Informacijos teorija : sistemos ir signalai : mokomoji knyga

Taikomosios informatikos katedraVytauto Didžiojo universiteta

Biologically inspired architecture of feedforward networks for signal classification

The hypothesis is that in the lowest bidden layers of biological systems "local subnetworks" are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and "unsupervised" training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as anew feature to perform a final classificationTaikomosios informatikos katedraVytauto Didžiojo universiteta

Parameter anglysis for steering angle prediction using neural networks

The paper presents analysis of steering angle prediction using neural networks on a curved country road. Data from real car driving on a country road is used both for training and testing of a neural network. Among parameters current steering angle, current speed of the car, and curve in front of the car are analyzed. The study aims to add to development of driver assistance systems for country road driving. Steering rules for flat and sharp curves are compared. Results show that predictions of sharp curve steering on a test set is much more accurate if learning was performed on a sharp curve, and predictions for the flat curve are not so sensitive to the learning set, but is to some extent better if learning is made using flat curve data. Further, steering prediction parameters for flat and sharp curves are compared. For flat curves “looking ahead” of 1s works best, while for sharp curves “looking ahead” of 0.5 provides the optimal prediction. Yet, the differences in error in the flat curve case were not large, and the optimum was shallow, while for the sharp curve it was much sharperInformatikos fakultetasVytauto Didžiojo universiteta

A neural network based investigation of high frequency components of the ECG

New information retrieval method is applied to detect low amplitude high frequency components of electrocardiogram (ECG). The special neural network using similarities to prototype features is suggested. Prognosis error is chosen as similarity measure of a signal to a prototype. This measure is preferable in the case of a poor signal to noise ratio. New technique was successfully applied for classification of ECG recordings of myocardial infarction (MI) patients with the complication of ventricular fibrillation (VF) vs. the MI patients who have not had the VF, a problem where standard methods failed to provide satisfactory separation of pattern classesInformatikos fakultetasVytauto Didžiojo universiteta

Approximation of unbiased convex classification error rate estimator

Convex classification error rate estimator is described as weighted combination of the low-biased estimator and the high-biased estimator. If the underlying data model is known, the coefficients (weights) can be optimized so that the bias and root-mean-square error of the estimator is minimized. However, in most situations, data model is unknown. In this paper we propose a new error estimation method, based on approximation of unbiased convex error rate estimator. Experiments with real world and synthetic data sets show that common error estimation methods, such as resubstitution, repeated 10-foldcross-validation, leave-one-out and random subsampling are outperformed (in terms of root-mean-square error) by the proposed methodMatematikos ir statistikos katedraVytauto Didžiojo universiteta

Population model based on cells with internal dynamics

A model of a population is constructed by bringing together individual model organisms (cells) which have explicit internal dynamics. An attempt is made to preserve analyzability of the relatively complex model by describing the nonlinear dynamics of each cell by a set of piece-wise linear equations. In the simplest case of three linear pieces, a population with inherent oscillations in the number of cells as a function of time is obtained. A proposal is made to approximate realistic internal dynamics of selected biological features by introducing the appropriate number of linear patches, and to simulate realistic populations in this way. Another extension of the model, the description of interactions between cells through released metabolites, is used to represent the situation of a chemostat.&nbsp

Model-free incremental learning of the semantics of manipulation actions

eISSN: 1872-793XUnderstanding and learning the semantics of complex manipulation actions are intriguing and non-trivial issues for the development of autonomous robots. In this paper, we present a novel method for an online, incremental learning of the semantics of manipulation actions by observation. Recently, we had introduced the Semantic Event Chains (SECs) as a new generic representation for manipulations, which can be directly computed from a stream of images and is based on the changes in the relationships between objects involved in a manipulation. We here show that the SEC concept can be used to bootstrap the learning of the semantics of manipulation actions without using any prior knowledge about actions or objects. We create a new manipulation action benchmark with 8 different manipulation tasks including in total 120 samples to learn an archetypal SEC model for each manipulation action. We then evaluate the learned SEC models with 20 long and complex chained manipulation sequences including in total 103 manipulation samples. Thereby we put the event chains to a decisive test asking how powerful is action classification when using this framework. We find that we reach up to 100% and 87% average precision and recall values in the validation phase and 99% and 92% in the testing phase. This supports the notion that SECs are a useful tool for classifying manipulation actions in a fully automatic wayTaikomosios informatikos katedraVytauto Didžiojo universiteta

Steering Angle Prediction Using Neural Networks and Look-up Table for Different Drivers

Individualized driving assistance system approach is not explored extensively. We are developing an algorithm that could aid to driver's assistance on a curved country road. Steering angle predictions were made using neural network and look-up table approaches. The predictions were made using four parameters based on analysis of the image sequence obtained from the camera installed in a car. Two drivers' steering angle prediction results were compared and evaluated by calculating the mean squared error between the true and predicted signals