105,992 research outputs found
Analog MIMO Radio-over-Copper: Prototype and Preliminary Experimental Results
Analog Multiple-Input Multiple-Output Radio-over-Copper (A-MIMO-RoC) is an
effective all-analog FrontHaul (FH) architecture that exploits any pre-existing
Local Area Network (LAN) cabling infrastructure of buildings to distribute
Radio-Frequency (RF) signals indoors. A-MIMO-RoC, by leveraging a fully analog
implementation, completely avoids any dedicated digital interface by using a
transparent end-to-end system, with consequent latency, bandwidth and cost
benefits. Usually, LAN cables are exploited mainly in the low-frequency
spectrum portion, mostly due to the moderate cable attenuation and crosstalk
among twisted-pairs. Unlike current systems based on LAN cables, the key
feature of the proposed platform is to exploit more efficiently the huge
bandwidth capability offered by LAN cables, that contain 4 twisted-pairs
reaching up to 500 MHz bandwidth/pair when the length is below 100 m. Several
works proposed numerical simulations that assert the feasibility of employing
LAN cables for indoor FH applications up to several hundreds of MHz, but an
A-MIMO-RoC experimental evaluation is still missing. Here, we present some
preliminary results obtained with an A-MIMO-RoC prototype made by low-cost
all-analog/all-passive devices along the signal path. This setup demonstrates
experimentally the feasibility of the proposed analog relaying of MIMO RF
signals over LAN cables up to 400 MHz, thus enabling an efficient exploitation
of the LAN cables transport capabilities for 5G indoor applications.Comment: Part of this work has been accepted as a conference publication to
ISWCS 201
Automated Test Input Generation for Android: Are We There Yet?
Mobile applications, often simply called "apps", are increasingly widespread,
and we use them daily to perform a number of activities. Like all software,
apps must be adequately tested to gain confidence that they behave correctly.
Therefore, in recent years, researchers and practitioners alike have begun to
investigate ways to automate apps testing. In particular, because of Android's
open source nature and its large share of the market, a great deal of research
has been performed on input generation techniques for apps that run on the
Android operating systems. At this point in time, there are in fact a number of
such techniques in the literature, which differ in the way they generate
inputs, the strategy they use to explore the behavior of the app under test,
and the specific heuristics they use. To better understand the strengths and
weaknesses of these existing approaches, and get general insight on ways they
could be made more effective, in this paper we perform a thorough comparison of
the main existing test input generation tools for Android. In our comparison,
we evaluate the effectiveness of these tools, and their corresponding
techniques, according to four metrics: code coverage, ability to detect faults,
ability to work on multiple platforms, and ease of use. Our results provide a
clear picture of the state of the art in input generation for Android apps and
identify future research directions that, if suitably investigated, could lead
to more effective and efficient testing tools for Android
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device,
non-intrusive and automatic-learning system for smartphone user authentication.
Operating in the background, our system implicitly observes users' phone
pick-up movements, the way they bend their arms when they pick up a smartphone
to interact with the device, to authenticate the users.
Our SPU outperforms the state-of-the-art implicit authentication mechanisms
in three main aspects: 1) SPU automatically learns the user's behavioral
pattern without requiring a large amount of training data (especially those of
other users) as previous methods did, making it more deployable. Towards this
end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW)
algorithm to effectively quantify similarities between users' pick-up
movements; 2) SPU does not rely on a remote server for providing further
computational power, making SPU efficient and usable even without network
access; and 3) our system can adaptively update a user's authentication model
to accommodate user's behavioral drift over time with negligible overhead.
Through extensive experiments on real world datasets, we demonstrate that SPU
can achieve authentication accuracy up to 96.3% with a very low latency of 2.4
milliseconds. It reduces the number of times a user has to do explicit
authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies
(SACMAT) 201
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
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