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This paper develops an integrated system for forest fire fighting facility location. We propose a mathematical model to deploy available fire fighting resources in proper positions so that any forest fire can be attacked within a specified response time. The proposed model considers budget constraints. This location problem is a variant of the well-known Maximal Covering Location Problem (MCLP) which is known to be NP-hard. We propose a hybrid algorithm that combines GRASP and Tabu Search procedures. According to preliminary computational experiments the hybrid algorithm provides good quality solutions with a reasonable amount of computer effort. This solution can give support to the decision maker for determining the location of fire-fighting resources.

A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.

The aim of this work is the design and implementation of a DWT architecture FPGA-based platform for up to three dimensional signal processing. First, filter banks are designed using a distributed arithmetic technique. Then, we design controllers, interfaces and protocols that handle, transmit and sequence all the data during the computing process. Data is sent via USB to the FPGA and the user interface is programmed in MATLAB. A graphical user interface manages the system operation and displays the results on a PC. Designed filters are compared with a fully parallel architecture in relation to the number of gates used, speed, and algorithm performance.

An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10–20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented.

In this paper we compare an analytical framework based on Real-Time Calculus with the existing analytical approaches commonly used for the performance evaluation of network interfaces such as probabilistic queuing models, parallel computation model, and protocol offload models (LAWS and EMO). In particular, we focus on the capabilities of these alternatives that can be employed for the performance evaluation of the NIC's buffer requirements in a network node.

Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead. However, each wavelet can yields into different results on compression ratio. Classic foveation filters also applies cutoff frequencies over the region of interest (ROI). A comparative analysis has then been performed showing the different error rate for different cutoff frequency windows that preserves the ROI coefficients intact against windows that apply cutoffs over the ROI. Simulation results show that with wavelet-based foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI.

The modeling and performance analysis of two novel approaches for motion prediction of objects in a limited sequence of images is presented in this paper. Particularly, these proposed approaches are based on linear or quadratic splines and on circular-queue vector correspondence. They use two basic motion detection techniques also proposed by authors that provide object tracing by its representation by envelope or by mass center grid. The performance and efficiency of the proposed motion prediction approaches are evaluated applying several simulated kinematics experiments either with constant velocity and accelerated motion, or with parabolic and circular motion.

This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner–Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection.

In this paper an approach to detect smoke columns from outdoor forest video sequences is proposed. The approach follows three basic steps. The first step is an image pre-processing block which resizes the image by applying a bicubic interpolation algorithm. The image is then transformed to its intensity values with a gray-scale transformation and finally the image is grouped by common areas with an image indexation. The second step consists of a smoke detection algorithm which performs a stationary wavelet transform (SWT) to remove high frequencies on horizontal, vertical, and diagonal details. The inverse SWT is then implemented and finally the image is compared to a non-smoke scene in order to determine the possible regions of interest (ROI). In order to reduce the number of false alarms, the final step of the proposed approach consists on a smoke verification algorithm, which determines whether the ROI is increasing its area or not. These results are combined to reach a final decision for detecting a smoke column on a sequence of static images from an outdoor video. Experimental results show that multi-resolution wavelet analysis is more accurate than the traditional low-pass filters on this application.

Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of real time algorithms suited to video image processing applications. The unique architecture of the FPGA has allowed the technology to be used in many applications encompassing all aspects of video image processing. Among those algorithms, linear filtering based on a 2D convolution, and non-linear 2D morphological filters, represent a basic set of image operations for a number of applications. In this work, an implementation of linear and morphological image filtering using a FPGA NexysII, Xilinx, Spartan 3E, with educational purposes, is presented. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station. The FPGA-based system is accessed through a Matlab graphical user interface, which handles the communication setup. A comparison between results obtained from MATLAB simulations and the described FPGA-based implementation is presented.

