Do men and women use similar adaptive locomotion to clear static and dynamic obstacles?

Purpose. The purpose of this study was to analyze the influence of gender on the adaptive locomotion in the clearance of obstacles. Specifically, it was evaluated if there are differences in the space-temporal parameters between male and female in the clearance of and dynamic obstacles moving at both slow and fast speeds. Basic procedures. Five young male adults and five young female adults took part in this study. The task was performed in three conditions: static obstacle and dynamic obstacle - clearance perpendicular to the participant's trajectory at slow speed (1.07 m/s) and at fast speed (1.71 m/s). The trials were recorded by two digital cameras and spatial-temporal information was obtained. Main findings. The dynamic obstacle conditions required more visual inspection. The results showed different adaptive locomotion between the sexes. The distinct gait patterns were evidenced for the spatial and temporal variables and cadence in the three conditions. Conclusions. The women presented a more conservative behavior, which was evidenced by the increase of the length in the penultimate step and in the toe clearance.

Adaptive walking in alzheimer's disease

The aim of this study is to analyze dual-task effects on free and adaptive gait in Alzheimer's disease (AD) patients. Nineteen elders with AD participated in the study. A veteran neuropsychiatrist established the degree of AD in the sample. To determine dual-task effects on free and adaptive gait, patients performed five trials for each experimental condition: free and adaptive gait with and without a dual-task (regressive countdown). Spatial and temporal parameters were collected through an optoelectronic tridimensional system. The central stride was analyzed in free gait, and the steps immediately before (approaching phase) and during the obstacle crossing were analyzed in adaptive gait. Results indicated that AD patients walked more slowly during adaptive gait and free gait, using conservative strategies when confronted either with an obstacle or a secondary task. Furthermore, patients sought for stability to perform the tasks, particularly for adaptive gait with dual task, who used anticipatory and online adjustments to perform the task. Therefore, the increase of task complexity enhances cognitive load and risk of falls for AD patients. © 2012 Diego Orcioli-Silva et al.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

Wind ensamble forecasting using an adaptive mass-consistent model

[EN]Ensemble forecasting [1] is a methodology to deal with uncertainties in the numerical wind prediction. In this work we propose to apply ensemble methods to the adaptive wind forecasting model presented in [2]. The wind _eld forecasting is based on a mass-consistent model and a log-linear wind pro_le using as input data the resulting forecast wind from Harmonie [3], a Non-Hydrostatic Dynamic model. The mass-consistent model parameters are estimated by using genetic algorithms [4]. The mesh is generated using the meccano method [5] and adapted to the geometry. The main source of uncertainties in this model is the parameter estimation and the in- trinsic uncertainties of the Harmonie Model…

Adaptive Finite Element Method with a local element parametrization nested meses

[EN]This work presents a novel approach to solve a two dimensional problem by using an adaptive finite element approach. The most common strategy to deal with nested adaptivity is to generate a mesh that represents the geometry and the input parameters correctly, and to refine this mesh locally to obtain the most accurate solution. As opposed to this approach, the authors propose a technique using independent meshes : geometry, input data and the unknowns. Each particular mesh is obtained by a local nested refinement of the same coarse mesh at the parametric space…

Ensemble wind forecasting based on the HARMONIE model and adaptive finite elements in complex orography

[EN]Ensemble forecasting is a methodology to deal with uncertainties in the numerical wind prediction. In this work we propose to apply ensemble methods to the adaptive wind forecasting model presented in. The wind field forecasting is based on a mass-consistent model and a log-linear wind profile using as input data the resulting forecast wind from Harmonie, a Non-Hydrostatic Dynamic model used experimentally at AEMET with promising results. The mass-consistent model parameters are estimated by using genetic algorithms. The mesh is generated using the meccano method and adapted to the geometry…

Brain-Computer Interfaces for augmented communication: Asynchronous and adaptive algorithms and evaluation with end users

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.

MaxMAC: a Maximally Traffic-Adaptive MAC Protocol for Wireless Sensor Networks

Energy efficiency is a major concern in the design of Wireless Sensor Networks (WSNs) and their communication protocols. As the radio transceiver typically accounts for a major portion of a WSN node’s power consumption, researchers have proposed Energy-Efficient Medium Access (E2-MAC) protocols that switch the radio transceiver off for a major part of the time. Such protocols typically trade off energy-efficiency versus classical quality of service parameters (throughput, latency, reliability). Today’s E2-MAC protocols are able to deliver little amounts of data with a low energy footprint, but introduce severe restrictions with respect to throughput and latency. Regrettably, they yet fail to adapt to varying traffic load at run-time. This paper presents MaxMAC, an E2-MAC protocol that targets at achieving maximal adaptivity with respect to throughput and latency. By adaptively tuning essential parameters at run-time, the protocol reaches the throughput and latency of energy-unconstrained CSMA in high-traffic phases, while still exhibiting a high energy-efficiency in periods of sparse traffic. The paper compares the protocol against a selection of today’s E2-MAC protocols and evaluates its advantages and drawbacks.

