Focused, eight-element transceive phased array coil for parallel magnetic resonance imaging of the chest-theoretical considerations

A new transceive system for chest imaging for MRI applications is presented. A focused, eight-element transceive torso phased array coil is designed to investigate transmitting a focused radiofrequency field deep within the torso and to enhance signal homogeneity in the heart region. The system is used in conjunction with the SENSE reconstruction technique to enable focused parallel imaging. A hybrid finite-difference-time-domain/method-of-moments method is used to accurately predict the radiofrequency behavior inside the human torso. The simulation results reported herein demonstrate the feasibility of the design concept, which shows that radiofrequency field focusing with SENSE reconstruction is theoretically achievable. (c) 2005 Wiley-Liss, Inc.

An object-oriented designed finite-difference time-domain simulator for electromagnetic analysis and design in MRI - application to high field analyses

This paper presents a finite-difference time-domain (FDTD) simulator for electromagnetic analysis and design applications in MRI. It is intended to be a complete FDTD model of an MRI system including all RF and low-frequency field generating units and electrical models of the patient. The pro-ram has been constructed in an object-oriented framework. The design procedure is detailed and the numerical solver has been verified against analytical solutions for simple cases and also applied to various field calculation problems. In particular, the simulator is demonstrated for inverse RF coil design, optimized source profile generation, and parallel imaging in high-frequency situations. The examples show new developments enabled by the simulator and demonstrate that the proposed FDTD framework can be used to analyze large-scale computational electromagnetic problems in modern MRI engineering. (C) 2004 Elsevier Inc. All rights reserved.

Minimising cold damage during reproductive development among temperate rice genotypes. I. Avoiding low temperature with the use of appropriate sowing time and photoperiod-sensitive varieties

Multiple-sown field trials in 4 consecutive years in the Riverina region of south-eastern Australia provided 24 different combinations of temperature and day length, which enabled the development of crop phenology models. A crop model was developed for 7 cultivars from diverse origins to identify if photoperiod sensitivity is involved in determining phenological development, and if that is advantageous in avoiding low-temperature damage. Cultivars that were mildly photoperiod-sensitive were identified from sowing to flowering and from panicle initiation to flowering. The crop models were run for 47 years of temperature data to quantify the risk of encountering low temperature during the critical young microspore stage for 5 different sowing dates. Cultivars that were mildly photoperiod-sensitive, such as Amaroo, had a reduced likelihood of encountering low temperature for a wider range of sowing dates compared with photoperiod-insensitive cultivars. The benefits of increased photoperiod sensitivity include greater sowing flexibility and reduced water use as growth duration is shortened when sowing is delayed. Determining the optimal sowing date also requires other considerations, e. g. the risk of cold damage at other sensitive stages such as flowering and the response of yield to a delay in flowering under non-limiting conditions. It was concluded that appropriate sowing time and the use of photoperiod-sensitive cultivars can be advantageous in the Riverina region in avoiding low temperature damage during reproductive development.

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.

Parallel 3D Simulation of a Fault Gouge using the Lattice Solid Model

Despite the insight gained from 2-D particle models, and given that the dynamics of crustal faults occur in 3-D space, the question remains, how do the 3-D fault gouge dynamics differ from those in 2-D? Traditionally, 2-D modeling has been preferred over 3-D simulations because of the computational cost of solving 3-D problems. However, modern high performance computing architectures, combined with a parallel implementation of the Lattice Solid Model (LSM), provide the opportunity to explore 3-D fault micro-mechanics and to advance understanding of effective constitutive relations of fault gouge layers. In this paper, macroscopic friction values from 2-D and 3-D LSM simulations, performed on an SGI Altix 3700 super-cluster, are compared. Two rectangular elastic blocks of bonded particles, with a rough fault plane and separated by a region of randomly sized non-bonded gouge particles, are sheared in opposite directions by normally-loaded driving plates. The results demonstrate that the gouge particles in the 3-D models undergo significant out-of-plane motion during shear. The 3-D models also exhibit a higher mean macroscopic friction than the 2-D models for varying values of interparticle friction. 2-D LSM gouge models have previously been shown to exhibit accelerating energy release in simulated earthquake cycles, supporting the Critical Point hypothesis. The 3-D models are shown to also display accelerating energy release, and good fits of power law time-to-failure functions to the cumulative energy release are obtained.

Towards platform-independent real-time systems

Real-time software systems are rarely developed once and left to run. They are subject to changes of requirements as the applications they support expand, and they commonly outlive the platforms they were designed to run on. A successful real-time system is duplicated and adapted to a variety of applications - it becomes a product line. Current methods for real-time software development are commonly based on low-level programming languages and involve considerable duplication of effort when a similar system is to be developed or the hardware platform changes. To provide more dependable, flexible and maintainable real-time systems at a lower cost what is needed is a platform-independent approach to real-time systems development. The development process is composed of two phases: a platform-independent phase, that defines the desired system behaviour and develops a platform-independent design and implementation, and a platform-dependent phase that maps the implementation onto the target platform. The last phase should be highly automated. For critical systems, assessing dependability is crucial. The partitioning into platform dependent and independent phases has to support verification of system properties through both phases.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

Termination of real-time programs: Definitely, definitely not, or maybe

Real-time control programs are often used in contexts where (conceptually) they run forever. Repetitions within such programs (or their specifications) may either (i) be guaranteed to terminate, (ii) be guaranteed to never terminate (loop forever), or (iii) may possibly terminate. In dealing with real-time programs and their specifications, we need to be able to represent these possibilities, and define suitable refinement orderings. A refinement ordering based on Dijkstra's weakest precondition only copes with the first alternative. Weakest liberal preconditions allow one to constrain behaviour provided the program terminates, which copes with the third alternative to some extent. However, neither of these handles the case when a program does not terminate. To handle this case a refinement ordering based on relational semantics can be used. In this paper we explore these issues and the definition of loops for real-time programs as well as corresponding refinement laws.

