Alberto Alvarez

A Neural Network with Evolutionary Neurons

A neural network, combining evolution and learning is introduced. The novel feature of the proposed network is the evolutionary character of its neurons. The argument of the transfer function performed by the neurons in the network is neither a linear nor polynomial function of the inputs to the neuron, but an unknown general function P(·). The adequate functional form P(·) for each neuron, is achieved during the learning period by means of genetic programming. The proposed neural network is applied to the problem domain of time series prediction of the Mackey-Glass delay differential equation. Simulation results indicate that the new neural network is effective.

Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called Aη, is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators.

Volumetric Reconstruction of Oceanographic Fields Estimated From Remote Sensing and In Situ Observations From Autonomous Underwater Vehicles of Opportunity

A main challenge of military oceanography (MILOC) is to assess the oceanographic conditions of denied/high-risk marine regions. Monitoring technologies are limited to those that can provide access to these regions. Remote sensing and autonomous underwater vehicles (AUVs) can support MILOC requirements. Unfortunately, the environmental information gathered by these technologies is not complete: remote sensing provides information about some surface conditions and water-column integrated variables, whereas operational priorities often constrain AUVs use during real crisis situations to missions with higher priority than environmental assessment. Under this scenario, data fusion techniques to maximize the information of the collected data are essential. This paper attempts to reconstruct thermal fields fusing data gathered by remote sensing platforms and AUVs performing missions not specifically designed for environmental data collection. The technique estimates the state that maximizes the posterior probability subjected to some smoothing constraints. A variational methodology allows remote sensing information to serve as boundary constraints. The approach uses 3-D finite elements to solve the maximization problem. The procedure investigated has been tested with different smoothing constraints in a simulated environment and in a real field experiment conducted by the Muscle AUV in the Gulf of Riga (Baltic Sea) on April 19, 2008. Results highlight the relevance of incorporating the surface information provided by remote sensors into the estimation.

Results on SSH neural network forecasting in the Mediterranean Sea

Nowadays, satellites are the only monitoring systems that cover almost continuously all possible ocean areas and are now an essential part of operational oceanography. A novel approach based on artificial intelligence (AI) concepts, exploits pasts time series of satellite images to infer near future ocean conditions at the surface by neural networks and genetic algorithms. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a 2D surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period 1992 to 1997. The learning procedure is applied to each mode individually. The final forecast is then reconstructed form the EOFs and the forecasted amplitudes and compared to the real observed field for validation of the method.

A methodology for calibration of hyperspectral and multispectral satellite data in coastal areas

The objective of this work is to determine the location(s) in any given oceanic area during different temporal periods where in situ sampling for Calibration/Validation (Cal/Val) provides the best capability to retrieve accurate radiometric and derived product data (lowest uncertainties). We present a method to merge satellite imagery with in situ measurements, to determine the best in situ sampling strategy suitable for satellite Cal/Val and to evaluate the present in situ locations through uncertainty indices. This analysis is required to determine if the present in situ sites are adequate for assessing uncertainty and where additional sites and ship programs should be located to improve Calibration/Validation (Cal/Val) procedures. Our methodology uses satellite acquisitions to build a covariance matrix encoding the spatial-temporal variability of the area of interest. The covariance matrix is used in a Bayesian framework to merge satellite and in situ data providing a product with lower uncertainty. The best in situ location for Cal/Val is then identified by using a design principle (A-optimum design) that looks for minimizing the estimated variance of the merged products. Satellite products investigated in this study include Ocean Color water leaving radiance, chlorophyll, and inherent and apparent optical properties (retrieved from MODIS and VIIRS). In situ measurements are obtained from systems operated on fixed deployment platforms (e.g., sites of the Ocean Color component of the AErosol RObotic NETwork- AERONET-OC), moorings (e.g, Marine Optical Buoy-MOBY), ships or autonomous vehicles (such as Autonomous Underwater Vehicles and/or Gliders).

Under the Sea: Rapid Characterization of Restricted Marine Environments

In crisis situations, military operating units require a rapid evaluation of the local meteorological and oceanographic (METOC) conditions affecting their missions. An important role of military oceanography (MILOC) is thus to provide a timely METOC characterization of denied littoral areas [1]. Environmental sampling procedures in MILOC must be easily relocatable, discreet, and secure. Until recently, marine sampling technologies meeting these requirements were scarce. Remote sensing is the technology mostly used by navies to assess environmental conditions in restricted areas [2]. Very-near-shore bathymetry, sea state, surface currents, ocean color, and sea surface temperature (SST) are among the pieces of environmental information that can be obtained by means of remote sensors. Although valuable, this information is not sufficient to fully assess the three-dimensional (3-D) variability of the environment. Numerical approaches that simulate the ocean dynamics may provide additional information on the environmental conditions. In the coastal regions, numerical ocean models of different spatiotemporal resolutions are generally nested to downscale METOC information to the region of interest [3]. Numerical procedures used to feed back dynamical information between the models with different resolutions inevitably introduce errors in this nesting process. In addition, at present, the uncertainties in physical parameterizations, forcing, and model initialization limit the accuracy of model forecasts.

SOFT project: a new forecasting system based on satellite data

The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build intelligent systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.

0.25-μm technology arithmetic codec for mobile multimedia communicators

Low power dissipation is a must when dealing with mobile devices due to the influence related to its weight and hence, its portability. In this paper, the implementation of a 0.25 mm technology arithmetic codec with a good power/area/performance trade-off is presented. One of the key aspects introduced in order to obtain good performance is the fact of using low precision arithmetic rather than full precision, allowing the elimination of multiplications and divisions needed in order to process symbols and coefficients. These operations are replaced by shift/add operations, minimizing the complexity of the algorithm and improving the encoding and decoding process. The chip has been described in a high level language, ensuring its portability to other technologies. The implementation gives as result a 25 mm2 chip, pads included, with a total power dissipation of 300 mW and a frequency of operation of 10 MHz.