within a local authority; and The Office for Common Sound (2016–), a project space that fosters dialogue concerning sound within specific regional and institutional contexts. These projects expand the role of artistic practice within the context of urban design and spatial planning by activating the field of urban sound design within diverse spatial, administrative and social contexts. These projects extend established methodologies drawn from sound art, site-specific art and sound installation practices with tactics inherited from public, participatory and socially engaged art, demonstrating how artist-led strategies for urban sound design can advance new forms of spatial production through collaboration with diverse urban actors

Atas do XVII Simpósio Internacional de Informática Educativa

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