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
A fingerprint segmentation method using a recurrent neural network
- 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
Improving neural classifiers for ATR using a kernel method for generating synthetic training sets
- 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
Blind signal extraction of signals with specified frequency band
- Trang 515-524
Blind sources separation, independent component analysis (ICA) and related methods are promising approaches for analysis of biomedical signals, especially for EEG/MEG and fMRI data. However, most of the methods extract all sources simultaneously, so it is time consuming and not reliable especially, when the number of sensors is large (more than 100 sensors) and signals are contaminated by huge noi......
#Frequency #Data mining #Independent component analysis #Biosensors #Signal analysis #Electroencephalography #Band pass filters #Stochastic resonance #Narrowband #Bandwidth
Analog implementation for networks of integrate-and-fire neurons with adaptive local connectivity
- Trang 657-666
An analog VLSI implementation for pulse coupled neural networks of leakage free integrate-and-fire neurons with adaptive connections is presented. Weight adaptation is based on existing adaptation rules for image segmentation. Although both integrate-and-fire neurons and adaptive weights can be implementation only approximately, simulations have shown, that synchronization properties of the origin......
#Neurons #Image segmentation #Frequency synchronization #Very large scale integration #Hardware #Neural networks #Robustness #Signal processing #Nearest neighbor searches #Information technology
An efficient SMO-like algorithm for multiclass SVM
- Trang 297-306
Starting from a reformulation of Cramer and Singer (see Journal of Machine Learning Research, vol.2, p.265-92, Dec. 2001) multiclass kernel machine, we propose a sequential minimal optimization (SMO) like algorithm for incremental and fast optimization of the Lagrangian. The proposed formulation allowed us to define very effective new pattern selection strategies which lead to better empirical res......
#Support vector machines #Kernel #Support vector machine classification #Lagrangian functions #Prototypes
Blind identification problems with constraints
- 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
Face recognition using kernel principal component analysis and genetic algorithms
- Trang 337-343
Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can b......
#Face recognition #Kernel #Principal component analysis #Genetic algorithms #Support vector machines #Support vector machine classification #Feature extraction #Classification algorithms #Polynomials #Spatial databases
Decision templates for the classification of bioacoustic time series
- 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
Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data
- Trang 77-86
This manuscript describes a combined approach of unsupervised clustering followed by supervised learning that provides an efficient classification of conditions in DNA array gene expression experiments (different cell lines including some cancer types, in the cases shown). Firstly the dimensionality of the dataset of gene expression profiles is reduced to a number of non-redundant clusters of co-e......
#Supervised learning #Gene expression #DNA #Cancer #Neural networks #Principal component analysis #Clustering algorithms #Support vector machines #Support vector machine classification #Biotechnology