415 research outputs found
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in
using the smartphones for indoor positioning. This article is dedicated to
review the most recent and interesting smartphones based indoor navigation
systems, ranging from electromagnetic to inertia to visible light ones, with an
emphasis on their unique challenges and potential real-world applications. A
taxonomy of smartphones sensors will be introduced, which serves as the basis
to categorise different positioning systems for reviewing. A set of criteria to
be used for the evaluation purpose will be devised. For each sensor category,
the most recent, interesting and practical systems will be examined, with
detailed discussion on the open research questions for the academics, and the
practicality for the potential clients
Low-Cost Multiple-MAV SLAM Using Open Source Software
We demonstrate a multiple micro aerial vehicle (MAV) system capable of supporting autonomous exploration and navigation in unknown environments using only a sensor commonly found in low-cost, commercially available MAVsâa front-facing monocular camera. We adapt a popular open source monocular SLAM library, ORB-SLAM, to support multiple inputs and present a system capable of effective cross-map alignment that can be theoretically generalized for use with other monocular SLAM libraries. Using our system, a single central ground control station is capable of supporting up to five MAVs simultaneously without a loss in mapping quality as compared to single-MAV ORB-SLAM. We conduct testing using both benchmark datasets and real-world trials to demonstrate the capability and real-time effectiveness
Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network
Visible light positioning has the potential to yield sub-centimeter accuracy
in indoor environments, yet conventional received signal strength (RSS)-based
localization algorithms cannot achieve this because their performance degrades
from optical multipath reflection. However, this part of the optical received
signal is deterministic due to the often static and predictable nature of the
optical wireless channel. In this paper, the performance of optical channel
impulse response (OCIR)-based localization is studied using an artificial
neural network (ANN) to map embedded features of the OCIR to the user
equipment's location. Numerical results show that OCIR-based localization
outperforms conventional RSS techniques by two orders of magnitude using only
two photodetectors as anchor points. The ANN technique can take advantage of
multipath features in a wide range of scenarios, from using only the DC value
to relying on high-resolution time sampling that can result in sub-centimeter
accuracy
Software-Defined Lighting.
For much of the past century, indoor lighting has been based on incandescent or gas-discharge technology. But, with LED lighting experiencing a 20x/decade increase in flux density, 10x/decade decrease in cost, and linear improvements in luminous efficiency, solid-state lighting is finally cost-competitive with the status quo. As a result, LED lighting is projected to reach over 70% market penetration by 2030. This dissertation claims that solid-state lightingâs real potential has been barely explored, that now is the time to explore it, and that new lighting platforms and applications can drive lighting far beyond its roots as an illumination technology. Scaling laws make solid-state lighting competitive with conventional lighting, but two key features make solid-state lighting an enabler for many new applications: the high switching speeds possible using LEDs and the color palettes realizable with Red-Green-Blue-White (RGBW) multi-chip assemblies.
For this dissertation, we have explored the post-illumination potential of LED lighting in applications as diverse as visible light communications, indoor positioning, smart dust time synchronization, and embedded device configuration, with an eventual eye toward supporting all of them using a shared lighting infrastructure under a unified system architecture that provides software-control over lighting. To explore the space of software-defined lighting (SDL), we design a compact, flexible, and networked SDL platform to allow researchers to rapidly test new ideas. Using this platform, we demonstrate the viability of several applications, including multi-luminaire synchronized communication to a photodiode receiver, communication to mobile phone cameras, and indoor positioning using unmodified mobile phones. We show that all these applications and many other potential applications can be simultaneously supported by a single lighting infrastructure under software control.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111482/1/samkuo_1.pd
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Multi-Mobile Computing
With mobile systems evermore ubiquitous, individual users often own multiple mobile systems and groups of users often have many mobile systems at their disposal. As a result, there is a growing demand for multi-mobile computing, the ability to combine the functionality of multiple mobile systems into a more capable one. However, there are several key challenges. First, mobile systems are highly heterogeneous with different software and hardware, each with their own interfaces and data formats. Second, there are no effective ways to allow users to easily and dynamically compose together multiple mobile systems for the quick interactions that typically take place with mobile systems. Finally, there is a lack of system infrastructure to allow existing apps to make use of multiple mobile systems, or to enable developers to write new multi-mobile aware apps. My thesis is that higher-level abstractions of mobile operating systems can be reused to combine heterogeneous mobile systems into a more capable one and enable existing and new apps to provide new functionality across multiple mobile systems.
