Linear versus nonlinear neural modeling for 2-D pattern recognition

This paper compares the classification performance of linear-system- and neural-network-based models in handwritten-digit classification and face recognition. In inputs to a linear classifier, nonlinear inputs are generated based on linear inputs, using different forms of generating products. Using a genetic algorithm, linear and nonlinear inputs to the linear classifier are selected to improve classification performance. Results show that an appropriate set of linear and nonlinear inputs to the linear classifier were selected, improving significantly its classification performance in both problems. It is also shown that the linear classifier reached a classification performance similar to or better than those obtained by nonlinear neural-network classifiers with linear inputs.     

Performance degradation in feedback control due to constraints

In this note, we present a method to characterize the degradation in performance that arises in linear systems due to constraints imposed on the magnitude of the control signal to avoid saturation effects. We do this in the context of cheap control for tracking step signals.     

Linear estimation for discrete-time systems in the presence oftime-correlated disturbances and uncertain observations

This note considers optimum linear recursive estimation for linear discrete-time systems with uncertain observations in cases where state and measurement noises are correlated. The different kinds of estimation problems treated include one-stage prediction and filtering     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Legacy code and parallel computing: updating and parallelizing a numerical model

In this paper, we present several important details in the process of legacy code parallelization, mostly related to the problem of maintaining numerical output of a legacy code while obtaining a balanced workload for parallel processing. Since we maintained the non-uniform mesh imposed by the original finite element code, we have to develop a specially designed data distribution among processors so that data restrictions are met in the finite element method. In particular, we introduce a data distribution method that is initially used in shared memory parallel processing and obtain better performance than the previous parallel program version. Besides, this method can be extended to other parallel platforms such as distributed memory parallel computers. We present results including several problems related to performance profiling on different (development and production) parallel platforms. The use of new and old parallel computing architectures leads to different behavior of the same code, which in all cases provides better performance in multiprocessor hardware.

     

Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state‐independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.     

A Ground Control Station for a Multi-UAV Surveillance System

This paper presents the ground control station developed for a platform composed by multiple unmanned aerial vehicles for surveillance missions. The software application is fully based on open source libraries and it has been designed as a robust and decentralized system. It allows the operator to dynamically allocate different tasks to the UAVs and to show their operational information in a 3D realistic environment in real time. The ground control station has been designed to assist the operator in the challenging task of managing a system with multiple UAVs, trying to reduce his workload. The multi-UAV surveillance system has been demonstrated in field experiments using two quadrotors equipped with visual cameras.     

Prediction of cracks in continuously cast steel beam blank through fully coupled analysis of fluid flow, heat transfer, and deformation behavior of a solidifying shell

A mathematical model has been developed for the prediction of cracks in the continuously cast steel beam blank through the fully coupled analysis of fluid flow, heat transfer, and deformation behavior of a solidifying shell. Fluid flow and heat transfer in the strand mold were analyzed with a three-dimensional (3-D) finite-volume method (FVM). For the complex geometry of the beam blank, a body-fitted coordinate (BFC) system was employed. Thermo-elastic-plastic deformation behavior in the strand was analyzed using the finite-element method (FEM) based on the two-dimensional (2-D) slice model. The thermal fields of the strand calculated with the FVM were used in the analysis of the deformation behavior of the strand. Through the iterative analysis of the fluid flow, heat-transfer, and deformation behavior, the coupling parameter of the heat-transfer coefficient between the strand and the mold was obtained. In order to describe the thermophysical properties and thermomechanical behavior of steel in the mushy zone, the microsegregation of solute elements was assessed. Consequently, some characteristic temperatures of steel as well as variations of phase fractions with temperature were determined. The probability of cracking in the strand, originating from an interdendritic liquid film, was quantified as a crack susceptibility coefficient. Recirculating flows were developed in the web and flange-tip regions. The development of a solidifying shell in the flange-center region was retarded by the inlet flow from a submerged entry nozzle (SEN). An air gap was formed mainly near the flange-tip corner. Surface cracks in the web and fillet regions and internal cracks in the flange-tip region were predicted.     

Modularizing application and database evolution – an aspect‐oriented framework for orthogonal persistence

In the maintenance of software applications, database evolution is one common difficulty. In object‐oriented databases, this process comprises schema evolution and instance adaptation. Both tasks usually require significant effort from programmers and database administrators. In this paper, we propose orthogonal persistence and aspect‐oriented programming to support semi‐transparent database evolution. A default mechanism for instance evolution is defined, but the user may provide modularized solutions using the aspect‐oriented paradigm. We present our framework AOF4OOP to test the feasibility of our proposed approach. This prototype allows programmes to transparently access data in other versions of the database schema. We evaluate our framework, comparing it to related approaches using two real applications and measuring the improvement of the productivity of the programmer. Copyright © 2016 John Wiley & Sons, Ltd.