17,505 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
On Correlated Knowledge Distillation for Monitoring Human Pose with Radios
In this work, we propose and develop a simple experimental testbed to study
the feasibility of a novel idea by coupling radio frequency (RF) sensing
technology with Correlated Knowledge Distillation (CKD) theory towards
designing lightweight, near real-time and precise human pose monitoring
systems. The proposed CKD framework transfers and fuses pose knowledge from a
robust "Teacher" model to a parameterized "Student" model, which can be a
promising technique for obtaining accurate yet lightweight pose estimates. To
assure its efficacy, we implemented CKD for distilling logits in our integrated
Software Defined Radio (SDR)-based experimental setup and investigated the
RF-visual signal correlation. Our CKD-RF sensing technique is characterized by
two modes -- a camera-fed Teacher Class Network (e.g., images, videos) with an
SDR-fed Student Class Network (e.g., RF signals). Specifically, our CKD model
trains a dual multi-branch teacher and student network by distilling and fusing
knowledge bases. The resulting CKD models are then subsequently used to
identify the multimodal correlation and teach the student branch in reverse.
Instead of simply aggregating their learnings, CKD training comprised multiple
parallel transformations with the two domains, i.e., visual images and RF
signals. Once trained, our CKD model can efficiently preserve privacy and
utilize the multimodal correlated logits from the two different neural networks
for estimating poses without using visual signals/video frames (by using only
the RF signals)
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing
them. Automating the process of action recognition from visual data has been
the topic of much research in the computer vision community. But what if it is
too dark, or if the person is occluded or behind a wall? In this paper, we
introduce a neural network model that can detect human actions through walls
and occlusions, and in poor lighting conditions. Our model takes radio
frequency (RF) signals as input, generates 3D human skeletons as an
intermediate representation, and recognizes actions and interactions of
multiple people over time. By translating the input to an intermediate
skeleton-based representation, our model can learn from both vision-based and
RF-based datasets, and allow the two tasks to help each other. We show that our
model achieves comparable accuracy to vision-based action recognition systems
in visible scenarios, yet continues to work accurately when people are not
visible, hence addressing scenarios that are beyond the limit of today's
vision-based action recognition.Comment: ICCV 2019. The first two authors contributed equally to this pape
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