83,551 research outputs found
A Reconfigurable Linear RF Analog Processor for Realizing Microwave Artificial Neural Network
Owing to the data explosion and rapid development of artificial intelligence
(AI), particularly deep neural networks (DNNs), the ever-increasing demand for
large-scale matrix-vector multiplication has become one of the major issues in
machine learning (ML). Training and evaluating such neural networks rely on
heavy computational resources, resulting in significant system latency and
power consumption. To overcome these issues, analog computing using optical
interferometric-based linear processors have recently appeared as promising
candidates in accelerating matrix-vector multiplication and lowering power
consumption. On the other hand, radio frequency (RF) electromagnetic waves can
also exhibit similar advantages as the optical counterpart by performing analog
computation at light speed with lower power. Furthermore, RF devices have extra
benefits such as lower cost, mature fabrication, and analog-digital mixed
design simplicity, which has great potential in realizing affordable, scalable,
low latency, low power, near-sensor radio frequency neural network (RFNN) that
may greatly enrich RF signal processing capability. In this work, we propose a
2X2 reconfigurable linear RF analog processor in theory and experiment, which
can be applied as a matrix multiplier in an artificial neural network (ANN).
The proposed device can be utilized to realize a 2X2 simple RFNN for data
classification. An 8X8 linear analog processor formed by 28 RFNN devices are
also applied in a 4-layer ANN for Modified National Institute of Standards and
Technology (MNIST) dataset classification.Comment: 11 pages, 16 figure
PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications
A cascading system of hierarchical, artificial neural networks (named
PRED-CLASS) is presented for the generalized classification of proteins into
four distinct classes-transmembrane, fibrous, globular, and mixed-from
information solely encoded in their amino acid sequences. The architecture of
the individual component networks is kept very simple, reducing the number of
free parameters (network synaptic weights) for faster training, improved
generalization, and the avoidance of data overfitting. Capturing information
from as few as 50 protein sequences spread among the four target classes (6
transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to
obtain 371 correct predictions out of a set of 387 proteins (success rate
approximately 96%) unambiguously assigned into one of the target classes. The
application of PRED-CLASS to several test sets and complete proteomes of
several organisms demonstrates that such a method could serve as a valuable
tool in the annotation of genomic open reading frames with no functional
assignment or as a preliminary step in fold recognition and ab initio structure
prediction methods. Detailed results obtained for various data sets and
completed genomes, along with a web sever running the PRED-CLASS algorithm, can
be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS
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Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network
A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002-2003 cool season (1 November-31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as "truth." The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small. © 2007 American Meteorological Society
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
automatic stage scoring based on machine learning techniques have been carried
out over the last years. In this work, we resort to multitaper spectral
analysis to create visually interpretable images of sleep patterns from EEG
signals as inputs to a deep convolutional network trained to solve visual
recognition tasks. As a working example of transfer learning, a system able to
accurately classify sleep stages in new unseen patients is presented.
Evaluations in a widely-used publicly available dataset favourably compare to
state-of-the-art results, while providing a framework for visual interpretation
of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on
Machine Learning for Signal Processin
A geographically distributed bio-hybrid neural network with memristive plasticity
Throughout evolution the brain has mastered the art of processing real-world
inputs through networks of interlinked spiking neurons. Synapses have emerged
as key elements that, owing to their plasticity, are merging neuron-to-neuron
signalling with memory storage and computation. Electronics has made important
steps in emulating neurons through neuromorphic circuits and synapses with
nanoscale memristors, yet novel applications that interlink them in
heterogeneous bio-inspired and bio-hybrid architectures are just beginning to
materialise. The use of memristive technologies in brain-inspired architectures
for computing or for sensing spiking activity of biological neurons8 are only
recent examples, however interlinking brain and electronic neurons through
plasticity-driven synaptic elements has remained so far in the realm of the
imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where
memristors work as "synaptors" between rat neural circuits and VLSI neurons.
The two fundamental synaptors, from artificial-to-biological (ABsyn) and from
biological-to- artificial (BAsyn), are interconnected over the Internet. The
bNN extends across Europe, collapsing spatial boundaries existing in natural
brain networks and laying the foundations of a new geographically distributed
and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
Six Noise Type Military Sound Classifier
Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives
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