A first-order time-domain Green's function approach to supersonic unsteady flow

A time-domain Green's Function Method for unsteady supersonic potential flow around complex aircraft configurations is presented. The focus is on the supersonic range wherein the linear potential flow assumption is valid. The Green's function method is employed in order to convert the potential-flow differential equation into an integral one. This integral equation is then discretized, in space through standard finite-element technique, and in time through finite-difference, to yield a linear algebraic system of equations relating the unknown potential to its prescribed co-normalwash (boundary condition) on the surface of the aircraft. The arbitrary complex aircraft configuration is discretized into hyperboloidal (twisted quadrilateral) panels. The potential and co-normalwash are assumed to vary linearly within each panel. Consistent with the spatial linear (first-order) finite-element approximations, the potential and co-normalwash are assumed to vary linearly in time. The long range goal of our research is to develop a comprehensive theory for unsteady supersonic potential aerodynamics which is capable of yielding accurate results even in the low supersonic (i.e., high transonic) range

Identifying Biomagnetic Sources in the Brain by the Maximum Entropy Approach

Magnetoencephalographic (MEG) measurements record magnetic fields generated from neurons while information is being processed in the brain. The inverse problem of identifying sources of biomagnetic fields and deducing their intensities from MEG measurements is ill-posed when the number of field detectors is far less than the number of sources. This problem is less severe if there is already a reasonable prior knowledge in the form of a distribution in the intensity of source activation. In this case the problem of identifying and deducing source intensities may be transformed to one of using the MEG data to update a prior distribution to a posterior distribution. Here we report on some work done using the maximum entropy method (ME) as an updating tool. Specifically, we propose an implementation of the ME method in cases when the prior contain almost no knowledge of source activation. Two examples are studied, in which part of motor cortex is activated with uniform and varying intensities, respectively.Comment: 8 pages, 8 figures. Presented at 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, San Jose, CA, USA Aug 7-12, 200

A first-order Green's function approach to supersonic oscillatory flow: A mixed analytic and numeric treatment

A frequency domain Green's Function Method for unsteady supersonic potential flow around complex aircraft configurations is presented. The focus is on the supersonic range wherein the linear potential flow assumption is valid. In this range the effects of the nonlinear terms in the unsteady supersonic compressible velocity potential equation are negligible and therefore these terms will be omitted. The Green's function method is employed in order to convert the potential flow differential equation into an integral one. This integral equation is then discretized, through standard finite element technique, to yield a linear algebraic system of equations relating the unknown potential to its prescribed co-normalwash (boundary condition) on the surface of the aircraft. The arbitrary complex aircraft configuration (e.g., finite-thickness wing, wing-body-tail) is discretized into hyperboloidal (twisted quadrilateral) panels. The potential and co-normalwash are assumed to vary linearly within each panel. The long range goal is to develop a comprehensive theory for unsteady supersonic potential aerodynamic which is capable of yielding accurate results even in the low supersonic (i.e., high transonic) range

A complete framework for Web mining

With the rapid growing number of WWW users, hidden information becomes ever increasingly valuable. As a consequence of this phenomenon, mining Web data and analysing on-line users' behaviour and their on-line traversal pattern have emerged as a new area of research. Primarily based on the Web servers' log files, the main objective of traversal pattern mining is to discover the frequent patterns in users' browsing paths and behaviors. This paper presents a complete framework for Web mining, allowing users to pre-define physical constraints when analysing complex traversal patterns in order to improve the efficiency of algorithms and offer flexibility in producing the results

A Layout-Aware Circuit Sizing Model Using Parametric Analysis

We propose a circuit sizing model that takes layout parasitics into account. The circuit and layout parameters are stored in a parameterized layout description format, GBLD. The layout parasitics are stored as closed form expressions. Layout optimization tools can modify the layout and recalculate parasitics on the fly. If the results of sensitivity analysis are passed to those tools, optimization for performance can be achieved with relatively few iterations involving time consuming circuit simulations

GBLD: A Formal Model for Layout Description and Generation

In this paper, we introduce a layout description and generation model, GBLD, based on the notions and elements of L-systems and context-free grammars. Our layout model is compatible with geometric layout formats, such as GDSII or CIF. However, it is more powerful and more concise. The layouts represented by GBLD are sizeable, parameterised, and can incorporate design rules. GBLD has the potential to be used as a format for analog layout templates, analog layout retargeting, as well as the final layout format

An Improved Approximate Consensus Algorithm in the Presence of Mobile Faults

This paper explores the problem of reaching approximate consensus in synchronous point-to-point networks, where each pair of nodes is able to communicate with each other directly and reliably. We consider the mobile Byzantine fault model proposed by Garay '94 -- in the model, an omniscient adversary can corrupt up to $f$ nodes in each round, and at the beginning of each round, faults may "move" in the system (i.e., different sets of nodes may become faulty in different rounds). Recent work by Bonomi et al. '16 proposed a simple iterative approximate consensus algorithm which requires at least $4f+1$ nodes. This paper proposes a novel technique of using "confession" (a mechanism to allow others to ignore past behavior) and a variant of reliable broadcast to improve the fault-tolerance level. In particular, we present an approximate consensus algorithm that requires only $\lceil 7f/2\rceil + 1$ nodes, an $\lfloor f/2 \rfloor$ improvement over the state-of-the-art algorithms. Moreover, we also show that the proposed algorithm is optimal within a family of round-based algorithms