ARIoT: scalable augmented reality framework for interacting with Internet of Things appliances everywhere

Jo, Dongsik; Kim, Gerard Jounghyun

doi:10.1109/tce.2016.7613201

Cited by 98 publications

(56 citation statements)

References 27 publications

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“…5. The dependencies of voltage V(D0) at the diode on current I(I0) for the following temperature values: Temperature = 0C (1); 20C (2); 40C (4); 60C (4); 80C (5); 100C (6) At the second stage, we carried out an investigation aimed at the adjustment of the diode SPICE model parameters taking into account the discrepancies between the diode characteristic and the theoretical mathematical dependencies for p-n junctions. For the most part, such discrepancies are attributed to the fact that the diode volt-ampere characteristic is influenced by the emission of charge carriers in the p-n barrier and parasitic resistance of its semiconductor structure.…”

Section: Signal Converter Verificationmentioning

confidence: 99%

“…Fig. 7 depicts the simulation results for discrete values of parasitic diode resistance RS = 0 (1), 10 Ohm (2), 20 Ohm (3), 30 Ohm (4), 40 Ohm (5). The obtained modeling results demonstrate discrepancies between the actual volt-ampere characteristic and its nominal logarithmic dependency.…”

Section: Signal Converter Verificationmentioning

confidence: 99%

“…Modern solutions for magnetic tracking include AR IoT, a universal framework that conforms with the AR and Internet of Things (IoT) concepts [5]; data fusion in medical imaging devices [6]; surgical instruments navigation in medical equipment [7]; biomedical engineering technologies including those for monitoring orofacial kinematics [8]; flexible robot mechatronics tools [9], etc. Magnetic tracking systems can be implemented upon specialized or unified coils [10].…”

Section: Introductionmentioning

confidence: 99%

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Measuring Logarithmic Signal Converter for Magnetic Tracking Systems

Holyaka¹,

Marusenkova²,

Fedasyuk³

2020

MEM

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The work deals with the problem of signal conversion in magnetic tracking systems. Magnetic tracking systems are a novel development trend of navigation sensors within the concepts of the Internet of Things and virtual and augmented reality. In contrast to optical tracking systems, magnetic ones do not suffer from occlusions. In comparison with tracking systems built upon inertial sensors, they are not susceptible to bias drift and provide better accuracy. Magnetic tracking technology is based on calculating the position of objects upon the dynamic measurement of the reference magnetic field vectors. The reference magnetic fields are formed by arrays of actuator coils in the low-frequency electromagnetic radiation spectrum. Those who implement a magnetic tracking system have to ensure noise-immune measurements of signals coming from sensor coils in a wide dynamic measurement range. The range changes from microvolts for distances of several meters in couples "actuator-sensor" to hundreds of millivolts in the case if the distances in "actuator-sensor" couples reduce to several centimeters. Thus, one requires signal converters able to provide highly noise-immune measurements in a dynamic measurement range covering six orders of magnitude. The work presents the results of development, simulation, and investigation into a signal converter for magnetic tracking systems, whose novelty consists in combining the methods of logarithmic amplification and synchronous demodulation of the output signals of the sensor coils. The main nodes of the developed signal converter are a control unit, a logarithmic amplifier, a synchronous demodulator, a low-pass filter, an actuator driver and an analog-to-digital converter. Voltage logarithm compression has been performed upon volt-ampere characteristics of semiconductor p-n junctions. The synchronous demodulator provides a high level of selection of the useful signal out of electromagnetic noise. The results presented in this paper are part of our complex research work related to the development of the Magnetic Tracking System Integrated Development Environment (MTS-IDE). The latter is being developed by a team of scholars within different projects and is aimed at enhancing the efficacy of parametric optimization and synthesizing firmware of embedded systems implementing integrated magnetic tracking sensors. Simulation and experiments have shown that the dynamic range of noise-immune signal measurement using the developed converter covers six orders of magnitude, from 1E-6 V to 1 V. Investigation into functionality were conducted by oscillograph methods. The characteristics of the proposed solution were measured by the above-mentioned MTS-IDE. The obtained results are of key importance for further improvement of magnetic tracking systems, particularly, for their noise-immune measurement volume expansion.

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Section: Signal Converter Verificationmentioning

confidence: 99%

Section: Signal Converter Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Logarithmic Signal Converter for Magnetic Tracking Systems

Holyaka¹,

Marusenkova²,

Fedasyuk³

2020

MEM

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“…The ARIoT framework [12], consolidates the technology of Augmented Reality (AR) by slightly extending the IoT infrastructure. The core of the framework is to: dynamically identify IoT targeted objects within proximity of an IoT space, recognition and tracking of IoT objects feature information (examples are, object id, attribute values, object manufacturers, etc.)…”

Section: Ariotmentioning

confidence: 99%

IoT Architectural Framework: Connection and Integration Framework for IoT Systems

Uviase¹,

Kotonya²

2018

Electron. Proc. Theor. Comput. Sci.

