Performance analysis of the unconstrained FxLMS algorithm for active noise control

The paper analyzes the performance of the unconstrained filtered-x LMS (FxLMS) algorithm for active noise control (ANC), where we remove the constraints on the controller that it must be causal and has finite impulse response. It is shown that the unconstrained FxLMS algorithm always converges to, if stable, the true optimum filter, even if the estimation of the secondary path is not perfect, and its final mean square error is independent of the secondary path. Moreover, we show that the sufficient and necessary stability condition for the feedforward unconstrained FxLMS is that the maximum phase error of the secondary path estimation must be within 90°, which is the only necessary condition for the feedback unconstrained FxLMS. The significance of the analysis on a practical system is also discussed. Finally we show how the obtained results can guide us to design a robust feedback ANC headset.

Tephra fallout hazard assessment for a Plinian eruption scenario at Volcán de Colima (Mexico)

Volcanic ash fallout associated with renewal of explosive activity at Colima, represents a serious threat to the surrounding urbanized area. Here we assess the tephra fallout hazard associated with a Plinian eruption scenario. The eruptive history of Volcán de Colima shows that Plinian eruptions occur approximately every 100 years and the last eruption, the 1913, represents the largest historic eruption of this volcano. We used the last eruption as a reference to discuss volcanic hazard and risk scenarios connected with ash fallout. Tephra fallout deposits are modeled using HAZMAP, a model based on a semi-analytical solution of the advection– diffusion–sedimentation equation for volcanic particles. Based on a statistical study of wind profiles at Colima region, we first reconstructed ash loading maps and then computed ground load probability maps for different seasons. The obtained results show that a Plinian eruptive scenario at Volcán de Colima, could seriously damage more than 10 small towns and ranches, and potentially affect big cities located at tens of kilometers from the eruptive center. The probability maps obtained are aimed to give support to the risk mitigation strategies

Design of a biomimetic skin for an octopus-inspired robot– Part I: characterising octopus skin

Octopus skin samples were tested under quasi-static and scissor cutting conditions to measure the in-plane material properties and fracture toughness. Samples from all eight arms of one octopus were tested statically to investigate how properties vary from arm to arm. Another nine octopus skins were measured to study the influence of body mass on skin properties. Influence of specimen location on skin mechanical properties was also studied. Material properties of skin, i.e. the Young's modulus, ultimate stress, failure strain and fracture toughness have been plotted against the position of skin along the length of arm or body. Statistical studies were carried out to help analyzing experimental data obtained. Results of this work will be used as guidelines for the design and development of artificial skins for an octopus-inspired robot.

A study of COFDM in a terrestrial multipath environment

Coded orthogonal frequency division multiplexing (COFDM) has existed for many years but it was not until 1997 when the European Telecommunications Standards Institute proposed its use for the transmission of digital television through a terrestrial channel. Up to date, an assumption has been made concerning the resilience of COFDM in a multipath environment. This paper discusses this assumption, give results of a DVB-T compliant simulation and discuss the validity of this assumption based on the obtained results.

Axonal velocity distributions in neural feld equations

By modelling the average activity of large neuronal populations, continuum mean field models (MFMs) have become an increasingly important theoretical tool for understanding the emergent activity of cortical tissue. In order to be computationally tractable, long-range propagation of activity in MFMs is often approximated with partial differential equations (PDEs). However, PDE approximations in current use correspond to underlying axonal velocity distributions incompatible with experimental measurements. In order to rectify this deficiency, we here introduce novel propagation PDEs that give rise to smooth unimodal distributions of axonal conduction velocities. We also argue that velocities estimated from fibre diameters in slice and from latency measurements, respectively, relate quite differently to such distributions, a significant point for any phenomenological description. Our PDEs are then successfully fit to fibre diameter data from human corpus callosum and rat subcortical white matter. This allows for the first time to simulate long-range conduction in the mammalian brain with realistic, convenient PDEs. Furthermore, the obtained results suggest that the propagation of activity in rat and human differs significantly beyond mere scaling. The dynamical consequences of our new formulation are investigated in the context of a well known neural field model. On the basis of Turing instability analyses, we conclude that pattern formation is more easily initiated using our more realistic propagator. By increasing characteristic conduction velocities, a smooth transition can occur from self-sustaining bulk oscillations to travelling waves of various wavelengths, which may influence axonal growth during development. Our analytic results are also corroborated numerically using simulations on a large spatial grid. Thus we provide here a comprehensive analysis of empirically constrained activity propagation in the context of MFMs, which will allow more realistic studies of mammalian brain activity in the future.