First, we present M2, a system for multi-mobile computing that enables existing unmodified mobile apps to share and combine multiple devices, including cameras, displays, speakers, microphones, sensors, GPS, and input. To support heterogeneous devices, M2 introduces a new data-centric approach that leverages higher-level device abstractions and hardware acceleration to efficiently share device data, not API calls. M2 introduces device transformation, a new technique to mix and match heterogeneous devices, enabling, for example, existing apps to leverage a single larger display fused from multiple displays for better viewing, or use a Nintendo Wii-like gaming experience by translating accelerometer to touchscreen input. We have implemented M2 and show that it operates across heterogeneous systems, including multiple versions of Android and iOS, and can run existing apps across mobile systems with modest overhead and qualitative performance indistinguishable from using local device hardware.
Second, we present Tap, a framework that leverages M2âs data-centric architecture to make it easy for users to dynamically compose collections of mobile systems and developers to write new multi-mobile apps that make use of those impromptu collections. Tap allows users to simply tap systems together to compose them into a collection without the need for users to register or connect to any cloud infrastructure. Tap makes it possible for apps to use existing mobile platform APIs across multiple mobile systems by virtualizing data sources so that local and remote data sources can be combined together upon tapping. Virtualized data sources can be hardware or software features, including media, clipboard, calendar events, and devices such as cameras and microphones. Leveraging existing mobile platform APIs make it easy for developers to write apps that use hard- ware and software features across dynamically composed collections of mobile systems. We have implemented Tap and show that it provides good usability for dynamically composing multiple mobile systems and good performance for sharing hardware devices and software features across multiple mobile systems.
Finally, using M2 and Tap, we present various apps that show how existing apps can provide useful functionality across multiple mobile systems and how new apps can be easily developed to provide new multi-mobile functionality. Examples include panoramic video recording using cameras from multiple mobile systems, surround sound music player app that configures itself based on automatically detecting the location of multiple mobile systems, and an added feature to the Snapchat app that allows multiple users to share a live Snap, using their own cameras and filters. Our user studies with these apps show that multi-mobile computing offers a richer and more enhanced experience for users and a much simpler development effort for developers
Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems
Mobile and embedded devices are increasingly using microphones and
audio-based computational models to infer user context. A major challenge in
building systems that combine audio models with commodity microphones is to
guarantee their accuracy and robustness in the real-world. Besides many
environmental dynamics, a primary factor that impacts the robustness of audio
models is microphone variability. In this work, we propose Mic2Mic -- a
machine-learned system component -- which resides in the inference pipeline of
audio models and at real-time reduces the variability in audio data caused by
microphone-specific factors. Two key considerations for the design of Mic2Mic
were: a) to decouple the problem of microphone variability from the audio task,
and b) put a minimal burden on end-users to provide training data. With these
in mind, we apply the principles of cycle-consistent generative adversarial
networks (CycleGANs) to learn Mic2Mic using unlabeled and unpaired data
collected from different microphones. Our experiments show that Mic2Mic can
recover between 66% to 89% of the accuracy lost due to microphone variability
for two common audio tasks.Comment: Published at ACM IPSN 201
Simultaneous Localization and Mapping with Power Network Electromagnetic Field
Various sensing modalities have been exploited for indoor location sensing, each of which has well understood limitations, however. This paper presents a first systematic study on using the electromagnetic field (EMF) induced by a building's electric power network for simultaneous localization and mapping (SLAM). A basis of this work is a measurement study showing that the power network EMF sensed by either a customized sensor or smartphone's microphone as a side-channel sensor is spatially distinct and temporally stable. Based on this, we design a SLAM approach that can reliably detect loop closures based on EMF sensing results. With the EMF feature map constructed by SLAM, we also design an efficient online localization scheme for resource-constrained mobiles. Evaluation in three indoor spaces shows that the power network EMF is a promising modality for location sensing on mobile devices, which is able to run in real time and achieve sub-meter accuracy
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