Optimum switching point assessment of parallel braking resistor and serial current limiter for power system stability enhancement

A new control algorithm using parallel braking resistor (BR) and serial fault current limiter (FCL) for power system transient stability enhancement is presented in this paper. The proposed control algorithm can prevent transient instability during first swing by immediately taking away the transient energy gained in faulted period. It can also reduce generator oscillation time and efficiently make system back to the post-fault equilibrium. The algorithm is based on a new system energy function based method to choose optimal switching point. The parallel BR and serial FCL resistor can be switched at the calculated optimal point to get the best control result. This method allows optimum dissipation of the transient energy caused by disturbance so to make system back to equilibrium in minimum time. Case studies are given to verify the efficiency and effectiveness of this new control algorithm.

A real-time asymmetric multiprocessor-reconfigurable system-on-chip architecture

We propose an asymmetric multi-processor SoC architecture, featuring a master CPU running uClinux, and multiple loosely-coupled slave CPUs running real-time threads assigned by the master CPU. Real-time SoC architectures often demand a compromise between a generic platform for different applications, and application-specific customizations to achieve performance requirements. Our proposed architecture offers a generic platform running a conventional embedded operating system providing a traditional software-oriented development approach, while multiple slave CPUs act as a dedicated independent real-time threads execution unit running in parallel of master CPU to achieve performance requirements. In this paper, the architecture is described, including the application / threading development environment. The performance of the architecture with several standard benchmark routines is also analysed.

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.

Real-time imaging of human cortical activity evoked by painful esophageal stimulation

Background & Aims: Current models of visceral pain processing derived from metabolic brain imaging techniques fail to differentiate between exogenous (stimulus-dependent) and endogenous (non-stimulus-specific) neural activity. The aim of this study was to determine the spatiotemporal correlates of exogenous neural activity evoked by painful esophageal stimulation. Methods: In 16 healthy subjects (8 men; mean age, 30.2 ± 2.2 years), we recorded magnetoencephalographic responses to 2 runs of 50 painful esophageal electrical stimuli originating from 8 brain subregions. Subsequently, 11 subjects (6 men; mean age, 31.2 ± 1.8 years) had esophageal cortical evoked potentials recorded on a separate occasion by using similar experimental parameters. Results: Earliest cortical activity (P1) was recorded in parallel in the primary/secondary somatosensory cortex and posterior insula (∼85 ms). Significantly later activity was seen in the anterior insula (∼103 ms) and cingulate cortex (∼106 ms; P = .0001). There was no difference between the P1 latency for magnetoencephalography and cortical evoked potential (P = .16); however, neural activity recorded with cortical evoked potential was longer than with magnetoencephalography (P = .001). No sex differences were seen for psychophysical or neurophysiological measures. Conclusions: This study shows that exogenous cortical neural activity evoked by experimental esophageal pain is processed simultaneously in somatosensory and posterior insula regions. Activity in the anterior insula and cingulate - brain regions that process the affective aspects of esophageal pain - occurs significantly later than in the somatosensory regions, and no sex differences were observed with this experimental paradigm. Cortical evoked potential reflects the summation of cortical activity from these brain regions and has sufficient temporal resolution to separate exogenous and endogenous neural activity. © 2005 by the American Gastroenterological Association.

Parallel geostatistics for sparse and dense datasets

Very large spatially-referenced datasets, for example, those derived from satellite-based sensors which sample across the globe or large monitoring networks of individual sensors, are becoming increasingly common and more widely available for use in environmental decision making. In large or dense sensor networks, huge quantities of data can be collected over small time periods. In many applications the generation of maps, or predictions at specific locations, from the data in (near) real-time is crucial. Geostatistical operations such as interpolation are vital in this map-generation process and in emergency situations, the resulting predictions need to be available almost instantly, so that decision makers can make informed decisions and define risk and evacuation zones. It is also helpful when analysing data in less time critical applications, for example when interacting directly with the data for exploratory analysis, that the algorithms are responsive within a reasonable time frame. Performing geostatistical analysis on such large spatial datasets can present a number of problems, particularly in the case where maximum likelihood. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively. Most modern commodity hardware has at least 2 processor cores if not more. Other mechanisms for allowing parallel computation such as Grid based systems are also becoming increasingly commonly available. However, currently there seems to be little interest in exploiting this extra processing power within the context of geostatistics. In this paper we review the existing parallel approaches for geostatistics. By recognising that diffeerent natural parallelisms exist and can be exploited depending on whether the dataset is sparsely or densely sampled with respect to the range of variation, we introduce two contrasting novel implementations of parallel algorithms based on approximating the data likelihood extending the methods of Vecchia [1988] and Tresp [2000]. Using parallel maximum likelihood variogram estimation and parallel prediction algorithms we show that computational time can be significantly reduced. We demonstrate this with both sparsely sampled data and densely sampled data on a variety of architectures ranging from the common dual core processor, found in many modern desktop computers, to large multi-node super computers. To highlight the strengths and weaknesses of the diffeerent methods we employ synthetic data sets and go on to show how the methods allow maximum likelihood based inference on the exhaustive Walker Lake data set.

Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.