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
On learning feedforward neural networks with noise injection into inputs
- Trang 149-158
Injecting noise to the inputs during the training of feedforward neural networks (FNN) can improve their generalization performance remarkably. Reported works justify this fact arguing that noise injection is equivalent to a smoothing regularization with the input noise variance playing the role of the regularization parameter. The success of this approach depends on the appropriate choice of the ......
#Neural networks #Feedforward neural networks #Cost function #Neurons #Function approximation #Smoothing methods #Pattern classification #Approximation algorithms #Electronic mail #Fuzzy control
Parallel and separable recursive Levenberg-Marquardt training algorithm
- 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 recursive Renyi's entropy estimator
- Trang 209-217
Estimating the entropy of a sample set is required, in solving numerous learning scenarios involving information theoretic optimization criteria. A number of entropy estimators are available in the literature; however, these require a batch of samples to operate on in order to yield an estimate. We derive a recursive formula to estimate Renyi's (1970) quadratic entropy on-line, using each new samp......
#Entropy #Recursive estimation #Information theory #Adaptive systems #Biomedical computing #Biomedical engineering #Yield estimation #Digital communication #Neural networks #Stochastic processes
Multi-layer perceptron mapping on a SIMD architecture
- Trang 667-675
An automatic road sign recognition system, A(RS)/sup 2/, is aimed at the detection and recognition of one or more road signs from real-world color images. The authors have proposed an A(RS)/sup 2/ able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using multi-layer perceptron......
#Multilayer perceptrons #Roads #Image recognition #Concurrent computing #Computer architecture #Layout #Shape #Neural networks #Multi-layer neural network #Real time systems
High-speed voiceband QAM constellation classification in multipath environment
- Trang 455-464
We describe two real-time classifiers of unknown finite point QAM constellations over an nonideal channel. In the proposed schemes, first the transmitted symbols are recovered over a band-limited channel using the inherent cyclostationary characteristics of QAM signals. After equalization, the constellation is determined in the face of an unknown rotation due to an equalizer using a clustering app......
#Quadrature amplitude modulation #Equalizers #Constellation diagram #Bit rate #Pattern classification #Intersymbol interference #Statistics #AWGN #Telephony #Resource management
A hierarchical feedforward adaptive filter for system identification
- Trang 269-278
An architecture for adaptive filtering based upon the previously introduced hierarchical least mean square algorithm is proposed. This pyramidal architecture incorporates sparse connections between the architectural layers with a certain variable degree of overlapping between the neighboring subfilters of the same level. A learning algorithm for this class of structures is derived, based on the ba......
#Adaptive filters #System identification #Signal processing algorithms #Neurons #Finite impulse response filter #Neural networks #Adaptive signal processing #Biomedical signal processing #Least squares approximation #Feedforward neural networks
Finding temporal structure in music: blues improvisation with LSTM recurrent networks
- 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
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
Detection of unusual human behavior in intelligent house
- Trang 697-706
This paper describes a model, based on a Markov process model, of daily human behavior in an intelligent house where human behavior is observed with small motion detectors. The number of sensor states is reduced to a few dozen by a vector quantization method, and transitions within this reduced set of states are observed. Then, the state transition probability and the transition duration time dist......
#Humans #Intelligent sensors #Home appliances #Vector quantization #Infrared detectors #Motion detection #Programming #Computer industry #Research and development #Cities and towns