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In this work, we present a novel method for accurate affine transformation estimation of image regions. We illustrate the benefits of using such a method in a point matching mechanism that enables locating large amount of point matches with high geometric precision and low rate of false matches. Recent publications have shown that considering the affine transformation model of local regions, is extremely...
In order to differentiate the affective state of a computer user as it changes from relaxation to stress, features derived from pupil dilation and periorbital temperature are processed with machine learning techniques. When absolute signal values are used together with entropy based features, the accuracy of affective classification is observed to increase. When decision tree (C4.5) is tested for...
Usage of Computer Aided Diagnostic (CAD) systems is increasing rapidly. Blood vessel segmentation on retinal fundus images could be used as a CAD system for diagnosis of various retinal diseases. In this paper, blood vessels are segmented on retinal fundus images. Firstly, Gabor filter and morphological top-hat transform are applied after preprocessing step in order to enhance blood vessels. Afterward,...
Brain computer interface technology comes at the beginning of the popular study subject for scientist that of excite all of humanity. By means of that technology it is allowed to control electronic devices for paralyzed or partial paralysis humans to make their lives easier. In literature there have been many cursor movement imagery studies based on electroencephalogram (EEG) signals. However, the...
In this study, a graph-based method based on Gaussian Mixture Modeling (GMM) to classify agricultural products is proposed. The effects of different number of components and smoothing constants to the classification accuracies are investigated during the analyses. Tests are performed over two 4-channel Kompsat-2 satellite images which cover approximately 100 km2 of the Karacabey Plain of the city...
Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability...
In this work a high precision sub-meter accuracy indoor localization system was developed using low cost infrared (IR) transmitters and receivers. With the help of the optical lenses and orienting mechanisms designed for the IR beacons, at about 0,75 m wide grid zone definitions could be achieved. An FPGA is armed for the evaluation of the data from multiple IR receivers at the mobile user. 0,5 m...
The possibility of using smart phone accelerometer to detect earthquake is investigated in this research. Experiments are designed to learn the pattern of an earthquake signal recorded from smart phone's accelerometer. The signal is processed using N-gram modeling as feature extractor for machine learning. For the classifier, this study use Naïve Bayes, Multi-Layer Perceptron (MLP), and Random Forest...
Surveillance systems have become increasingly popular in the globalization process. Intelligent video surveillance system based on image recognition is widely used to effectively prevent many crimes and helps to provide public security. Due to the high complexity in techniques such as real time processing and image contents analysis/understanding, a well-developed product is not available until now...
This paper presents classification and recognition of monophonic isolated musical instrument sounds using higher order spectra such as Bispectrum and Trispectrum. Experimental results on a widely used dataset shows that higher order spectra based features improve the recognition accuracy, when combined with conventional features such as Mel Frequency Cepstral Coefficient (MFCC), Cepstral, Spectral...
The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which...
This article addresses the problem of signal reconstruction, spectral estimation and linear filtering directly from irregularly-spaced samples of a continuous signal (or autocorrelation function in the case of random signals) when signal spectrum is assumed to be bounded. The number 2L of samples is assumed to be large enough so that the variation of the spectrum on intervals of width π/L is small...
Approximate computing is a promising approach in the design of low-power digital systems. There are two main approaches in approximate computing: the first relies on lowering the supply voltage level on specific computational blocks, while the second relies on completely truncating specific computational blocks. The second option is more aggressive; however, it is deterministic and leads to better...
The sparse representation of signals with respect to an over-complete dictionary has been of recent interest in a broad range of applications. One of the most used methods for obtaining sparse codes, the Lasso problem, becomes computationally costly for large dictionaries and this hinders the use of this approach to large-scale decision tasks. Recently, dictionary screening has been used to address...
We present a novel data classifier that is based on the regularization of graph signals. Our approach is based on the theory of discrete signal processing on graphs where the graph represents similarities between data and we interpret labels for the dataset elements as a signal indexed by the nodes of the graph. We postulate that true labels form a low-frequency graph signal and the classifier finds...
Medical embedded systems hold the promise to improve health outcomes, decrease isolation, reduce health disparities, and substantially reduce costs. In spite of their revolutionary potentials, these systems face a number of challenges in design and architecture that form stumbling blocks in their path to success. On one hand, as the sensor units continue to become more miniaturized, the underlying...
We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally efficient...
In the paper, the hybrid model of particle swarm optimization and least square support vector machine is proposed to network signal processing and network intrusion detection, and PSO is utilized to select the parameters of support vector machine simultaneously. In the study, KDDCUP99 datasets are adopted to research the network intrusion detection performance of the hybrid model of particle swarm...
Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. These devices open wide range of opportunities of using their...
Due to the rapid and continuous increase of network intrusion, the need of protecting our systems becomes more and more compelling. In many situations, there exists a weak anomaly signal detection problem: due to the little number of anomalous system calls, the anomalous patterns of some intrusions may not be enough to distinguish themselves from normal activities so the existing anomaly detection...
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