California Institute of Technology

About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.

Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview

Abstract: The Multi-angle Imaging SpectroRadiometer (MISR) instrument is scheduled for launch aboard the first of the Earth Observing System (EOS) spacecraft, EOS-AM1. MISR will provide global, radiometrically calibrated, georectified, and spatially coregistered imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. Algorithms specifically developed to capitalize on this measurement strategy will be used to retrieve geophysical products for studies of clouds, aerosols, and surface radiation. This paper provides an overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth.

Self-similarity through high-variability: statistical analysis of ethernet LAN traffic at the source level

Abstract: A number of recent empirical studies of traffic measurements from a variety of working packet networks have convincingly demonstrated that actual network traffic is self-similar or long-range dependent in nature (i.e., bursty over a wide range of time scales) - in sharp contrast to commonly made traffic modeling assumptions. In this paper, we provide a plausible physical explanation for the occurrence of self-similarity in high-speed network traffic. Our explanation is based on convergence results for processes that exhibit high variability (i.e., infinite variance) and is supported by detailed statistical analyses of real-time traffic measurements from Ethernet LAN's at the level of individual sources.Our key mathematical result states that the superposition of many ON/OFF sources (also known as packet trains) whose ON-periods and OFF-periods exhibit the Noah Effect (i.e., have high variability or infinite variance) produces aggregate network traffic that features the Joseph Effect (i.e., is self-similar or long-range dependent). There is, moreover, a simple relation between the parameters describing the intensities of the Noah Effect (high variability) and the Joseph Effect (self-similarity). An extensive statistical analysis of two sets of high time-resolution traffic measurements from two Ethernet LAN's (involving a few hundred active source-destination pairs) confirms that the data at the level of individual sources or source-destination pairs are consistent with the Noah Effect. We also discuss implications of this simple physical explanation for the presence of self-similar traffic patterns in modern high-speed network traffic for (i) parsimonious traffic modeling (ii) efficient synthetic generation of realistic traffic patterns, and (iii) relevant network performance and protocol analysis.

Detection of an Extrasolar Planet Atmosphere

Abstract: We report high precision spectrophotometric observations of four planetary transits of HD 209458, in the region of the sodium resonance doublet at 589.3 nm. We find that the photometric dimming during transit in a bandpass centered on the sodium feature is deeper by (2.32 +/- 0.57) x 10^{-4} relative to simultaneous observations of the transit in adjacent bands. We interpret this additional dimming as absorption from sodium in the planetary atmosphere, as recently predicted from several theoretical modeling efforts. Our model for a cloudless planetary atmosphere with a solar abundance of sodium in atomic form predicts more sodium absorption than we observe. There are several possibilities that may account for this reduced amplitude, including reaction of atomic sodium into molecular gases and/or condensates, photoionization of sodium by the stellar flux, a low primordial abundance of sodium, or the presence of clouds high in the atmosphere.

Spurious dimension from correlation algorithms applied to limited time-series data

Abstract: An algorithm devised for measuring the dimension of a strange attractor from a time series is applied both to autocorrelated Gaussian noise and to a dynamical system. It is analytically shown that a finite sequence of stochastic data---where by ``finite'' it is meant that N2${\ensuremath{\tau}}^{m/2}$, where N is the number of points in the sequence, \ensuremath{\tau} is the autocorrelation time (in units of sampling period), and m is the embedding dimension---exhibits anomalous structure in its correlation integral. The anomaly is seen numerically in both stochastic and dynamical data. Unrecognized, it can lead to unnecessarily inaccurate and possibly spurious estimates of dimension. We propose a slight modification of the standard algorithm which eliminates this difficulty.