Multi‐agent system for knowledge‐based event recognition and composition

This work presents a multi‐agent system for knowledge‐based high‐level event composition, which interprets activities, behaviour and situations semantically in a scenario with multi‐sensory monitoring. A perception agent (plurisensory agent and visual agent)‐based structure is presented. The agents process the sensor information and identify (agent decision system) significant changes in the monitored signals, which they send as simple events to the composition agent that searches for and identifies pre‐defined patterns as higher‐level semantic composed events. The structure has a methodology and a set of tools that facilitate its development and application to different fields without having to start from scratch. This creates an environment to develop knowledge‐based systems generally for event composition. The application task of our work is surveillance, and event composition/inference examples are shown which characterize an alarming situation in the scene and resolve identification and tracking problems of people in the scenario being monitored.     

Applying intelligent agents and semantic web services in eGovernment environments

The increasing volume of eGovernment‐related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof‐of‐concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.     

Frequency domain regularization of d-dimensional structure tensor-based directional fields

The present work is intended to address two of the major difficulties that can be found when tackling the estimation of the local orientation of the data in a scene, a task which is usually accomplished by means of the computation of the structure tensor-based directional field. On one hand, the orientation information only exists in the non-homogeneous regions of the dataset, while it is zero in the areas where the gradient (i.e. the first-order intensity variation) remains constant. Due to this lack of information, there are many cases in which the overall shape of the represented objects cannot be precisely inferred from the directional field. On the other hand, the orientation estimation is highly dependent on the particular choice of the averaging window used for its computation (since a collection of neighboring gradient vectors is needed to obtain a dominant orientation), typically resulting in vector fields which vary from very irregular (thus yielding a noisy estimation) to very uniform (but at the expense of a loss of angular resolution). The proposed solution to both drawbacks is the regularization of the directional field; this process extends smoothly the previously computed vectors to the whole dataset while preserving the angular information of relevant structures. With this purpose, the paper introduces a suitable mathematical framework and deals with the d-dimensional variational formulation which is derived from it. The proposed formulation is finally translated into the frequency domain in order to obtain an increase of insight on the regularization problem, which can be understood as a low-pass filtering of the directional field. The frequency domain point of view also allows for an efficient implementation of the resulting iterative algorithm. Simulation experiments involving datasets of different dimensionality prove the validity of the theoretical approach.     

Mass-controlled capillary viscometer for a Newtonian liquid: viscosity of water at different temperatures

The operation principle of the mass-controlled capillary viscometer is presented for a Newtonian liquid. The derived equation for the temporal changes of the mass in a liquid column draining under gravity through a discharge capillary tube accounts self-consistently for the inertial convective term associated with the acceleration effect. The viscosity of water measured at different temperatures using the new approach is in good agreement with literature data.     

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.     

A wavelet-VQ system for real-time video compression

This paper presents a scheme and its Field Programmable Gate Array (FPGA) implementation for a system based on combining the bi-dimensional discrete wavelet transformation (2D-DWT) and vector quantization (VQ) for image compression. The 2D-DWT works in a non-separable fashion using a parallel filter structure with distributed control to compute two resolution levels. The wavelet coefficients of the higher frequency sub-bands are vector quantized using multi-resolution codebook and those of the lower frequency sub-band at level two are scalar quantized and entropy encoded. VQ is carried out by self organizing feature map (SOFM) neural nets working at the recall phase. Codebooks are quickly generated off-line using the same nets functioning at the training phase. The complete system, including the 2D-DWT, the multi-resolution codebook VQ, and the statistical encoder, was implemented on a Xilinx Virtex 4 FPGA and is capable of performing real-time compression for digital video when dealing with grayscale 512 × 512 pixels images. It offers high compression quality (PSNR values around 35 dB) and acceptable compression rate values (0.62 bpp).

Catalytic oxidation with Al-Ce-Fe-PILC as a post-treatment system for coffee wet processing wastewater

The effluent from the anaerobic biological treatment of coffee wet processing wastewater (CWPW) contains a non-biodegradable compound that must be treated before it is discharged into a water source. In this paper, the wet hydrogen peroxide catalytic oxidation (WHPCO) process using Al-Ce-Fe-PILC catalysts was researched as a post-treatment system for CWPW and tested in a semi-batch reactor at atmospheric pressure and 25 °C. The Al-Ce-Fe-PILC achieved a high conversion rate of total phenolic compounds (70%) and mineralization to CO(2) (50%) after 5 h reaction time. The chemical oxygen demand (COD) of coffee processing wastewater after wet hydrogen peroxide catalytic oxidation was reduced in 66%. The combination of the two treatment methods, biological (developed by Cenicafé) and catalytic oxidation with Al-Ce-Fe-PILC, achieved a 97% reduction of COD in CWPW. Therefore, the WHPCO using Al-Ce-Fe-PILC catalysts is a viable alternative for the post-treatment of coffee processing wastewater.     

The Social Sustainable Aquifer Yield: An Indicator for the Analysis and Assessment of the Integrated Aquifers Management

Integrated Aquifers Management (IAM) demands innovative tools and methods that are able to consider as much perspectives as possible. This research is aimed to design, apply and provide an indicator named Social Sustainable Aquifer Yield (SSAY), expressed in units of time that includes pure hydrological variables as well as social ones. The indicator is defined as the relation between the Residence Time, which is the relation between aquifer Storage (S) and Recharge (R) (S/R), and the relation between the aquifer Pumping (P) and the new variable named Aquifer Social Yield (ASY). The whole indicator is defined by this formula: (S/R)/(P/ASY). The assessment of the residence time is essential in aquifers with at least one of the following features: i) high hydraulic diffusivity, and ii) small volume of reserves. Finally, the variable ASY is defined as the average perception from the stakeholders about the maximum acceptable aquifer exploitation. This indicator has been successfully applied in the aquifers located in southern Jaen province (South Spain) belonging to the Water System SE4-Jaén Water Supply. The results probe the high utility of the indicator, especially in regards to the public participation processes.     

A consistent stable numerical scheme for a nonlinear option pricing model in illiquid markets

Markets liquidity is an issue of very high concern in financial risk management. In a perfect liquid market the option pricing model becomes the well-known linear Black–Scholes problem. Nonlinear models appear when transaction costs or illiquid market effects are taken into account. This paper deals with the numerical analysis of nonlinear Black–Scholes equations modeling illiquid markets when price impact in the underlying asset market affects the replication of a European contingent claim. Numerical analysis of a nonlinear model is necessary because disregarded computations may waste a good mathematical model. In this paper we propose a finite-difference numerical scheme that guarantees positivity of the solution as well as stability and consistency.     

Asymptotic synchronization of the Colpitts oscillator

In this paper we deal with the observer-based asymptotic synchronization problem for a class of chaotic oscillators. Some results based on a differential algebraic approach are used in order to determine the algebraic observability of unknown variables. The strategy consists of proposing a slave system (observer) which tends to follow asymptotically the master system. The methodology is tested in the real-time asymptotic synchronization of the Colpitts oscillator by means of a proportional reduced order observer (PROO) of free-model type.