Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
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Signal reconstruction from sampled data using neural network
- Trang 707-715
For reconstructing a signal from sampling data, the method based on Shannon's
sampling theorem is usually employed. The reconstruction error appears when the
signal does not satisfy the Nyquist condition. This paper proposes a new
reconstruction method by using a linear perceptron and multilayer perceptron as
FIR filter. The perceptron, which has weights obtained by learning when adapting
the orig...
#Signal reconstruction #Neural networks #Image reconstruction #Sampling methods #Finite impulse response filter #Frequency #Adaptive filters #Image sampling #Information retrieval #Physics
Minimax strategies for training classifiers under unknown priors
- Trang 249-258
Most supervised learning algorithms are based on the assumption that the
training data set reflects the underlying statistical model of the real data.
However, this stationarity assumption is not always satisfied in practice: quite
frequently, class prior probabilities are not in accordance with the class
proportions in the training data set. The minimax approach is based on selecting
the classifi...
#Minimax techniques #Neural networks #Training data #Error analysis #Robustness #Supervised learning #Error probability #Cost function #Proposals
Accurate estimation of the signal baseline in DNA chromatograms
- Trang 35-44
Estimating accurately the varying baseline level in different parts of a DNA
chromatogram is a challenging and important problem for accurate base-calling.
We are formulating the problem in a statistical learning framework and propose
an Expectation-Maximization algorithm for its solution. In addition we also
present a faster, iterative histogram based method for estimating the background
of the s...
#Statistical learning #Bayesian methods #Signal resolution #Signal to noise ratio #Data mining #Digital signal processing #DNA computing #Expectation-maximization algorithms #Iterative methods #Histograms
Fully adaptive neural nonlinear FIR filters
- Trang 279-288
A class of algorithms for training neural adaptive filters employed for
nonlinear adaptive filtering is introduced. Sign algorithms incorporated with
the fully adaptive normalised nonlinear gradient descent (SFANNGD) algorithm,
normalised nonlinear gradient descent (SNNGD) algorithm and nonlinear gradient
descent (SNGD) algorithm are proposed. The SFANNGD, SNNGD and the SNGD are
derived based upon...
#Finite impulse response filter #Adaptive filters #Computational complexity #Mathematical model #Cost function #Convergence #Taylor series #Educational institutions #Information systems #Filtering algorithms
Scaling of a length scale for regression and prediction
- Trang 179-187
We analyze the prediction from noised data, based on a regression formulation of
the problem. For the regression, we construct a model with a length scale to
smooth the data, which is determined by the variance of noise and the speed of
the variation of original signals. The model is found to be effective also for
prediction. This is because it decreases an uncertain region near a boundary as
the ...
#Gaussian noise #Predictive models #Sampling methods #Information processing #Elementary particles #Fractals #Shape #Information analysis #Performance analysis #Algorithm design and analysis
Towards a tunable tactile communication system: concept and first experiments
- Trang 767-776
We present a novel concept of a tactile communication system with dialog-based
tuning possibilities for the exploration of tactile language developments. An
experimental implementation of the proposed tactile intelligent sensory
substitution system (TIS/sup 3/) is being tested in a closed loop set up with
human subjects. TIS/sup 3/ consists of a tactile encoder (TE) to map desired
objects onto a p...
#Humans #Tellurium #Intelligent systems #Intelligent sensors #Skin #Signal generators #Computer science #System testing #Information processing #Digital signal processing
Fast edge-based stereo matching algorithm based on search space reduction
- Trang 587-596
The reduction of the search region in stereo correspondence can increase the
performance of the matching process, in the context of execution time and
accuracy. For edge-based stereo matching, we establish the relationship between
the search space and parameters like relative displacement of the edges, the
disparity under consideration, the image resolution, the CCD dimensions and the
focal length...
#Cameras #Space technology #Layout #Electronic mail #Image resolution #Charge coupled devices #Robot vision systems #Stereo vision #Wavelet transforms #Joining processes
Time domain blind source separation of non-stationary convolved signals by utilizing geometric beamforming
- Trang 445-454
We propose a time-domain blind source separation (BSS) algorithm that utilizes
geometric information such as sensor positions and assumed locations of sources.
The algorithm tackles the problem of convolved mixtures by explicitly exploiting
the non-stationarity of the acoustic sources. The learning rule is based on
second-order statistics and is derived by natural gradient minimization. The
propos...
#Blind source separation #Source separation #Finite impulse response filter #Speech #Frequency domain analysis #Array signal processing #Acoustic sensors #Laboratories #Time domain analysis #Statistics
Dynamic Bayesian network based speech recognition with pitch and energy as auxiliary variables
- Trang 637-646
Pitch and energy are two fundamental features describing speech, having
importance in human speech recognition. However, when incorporated as features
in automatic speech recognition (ASR), they usually result in a significant
degradation on recognition performance due to the noise inherent in estimating
or modeling them. We show experimentally how this can be corrected by either
conditioning the ...
#Bayesian methods #Speech recognition #Automatic speech recognition #Degradation #Hidden Markov models #Acoustic emission #Artificial intelligence #Humans #Speech enhancement #Network topology
A stochastic method for minimizing functions with many minima
- Trang 289-296
An efficient stochastic method for continuous optimization problems is
presented. Combining a novel global search with typical local optimization
methods, the proposed method specializes in hard optimization problems such as
minimizing multimodal or ill-conditioned unimodal objective functions. Extensive
numerical studies show that, starting from a random initial point, the proposed
method is alwa...
#Stochastic processes #Optimization methods #Minimization methods #Newton method #Least squares methods #Recursive estimation #Computational modeling #Simulated annealing #Genetics #Algorithm design and analysis