EEG reactivity and EEG activity in never-treated acute schizophrenics, measured with spectral parameters and dimensional complexity

Our approaches to the use of EEG studies for the understanding of the pathogenesis of schizophrenic symptoms are presented. The basic assumptions of a heuristic and multifactorial model of the psychobiological brain mechanisms underlying the organization of normal behavior is described and used in order to formulate and test hypotheses about the pathogenesis of schizophrenic behavior using EEG measures. Results from our studies on EEG activity and EEG reactivity (= EEG components of a memory-driven, adaptive, non-unitary orienting response) as analyzed with spectral parameters and "chaotic" dimensionality (correlation dimension) are summarized. Both analysis procedures showed a deviant brain functional organization in never-treated first-episode schizophrenia which, within the framework of the model, suggests as common denominator for the pathogenesis of the symptoms a deviation of working memory, the nature of which is functional and not structural.

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

A Cooperative and Adaptive Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

In this paper we propose two cooperation schemes to compose new parallel variants of the Variable Neighborhood Search (VNS). On the one hand, a coarse-grained cooperation scheme is introduced which is well suited for being enhanced with a solution warehouse to store and manage the so far best found solutions and a self-adapting mechanism for the most important search parameters. This makes an a priori parameter tuning obsolete. On the other hand, a fine-grained scheme was designed to reproduce the successful properties of the sequential VNS. In combination with the use of parallel exploration threads all of the best solutions and 11 out of 20 new best solutions for the Multi Depot Vehicle Routing Problem with Time Windows were found.

Adaptive variability to low-pH river discharges in Acartia tonsa and stress responses to high PCO2 conditions

Environmental transitions leading to spatial physical-chemical gradients are of ecological and evolutionary interest because they are able to induce variations in phenotypic plasticity. Thus, the adaptive variability to low-pH river discharges may drive divergent stress responses [ingestion rates (IR) and expression of stress-related genes such as Heat shock protein 70 (Hsp70) and Ferritin] in the neritic copepod Acartia tonsa facing changes in the marine chemistry associated to ocean acidification (OA). These responses were tested in copepod populations inhabiting two environments with contrasting carbonate system parameters (an estuarine versus coastal area) in the Southern Pacific Ocean, and assessing an in situ and 96-h experimental incubation under conditions of high pressure of CO2 (PCO2 1200 ppm). Adaptive variability was a determining factor in driving variability of copepods' responses. Thus, the food-rich but colder and corrosive estuary induced a traits trade-off expressed as depressed IR under in situ conditions. However, this experience allowed these copepods to tolerate further exposure to high PCO2 levels better, as their IRs were on average 43% higher than those of the coastal individuals. Indeed, expression of both the Hsp70 and Ferritin genes in coastal copepods was significantly higher after acclimation to high PCO2 conditions. Along with other recent evidence, our findings confirm that adaptation to local fluctuations in seawater pH seems to play a significant role in the response of planktonic populations to OA-associated conditions. Facing the environmental threat represented by the inter-play between multiple drivers of climate change, this biological feature should be examined in detail as a potential tool for risk mitigation policies in coastal management arrangements.

Adaptive Multi-Camera System for Real Time Object Detection

In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy.

Goal-oriented self-adaptive hp-strategies for finite element analysis of electromagnetic scattering and radiation problems

In this paper, a fully automatic goal-oriented hp-adaptive finite element strategy for open region electromagnetic problems (radiation and scattering) is presented. The methodology leads to exponential rates of convergence in terms of an upper bound of an user-prescribed quantity of interest. Thus, the adaptivity may be guided to provide an optimal error, not globally for the field in the whole finite element domain, but for specific parameters of engineering interest. For instance, the error on the numerical computation of the S-parameters of an antenna array, the field radiated by an antenna, or the Radar Cross Section on given directions, can be minimized. The efficiency of the approach is illustrated with several numerical simulations with two dimensional problem domains. Results include the comparison with the previously developed energy-norm based hp-adaptivity.