Structural analysis of light aircraft using NASTRAN

An application of NASTRAN to the structural analysis of light aircraft was conducted to determine the cost effectiveness. A model of the Baby Ace D model homebuilt aircraft was used. The NASTRAN model of the aircraft consists of 193 grid points connected by 352 structural members. All members are either rod or beam elements, including bending of unsymmetrical cross sections and torsion of noncircular cross sections. The aerodynamic loads applied to the aircraft were in accordance with FAA regulations governing the utility category aircraft

Minimum entropy restoration using FPGAs and high-level techniques

One of the greatest perceived barriers to the widespread use of FPGAs in image processing is the difficulty for application specialists of developing algorithms on reconfigurable hardware. Minimum entropy deconvolution (MED) techniques have been shown to be effective in the restoration of star-field images. This paper reports on an attempt to implement a MED algorithm using simulated annealing, first on a microprocessor, then on an FPGA. The FPGA implementation uses DIME-C, a C-to-gates compiler, coupled with a low-level core library to simplify the design task. Analysis of the C code and output from the DIME-C compiler guided the code optimisation. The paper reports on the design effort that this entailed and the resultant performance improvements

Restoration of star-field images using high-level languages and core libraries

Research into the use of FPGAs in Image Processing began in earnest at the beginning of the 1990s. Since then, many thousands of publications have pointed to the computational capabilities of FPGAs. During this time, FPGAs have seen the application space to which they are applicable grow in tandem with their logic densities. When investigating a particular application, researchers compare FPGAs with alternative technologies such as Digital Signal Processors (DSPs), Application-Specific Integrated Cir-cuits (ASICs), microprocessors and vector processors. The metrics for comparison depend on the needs of the application, and include such measurements as: raw performance, power consumption, unit cost, board footprint, non-recurring engineering cost, design time and design cost. The key metrics for a par-ticular application may also include ratios of these metrics, e.g. power/performance, or performance/unit cost. The work detailed in this paper compares a 90nm-process commodity microprocessor with a plat-form based around a 90nm-process FPGA, focussing on design time and raw performance. The application chosen for implementation was a minimum entropy restoration of star-field images (see [1] for an introduction), with simulated annealing used to converge towards the globally-optimum solution. This application was not chosen in the belief that it would particularly suit one technology over another, but was instead selected as being representative of a computationally intense image-processing application

Real, virtual, and other personas in an online collaborative environment

This presentation reports on a study of an unusual online course, which incorporates collaboration across campuses in teaching about evaluation of information technologies. Issues raised by new information technologies are major foci within the course, and also entry points for our study of its implementation.published or submitted for publicationis peer reviewe

DISTRIBUTIONAL IMPACTS OF CAPPING ELIGIBILITY FOR COMMODITY PROGRAM PAYMENTS

Adjusted Gross Income, Commodity Payments, Eligibility, Means Test, Resource /Energy Economics and Policy, Q12, Q18,

Financing Constraints and Corporate Investment

macroeconomics, Financing Constraints, Corporate Investment

Inventory Investment, Internal-Finance Fluctuation, and the Business Cycle

macroeconomics, inventory investment, internal-finance fluctuation, business cycle

Financing Constraints and Corporate Investment: Response to Kaplan and Zingales

Kaplan and Zingales (1995, hereafter KZ) criticize Fazzari, Hubbard and Petersen (1988, hereafter FHP) and much ensuing research that uses cross-sectional differences in firm behavior to test for financing constraints on investment. This reply identifies flaws in the KZ analysis. The questions KZ raise have been considered extensively and rigorously in the literature (most of which is not addressed in KZ), with results broadly similar to those of FHP. We also challenge both of KZ's main results. First, their finding that most of the FHP firms are not financially constrained relies on an inappropriate operational definition of what it means to be constrained. Their definition ignores the incentives for firms that operate in imperfect capital markets to accumulate stocks of cash or maintain unused debt capacity to offset partially shocks to the flow of internal finance. Second, the KZ regression results (lower sensitivity of investment to cash flow for firms classified as constrained than for those classified as unconstrained) are uninformative. Their classification approach relies on possibly self- serving managerial statements that may present a distorted picture of firm's availability of finance. It also employs misleading criteria to make unrealistically fine distinctions in the degree of financing constraints, and emphasizes financial distress rather than financing constraints. Finally, econometric problems affect the interpretation of the KZ regressions. We conclude that the KZ findings do not contradict the interpretation of the empirical results in FHP and subsequent research.