A Kalman filter based methodology for EEG spike enhancement

In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.     

A finite‐strain gradient‐inelastic beam theory and a corresponding force‐based frame element formulation

This paper extends the gradient‐inelastic (GI) beam theory, introduced by the authors to simulate material softening phenomena, to further account for geometric nonlinearities and formulates a corresponding force‐based (FB) frame element computational formulation. Geometric nonlinearities are considered via a rigorously derived finite‐strain beam formulation, which is shown to coincide with Reissner's geometrically nonlinear beam formulation. The resulting finite‐strain GI beam theory: (i) accounts for large strains and rotations, unlike the majority of geometrically nonlinear beam formulations used in structural modeling that consider small strains and moderate rotations; (ii) ensures spatial continuity and boundedness of the finite section strain field during material softening via the gradient nonlocality relations, eliminating strain singularities in beams with softening materials; and (iii) decouples the gradient nonlocality relations from the constitutive relations, allowing use of any material model. On the basis of the proposed finite‐strain GI beam theory, an exact FB frame element formulation is derived, which is particularly novel in that it: (a) expresses the compatibility relations in terms of total strains/displacements, as opposed to strain/displacement rates that introduce accumulated computational error during their numerical time integration, and (b) directly integrates the strain‐displacement equations via a composite two‐point integration method derived from a cubic Hermite interpolating polynomial to calculate the displacement field over the element length and, thus, address the coupling between equilibrium and strain‐displacement equations. This approach achieves high accuracy and mesh convergence rate and avoids polynomial interpolations of individual section fields, which often lead to instabilities with mesh refinements. The FB formulation is then integrated into a corotational framework and is used to study the response of structures, simultaneously accounting for geometric nonlinearities and material softening. The FB formulation is further extended to capture member buckling triggered by minor perturbations/imperfections of the initial member geometry.     

Focusing of macromolecules using equilibrium between centrifugal and electric force

A focusing separation model for macromolecules has been theoretically investigated. The method involves an ultracentrifugation device, which however, deploys an electric field gradient oriented longitudinally along the radial direction. When a macromolecular sample solution is centrifuged, the molecules which have different density to the surrounding solvent and a non-zero electric charge, experience a combination of centrifugal and electric forces. This forces the molecules to move to the equilibrium positions along the radius of the rotor, which are characterised by the charge over mass ratio of the molecules. Therefore a molecular sample will be separated into its constituents and bands will form, akin to electrophoresis. The bands are, however, focused at their equilibrium position. An example configuration has been examined whereby four proteins with masses between 20 and 100 kDa can be separated within a radial distance of 20 cm, for a rotor spinning at approximately 130,000 rpm and with a varying electric field between 0 and 100 V/cm.     

A framework for fuzzy expert system creation--application to cardiovascular diseases.

A methodology for the automated development of fuzzy expert systems is presented. The idea is to start with a crisp model described by crisp rules and then transform them into a set of fuzzy rules, thus creating a fuzzy model. The adjustment of the model's parameters is performed via a stochastic global optimization procedure. The proposed methodology is tested by applying it to problems related to cardiovascular diseases, such as automated arrhythmic beat classification and automated ischemic beat classification, which, besides being well-known benchmarks, are of particular interest due to their obvious medical diagnostic importance. For both problems, the initial set of rules was determined by expert cardiologists, and the MIT-BIH arrhythmia database and the European ST-T database are used for optimizing the fuzzy model's parameters and evaluating the fuzzy expert system. In both cases, the results indicate an escalation of the performance from the simple initial crisp model to the more sophisticated fuzzy models, proving the scientific added value of the proposed framework. Also, the ability to interpret the decisions of the created fuzzy expert systems is a major advantage compared to "black box" approaches, such as neural networks and other techniques.     

Growth,grain yield and nitrogen use efficiency of Mediterranean wheat in soils amended with municipal sewage sludge

The application of sewage sludge (SS) to agricultural land can improve soil fertility and physical properties, and enhance crop production. This field study was conducted for two consecutive growing seasons to investigate the influence of SS application on winter wheat growth, grain yield, N accumulation, translocation and use, and on trace elements concentrations in soil and wheat plants under Mediterranean conditions. Treatments consisted of three rates of SS, i.e. 20, 40, and 60 Mg dry weight ha^?1 year^?1, one rate of inorganic fertilizer (IF, 120 kg N ha^?1 year^?1 plus 80 kg P₂O₅ ha^?1 year^?1), and an unamended control. The application of SS resulted in tall plants with high early dry matter and N accumulation similar to or significantly higher than those obtained with IF. The lowest SS application rate resulted in grain yield similar to that obtained with IF. Nitrogen use efficiency (NUE) in SS treatments was mainly determined by uptake efficiency, which decreased with increasing SS application rate. Values of NUE and biomass production efficiency with the lowest SS rate were similar to those obtained with IF. SS application resulted in increased concentrations of total and DTPA-extractable trace elements in the soil after the first year, but concentrations were much lower than the regulation limits. Concentrations of Cu, Mn and Zn in wheat plants did not exceed those obtained with IF. Overall, SS could be considered for use as a fertilizer in wheat production systems in the area, serving also as an alternative method of SS disposal.     

