United States Department of Commerce

About: United States Department of Commerce is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Video quality. The organization has 1360 authors who have published 1159 publications receiving 37140 citations. The organization is also known as: Commerce & US Department of Commerce.

Frequency assignment: Theory and applications

Abstract: In this paper we introduce the minimum-order approach to frequency assignment and present a theory which relates this approach to the traditional one. This new approach is potentially more desirable than the traditional one. We model assignment problems as both frequency-distance constrained and frequency constrained optimization problems. The frequency constrained approach should be avoided if distance separation is employed to mitigate interference. A restricted class of graphs, called disk graphs, plays a central role in frequency-distance constrained problems. We introduce two generalizations of chromatic number and show that many frequency assignment problems are equivalent to generalized graph coloring problems. Using these equivalences and recent results concerning the complexity of graph coloring, we classify many frequency assignment problems according to the "execution time efficiency" of algorithms that may be devised for their solution. We discuss applications to important real world problems and identify areas for further work.

The Probability Plot Correlation Coefficient Test for Normality

Abstract: This paper introdLlces the normal probability plot correlation coefficient as a test statistic in complete samples for the composite hypothesis of normality. The proposed test statistic is conceptnally simple, is compntationally convenient, and is readily extendible to testing non-normal distributional hypotheses. An empirical power strldy shows that the normal probability plot correlation coefficient, compares favorably with 7 other normal test statistics. Percent points are tabulated for n = 3(l)50(5)100.

Statistical-physical models of electromagnetic interference

Abstract: Most man-made and natural electromagnetic interference, or "noise," are highly non-Gaussian random processes, whose degrading effects on system performance can be severe, particularly on most conventional systems, which are designed for optimal or near optimal performance against normal noise. In addition, the nature, origins, measurement, and prediction of the general EM interference environment are a major concern of any adequate spectral management program. Accordingly, this study is devoted to the development of analytically tractable, experimentally verifiable, statistical-physical models of such electromagnetic interference. Here, classification into three major types of noise is made: Class A (narrow band vis-a-vis the receiver), Class B (broad band vis-a-vis the receiver), and Class C (= Class A + Class B). First-order statistical models are constructed for the Class A and Class B cases. In particular, the APD (a posteriori probability distribution) or exceedance probability, PD, vis;P1 (? > ?o)A,B, (and the associated probability densities, pdf's w1(?)A,B,[1]) of the envelope are obtained; (the phase is shown to be uniformly distributed in (0, 2?). These results are canonical, i.e., their analytic forms are invariant of the particular noise source and its quantifying parameter values, levels, etc. Class A interference is described by a 3-parameter model, Class B noise by a 6-parameter model.

Algorithm 526: Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points [E1]

Abstract: A method of blvariate interpolation and smooth surface fitting is developed for z values given at points irregularly distributed in the x-y plane. The interpolating function is a fifth-degree polynomial in x and y defined in each triangular cell whmh has projections of three data points in the x-y plane as its vertexes. Each polynomial is determined by the given values of z and estimated values of partial derivatives at the vertexes of the triangle. Procedures for dividing the x-y plane into a number of triangles, for estimating partial derivatives at each data point, and for determining the polynomial in each triangle are described A simple example of the application of the proposed method is shown. Key W6rds and Phrases bivariate interpolation, interpolation, partial derivative, polynomial, smooth surface fitting CR Categories: 5.13 The Algorithm Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points ACM Trans. Math. Software 2, 1(June 1978), 160-164.

Women in STEM: A Gender Gap to Innovation

Abstract: Our science, technology, engineering and math (STEM) workforce is crucial to America’s innovative capacity and global competitiveness. Yet women are vastly underrepresented in STEM jobs and among STEM degree holders despite making up nearly half of the U.S. workforce and half of the college-educated workforce. That leaves an untapped opportunity to expand STEM employment in the United States, even as there is wide agreement that the nation must do more to improve its competitiveness.Although women fill close to half of all jobs in the U.S. economy, they hold less than 25 percent of STEM jobs. This has been the case throughout the past decade, even as college-educated women have increased their share of the overall workforce.Women with STEM jobs earned 33 percent more than comparable women in non-STEM jobs – considerably higher than the STEM premium for men. As a result, the gender wage gap is smaller in STEM jobs than in non-STEM jobs.Women hold a disproportionately low share of STEM undergraduate degrees, particularly in engineering.Women with a STEM degree are less likely than their male counterparts to work in a STEM occupation; they are more likely to work in education or healthcare.There are many possible factors contributing to the discrepancy of women and men in STEM jobs, including: a lack of female role models, gender stereotyping, and less family-friendly flexibility in the STEM fields. Regardless of the causes, the findings of this report provide evidence of a need to encourage and support women in STEM.