Limits on the HI content of the dwarf galaxy Hydra II

Sensitive 21cm HI observations have been made with the Green Bank Telescope toward the newly-discovered Local Group dwarf galaxy Hydra II, which may lie within the leading arm of the Magellanic Stream. No neutral hydrogen was detected. Our 5-sigma limit of MHI < 210 solar masses for a 15 km/s linewidth gives a gas-to-luminosity ratio MHI/L_V < 2.6 x 10^{-2} Mo / Lo. The limits on HI mass and MHI/L_V are typical of dwarf galaxies found within a few hundred kpc of the Milky Way. Whatever the origin of Hydra II, its neutral gas properties are not unusual.Comment: Accepted for publication in Astronomy & Astrophysic

Problems in Sensor Networks: Inference, *Localization, and Motion Estimation

114 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Finally, we introduce a framework to study the development of efficient algorithms in systems with dependent tasks. We use it to identify the structure of the algorithms we designed, and a classification that may be useful in other real-time systems.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Tracking Objects with Networked Scattered Directional Sensors

We study the problem of object tracking using highly directional sensors—sensors whose field of vision is a line or a line segment. A network of such sensors monitors a certain region of the plane. Sporadically, objects moving in straight lines and at a constant speed cross the region. A sensor detects an object when it crosses its line of sight, and records the time of the detection. No distance or angle measurements are available. The task of the sensors is to estimate the directions and speeds of the objects, and the sensor lines, which are unknown a priori. This estimation problem involves the minimization of a highly nonconvex cost function. To overcome this difficulty, we introduce an algorithm, which we call “adaptive basis algorithm.†This algorithm is divided into three phases: in the first phase, the algorithm is initialized using data from six sensors and four objects; in the second phase, the estimates are updated as data from more sensors and objects are incorporated. The third phase is an optional coordinated transformation. The estimation is done in an “ad-hoc†coordinate system, which we call “adaptive coordinate system.†When more information is available, for example, the location of six sensors, the estimates can be transformed to the “real-world†coordinate system. This constitutes the third phase

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices

In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node.To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and delivering just- in-time adaptive interventions. We apply our framework to two activity recognition datasets as well as the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data.Comment: arXiv admin note: substantial text overlap with arXiv:1607.0373