Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Error Modeling and Accuracy Analysis of a Novel Mobile Hybrid Parallel Robot

Over the last decades, calibration techniques have been widely used to improve the accuracy of robots and machine tools since they only involve software modification instead of changing the design and manufacture of the hardware. Traditionally, there are four steps are required for a calibration, i.e. error modeling, measurement, parameter identification and compensation. The objective of this thesis is to propose a method for the kinematics analysis and error modeling of a newly developed hybrid redundant robot IWR (Intersector Welding Robot), which possesses ten degrees of freedom (DOF) where 6-DOF in parallel and additional 4-DOF in serial. In this article, the problem of kinematics modeling and error modeling of the proposed IWR robot are discussed. Based on the vector arithmetic method, the kinematics model and the sensitivity model of the end-effector subject to the structure parameters is derived and analyzed. The relations between the pose (position and orientation) accuracy and manufacturing tolerances, actuation errors, and connection errors are formulated. Computer simulation is performed to examine the validity and effectiveness of the proposed method.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Multiple-acquisition parallel imaging combined with a transceive array for the amelioration of high-field RF distortion: a modeling study

A new method for ameliorating high-field image distortion caused by radio frequency/tissue interaction is presented and modeled, The proposed method uses, but is not restricted to, a shielded four-element transceive phased array coil and involves performing two separate scans of the same slice with each scan using different excitations during transmission. By optimizing the amplitudes and phases for each scan, antipodal signal profiles can be obtained, and by combining both images together, the image distortion can be reduced several-fold. A hybrid finite-difference time-domain/method-of-moments method is used to theoretically demonstrate the method and also to predict the radio frequency behavior inside the human head. in addition, the proposed method is used in conjunction with the GRAPPA reconstruction technique to enable rapid imaging. Simulation results reported herein for IIT (470 MHz) brain imaging applications demonstrate the feasibility of the concept where multiple acquisitions using parallel imaging elements with GRAPPA reconstruction results in improved image quality. (c) 2006 Wiley Periodicals, Inc.

High performance numerical modeling of ultra-short laser pulse propagation based on multithreaded parallel hardware

The focus of this study is development of parallelised version of severely sequential and iterative numerical algorithms based on multi-threaded parallel platform such as a graphics processing unit. This requires design and development of a platform-specific numerical solution that can benefit from the parallel capabilities of the chosen platform. Graphics processing unit was chosen as a parallel platform for design and development of a numerical solution for a specific physical model in non-linear optics. This problem appears in describing ultra-short pulse propagation in bulk transparent media that has recently been subject to several theoretical and numerical studies. The mathematical model describing this phenomenon is a challenging and complex problem and its numerical modeling limited on current modern workstations. Numerical modeling of this problem requires a parallelisation of an essentially serial algorithms and elimination of numerical bottlenecks. The main challenge to overcome is parallelisation of the globally non-local mathematical model. This thesis presents a numerical solution for elimination of numerical bottleneck associated with the non-local nature of the mathematical model. The accuracy and performance of the parallel code is identified by back-to-back testing with a similar serial version.

Integrating structural and input design of a 2-DOF high-speed parallel manipulator: A flexible model-based approach

This paper discusses the integrated design of parallel manipulators, which exhibit varying dynamics. This characteristic affects the machine stability and performance. The design methodology consists of four main steps: (i) the system modeling using flexible multibody technique, (ii) the synthesis of reduced-order models suitable for control design, (iii) the systematic flexible model-based input signal design, and (iv) the evaluation of some possible machine designs. The novelty in this methodology is to take structural flexibilities into consideration during the input signal design; therefore, enhancing the standard design process which mainly considers rigid bodies dynamics. The potential of the proposed strategy is exploited for the design evaluation of a two degree-of-freedom high-speed parallel manipulator. The results are experimentally validated. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling of Plastic Deformation Effects in Ferromagnetic Thin Films

To explain the magnetic behavior of plastic deformation of thin magnetic films (Fe and permalloy) on an elastic substrate (nitinol), it is noted that unlike in the bulk, the dislocation density does not increase dramatically because of the dimensional constraint. As a result, the resulting residual stress, even though strain hardening is limited, dominates the observed magnetic behavior. Thus, with the field parallel to the stress axis, the compressive residual stress resulting from plastic deformation causes a decrease in remanence and an increase in coercivity; and with the field perpendicular to the stress axis, the resulting compressive residual stress causes an increase in remanence and a decrease in coercivity. These elements have been inserted into the model previously developed for plastic deformation in the bulk, producing the aforementioned behavior, which has been observed experimentally in the films.

