11,986 research outputs found
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices
Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi
devices has gained attention with recent advances in wireless technology. HGR recognizes the human
activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing
them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction
and transformation to pre-process the raw CSI traces. However, these methods fail to capture
the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal
representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts
higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the
recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order
cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods
derived from information theory construct a robust and highly informative feature subset, fed as
input to the multilevel support vector machine (SVM) classifier in order to measure the performance.
The proposed methodology is validated using a public database SignFi, consisting of 276 gestures
with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home
environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of
97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average
recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was
96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
Laser range data based semantic labeling of places
Extending metric space representations of an environment with other high level information, such as semantic and topological representations enable a robotic device to efficiently operate in complex environments. This paper proposes a methodology for a robot to classify indoor environments into semantic categories. Classification task, using data collected from a laser range finder, is achieved by a machine learning approach based on the logistic regression algorithm. The classification is followed by a probabilistic temporal update of the semantic labels of places. The innovation here is that the new algorithm is able to classify parts of a single laser scan into different semantic labels rather than the conventional approach of gross categorization of locations based on the whole laser scan. We demonstrate the effectiveness of the algorithm using a data set available in the public domain. ©2010 IEEE
OnionBots: Subverting Privacy Infrastructure for Cyber Attacks
Over the last decade botnets survived by adopting a sequence of increasingly
sophisticated strategies to evade detection and take overs, and to monetize
their infrastructure. At the same time, the success of privacy infrastructures
such as Tor opened the door to illegal activities, including botnets,
ransomware, and a marketplace for drugs and contraband. We contend that the
next waves of botnets will extensively subvert privacy infrastructure and
cryptographic mechanisms. In this work we propose to preemptively investigate
the design and mitigation of such botnets. We first, introduce OnionBots, what
we believe will be the next generation of resilient, stealthy botnets.
OnionBots use privacy infrastructures for cyber attacks by completely
decoupling their operation from the infected host IP address and by carrying
traffic that does not leak information about its source, destination, and
nature. Such bots live symbiotically within the privacy infrastructures to
evade detection, measurement, scale estimation, observation, and in general all
IP-based current mitigation techniques. Furthermore, we show that with an
adequate self-healing network maintenance scheme, that is simple to implement,
OnionBots achieve a low diameter and a low degree and are robust to
partitioning under node deletions. We developed a mitigation technique, called
SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and
discuss a set of techniques that can enable subsequent waves of Super
OnionBots. In light of the potential of such botnets, we believe that the
research community should proactively develop detection and mitigation methods
to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
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