4,837 research outputs found
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful
application-specific design to ensure that the most informative relationships
in the data are learnable. In this paper, we apply deep neural networks to the
nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We
consider problems of estimating macroscopic quantities (e.g., the queue at an
intersection) at a lane level. First-principles modeling at the lane scale has
been a challenge due to complexities in modeling social behaviors like lane
changes, and those behaviors' resultant macro-scale effects. Following domain
knowledge that upstream/downstream lanes and neighboring lanes affect each
others' traffic flows in distinct ways, we apply a form of neural attention
that allows the neural network layers to aggregate information from different
lanes in different manners. Using a microscopic traffic simulator as a testbed,
we obtain results showing that an attentional neural network model can use
information from nearby lanes to improve predictions, and, that explicitly
encoding the lane-to-lane relationship types significantly improves
performance. We also demonstrate the transfer of our learned neural network to
a more complex road network, discuss how its performance degradation may be
attributable to new traffic behaviors induced by increased topological
complexity, and motivate learning dynamics models from many road network
topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation
System
FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications
The miniaturized calcium imaging is an emerging neural recording technique
that can monitor neural activity at large scale at a specific brain region of a
rat or mice. It has been widely used in the study of brain functions in
experimental neuroscientific research. Most calcium-image analysis pipelines
operate offline, which incurs long processing latency thus are hard to be used
for closed-loop feedback stimulation targeting certain neural circuits. In this
paper, we propose our FPGA-based design that enables real-time calcium image
processing and position decoding for closed-loop feedback applications. Our
design can perform real-time calcium image motion correction, enhancement, and
fast trace extraction based on predefined cell contours and tiles. With that,
we evaluated a variety of machine learning methods to decode positions from the
extracted traces. Our proposed design and implementation can achieve position
decoding with less than 1 ms latency under 300 MHz on FPGA for a variety of
mainstream 1-photon miniscope sensors. We benchmarked the position decoding
accuracy on open-sourced datasets collected from six different rats, and we
show that by taking advantage of the ordinal encoding in the decoding task, we
can consistently improve decoding accuracy without any overhead on hardware
implementation and runtime across the subjects.Comment: 11 pages, 15 figure
Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach
In this paper, the existing Scheduling Dimension Reduction (SDR) methods for
Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network
(DNN) approach is developed that achieves higher model accuracy under
scheduling dimension reduction. The proposed DNN method and existing SDR
methods are compared on a two-link robotic manipulator, both in terms of model
accuracy and performance of controllers synthesized with the reduced models.
The methods compared include SDR for state-space models using Principal
Component Analysis (PCA), Kernel PCA (KPCA) and Autoencoders (AE). On the
robotic manipulator example, the DNN method achieves improved representation of
the matrix variations of the original LPV model in terms of the Frobenius norm
compared to the current methods. Moreover, when the resulting model is used to
accommodate synthesis, improved closed-loop performance is obtained compared to
the current methods.Comment: Accepted to American Control Conference (ACC) 2020, Denve
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