In this paper we present an approach for biometric key generation using wavelets and electrocardiogram (ECG) signals. The stages that comprise the approach are one time enrollment and key derivation. This work is based on the uniqueness and quasi-stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different information is generated and then stored in a token for authentication purposes. Also, this approach implements an error-correction layer using the Hadamard code. The performance of the proposed approach is assessed using ECG signals from MIT-BIH database. Simulation results show a false acceptance rate (FAR) of 4.60 and a false rejection rate (FRR) of 7.90. The random biometric key released by the proposed approach can be used in several encryption algorithms.

In this paper, we present a wavelet-based 128-bit key generator using electrocardiogram (ECG) signals. The key generator comprises two independent stages, namely, enrollment and verification-generation. In the latter, an algorithm for determining the keys is also proposed. This work is based on the uniqueness and quasi-stationary behavior of ECG signals with respect to an individual. This lets to consider the ECG signal as a biometric characteristic and guarantees that different keys are released to different individuals. The performance of the proposed key generator is assessed using ECG signals from MIT-BIH database. Simulation results show a false accept rate (FAR) of 22.3% and a false reject rate (FRR) of 18.1%. The 128-bit key released by the generator proposed in this work can be used in several encryption algorithms.

In this paper, we present the design and implementation of a PLC (Power-Line Communication) Modem based on Orthogonal Frequency Division Multiplexing (OFDM). The PLC device implements OFDM in both transmitter and receiver using VHDL programming. The OFDM processor is synthesized in a Field Programmable Gate Array (FPGA) that acts as a Core Processor in the PLC Modem. Furthermore, the prototype includes the design and implementation of the stages needed to inject the signal into the power-line and to recover the same signal in other point of the PLC network. An analysis of several noise sources show that the PLC Modem performance produced a bit error percentage of 4.46% in the worst case, while 0.89% in the best case for the transmission/reception tested.

In this paper we present a bimodal biometric system for cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric samples, and a token. The stages that comprise the architecture are \textit{one time enrollment} and \textit{key derivation}. The system architecture is able to verify the identity of individuals off-line avoiding the use of a centralized database for storing the biometric information. The system also implements an error-correction layer using the Hadamard code. The performance of the system is assessed using ECG signals from the MIT-BIH database and speech signals from a speech database created for testing purposes. Simulation results report a false acceptance rate (FAR) of 1.27% and a false rejection rate (FRR) of 10.62% for the system. The random cryptographic key released by the system may be used in several encryption algorithms.

This paper presents an analysis of some design concepts and development of advanced techniques for incrementing speed and accuracy of CLR meters of low cost. Some measuring systems have been designed in order to estimate performance of novel approaches, such as a structural method of error correction and iterating correction method of errors in immitance measurement. The expressions of functional conversion (transfer functions) and block diagrams of designed equipment are presented and discussed for these algorithmic methods. Using the best iterating correction method the E7-13a LCR meter has been designed and tested for estimation of its accuracy and speed during measurement of capacitance, inductance, resistance, and conductance. The obtained results are evaluated to define efficiency of proposed methods, their utility and performance. These results encourage more researches in this open problem of immitance measurement.

Wavelet-networks are inspired by both the feed forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other networks schemes, particularly in approximation and prediction problems. In this paper a novel approach, based on a wavelet neural network structure with correlation-based initialisation and training algorithm, is introduced in order to face with the problem of pollutant estimation in a metropolitan area. In particular a short-term prediction of the maximum ozone pollutant value has been performed. Ozone gas is considered one of the most common and damaging air contaminants. The results reported in this work show clearly that wavelet networks have good prediction properties and seriously represent a novel alternative to the traditional ozone forecasting methods

Machine learning algorithms are commonly used in real-world applications for solving complex problems where it is difficult to get a mathematical model. The goal of machine learning algorithms is to learn an objective function from a set of training examples where each example is defined by a feature set. Regularly, real world applications have many examples with many features; however, the objective function depends on few of them. The presence of noisy examples or irrelevant features in a dataset degrades the performance of machine learning algorithms; such is the case of k-nearest neighbor machine learning algorithm (k-NN). Thus choosing good instance and feature subsets may improve the algorithm's performance. Evolutionary algorithms proved to be good techniques for finding solutions in a large solution space and to be stable in the presence of noise. In this work, we address the problem of instance selection and feature weighting for instance-based methods by means of a Genetic Algorithm (GA) and Evolution strategies (ES). We show that combining GA and ES with a k-NN algorithm can improve the predictive accuracy of the resulting classifier.