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The proliferation of the Internet of Things (IoT) has since seen a growing interest in architectural design and adaptive frameworks to promote the connection between heterogeneous IoT devices and IoT systems. The most widely favoured software architecture in IoT is the Service Oriented Architecture (SOA), which aims to provide a loosely coupled systems to leverage the use and reuse of IoT services at the middle-ware layer, to minimise system integration problems. However, despite the flexibility offered by SOA, the challenges of integrating, scaling and ensuring resilience in IoT systems persist. One of the key causes of poor integration in IoT systems is the lack of an intelligent, connection-aware framework to support interaction in IoT systems. This paper reviews existing architectural frameworks for integrating IoT devices and identifies the key areas that require further research improvements. The paper concludes by proposing a possible solution based on microservice. The proposed IoT integration framework benefits from an intelligent API layer that employs an external service assembler, service auditor, service monitor and service router component to coordinate service publishing, subscription, decoupling and service combination within the architecture.

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“…Most aged population tend to remain in their homes and support the significant cost of nursing care instead of been moved to nursing homes [6].On the one hand, the continuous technological advances turn possible the development of automatic and intelligent services with powerful sensing and connecting capabilities for enhanced living environments and occupational health [7][8][9]. In particular, the advances in networking, sensors, and embedded systems enable real-time monitoring for enhanced living environments and healthcare, as well as they are closely related to mHealth [10]. On the other hand, there is a vast array of challenges in the design and implementation of effective AAL and mHealth systems, such as information architecture, interaction design, human-computer interaction, ergonomics, usefulness, and accessibility [11].…”

mentioning

confidence: 99%

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

Marques

Pitarma

2019

JSAN

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Mobile health research field aims to provide access to healthcare anytime and anywhere through mobile computing technologies while using a cost-effective approach. Mobile health is closely related to ambient assisted living as both research fields address independence in elderly adults. Aging has become a relevant challenge, as it is anticipated that 20% of world population will be aged 60 years and older in 2050. Most people spend more than 90% of their time indoors, therefore the indoor environmental quality has a relevant impact on occupant's health and well-being. We intended to provide real-time indoor quality monitoring for enhanced living environments and occupational health. This paper presents the AirPlus real-time indoor environmental quality monitoring system, which incorporates several advantages when compared to other systems, such as scalability, flexibility, modularity, easy installation, and configuration, as well as mobile computing software for data consulting and notifications. The results that were obtained are promising and present a significant contribution to the monitoring solutions available in the literature. AirPlus provides a rich dataset to plan interventions for enhanced indoor quality, but also to support clinical diagnostics and correlate occupant's health problems with their living environment conditions. Keywords: ambient assisted living; enhanced living environments; health monitoring; indoor air quality; indoor environmental quality; Internet of Things; mobile health; occupational health IntroductionMobile health (mHealth) is a multifaceted research field to deliver healthcare services anytime and anywhere through mobile computing technologies while using a cost-effective approach. The mHealth services and applications are enhanced personalized healthcare systems that improve the communication between patient and doctor [1]. The development of mobile computing solutions for health improvement and well-being has significantly increased in the recent past. There is relevant interest in the study and development of reliable solutions based on mobile computing for enhanced healthcare systems [2]. However, most people do not use mobile health applications, and the individuals that use them tend to stop using it in a short period of time. Therefore, the software developers need to study new pervasive and ubiquitous methods and architectures to better address the consumer needs in order to promote the adoption of these mobile applications for enhanced living environments [3]. The proliferation of computer processing power and the evolution of wireless communication technologies have promoted mHealth and lead to the development of solutions and technologies that substantially contribute to the evolution of clinical research and healthcare [4].It is anticipated that 20% of the world population will be aged 60 years and older in 2050 [5], which will cause a significant increase of health disorders, medical services costs, and caregivers demands. Furthermore, Ambient Assisted Living (AAL)...

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ARIoT: scalable augmented reality framework for interacting with Internet of Things appliances everywhere

Cited by 98 publications

References 27 publications

Measuring Logarithmic Signal Converter for Magnetic Tracking Systems

Measuring Logarithmic Signal Converter for Magnetic Tracking Systems

IoT Architectural Framework: Connection and Integration Framework for IoT Systems

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

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