Raúl Vicen

• University of Alcalá

This paper presents a methodology to support the decision-making process during the planning of ship operations. The methodology is designed with the aim of identifying and providing the operator with the best Estimated Time of Departure (ETD)–Estimated Time of Arrival (ETA) window of opportunity to execute the journey/operation between two predefi...

This paper presents a novel weather-routing system based on a multi-criteria setup. The set of 3 conflicting criteria is: travel time, ship navigation added resistance (caused by wind and waves), and navigation risk/safety. To this aim, the International Maritime Organization (IMO) safety guidelines are exploited for the design of navigation risk c...

This paper presents the analysis of the weather routing scenario in a multi-criteria setup. The set of 3 conflicting criteria is: added navigation resistance (caused by wind and waves), navigation risk and travel time. To this aim the International Maritime Organization (IMO) safety guidelines are exploited for the design of navigation risk criteri...

An understanding of environmental variability (stability/instability) is important to support operational planning of expeditionary warfare and littoral operations, as well as for preparing the Recognized Environmental Picture (REP). Specifically, the identification of environmentally stable/unstable areas helps the planning of maritime operations,...

This paper presents the recent developments of an Optimal Path Planning — Decision Support System (OPP-DSS). The designed framework is based on multi-objective optimization algorithms providing a set of Pareto efficient solutions representing a trade-off among mission objectives. Meteorological and Oceanographic (METOC) and Automatic Identification...

This paper presents a decision support system (DSS) to help Command and Control (C2) operators. Hereby, the METOC-driven vessel interdiction system (MVIS) is presented. It is aimed to help the decision-making process in case of interdiction operations so that the success rate increases. In particular, MVIS yields the best location along a predicted...

In this paper, curve-fitting and intensity-level-selection (ILS) based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the IL...

Conference code: 116730, Export Date: 23 August 2016, References: An, W., Ayala, D.F., Sidoti, D., Mishra, M., Han, X., Pattipati, K., Hansen, J., Dynamic asset allocation approaches for counter-piracy operations (2012) Proc. of Int.Conference on Information Fusion;

This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatt...

One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-...

A novel method for detecting ships in marine envi- ronments is presented in this paper. For this purpose, the infor- mation contained in the marine images obtained by a measuring and monitoring marine system is used. The ship detection is done by multilayer perceptrons (MLPs). In the first approach, the MLP processes the information extracted from...

This study presents a novel way for detecting ships in sea clutter. For this purpose, the information contained in the Radar images obtained by an incoherent X-band maritime Radar is used. The ship detection is solved by feedforward artificial neural networks, such as the multilayer perceptrons (MLPs). In a first approach, the MLP processes the inf...

The mean-shift (MS) algorithm is applied for reduc- ing speckle noise and segmenting synthetic aperture radar (SAR) images. Two coastal images acquired by Envisat's advanced SAR (ASAR) (European Space Agency (ESA)) are used. Studies of the MS parameters are carried out according to the desired product: a speckle filtered image where textures and ed...

The detection of Swerling 0 targets in movement in sea-ice Weibull-distributed clutter by neural networks (NNs) is presented in this paper. Synthetic data generated for typical sea-ice Weibull parameters reported in the literature are used. Due to the capability of NNs for learning the statistical properties of the clutter and target signals during...

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low obj...

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achiev...

This study presents the problem of detecting known targets (Swerling 0 model) in simulated ground clutter (land cultivated). As a modelled low-resolution coherent radar system is used, the clutter is modelled by a complex-valued time-correlated Weibull distribution. The research exposed in this study looks for two objectives. First, finding a detec...

Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper w...

The feasible implementation of signal processing techniques on hearing aids is constrained by the finite precision required to represent numbers and by the limited number of instructions per second to implement the algorithms on the digital signal processor the hearing aid is based on. This adversely limits the design of a neural network-based clas...

This paper addresses the issue of automatic wood defect classification. A tree-structure support vector machine (SVM) is proposed to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by partitioning the knot images into three distinct areas, followed by utilizing...

Digital hearing aids usually suffer from acoustic feedback. This feedback corrupts the speech signal, causes instability, and damages the speech intelligibility. To solve these problems, an acoustic feedback reduction (AFR) subsystem using adaptive algorithms such as the least mean square (LMS) algorithm is needed. Although this algorithm has a red...

This paper presents a clutter reduction system when medium-high correlated Weibull-distributed clutter governs the environment of a coherent radar system. This proposal is based on the capabilities of learning of some artificial intelligence techniques, such as the neural networks. This capability of learning of the neural networks is used to learn...

In radio measurement systems, the backscatter from what is not a target, i.e. the clutter, is usually not desired. So, these systems try to incorporate clutter reduction techniques as efficient as possible. In this way, different signal processing techniques can be used. The case of study presented in this paper shows how to reduce the level of sea...