Factors contributing to fecal incontinence in older people and outcome of routine management in home, hospital and nursing home settings

Objective: Fecal loading, cognitive impairment, loose stools, functional disability, comorbidity and anorectal incontinence are recognized as factors contributing to loss of fecal continence in older adults. The objective of this project was to assess the relative distribution of these factors in a variety of settings along with the outcome of usual management. Methods: One hundred and twenty adults aged 65 years and over with fecal incontinence recruited by convenience sampling from four different settings were studied. They were either living at home or in a nursing home or receiving care on an acute or rehabilitation elderly care ward. A structured questionnaire was used to elicit which factors associated with fecal incontinence were present from subjects who had given written informed consent or for whom assent for inclusion in the study had been obtained. Results: Fecal loading (Homes 6 [20%]; Acute care wards 17 [57%]; Rehabilitation wards 19 [63%]; Nursing homes 21 [70%]) and functional disability (Homes 5 [17%]; Acute care wards 25 [83%]; Rehabilitation wards 25 [83%]; Nursing homes 20 [67%]) were significantly more prevalent in the hospital and nursing home settings than in those living at home (P < 0.01). Loose stools were more prevalent in the hospital setting than in the other settings (Homes 11 [37%]; Acute care wards 20 [67%]; Rehabilitation wards 17 [57%]; Nursing homes 6 [20%]) (P < 0.01). Cognitive impairment was significantly more common in the nursing home than in the other settings (Nursing homes 26 [87%], Homes 5 [17%], Acute care wards 13 [43%], Rehabilitation wards 14 [47%]) (P < 0.01). Loose stools were the most prevalent factor present at baseline in 13 of the 19 (68%) subjects whose fecal incontinence had resolved at 3 months. Conclusion: The distribution of the factors contributing to fecal incontinence in older people living at home differs from those cared for in nursing home and hospital wards settings. These differences need to be borne in mind when assessing people in different settings. Management appears to result in a cure for those who are not significantly disabled with loose stools as a cause for their fecal incontinence, but this would need to be confirmed by further research.

Climate change impacts in the design of drainage systems: case study of Portugal

This paper aims to assess the necessity of updating the intensity-duration-frequency (IDF) curves used in Portugal to design building storm-water drainage systems. A comparative analysis of the design was performed for the three predefined rainfall regions in Portugal using the IDF curves currently in use and estimated for future decades. Data for recent and future climate conditions simulated by a global and regional climate model chain are used to estimate possible changes of rainfall extremes and its implications for the drainage systems. The methodology includes the disaggregation of precipitation up to subhourly scales, the robust development of IDF curves, and the correction of model bias. Obtained results indicate that projected changes are largest for the plains in southern Portugal (5–33%) than for mountainous regions (3–9%) and that these trends are consistent with projected changes in the long-term 95th percentile of the daily precipitation throughout the 21st century. The authors conclude there is a need to review the current precipitation regime classification and change the new drainage systems towards larger dimensions to mitigate the projected changes in extreme precipitation.

Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV

This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Brazilian red propolis: unreported substances, antioxidant and antimicrobial activities

BACKGROUND: Chloroform, ethyl acetate and methanol extracts of a sample of red propolis from the state of Alagoas (northeast Brazil) were analyzed by gas chromatography-mass spectrometry and high-performance liquid chromatography-diode array detection-electrospray ionization-mass spectrometry. Antimicrobial and antioxidant activities were also obtained. RESULTS: The propolis sample contained low content of narigenin-8-C-hexoside, this being the first report of a C-glycoside in propolis. The main constituent found was characterized as 3,4,2`,3`-tetrahydroxychalcone. Other important constituents were the chalcone isoliquiritigenin, the isoflavans (3S)-vestitol, (3S)-7-O-methylvestitol, the pterocarpan medicarpin, the phenylpropenes trans-anethol, methyl eugenol, elimicin, methoxyeugenol and cis-asarone, and the triterpenic alcohols lupeol and alpha- and beta- amyrins. The methanol extract exhibited high antioxidant activities by 2,2-diphenyl-1-picrylhydrazyl and beta-carotene/linoleic acid assay methods, and antimicrobial activity toward Gram-positive and Gram-negative bacteria. CONCLUSION: Structures are suggested for new substances never before seen in any kind of propolis. This is the first report of 3,4,2`,3`-tetrahydroxychalcone and a flavone C-glycoside in a propolis sample. (C) 2011 Society of Chemical Industry

Involvement of the AT(1) receptor in the venoconstriction induced by angiotensin II in both the inferior vena cava and femoral vein

Although angiotensin II-induced venoconstriction has been demonstrated in the rat vena cava and femoral vein, the angiotensin II receptor subtypes (AT(1) or AT(2)) that mediate this phenomenon have not been precisely characterized. Therefore, the present study aimed to characterize the pharmacological receptors involved in the angiotensin II-induced constriction of rat venae cavae and femoral veins, as well as the opposing effects exerted by locally produced prostanoids and NO upon induction of these vasomotor responses. The obtained results suggest that both AT(1) and AT(2) angiotensin II receptors are expressed in both veins. Angiotensin II concentration-response curves were shifted toward the right by losartan but not by PD 123319 in both the vena cava and femoral vein. Moreover, it was observed that both 10(-5) M indomethacin and 10(-4) M L-NAME improve the angiotensin II responses in the vena cava and femoral vein. In conclusion, in the rat vena cava and femoral vein, angiotensin II stimulates AT(1) but not AT(2) to induce venoconstriction, which is blunted by vasodilator prostanoids and NO. (C) 2010 Elsevier Inc. All rights reserved.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.