Modeling of phase equilibrium and vapor adsorption on carbon black based on a combination of a lattice theory and equation of state

A thermodynamic approach is developed in this paper to describe the behavior of a subcritical fluid in the neighborhood of vapor-liquid interface and close to a graphite surface. The fluid is modeled as a system of parallel molecular layers. The Helmholtz free energy of the fluid is expressed as the sum of the intrinsic Helmholtz free energies of separate layers and the potential energy of their mutual interactions calculated by the 10-4 potential. This Helmholtz free energy is described by an equation of state (such as the Bender or Peng-Robinson equation), which allows us a convenient means to obtain the intrinsic Helmholtz free energy of each molecular layer as a function of its two-dimensional density. All molecular layers of the bulk fluid are in mechanical equilibrium corresponding to the minimum of the total potential energy. In the case of adsorption the external potential exerted by the graphite layers is added to the free energy. The state of the interface zone between the liquid and the vapor phases or the state of the adsorbed phase is determined by the minimum of the grand potential. In the case of phase equilibrium the approach leads to the distribution of density and pressure over the transition zone. The interrelation between the collision diameter and the potential well depth was determined by the surface tension. It was shown that the distance between neighboring molecular layers substantially changes in the vapor-liquid transition zone and in the adsorbed phase with loading. The approach is considered in this paper for the case of adsorption of argon and nitrogen on carbon black. In both cases an excellent agreement with the experimental data was achieved without additional assumptions and fitting parameters, except for the fluid-solid potential well depth. The approach has far-reaching consequences and can be readily extended to the model of adsorption in slit pores of carbonaceous materials and to the analysis of multicomponent adsorption systems. (C) 2002 Elsevier Science (USA).

Parallel run-time for CO-OPN

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

Including the workload effect in the parallel program signature

Performance prediction and application behavior modeling have been the subject of exten- sive research that aim to estimate applications performance with an acceptable precision. A novel approach to predict the performance of parallel applications is based in the con- cept of Parallel Application Signatures that consists in extract an application most relevant parts (phases) and the number of times they repeat (weights). Executing these phases in a target machine and multiplying its exeuction time by its weight an estimation of the application total execution time can be made. One of the problems is that the performance of an application depends on the program workload. Every type of workload affects differently how an application performs in a given system and so affects the signature execution time. Since the workloads used in most scientific parallel applications have dimensions and data ranges well known and the behavior of these applications are mostly deterministic, a model of how the programs workload affect its performance can be obtained. We create a new methodology to model how a program’s workload affect the parallel application signature. Using regression analysis we are able to generalize each phase time execution and weight function to predict an application performance in a target system for any type of workload within predefined range. We validate our methodology using a synthetic program, benchmarks applications and well known real scientific applications.

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (congruent with 73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r (2) = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pK(m) values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.

Measurements of glial metabolic fluxes with C-11-acetate using positron emission and an adapted NMR-based metabolic modeling approach

Objectives: Acetate brain metabolism has the particularity to occur specifically in glial cells. Labeling studies, using acetate labeled either with 13C (NMR) or 11C (PET), are governed by the same biochemical reactions and thus follow the same mathematical principles. In this study, the objective was to adapt an NMR acetate brain metabolism model to analyse [1-11C]acetate infusion in rats. Methods: Brain acetate infusion experiments were modeled using a two-compartment model approach used in NMR.1-3 The [1-11C]acetate labeling study was done using a beta scintillator.4 The measured radioactive signal represents the time evolution of the sum of all labeled metabolites in the brain. Using a coincidence counter in parallel, an arterial input curve was measured. The 11C at position C-1 of acetate is metabolized in the first turn of the TCA cycle to the position 5 of glutamate (Figure 1A). Through the neurotransmission process, it is further transported to the position 5 of glutamine and the position 5 of neuronal glutamate. After the second turn of the TCA cycle, tracer from [1-11C]acetate (and also a part from glial [5-11C]glutamate) is transferred to glial [1-11C]glutamate and further to [1-11C]glutamine and neuronal glutamate through the neurotransmission cycle. Brain poster session: oxidative mechanisms S460 Journal of Cerebral Blood Flow & Metabolism (2009) 29, S455-S466 Results: The standard acetate two-pool PET model describes the system by a plasma pool and a tissue pool linked by rate constants. Experimental data are not fully described with only one tissue compartment (Figure 1B). The modified NMR model was fitted successfully to tissue time-activity curves from 6 single animals, by varying the glial mitochondrial fluxes and the neurotransmission flux Vnt. A glial composite rate constant Kgtg=Vgtg/[Ace]plasma was extracted. Considering an average acetate concentration in plasma of 1 mmol/g5 and the negligible additional amount injected, we found an average Vgtg = 0.08±0.02 (n = 6), in agreement with previous NMR measurements.1 The tissue time-activity curve is dominated by glial glutamate and later by glutamine (Figure 1B). Labeling of neuronal pools has a low influence, at least for the 20 mins of beta-probe acquisition. Based on the high diffusivity of CO2 across the blood-brain barrier; 11CO2 is not predominant in the total tissue curve, even if the brain CO2 pool is big compared with other metabolites, due to its strong dilution through unlabeled CO2 from neuronal metabolism and diffusion from plasma. Conclusion: The two-compartment model presented here is also able to fit data of positron emission experiments and to extract specific glial metabolic fluxes. 11C-labeled acetate presents an alternative for faster measurements of glial oxidative metabolism compared to NMR, potentially applicable to human PET imaging. However, to quantify the relative value of the TCA cycle flux compared to the transmitochondrial flux, the chemical sensitivity of NMR is required. PET and NMR are thus complementary.