A microcontroller-based portable electrocardiograph recorder

We describe a low cost portable Holter design that can be implemented with off-the-shelf components. The recorder is battery powered and includes a graphical display and keyboard. The recorder is capable of acquiring up to 48 hours of continuous electrocardiogram data at a sample rate of up to 250 Hz.     

Latent communities of digital publications: The role of editors,followers, and advertisers

This research explores how digital publications may be turned into something more than just a meeting point for people who are seeking information and may become a latent community. The paper proposes that the network of editors, followers, and advertisers of digital publications determine the user sense of community towards the publication. While the pattern of connections among editors and followers reinforces the sense of community, advertisers act as intruders who weaken the feasibility of building a community. Data were collected from followers of digital publications. The dynamics of the sense of community is described as a process in which informational value and identification are essential to nurturing readers’ soft or hard commitment. Findings indicate that the strong ties between editors and followers as well as the diversity positively impacts on informational value and identification, whereas advertising intrusiveness diminishes the informational value.     

Diagnosis of learner dropout based on learning styles for online distance learning

The amount of data generated by computer systems in Online Distance Learning (ODL) contains rich information. One example of this information we define as the Learner Learning Trail (LLT), which is the sequence of interactions between the students and the virtual environment. Another example is the Learner Learning Style (LLS), which is associated with the student behavior and choices during the learning process. This information can be used to identify learner behavior and learning style. We perceived, after the study of related literature, that the research field of learner diagnosis for ODL does not apply the conjoint use of LLT and LLS. In this article, we propose a model capable of integrating data generated from the behavior of students in ODL with cognitive aspects of them, such as their Learning Styles, by crossing LLT with LLS. We also propose the CPAD method (Collect, Preprocessing, Analysis, Diagnosis), which is implemented by collecting the raw data regarding learning activities, preprocessing the data into structured time sequences, analyzing the sequences regarding the learning styles and using this analysis to diagnose the learner behavior. We selected the dropout to investigate, once the dropout rate in ODL is a real problem in universities around the world. In addition, the dropout is a student decision which can be associated with previous students behaviors. We performed a study with 202 learners to evaluate if learning styles are capable of explaining aspects of the student behavior. The results suggest that Sequential/Global learning style dimension is more capable of explaining the dropout than the other dimensions. Also, we performed four classification experiments to verify how the dimensions of Felder-Silverman Learning Style Model influence the learner diagnosis. We perceived that the Sequential/Global dimension could provide a higher accuracy average with lower variation independently of the diagnosis technique.     

Superoleophilic Magnetic Iron Oxide Nanoparticles for Effective Hydrocarbon Removal from Water

Various hydrocarbons are efficiently extracted from water by using a new sorbent material based on covalently functionalized magnetic nanoparticles. The functionalization of the magnetite nanoparticles with a self‐assembled monolayer of hexadecylphosphonic acid renders the nanoparticles oleophilic and the magnetic nature of magnetite allows for simple extraction of the hydrocarbon‐soaked sorbent. The sorbent material is capable of extracting single contaminants such as alkanes and aromatics and complex hydrocarbon mixtures such as crude oils in high extraction rates of up to 14 times the sorbent volume. Experimental results are explained by molecular dynamics simulations on the adsorption of single components from a hydrocarbon‐water mixture to the alkylphosphonic acid layer on the nanoparticles. The core–shell sorbent material is highly stable and therefore, reusable over several successive extraction cycles without degradation. The extraction performance is determined at different water temperatures, different water sources, and different magnetic core materials and evaluated compared to heptadecanoic acid functionalized magnetite. The new sorbent material provides the opportunity for an efficient, reliable, inexpensive, and environmental friendly removal of hydrocarbons from water.     