An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames

In this paper, we present a method for the automated detection of lumen and media-adventitia border in sequential intravascular ultrasound (IVUS) frames. The method is based on the use of deformable models. The energy function is appropriately modified and minimized using a Hopfield neural network. Proper modifications in the definition of the bias of the neurons have been introduced to incorporate image characteristics. A simulated annealing scheme is included to ensure convergence at a global minimum. The method overcomes distortions in the expected image pattern, due to the presence of calcium, employing a specialized structure of the neural network and boundary correction schemas which are based on a priori knowledge about the vessel geometry. The proposed method is evaluated using sequences of IVUS frames from 18 arterial segments, some of them indicating calcified regions. The obtained results demonstrate that our method is statistically accurate, reproducible, and capable to identify the regions of interest in sequences of IVUS frames.     

Robust discrete-time control design with time-varying parametric uncertainties

A novel multiplier approach for robust controller design in discrete-time systems with real, time-varying parametric uncertainty is presented. An important feature of our approach is that bounds on the rate of variation of the uncertain parameters are assumed and, unlike in most related approaches, dynamic multipliers are obtained that utilize this information. A convex minimization procedure formulated as an LMI problem is presented to obtain multipliers that satisfy the robustness conditions derived. Such conditions are transformed to an equivalent scaled H_∞ norm condition and a μ/k_m-synthesis approach is proposed to design robust controllers. Copyright © 1999 John Wiley & Sons, Ltd.     

A generalized methodology for the gridding of microarray images with rectangular or hexagonal grid

Microarrays provide a simple way to measure the level of hybridization of known probes of interest with one or more samples under different conditions. The rapid development of microarray technology requires the implementation of smart and flexible algorithms to deal either with the great amount of data or with the variations of the used hardware. In this paper, a generalized methodology for spot addressing and gridding of microarray images is presented. The methodology can cope with both rectangular and hexagonal grids, which are used for the probes placement onto the substrate. Initially, the methodology identifies the structure of the image, and an efficient spot-by-spot approach has been developed for the detection of all spots in the image. The evaluation of the methodology was performed using both rectangular and hexagonal structured images, merged in a single dataset. The methodology results in high accuracy in the spots detection, ranging from 92.8 to 99.8 % depending on the dataset used.     

High bandwidth tilt measurement using low-cost sensors

In this paper, a state estimation technique is developed for sensing inclination angles using low-cost sensors. A low-bandwidth tilt sensor is used along with an inaccurate rate gyro and a low-cost accelerometer to obtain the measurement. The rate gyro has an inherent bias along with sensor noise. The tilt sensor uses an internal pendulum and therefore has its own slow dynamics. These sensor dynamics were identified experimentally and combined to achieve high-bandwidth measurements using an optimal linear state estimator. Potential uses of the measurement technique range from robotics, to rehabilitation, to vehicle control.     

Comparison of wear properties of tool steels AISI D2 and O1 with the same hardness

Two commercial cold work tool steels, AISI D2 and O1, were heat treated in order to obtain the same hardness 700 HV (60 HRc) and were subsequently tested in three different modes of wear, namely in adhesion, three-body and two-body abrasion, by using pin-on-disk, dry sand/rubber wheel apparatus and pin abrasion on SiC, respectively. Even though AISI O1 and D2 steel are heat treated to the same hardness, they perform differently under the three modes of wear examined. The results show that the steel microstructures play the most important role in determining the wear properties. For relatively low sliding speeds AISI O1 steel performs up to 12 times better than AISI D2 steel in adhesive wear. For higher sliding speeds, however, this order is reversed due to oxidation taking place on the surface of the AISI D2 steel. The wear rate of both tool steels in three-body and two-body abrasion wear is proportional to the applied load. In three-body abrasive wear, AISI D2 exhibits a normalised wear rate about two times lower than the AISI O1 tool steel, and this is due to the presence of the plate-like hard carbides in its microstructure. Both tool steels perform 3–8 times better in three-body abrasive wear conditions than in two-body abrasive wear.