Speckle noise is an undesired effect in SAR (Synthetic Aperture Radar) images which degrades their quality. In this paper a filtering technique based on the algorithm ldquoMean Shiftrdquo is applied to reduce the speckle noise in SAR images, preserving their quality by maintaining textures, sharp edges and shapes. A image of the North-East coast of...

The presence of sea clutter in marine radar signals is sometimes not desired. So, efficient radar signal processing techniques are needed to reduce it. In this way, nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system. The developed experiments show promising results characterized b...

The design of digital hearing aids able to carry out advanced functionalities (such as, for instance, classify the acoustic environment and automatically select the best amplification program for the user's comfort) exhibits a great difficulty. Since hearing aids have to work at very low clock frequency in order to minimize power consumption and ma...

Obtaining analytical expressions for coherent detection of known signals in Weibul-distributed clutter and white Gaussian noise has been a hard task since the last decades. In fact, nowadays, these expressions have not been found yet. This problem lead us to use suboptimum solutions to solve this problem. Optimum approximations can be done by using...

Acoustic feedback phenomenon can deteriorate the performance of digital hearing aids performance working at high gains, causing instability and speech degradation. In order to restore a stable situation, it is needed a feedback reduction system based on adaptive algorithms. Depending on the adaptive flter impulse response, a feedback reduction algo...

The coherent detection of targets in presence of clutter and noise is considered in this study. Several clutter models are proposed in the literature, although the commonly used for sea and land clutter returns is the Weibull one. Our case of study involves that the target is known a priori, the clutter is Weibull-distributed and a white Gaussian n...

Multilayer perceptron (MLP) based detectors are proposed for detecting Gaussian signals with unknown correlation coefficient (rhos) in additive white Gaussian noise. After proving the low robustness of the likelihood ratio (LR) based detector with respect to rhos, the average likelihood ratio(ALR) based detector assuming a uniform distribution of r...

In this paper, the alignment of noisy high-resolution radar signals using the maximum position method is studied. The relationship between the shift estimation and the signal-to-noise ratio is considered. As a result, two analytical expressions are obtained that approximate the root-mean-square error of the difference in the shift estimation with a...

This paper addresses the issue of automatic wood defect classification. We propose a tree-structure support vector machine (SVM) to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by first partitioning the knot images into 3 distinct areas, followed by applying...

This paper deals with the application of Multilayer Perceptrons to radar detection. The dependence of the neural detector performance on the network size and on the signal-to-noise ratio selected for training is considered. Multilayer Perceptrons with different numbers of neurons in the hidden layer have been trained using different values of the s...

This paper studies the alignment of noisy high resolution radar signals using the Maximum Position method in auto- matic target recognition. The relationship between the shift estimation, the performance of the classifier and the signal to noise ratio is analyzed. Several experiments are carried out in order to study the influence of the alignment...

Constructive learning algorithms offer an attractive approach for the incremental construction of nearminimal neural-network architectures for pattern classification. This paper explores the feasibility of using a constructive algorithm for multilayer perceptrons (MLPs) applied to the problem of speech/non-speech classification in hearing aids. Whe...

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any bra...

The speech signal corrupted by the acoustic feedback in digital hearing aids can be restored by a feedback reduction system using adaptive algorithms such as the least-mean square (LMS) algorithm. The main disadvantage of the LMS algorithm is the instability. In order to avoid this situation, it is used another feedback reduction systems based on t...

The detection of gaussian signals with unknown correlation coefficient, rho_s, is considered. A strategy for designing mixture of experts in composite hypothesis test is proposed. It is based on designing a single multi-layer perceptron (MLP) trained with rho_s varying uniformly in [0,1] to approximate the average likelihood rat...

This paper deals with the description of a hearing aid simulation tool. This tool simulates the real behavior of digital DSP-based hearing aids with the aim of getting a very promising performance, which can be used for further design and research, and for a better fitting of the hearing impaired patient. The main parameters to program are the nois...

In this paper, a Multilayer Perceptron (MLP) is proposed as a radar detector of known targets in Weibull-distributed clutter. The MLP is trained in a supervised way using the Levenberg-Marquardt backpropagation algorithm to minimize the Mean Square Error, which is able to approximate the Neyman-Pearson detector. Due to the impossibility to find an...

MultiLayer Perceptrons (MLPs) trained in a supervised way to minimize the Mean Square Error are able to approximate the Neyman-Pearson detector. The known target detection in a Weibull-distributed clutter and white Gaussian noise is considered. Because the difficulty to obtain analytical expressions for the optimum detector under this environment,...

The Neyman-Pearson detector can be approximated by Mul-tiLayer Perceptrons (MLPs) trained in a supervised way to mini-mize the Mean Square Error. The detection of a known target in a Weibull-distributed clutter and white Gaussian noise is considered. Because of the difficulty to obtain analytical expressions for the op-timum detector under this env...

Radar detection of targets in clutter and noise is an usual problem presented in radar systems. Several schemes based on statistical signal processing are proposed as detectors. In some cases, the Neural Networks (NNs) are applied to this problem. In this article, a radar detector based in a class of NN, the MultiLayer Perceptron (MLP), is proposed...

