Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

A Procedure for Semantic Querying in the Adaptive Formalism

This paper contains a new proposal for the definition of the fundamental operation of query under the Adaptive Formalism, one capable of locating functional nuclei from descriptions of their semantics. To demonstrate the method`s applicability, an implementation of the query procedure constrained to a specific class of devices is shown, and its asymptotic computational complexity is discussed.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

This work deals with the problem of minimizing the waste of space that occurs on a rotational placement of a set of irregular bi-dimensional items inside a bi-dimensional container. This problem is approached with a heuristic based on Simulated Annealing (SA) with adaptive neighborhood. The objective function is evaluated in a constructive approach, where the items are placed sequentially. The placement is governed by three different types of parameters: sequence of placement, the rotation angle and the translation. The rotation applied and the translation of the polygon are cyclic continuous parameters, and the sequence of placement defines a combinatorial problem. This way, it is necessary to control cyclic continuous and discrete parameters. The approaches described in the literature deal with only type of parameter (sequence of placement or translation). In the proposed SA algorithm, the sensibility of each continuous parameter is evaluated at each iteration increasing the number of accepted solutions. The sensibility of each parameter is associated to its probability distribution in the definition of the next candidate.

Simulated annealing with adaptive neighborhood: A case study in off-line robot path planning

Simulated annealing (SA) is an optimization technique that can process cost functions with degrees of nonlinearities, discontinuities and stochasticity. It can process arbitrary boundary conditions and constraints imposed on these cost functions. The SA technique is applied to the problem of robot path planning. Three situations are considered here: the path is represented as a polyline; as a Bezier curve; and as a spline interpolated curve. In the proposed SA algorithm, the sensitivity of each continuous parameter is evaluated at each iteration increasing the number of accepted solutions. The sensitivity of each parameter is associated to its probability distribution in the definition of the next candidate. (C) 2010 Elsevier Ltd. All rights reserved.

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Transient and Steady-State Analysis of the Affine Combination of Two Adaptive Filters

In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow adaptive filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton`s method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques - combines computer engineering concepts with areas like dermatology and oncology. Its objective is to discern images of skin cancer, specifically melanoma, from others that show only common spots or other types of skin diseases, using image recognition. This work makes use of the ABCDE visual rule, which is often used by dermatologists for melanoma identification, to define which characteristics are analyzed by the software. It then applies various algorithms and techniques, including an ellipse-fitting algorithm, to extract and measure these characteristics and decide whether the spot is a melanoma or not. The achieved results are presented with special focus on the adaptive decision-making and its effect on the diagnosis. Finally, other applications of the software and its algorithms are presented.

The AdapLib Library for Execution of Adaptive Devices: Architecture and Use

In this paper it is presented the theoretical background, the architecture (using the ""4+1"" model), and the use of the library for execution of adaptive devices, AdapLib. This library was created seeking to be accurate to the adaptive devices theory, and to allow its easy extension considering the specific details of solutions that employ this kind of device. As an example, it is presented a case study in which the library was used to create a proof of concept to monitor and diagnose problems in an online news portal.

Memory with emotional content, brain amygdala and Alzheimer`s disease

Objectives - A highly adaptive aspect of human memory is the enhancement of explicit, consciously accessible memory by emotional stimuli. We studied the performance of Alzheimer`s disease (AD) patients and elderly controls using a memory battery with emotional content, and we correlated these results with the amygdala and hippocampus volume. Methods - Twenty controls and 20 early AD patients were subjected to the International Affective Picture System (IAPS) and to magnetic resonance imaging-based volumetric measurements of the medial temporal lobe structures. Results - The results show that excluding control group subjects with 5 or more years of schooling, both groups showed improvement with pleasant or unpleasant figures for the IAPS in an immediate free recall test. Likewise, in a delayed free recall test, both the controls and the AD group showed improvement for pleasant pictures, when education factor was not controlled. The AD group showed improvement in the immediate and delayed free recall test proportional to the medial temporal lobe structures, with no significant clinical correlation between affective valence and amygdala volume. Conclusion - AD patients can correctly identify emotions, at least at this early stage, but this does not improve their memory performance.

Plasmodium vivax recombinant vaccine candidate AMA-1 plays an important role in adaptive immune response eliciting differentiation of dendritic cells

The Apical Membrane Antigen-1 (AMA-1) is a well-characterized and functionally important merozoite protein and is currently considered a major candidate antigen for a malaria vaccine. Previously, we showed that AMA-1 has an influence on cellular immune responses of malaria-naive subjects, resulting in an alternative activation of monocyte-derived dendritic cells and induction of a pro-inflammatory response by stimulated PBMCs. Although there is evidence, from human and animal malaria model systems that cell-mediated immunity may contribute to both protection and pathogenesis, the knowledge on cellular immune responses in vivax malaria and the factors that may regulate this immunity are poorly understood. In the current work, we describe the maturation of monocyte-derived dendritic cells of P. vivax naturally infected individuals and the effect of P. vivax vaccine candidate Pv-AMA-1 on the immune responses of the same donors. We show that malaria-infected subjects present modulation of DC maturation, demonstrated by a significant decrease in expression of antigen-presenting molecules (CD1a, HLA-ABC and HLA-DR), accessory molecules (CD40, CD80 and CD86) and Fc gamma RI (CD64) receptor (P <= 0.05). Furthermore, Pv-AMA-1 elicits an upregulation of CD1a and HLA-DR molecules on the surface of monocyte-derived dendritic cells (P=0.0356 and P=0.0196, respectively), and it is presented by AMA-1-stimulated DCs. A significant pro-inflammatory response elicited by Pv-AMA-1-pulsed PBMCs is also demonstrated, as determined by significant production of TNF-alpha, IL-12p40 and IFN-gamma (P <= 0.05). Our results suggest that Pv-AMA-1 may partially revert DC down-modulation observed in infected subjects, and exert an important role in the initiation of pro-inflammatory immunity that might contribute substantially to protection. (c) 2009 Elsevier Ltd. All rights reserved.

Cdc42p controls yeast-cell shape and virulence of Paracoccidioides brasiliensis

Paracoccidioides brasiliensis is characterized by a multiple budding phenotype and a polymorphic cell growth, leading to the formation of cells with extreme variations in shape and size. Since Cdc42 is a pivotal molecule in establishing and maintaining polarized growth for diverse cell types, as well as during pathogenesis of certain fungi, we evaluated its role during cell growth and virulence of the yeast-form of P. brasiliensis. We used antisense technology to knock-down PbCDC42`s expression in P. brasiliensis yeast cells, promoting a decrease in cell size and more homogenous cell growth, altering the typical polymorphism of wild-type cells. Reduced expression levels also lead to increased phagocytosis and decreased virulence in a mouse model of infection. We provide genetic evidences underlying Pbcdc42p as an important protein during host-pathogen interaction and the relevance of the polymorphic nature and cell size in the pathogenesis of P. brasiliensis. (C) 2009 Elsevier Inc. All rights reserved.

A wavelet-based adaptive technique for adsorption problems involving steep gradients

A general, fast wavelet-based adaptive collocation method is formulated for heat and mass transfer problems involving a steep moving profile of the dependent variable. The technique of grid adaptation is based on sparse point representation (SPR). The method is applied and tested for the case of a gas–solid non-catalytic reaction in a porous solid at high Thiele modulus. Accurate and convergent steep profiles are obtained for Thiele modulus as large as 100 for the case of slab and found to match the analytical solution.