Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
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Blind identification problems with constraints
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 535-544
In many applications of independent component analysis (ICA) and blind source
separation (BSS) the mixing or separating matrices have some special structure
or some constraints are imposed for the matrices like symmetry, orthogonality,
nonnegativity, sparseness and unit (or specified invariant norm) of the matrix.
We present several algorithms and overview some known transformations which
allows u...
#Independent component analysis #Symmetric matrices #Source separation #Covariance matrix #Blind source separation #Signal processing algorithms #Infrared sensors #Matrix decomposition #Laboratories #Biomedical signal processing
Efficient total least squares method for system modeling using minor component analysis
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 259-268
We present two algorithms to solve the total least-squares (TLS) problem. The
algorithms are on-line with O(N/sup 2/) and O(N) complexity. The convergence of
the algorithms is significantly faster than the traditional methods. A
mathematical analysis of convergence is also provided along with simulations to
substantiate the claims. We also apply the TLS algorithms for FIR system
identification wit...
#Least squares methods #Modeling #Signal processing algorithms #Parameter estimation #Algorithm design and analysis #Convergence #Vectors #Analytical models #Neural engineering #Laboratories
Parallel and separable recursive Levenberg-Marquardt training algorithm
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 129-138
A novel decomposed recursive Levenberg Marquardt (RLM) algorithm is derived for
the training of feedforward neural networks. By neglecting interneuron weight
correlations the recently proposed RLM training algorithm can be decomposed at
neuron level enabling weights to be updated in an efficient parallel manner. A
separable least squares implementation of decomposed RLM is also introduced.
Experim...
#Neurons #Cost function #Neural networks #Least squares methods #Convergence #Partitioning algorithms #Feedforward neural networks #Training data #Backpropagation algorithms #Resonance light scattering
A new SOLPN-based rate control algorithm for MPEG video coding
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 577-586
A new SOLPN (self-organizing learning Petri net)-based rate control algorithm
for an MPEG encoder is proposed. The idea is to use SOLPN to realize the RD
(rate distortion) model, which is self-organized on line and adaptively updated
frame by frame. The method does not require off-line pre-training; hence it is
geared toward real-time coding. The comparative results on the examples suggest
that ou...
#Video coding #Communication system control #Video sequences #Delay estimation #Impedance matching #Cities and towns #Rate-distortion #PSNR #Computational intelligence #Image coding
Decision templates for the classification of bioacoustic time series
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 159-168
The classification of time series is topic of this paper. In particular we
discuss the combination of multiple classifier outputs with decision templates.
The decision templates are calculated over a set of feature vectors which are
extracted in local time windows. To learn characteristic classifier outputs of
time series a set of decision templates is determined for the individual
classes. We pre...
#Biomedical acoustics #Information processing #Data mining #Speech recognition #Signal processing #Speech processing #Recurrent neural networks #Hidden Markov models #Neural networks #Supervised learning
Improving neural classifiers for ATR using a kernel method for generating synthetic training sets
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 425-434
An important problem with the use of neural networks in HRR radar target
classification is the difficulty in obtaining training data. Training sets are
small because of this, making generalization to new data difficult. In order to
improve generalization capability, synthetic radar targets are obtained using a
novel kernel method for estimating the probability density function of each
class of rad...
#Kernel #Neural networks #Statistical analysis #Training data #Chirp modulation #Azimuth #Radar scattering #Gaussian distribution #Probability density function #Radar measurements
A fingerprint segmentation method using a recurrent neural network
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 345-354
In this paper, we propose a segmentation method for identifying a fingerprint
image with the variation of vertical length using a recurrent neural network
(RNN). Group delay spectra and histograms of horizontal pixel line are used as
input features fed into the RNN and two target output patterns with and without
consideration of state dependency are introduced for learning. The method
composed of ...
#Fingerprint recognition #Recurrent neural networks #Image sensors #Fingers #Neurons #Image matching #Image segmentation #Histograms #Optical sensors #Sensor systems
Adaptive BP neural network (ABPNN) based PN code acquisition system via recursive accumulator
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 737-745
An adaptive back propagation (BP) neural network based PN code acquisition
system is presented. Conventional neural network based acquisition systems are
usually trained on PN code, but this system is based on training a back
propagation neural network at all possible phases of the output of a correlation
detector which is modified by a recursive accumulator. The recursive accumulator
can converge...
#Adaptive systems #Neural networks #Signal to noise ratio #Phase detection #Detectors #Control systems #Training data #Additive white noise #Gaussian noise #Computer simulation
Clustering of Sun exposure measurements
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 727-735
In a medically motivated Sun-exposure study, questionnaires concerning
Sun-habits were collected from a number of subjects together with UV radiation
measurements. This paper focuses on identifying clusters in the heterogeneous
set of data for the purpose of understanding possible relations between
Sun-habits exposure and eventually assessing the risk of skin cancer. A general
probabilistic framew...
#Sun #Matrix decomposition #Skin cancer #Data mining #Training data #Mathematical model #Electronic mail #Hospitals #Sampling methods #Biomedical imaging
Finding temporal structure in music: blues improvisation with LSTM recurrent networks
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing - - Trang 747-756
We consider the problem of extracting essential ingredients of music signals,
such as a well-defined global temporal structure in the form of nested
periodicities (or meter). We investigate whether we can construct an adaptive
signal processing device that learns by example how to generate new instances of
a given musical style. Because recurrent neural networks (RNNs) can, in
principle, learn the...
#Intelligent networks #Multiple signal classification #Recurrent neural networks #Timing #Adaptive signal processing #Signal generators #Signal processing #Machine learning #Bars #Learning systems
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