Performance of GPRS coding scheme detection under severe multipath and co-channel interference as a function of soft-bit width

The General Packet Radio Service (GPRS) has been developed for the mobile radio environment to allow the migration from the traditional circuit switched connection to a more efficient packet based communication link particularly for data transfer. GPRS requires the addition of not only the GPRS software protocol stack, but also more baseband functionality for the mobile as new coding schemes have be en defined, uplink status flag detection, multislot operation and dynamic coding scheme detect. This paper concentrates on evaluating the performance of the GPRS coding scheme detection methods in the presence of a multipath fading channel with a single co-channel interferer as a function of various soft-bit data widths. It has been found that compressing the soft-bit data widths from the output of the equalizer to save memory can influence the likelihood decision of the coding scheme detect function and hence contribute to the overall performance loss of the system. Coding scheme detection errors can therefore force the channel decoder to either select the incorrect decoding scheme or have no clear decision which coding scheme to use resulting in the decoded radio block failing the block check sequence and contribute to the block error rate. For correct performance simulation, the performance of the full coding scheme detection must be taken into account.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Influence of education and depressive symptoms on cognitive function in the elderly

Objective: The purpose of the present study was to investigate the influence that education and depression have on the performance of elderly people in neuropsychological tests. Methods: The study was conducted at the Institute of Psychiatry, University of Sao Paulo School of Medicine, Hospital das Clinicas. All of the individuals evaluated were aged 60 or older. The study sample consisted of 59 outpatients with depressive disorders and 51 healthy controls. We stratified the sample by level of education: low = 1-4 years of schooling; high = 5 or more years of schooling. Evaluations consisted of psychiatric assessment, cognitive assessment, laboratory tests and cerebral magnetic resonance imaging. Results: We found that level of education influenced all the measures of cognitive domains investigated (intellectual efficiency, processing speed, attention, executive function and memory) except the Digit Span Forward and Fuld Object Memory Evaluation (immediate and delayed recall), whereas depressive symptoms influenced some measures of memory, attention, executive function and processing speed. Although the combination of a low level of education and depression had a significant negative influence on Stroop Test part B, Trail Making Test part B and Logical Memory (immediate recall), we found no other significant effects of the interaction between level of education and depression. Conclusion: The results of this study underscore the importance of considering the level of education in the analysis of cognitive performance in depressed elderly patients, as well as the relevance of developing new cognitive function tests in which level of education has a reduced impact on the results.

The combination of neural estimates in prediction and decision problems

In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

Increase in Hippocampal Theta Oscillations during Spatial Decision Making

The processing of spatial and mnemonic information is believed to depend on hippocampal theta oscillations (5–12 Hz). However, in rats both the power and the frequency of the theta rhythm are modulated by locomotor activity, which is a major confounding factor when estimating its cognitive correlates. Previous studies have suggested that hippocampal theta oscillations support decision-making processes. In this study, we investigated to what extent spatial decision making modulates hippocampal theta oscillations when controlling for variations in locomotion speed. We recorded local field potentials from the CA1 region of rats while animals had to choose one arm to enter for reward (goal) in a four-arm radial maze. We observed prominent theta oscillations during the decision-making period of the task, which occurred in the center of the maze before animals deliberately ran through an arm toward goal location. In speed-controlled analyses, theta power and frequency were higher during the decision period when compared to either an intertrial delay period (also at the maze center), or to the period of running toward goal location. In addition, theta activity was higher during decision periods preceding correct choices than during decision periods preceding incorrect choices. Altogether, our data support a cognitive function for the hippocampal theta rhythm in spatial decision making

Uma investigação de algoritmos exatos e metaheurísticos aplicados ao nonograma

Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N  M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms

Evolução da terminoliga princípio contábil baseada na Escola Norte-Americana

This research has as objective of study the evolution of the accountancy princliple terminology which is present in the accounting conceptual framework. The scene of this research will have as target the North American School of Accounting. The choice of the searched terminology is its relevance in the study of the Accounting Theory. To understand the evolution of the accountancy thought, will be boarded: the influence of the Feudal System and the Mercantilism in the European economic conception; the importance of the Industrial Revolution in the beginning of the accounting standards and the influence of England in the formation of the North American School of Accounting. With relation to U.S.A., the development of the economic-financial scene of the American society will be evaluated, focusing the contribution in the search of the construction of an applied theoretical framework to the Accounting. The economic-financial development of U.S.A. provided the sprouting of new users with specific necessities. The necessity of the user for useful information for the decision taking, unchained the process of research directed toward the establishment of an applied Accountancy terminology. In this process, the paper exerted for the responsible accountancy organisms for the accounting standards will be boarded, as well as the professionals associations which had invested in researches, aiming at to elaborate a body of accountancy principles and to adjust the accountancy procedures to the necessities of the users. To reach the research objective, a bibliographical revision in specialized literature will be effected, adopting the historical method, in the period that understands the development of the North American School of Accounting. As result of the research, it can conclude that the evolution process of the terminology which is studied presents a structural logical problem, because the impossibility of the construction of a theoretical framework, having as bases the principle terminology. The impossibility occurred in function of the reach attributed to the term, which made a difficult in its application in the elaboration of the accountancy procedures

