1,272 research outputs found
Collaborative Beamforming for Distributed Wireless Ad Hoc Sensor Networks
The performance of collaborative beamforming is analyzed using the theory of
random arrays. The statistical average and distribution of the beampattern of
randomly generated phased arrays is derived in the framework of wireless ad hoc
sensor networks. Each sensor node is assumed to have a single isotropic antenna
and nodes in the cluster collaboratively transmit the signal such that the
signal in the target direction is coherently added in the far- eld region. It
is shown that with N sensor nodes uniformly distributed over a disk, the
directivity can approach N, provided that the nodes are located sparsely
enough. The distribution of the maximum sidelobe peak is also studied. With the
application to ad hoc networks in mind, two scenarios, closed-loop and
open-loop, are considered. Associated with these scenarios, the effects of
phase jitter and location estimation errors on the average beampattern are also
analyzed.Comment: To appear in the IEEE Transactions on Signal Processin
Accurate and efficient spin integration for particle accelerators
Accurate spin tracking is a valuable tool for understanding spin dynamics in
particle accelerators and can help improve the performance of an accelerator.
In this paper, we present a detailed discussion of the integrators in the spin
tracking code gpuSpinTrack. We have implemented orbital integrators based on
drift-kick, bend-kick, and matrix-kick splits. On top of the orbital
integrators, we have implemented various integrators for the spin motion. These
integrators use quaternions and Romberg quadratures to accelerate both the
computation and the convergence of spin rotations. We evaluate their
performance and accuracy in quantitative detail for individual elements as well
as for the entire RHIC lattice. We exploit the inherently data-parallel nature
of spin tracking to accelerate our algorithms on graphics processing units.Comment: 43 pages, 17 figure
Quasi-dynamic Load and Battery Sizing and Scheduling for Stand-Alone Solar System Using Mixed-integer Linear Programming
Considering the intermittency of renewable energy systems, a sizing and
scheduling model is proposed for a finite number of static electric loads. The
model objective is to maximize solar energy utilization with and without
storage. For the application of optimal load size selection, the energy
production of a solar photovoltaic is assumed to be consumed by a finite number
of discrete loads in an off-grid system using mixed-integer linear programming.
Additional constraints are battery charge and discharge limitations and minimum
uptime and downtime for each unit. For a certain solar power profile the model
outputs optimal unit size as well as the optimal scheduling for both units and
battery charge and discharge (if applicable). The impact of different solar
power profiles and minimum up and down time constraints on the optimal unit and
battery sizes are studied. The battery size required to achieve full solar
energy utilization decreases with the number of units and with increased
flexibility of the units (shorter on and off-time). A novel formulation is
introduced to model quasi-dynamic units that gradually start and stop and the
quasi-dynamic units increase solar energy utilization. The model can also be
applied to search for the optimal number of units for a given cost function.Comment: 6 pages, 3 figures, accepted at The IEEE Conference on Control
Applications (CCA
Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios
This paper presents and evaluates the performance of an optimal scheduling
algorithm that selects the on/off combinations and timing of a finite set of
dynamic electric loads on the basis of short term predictions of the power
delivery from a photovoltaic source. In the algorithm for optimal scheduling,
each load is modeled with a dynamic power profile that may be different for on
and off switching. Optimal scheduling is achieved by the evaluation of a
user-specified criterion function with possible power constraints. The
scheduling algorithm exploits the use of a moving finite time horizon and the
resulting finite number of scheduling combinations to achieve real-time
computation of the optimal timing and switching of loads. The moving time
horizon in the proposed optimal scheduling algorithm provides an opportunity to
use short term (time moving) predictions of solar power based on advection of
clouds detected in sky images. Advection, persistence, and perfect forecast
scenarios are used as input to the load scheduling algorithm to elucidate the
effect of forecast errors on mis-scheduling. The advection forecast creates
less events where the load demand is greater than the available solar energy,
as compared to persistence. Increasing the decision horizon leads to increasing
error and decreased efficiency of the system, measured as the amount of power
consumed by the aggregate loads normalized by total solar power. For a
standalone system with a real forecast, energy reserves are necessary to
provide the excess energy required by mis-scheduled loads. A method for battery
sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201
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The ASD Living Biology: from cell proliferation to clinical phenotype.
Autism spectrum disorder (ASD) has captured the attention of scientists, clinicians and the lay public because of its uncertain origins and striking and unexplained clinical heterogeneity. Here we review genetic, genomic, cellular, postmortem, animal model, and cell model evidence that shows ASD begins in the womb. This evidence leads to a new theory that ASD is a multistage, progressive disorder of brain development, spanning nearly all of prenatal life. ASD can begin as early as the 1st and 2nd trimester with disruption of cell proliferation and differentiation. It continues with disruption of neural migration, laminar disorganization, altered neuron maturation and neurite outgrowth, disruption of synaptogenesis and reduced neural network functioning. Among the most commonly reported high-confidence ASD (hcASD) genes, 94% express during prenatal life and affect these fetal processes in neocortex, amygdala, hippocampus, striatum and cerebellum. A majority of hcASD genes are pleiotropic, and affect proliferation/differentiation and/or synapse development. Proliferation and subsequent fetal stages can also be disrupted by maternal immune activation in the 1st trimester. Commonly implicated pathways, PI3K/AKT and RAS/ERK, are also pleiotropic and affect multiple fetal processes from proliferation through synapse and neural functional development. In different ASD individuals, variation in how and when these pleiotropic pathways are dysregulated, will lead to different, even opposing effects, producing prenatal as well as later neural and clinical heterogeneity. Thus, the pathogenesis of ASD is not set at one point in time and does not reside in one process, but rather is a cascade of prenatal pathogenic processes in the vast majority of ASD toddlers. Despite this new knowledge and theory that ASD biology begins in the womb, current research methods have not provided individualized information: What are the fetal processes and early-age molecular and cellular differences that underlie ASD in each individual child? Without such individualized knowledge, rapid advances in biological-based diagnostic, prognostic, and precision medicine treatments cannot occur. Missing, therefore, is what we call ASD Living Biology. This is a conceptual and paradigm shift towards a focus on the abnormal prenatal processes underlying ASD within each living individual. The concept emphasizes the specific need for foundational knowledge of a living child's development from abnormal prenatal beginnings to early clinical stages. The ASD Living Biology paradigm seeks this knowledge by linking genetic and in vitro prenatal molecular, cellular and neural measurements with in vivo post-natal molecular, neural and clinical presentation and progression in each ASD child. We review the first such study, which confirms the multistage fetal nature of ASD and provides the first in vitro fetal-stage explanation for in vivo early brain overgrowth. Within-child ASD Living Biology is a novel research concept we coin here that advocates the integration of in vitro prenatal and in vivo early post-natal information to generate individualized and group-level explanations, clinically useful prognoses, and precision medicine approaches that are truly beneficial for the individual infant and toddler with ASD
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