18 research outputs found
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
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Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
Deep learning has been successfully applied in several fields such as machine
translation, manufacturing, and pattern recognition. However, successful
application of deep learning depends upon appropriately setting its parameters
to achieve high quality results. The number of hidden layers and the number of
neurons in each layer of a deep machine learning network are two key
parameters, which have main influence on the performance of the algorithm.
Manual parameter setting and grid search approaches somewhat ease the users
tasks in setting these important parameters. Nonetheless, these two techniques
can be very time consuming. In this paper, we show that the Particle swarm
optimization (PSO) technique holds great potential to optimize parameter
settings and thus saves valuable computational resources during the tuning
process of deep learning models. Specifically, we use a dataset collected from
a Wi-Fi campus network to train deep learning models to predict the number of
occupants and their locations. Our preliminary experiments indicate that PSO
provides an efficient approach for tuning the optimal number of hidden layers
and the number of neurons in each layer of the deep learning algorithm when
compared to the grid search method. Our experiments illustrate that the
exploration process of the landscape of configurations to find the optimal
parameters is decreased by 77%-85%. In fact, the PSO yields even better
accuracy results