4 research outputs found
Visible light communications-based indoor positioning via compressed sensing
This paper presents an approach for visible light communication-based indoor
positioning using compressed sensing. We consider a large number of light
emitting diodes (LEDs) simultaneously transmitting their positional information
and a user device equipped with a photo-diode. By casting the LED signal
separation problem into an equivalent compressed sensing framework, the user
device is able to detect the set of nearby LEDs using sparse signal recovery
algorithms. From this set, and using proximity method, position estimation is
proposed based on the concept that if signal separation is possible, then
overlapping light beam regions lead to decrease in positioning error due to
increase in the number of reference points. The proposed method is evaluated in
a LED-illuminated large-scale indoor open-plan office space scenario. The
positioning accuracy is compared against the positioning error lower bound of
the proximity method, for various system parameters.Comment: to appear in IEEE Communication Letter
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
A lack of sufficient training data, both in terms of variety and quantity, is
often the bottleneck in the development of machine learning (ML) applications
in any domain. For agricultural applications, ML-based models designed to
perform tasks such as autonomous plant classification will typically be coupled
to just one or perhaps a few plant species. As a consequence, each
crop-specific task is very likely to require its own specialized training data,
and the question of how to serve this need for data now often overshadows the
more routine exercise of actually training such models. To tackle this problem,
we have developed an embedded robotic system to automatically generate and
label large datasets of plant images for ML applications in agriculture. The
system can image plants from virtually any angle, thereby ensuring a wide
variety of data; and with an imaging rate of up to one image per second, it can
produce lableled datasets on the scale of thousands to tens of thousands of
images per day. As such, this system offers an important alternative to time-
and cost-intensive methods of manual generation and labeling. Furthermore, the
use of a uniform background made of blue keying fabric enables additional image
processing techniques such as background replacement and plant segmentation. It
also helps in the training process, essentially forcing the model to focus on
the plant features and eliminating random correlations. To demonstrate the
capabilities of our system, we generated a dataset of over 34,000 labeled
images, with which we trained an ML-model to distinguish grasses from
non-grasses in test data from a variety of sources. We now plan to generate
much larger datasets of Canadian crop plants and weeds that will be made
publicly available in the hope of further enabling ML applications in the
agriculture sector.Comment: 35 pages, 8 figures, Preprint submitted to PLoS On