Early initiation of dialysis: mortality and renal function recovery in acute kidney injury patients

INTRODUÇÃO: A decisão de quando iniciar a diálise em pacientes com lesão renal aguda (LRA) que apresentam síndrome urêmica está bem estabelecida, entretanto, com ureia < 200 mg/dl o melhor momento para iniciar a diálise torna-se incerto. OBJETIVO: Este estudo teve como objetivo avaliar a mortalidade e a recuperação da função renal em pacientes com LRA, cujo início da diálise ocorreu em diferentes níveis de ureia. MÉTODOS: Estudo retrospectivo desenvolvido em hospital escola, no estado de São Paulo, Brasil, envolvendo 86 pacientes submetidos à diálise. RESULTADOS: A diálise foi iniciada com uréia > 150 mg/dl em 23 pacientes (grupo I) e uréia > 150 mg/dl em 63 pacientes (grupo II). Hipervolemia e mortalidade foram mais frequentes no grupo I que no grupo II (65,2 x 14,2% - p < 0,05; 39,1 x 68,9% - p < 0,05, respectivamente). Entre os sobreviventes, a recuperação renal foi maior no grupo I (71,4 e 36,8%, respectivamente, p < 0,05). A análise multivariada mostrou risco independente de mortalidade relacionado à sepse, idade > 60 anos, diálise peritoneal e uréia > 150 mg/dl no início da diálise. CONCLUSÃO: Menor mortalidade e maior recuperação renal estão associadas com o diálise iniciada precocemente, conforme baixos níveis de ureia, em pacientes com LRA.

Effects of age on aerobic capacity in heart failure patients under beta-blocker therapy: Possible impact in clinical decision-making?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatio-Temporal Event Model for Cyber-Physical Systems

The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatiotemporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Cognitive function in patients with chronic pain treated with opioids: characteristics and associated factors

Background The paucity of studies regarding cognitive function in patients with chronic pain, and growing evidence regarding the cognitive effects of pain and opioids on cognitive function prompted us to assess cognition via neuropsychological measurement in patients with chronic non-cancer pain treated with opioids. Methods In this cross-sectional study, 49 patients were assessed by Continuous Reaction Time, Finger Tapping, Digit Span, Trail Making Test-B and Mini-mental State Examination tests. Linear regressions were applied. Results Patients scored poorly in the Trail Making Test-B (mean?=?107.6?s, SD?=?61.0, cut-off?=?91?s); and adequately on all other tests. Several associations among independent variables and cognitive tests were observed. In the multiple regression analyses, the variables associated with statistically significant poor cognitive performance were female sex, higher age, lower annual income, lower schooling, anxiety, depression, tiredness, lower opioid dose, and more than 5?h of sleep the night before assessment (P?<?0.05). Conclusions Patients with chronic pain may have cognitive dysfunction related to some reversible factors, which can be optimized by therapeutic interventions.

Average control of Markov decision processes with Feller transition probabilities and general action spaces

This paper studies the average control problem of discrete-time Markov Decision Processes (MDPs for short) with general state space, Feller transition probabilities, and possibly non-compact control constraint sets A(x). Two hypotheses are considered: either the cost function c is strictly unbounded or the multifunctions A(r)(x) = {a is an element of A(x) : c(x, a) <= r} are upper-semicontinuous and compact-valued for each real r. For these two cases we provide new results for the existence of a solution to the average-cost optimality equality and inequality using the vanishing discount approach. We also study the convergence of the policy iteration approach under these conditions. It should be pointed out that we do not make any assumptions regarding the convergence and the continuity of the limit function generated by the sequence of relative difference of the alpha-discounted value functions and the Poisson equations as often encountered in the literature. (C) 2012 Elsevier Inc. All rights reserved.

Nonlinear estimation of neural processing time from BOLD signal with application to decision-making

The extraction of information about neural activity timing from BOLD signal is a challenging task as the shape of the BOLD curve does not directly reflect the temporal characteristics of electrical activity of neurons. In this work, we introduce the concept of neural processing time (NPT) as a parameter of the biophysical model of the hemodynamic response function (HRF). Through this new concept we aim to infer more accurately the duration of neuronal response from the highly nonlinear BOLD effect. The face validity and applicability of the concept of NPT are evaluated through simulations and analysis of experimental time series. The results of both simulation and application were compared with summary measures of HRF shape. The experiment that was analyzed consisted of a decision-making paradigm with simultaneous emotional distracters. We hypothesize that the NPT in primary sensory areas, like the fusiform gyrus, is approximately the stimulus presentation duration. On the other hand, in areas related to processing of an emotional distracter, the NPT should depend on the experimental condition. As predicted, the NPT in fusiform gyrus is close to the stimulus duration and the NPT in dorsal anterior cingulate gyrus depends on the presence of an emotional distracter. Interestingly, the NPT in right but not left dorsal lateral prefrontal cortex depends on the stimulus emotional content. The summary measures of HRF obtained by a standard approach did not detect the variations observed in the NPT. Hum Brain Mapp, 2012. (C) 2010 Wiley Periodicals, Inc.