Neural networks (NNs) are proposed for approximating the Average Likelihood Ratio (ALR). The detection of gaussian targets with gaussian autocorrelation function and unknown one-lag correlation coefficient, ρ s , in Additive White Gaussian Noise (AWGN) is considered. After proving the low robustness of the likelihood ratio (LR) detector with respec...

The clutter is always present in the radar signal, so it is important to generate models that give us the possibility to minimize its effect in the detection of targets in a radar space. In that way, it is focused this paper, where, we try to propose a model that generates discrete time coherent sequences with a Weibull distribution for its modulus...

In this paper the alignment of noisy signals using dif- ferent methods is studied. The methods studied in this pa- per are the Maximum Position method, the Cross-correlation method and the Zero Phase method. In order to evaluate the performance of the alignment methods, a database of high range resolution radar profiles containing patterns belongin...

The 20th century Spanish sound history can now be consulted online. This is a pioneer project in the broadcasting industry around the world. The Radio Nacional de España (RNE) sound archive has been massively digitized and several applications to access this information online have been developed. This archive is considered the most important audio...

This paper deals with the application of neural networks to approximate the Neyman-Pearson detector. The detection of Swerling I targets in white gaussian noise is considered. For this case, the optimum detector and the optimum decision boundaries are calculated. Results prove that the optimum detector is independent on TSNR, so, under good trainin...

The work presented in this paper suggests a Traffic Sign Recognition (TSR) system whose core is based on a Multilayer Perceptron (MLP). A pre-processing of the traffic sign image (blob) is applied before the core. This operation is made to reduce the redundancy contained in the blob, to reduce the computational cost of the core and to improve its p...

This paper deals with the study of the performance of different methods to align noisy one-dimensional signals shifted in time. The methods studied in this paper are the Maximum Position method, the Cross-correlation method and the Zero Phase method. Results show the best performance of the Zero Phase method for middle and high values of SNR. A sta...

This paper deals with the application of Neural Networks (NNs) to the problem of Traffic Sign Recognition (TSR). The NN chosen to implement the TSR system is the Multilayer Perceptron (MLP). Two ways to reduce the computational cost in order to facilitate the real time implementation are proposed. The first one reduces the number of MLP inputs by p...

Structure noise from inhomogeneous micro-structures makes the detection of flaws present in highly scattering materials difficult. Several techniques have been applied to improve the signal-to-noise ratio (SNR) in order to make flaw detection easier. Linear filtering does not provide good results because both structure noise and flaw signal concent...

Ultrasonic flaw detection has been studied many times in the literature. Schemes based on thresholding after a previous matched filter use to be the best solution, but results obtained with this method are only satisfactory when scattering and attenuation are not considered. In this paper, we propose an alternative solution to thresholding detectio...

In this paper the application of neural networks to Automatic Target Recognition (ATR) using a High Range Resolution radar is studied. Both Multi-layer Perceptrons (MLP) and Radial Basis Function Networks (RBFN) have been used. RBFNs can achieve very good results with a considerably small size of the training set, but they require a high number of...

This work introduces a new segmentation technique that is based on the Support Vector Machines (SVMs). This segmentation procedure classifies the different pixels of an image between those that belongs to a region and the others that do not belongs to it. The technique is applied to color medical images in order to separate the different color regi...

Among the tasks a computer vision system has to do, one of them could be the separation between those parts that belong to the background and don't. When the video sensor is fixed and there is no movement between consecutive frames, the easiest way to perform this task is to compare the current frame with the previous frame or frames. In these case...

In this paper three solutions that contribute to improve the use of the instrumentation of a RF laboratory are escribed. In the first place, we have connected a vector network analyzer to a PC using their TCP/IP ports by means of an application developed in Visual Basic. In second place we have connected a spectrum analyzer to another PC through th...

The aim of this work is to design a Traffic Sign Classification system that combines different image preprocessing techniques with Neural Networks. It must be robust against image problems like rotation, deterioration, vandalism, and so on. The preprocessings applied to the gray scale transformed image are: the median filter (MF), the histogram equ...

http://www2.uah.es/teose/ Abstract: - A neural network based coherent detector is proposed for detecting gaussian targets in gaussian clutter. Target and clutter ACF are supposed gaussian with different powers and one lag correlation coefficients. While clutter mean Doppler frequency is set to 1, the influence of target mean Doppler frequency is co...

This document presents an application where neural networks with variable size and parameters are applied to radar targets detection. These parameters are: number of layers, neurons in every layer, learning rate and momentum. These parameters allow the choice of the network that match better with your problem. We have successfully used neural netwo...

The computational cost associated to the k-nearest neigh-bor classifier depends on the amount of available patterns, which makes this method impractical in many real-time ap-plications. This fact makes interesting the study of fast al-gorithms for finding the k-nearest patterns, like, for exam-ple, the kLAESA algorithm. In this paper we propose a n...