Wavelet-networks are inspired by both the feed-forward neural networks and the theory underlying wavelet decompositions. This special kind of networks has proved its advantages over other network schemes, particularly in approximation and prediction problems. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets, but unfortunately, most of powerful and used wavelets do not satisfy this property. This paper presents a new initialisation procedure and a new training algorithm for wavelet neural-networks that improve its performance allowing the use of different kind of wavelets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet-network.

This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks.. This kind of network uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feed-forward networks trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than its similar backpropagation networks.

This paper presents a hierarchical approach for modelling an MPLS (Multi-Protocol Label Switching) network using VHDL (Very high-speed integrated circuits Hardware Description Language). The MPLS technology is used because it offers a better performance and flexibility than IP routing. Six MPLS switches are modelled and simulated as follows. A label is assigned to the IP packet header by the first switch, which indicates the route that the frame must follow in the MPLS network. This label is then used by the MPLS middle switches for its management inside the network. Four middle switches change the label depending on the destination of the packet. The final switch removes the label of the IP packet header and the frame continues its path outside the MPLS network. Simulation results show that this approach allows the simulation of large networks consisting of even 256 MPLS switches.

A software that calculates satellite indoor loss path for three different systems of mobile satellite communications is presented. The systems are GLOBALSTAR , IRIDIUM and a third option for a GENERAL system in which the user have the possibility of change some parameters of the calculations. The user can choose from 5 different indoor propagation models. The software was developed using Visual Basic.

This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet networks are a class of neural network that take advantage of good localization and approximation properties of multiresolution analysis. These networks use wavelets as activation functions in the hidden layer and a hierarchical method is used for learning. Comparisons are made between a wavelet network, tested with two different wavelets, and the typical feedforward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than back-propagation networks.

In this paper, we present a comparative simulation study of three denoising algorithms using wavelets. The denoising algorithms (i.e., universal threshold, minimax threshold and rigorous SURE threshold) have been used to remove white Gaussian noise from synthetic and real signals. The analysis is done by applying soft and hard thresholds to signals with different sample sizes. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the rigorous SURE algorithm with a hard threshold has a better performance than other algorithms in synthetic signals. On the other hand, the universal threshold algorithm with a soft threshold shows the best performance in real signals when using the Daubechies wavelet with 5 vanishing moments.

In this paper, we present a generalized optimal stable all-pass digital filter design algorithm that supports (1) arbitrary phase response specifications, (2) a corrective system designed to make the phase delay of a magnitude-filter substantially constant over a desired frequency range, (3) estimation of the delay 3 of the best performed equalizer, (4) stability, and (5) wavelet-based non-uniform sampling of the phase response of the magnitude-filter whose delay is to be equalized.

In order to correctly match two sets of images from different modalities, our method applies a non-rigid transformation to one set to get as close as possible to the other. This requires the estimation of the optimal similarity transformation between the two set of images. Estimation of the non-rigid deformation between the two sets of images is referred to as the deformation estimation between the pair of three-dimensional object extracted from both sets. We present a new methodology for image registration by first extracting objects from the set of images by reconstructing the object surfaces where this extraction supports semi-automatic segmentation of sets of 3-D medical images and then finding the best similarity transformation based on matching of two sets of surface points, but also incorporates the matching of two sets of feature points, and we have shown that deformation estimates based on simultaneous matching of surfaces and features are more accurate than those based on surface matching alone. This is especially true when the deformation involves physically realistic cases, such as those in human organs. Our technique uses free-form deformation models and applies the wavelet transform to extract feature points in the 3-D space. Feature point extraction also provides a means to compute the error in our estimates. We have applied our method to register sequences of MRI images to histology images of the carotid artery.