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Neural Cross Correlation For Radio Astronomy
Chipo N Ngongoni Supervisor: Professor J Tapson
Department of Electrical Engineering, University of Cape Town Rondebosch, 7701, South Africa
[email protected]@uct.ac.za
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Neural Cross Correlation For Radio Astronomy
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Outline
Neural Computation description
Outline of Research
Relevance to Radio Astronomy
Work Update
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Neural Computation...• Modelling of systems according to brain response
and neural system in living organisms
• Types of models: compartmental models, rate models, spiking models
• Modeling platforms: mathematical, hardware and software
• Application areas: Wireless communications, biomedical prosthetics, pattern and speech recognition, financial analysis….
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Neural Computation...
• Not all neural networks are based on training and evolving an algorithm
• J Tapson( 1998)¹ , J Tapson (2009)
• Benefits are found inherently from modelling close likeness of a biological model and extracting relevant information
J. Tapson ,1998,Autocorrelation Properties of Single neurons
J.Tapson, C.Jin et.al..2009 A First Order Non-Homogeneous Markov Model for the response of Spiking Neurons Stimulated by small phase continuous signals
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Research Outline
• Neural based analysis of auto/cross correlation
• Simulate/ build a biologically inspired correlator module ( ASIC to Reconfigurable)
• Test applicability to Radio Astronomy correlation requirements
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Spiking Neuron
• Basic function of spiking neuron• Integrate-and-fire model: membrane
potential• Stochastic Resonance
v t =∫m+ξ t +g t dt
drift noisesignal
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Spiking Neuron
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Neuron Spike Interpretation
• Wolfgang Maass: Information contained in spikes
• Spike information is contained in the spike time independent of shape and size of the spike.
• Spikes analyzed in the form ISIH and post processing logic
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The Selected Model• Equivalent analog electronic circuit model • Leaky integrator which resets at hysteretic
comparator thresholds
x(t)
nx(t)
mx
y(t)
ny(t)
my
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The Selected Model
• Digital analogy of the same model adopted from FPGA Based Silicon Neural Array by Andrew Cassidy et al…
• Built on Altera FPGA with VHDL and Quartus software
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Digital Platforms
• 1. Digital neuron implemented on VHDL-AMS (Analog Mixed Signal).
– Ease of modelling
• 2. Field Programmable Analog Arrays:- availability
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Proposed Architecture
• Signal processor CMAC in correlator
• Based on the functionalities of analog correlators and neurons
N
BU
Vxl
Vxh
x(t)
y(t) VyhVyl
Rb
Rb
Rb
RbRb Rb
N
BU
N
BU
N
BU
N
BU
N
BU
N
BU
N
BU
N
BU
N
BU
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BU
N
BU
N
BU
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BU
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Proposed Architecture
clk
reset
a[7..0]
result[7..0]
clk
a[7..0]z
clk
enable
reset
q[7..0]
BUF (LCELL)
counter:Gate4
wire3
clkmain
enable
input1[7..0]
output[7..0]
comparator:Gate3basic:Gate2
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Model Results
• Cross correlation
MathematicalCross Correlation
Signals
Neural Cross Correlation
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Model Results
• Cross correlation
MathematicalCross Correlation
Signals
Neural Cross Correlation
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Relevance to Radio Astronomy
• Neural networks not a new phenomenon to astronomy .
• Used in cluster identification, signal processing
• Spike interpretation can be analysed as bit stream correlators.
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Relevance to Radio Astronomy
• Alternative technique for correlation that can switch from parallel to serial
• Cost-space allocation on FPGA
• Power Consumption
• Computation effectiveness