An anisotropic microsphere-based approach for fiber orientation adaptation in soft tissue

Evolutionary processes in biological tissue, such as adaptation or remodeling, represent an enterprising area of research. In this paper, we present a multiscale model for the remodeling of fibered structures, such as bundles of collagen fibrils. With this aim, we introduce a von Mises statistical distribution function to account for the directional dispersion of the fibrils, and we remodel the underlying fibrils by changing their orientation. To numerically compute this process, we make use of the microsphere approach, which provides a useful multiscale tool for homogenizing the microstructure behavior, related to the fibrils of the bundle, in the macroscale of the problem. The results show how the fibrils respond to the stimulus by reorientation of their structure. This process leads to a stiffer material eventually reaching a stationary state. These results are in agreement with those reported in the literature, and they characterize the adaptation of biological tissue to external stimuli.     

Extraction and analysis of T waves in electrocardiograms during atrial flutter

Analysis of T waves in the ECG is an essential clinical tool for diagnosis, monitoring, and follow-up of patients with heart dysfunction. During atrial flutter, this analysis has been so far limited by the perturbation of flutter waves superimposed over the T wave. This paper presents a method based on missing data interpolation for eliminating flutter waves from the ECG during atrial flutter. To cope with the correlation between atrial and ventricular electrical activations, the CLEAN deconvolution algorithm was applied to reconstruct the spectrum of the atrial component of the ECG from signal segments corresponding to TQ intervals. The locations of these TQ intervals, where the atrial contribution is presumably dominant, were identified iteratively. The algorithm yields the extracted atrial and ventricular contributions to the ECG. Standard T-wave morphology parameters (T-wave amplitude, T peak-T end duration, QT interval) were measured. This technique was validated using synthetic signals, compared to average beat subtraction in a patient with a pacemaker, and tested on pseudo-orthogonal ECGs from patients in atrial flutter. Results demonstrated improvements in accuracy and robustness of T-wave analysis as compared to current clinical practice.     

Rayleigh mixture model for plaque characterization in intravascular ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.     

Motion Compensated Three-Dimensional Frequency Selective Extrapolation for improved error concealment in video communication

During transmission of video data over error-prone channels the risk of getting severe image distortions due to transmission errors is ubiquitous. To deal with image distortions at decoder side, error concealment is applied. This article presents Motion Compensated Three-Dimensional Frequency Selective Extrapolation, a novel spatio-temporal error concealment algorithm. The algorithm uses fractional-pel motion estimation and compensation as initial step, being followed by the generation of a model of the distorted signal. The model generation is conducted by an enhanced version of Three-Dimensional Frequency Selective Extrapolation, an existing error concealment algorithm. Compared to this existent algorithm, the proposed one yields an improvement in concealment quality of up to 1.64 dB PSNR. Altogether, the incorporation of motion compensation and the improved model generation extends the already high extrapolation quality of the underlying Frequency Selective Extrapolation, resulting in a gain of more than 3 dB compared to other well-known error concealment algorithms.     

A framework for network quality monitoring in the VoIP environment

Monitoring speech quality in Voice over IP (VoIP) networks is important to ensure a minimal acceptable level of speech quality for IP calls running through a managed network. Information such as packet loss, codec type, jitter, end‐to‐end delay and overall speech quality enables the network manager to verify and accurately tune parameters in order to adjust network problems. The present article proposes the deployment of a monitoring architecture that collects, stores and displays speech quality information about concluded voice calls. This architecture is based on our proposed MIB (Management Information Base) VOIPQOS, deployed for speech quality monitoring purposes. Currently, the architecture is totally implemented, but under adjustment and validation tests. In the future, the VOIPQOS MIB can be expanded to automatically analyze collected data and control VoIP clients and network parameters for tuning the overall speech quality of ongoing calls. Copyright © 2006 John Wiley & Sons, Ltd.     

Characterization of the Main Semiconductor Laser Static and Dynamic Working Parameters From CW Optical Spectrum Measurements

We present a complete characterization of the work parameters of several types of semiconductor lasers. Static parameters as: power, linewidth and linewidth enhancement factor and also dynamic parameters such as: relaxation oscillations, relative intensity noise and damping rates are calculated using measurements of the optical spectrum of the lasers operated in continuous-wave mode. Methods for the calculation of these parameters are described and applied to the lasers under test by means of a single general setup and a single set of measurements