16,785 research outputs found
Deep Room Recognition Using Inaudible Echos
Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).Comment: 29 page
Cloud-based or On-device: An Empirical Study of Mobile Deep Inference
Modern mobile applications are benefiting significantly from the advancement
in deep learning, e.g., implementing real-time image recognition and
conversational system. Given a trained deep learning model, applications
usually need to perform a series of matrix operations based on the input data,
in order to infer possible output values. Because of computational complexity
and size constraints, these trained models are often hosted in the cloud. To
utilize these cloud-based models, mobile apps will have to send input data over
the network. While cloud-based deep learning can provide reasonable response
time for mobile apps, it restricts the use case scenarios, e.g. mobile apps
need to have network access. With mobile specific deep learning optimizations,
it is now possible to employ on-device inference. However, because mobile
hardware, such as GPU and memory size, can be very limited when compared to its
desktop counterpart, it is important to understand the feasibility of this new
on-device deep learning inference architecture. In this paper, we empirically
evaluate the inference performance of three Convolutional Neural Networks
(CNNs) using a benchmark Android application we developed. Our measurement and
analysis suggest that on-device inference can cost up to two orders of
magnitude greater response time and energy when compared to cloud-based
inference, and that loading model and computing probability are two performance
bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering
(IC2E) conference 201
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
As an important application in the busy world today, mobile video
conferencing facilitates virtual face-to-face communication with friends,
families and colleagues, via their mobile devices on the move. However, how to
provision high-quality, multi-party video conferencing experiences over mobile
devices is still an open challenge. The fundamental reason behind is the lack
of computation and communication capacities on the mobile devices, to scale to
large conferencing sessions. In this paper, we present vSkyConf, a
cloud-assisted mobile video conferencing system to fundamentally improve the
quality and scale of multi-party mobile video conferencing. By novelly
employing a surrogate virtual machine in the cloud for each mobile user, we
allow fully scalable communication among the conference participants via their
surrogates, rather than directly. The surrogates exchange conferencing streams
among each other, transcode the streams to the most appropriate bit rates, and
buffer the streams for the most efficient delivery to the mobile recipients. A
fully decentralized, optimal algorithm is designed to decide the best paths of
streams and the most suitable surrogates for video transcoding along the paths,
such that the limited bandwidth is fully utilized to deliver streams of the
highest possible quality to the mobile recipients. We also carefully tailor a
buffering mechanism on each surrogate to cooperate with optimal stream
distribution. We have implemented vSkyConf based on Amazon EC2 and verified the
excellent performance of our design, as compared to the widely adopted unicast
solutions.Comment: 10 page
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