neural cross correlation for radio astronomy chipo n ngongoni supervisor: professor j tapson...
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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
Neural Cross Correlation For Radio Astronomy
Outline
Neural Computation description
Outline of Research
Relevance to Radio Astronomy
Work Update
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….
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
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
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
Spiking Neuron
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
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
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
Digital Platforms
• 1. Digital neuron implemented on VHDL-AMS (Analog Mixed Signal).
– Ease of modelling
• 2. Field Programmable Analog Arrays:- availability
Proposed Architecture
• Signal processor CMAC in correlator
• Based on the functionalities of analog correlators and neurons
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BU
Vxl
Vxh
x(t)
y(t) VyhVyl
Rb
Rb
Rb
RbRb Rb
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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
Model Results
• Cross correlation
MathematicalCross Correlation
Signals
Neural Cross Correlation
Model Results
• Cross correlation
MathematicalCross Correlation
Signals
Neural Cross Correlation
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.
Relevance to Radio Astronomy
• Alternative technique for correlation that can switch from parallel to serial
• Cost-space allocation on FPGA
• Power Consumption
• Computation effectiveness