Hybrid Cognition for Target Tracking in Cognitive Radar Networks

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel.Comment: 34 pages, single-column, 10 figure

Gray Whale Distribution and Catch by Alaskan Eskimos: A Replacement for the Bowhead Whale?

The catch of gray whales, Eschrichtius robustus, by Alaskan Eskimos from 1925 to 1980 has been documented to the extent possible by a search of the literature and personal communications with knowledgeable sources. During the period 1950-1980, 47 gray whales were landed by hunters at 12 villages. During this same period, 505 bowhead whales, Balaena mysticetus, were landed at nine coastal whaling villages. Alaskan Eskimos traditionally have been bowhead whalers, principally because of the predictive nature of the bowheads' migration. Gray whaling has never been an important subsistence activity. Because the bowhead population is thought to be depleted, gray whales have been suggested as a possible substitute for subsistence. The distribution of gray whales in Alaskan coastal waters is such that reliable annual whaling for this species is possible only at villages on the shores of the northern Bering Sea; it is unlikely for villages north of Bering Strait to Cape Lisburne, and more unlikely for villages north of Cape Lisburne and east of Point Barrow. Based on cultural and biological grounds, substituting gray whales for bowheads does not appear to be a reliable alternative for the residents of four to six of the nine Eskimo villages that currently participate in bowhead whaling.Key words: gray whale, Eschrichitus robustus; bowhead whale, Balaena mysticetus; Eskimos; subsistence whaling; AlaskaMots clés: baleine grise de Californie, Eschrichtius robustus; balaine boréale, Balaena mysticetus; Esquimaux, chasse à la baleine comme activité de subsistance, Alask

Kepler-18b,c, and d: A System of Three Planets Confirmed by Transit Timing Variations, Light Curve Validation, Warm-Spitzer Photometry, and Radial Velocity Measurements

We report the detection of three transiting planets around a Sun-like star, which we designate Kepler-18. The transit signals were detected in photometric data from the Kepler satellite, and were confirmed to arise from planets using a combination of large transit-timing variations (TTVs), radial velocity variations, Warm-Spitzer observations, and statistical analysis of false-positive probabilities. The Kepler-18 star has a mass of 0.97 M_☉, a radius of 1.1 R_☉, an effective temperature of 5345 K, and an iron abundance of [Fe/H] = +0.19. The planets have orbital periods of approximately 3.5, 7.6, and 14.9 days. The innermost planet "b" is a "super-Earth" with a mass of 6.9 ± 3.4 M_⊕, a radius of 2.00 ± 0.10 R_⊕, and a mean density of 4.9 ± 2.4 g cm^3. The two outer planets "c" and "d" are both low-density Neptune-mass planets. Kepler-18c has a mass of 17.3 ± 1.9 M_⊕, a radius of 5.49 ± 0.26 R_⊕, and a mean density of 0.59 ± 0.07 g cm^3, while Kepler-18d has a mass of 16.4 ± 1.4 M_⊕, a radius of 6.98 ± 0.33 R_⊕ and a mean density of 0.27 ± 0.03 g cm^3. Kepler-18c and Kepler-18d have orbital periods near a 2:1 mean-motion resonance, leading to large and readily detected TTVs

Report on the test set-up for the structural testing of the Airmass Sunburst Ultralight Aircraft

This report reviews the test set-up and procedure for the structural testing of the Airmass Sunburst Ultralight Aircraft. In general aviation today, there is a growing need for more stringent design criteria for ultralight aircraft. Unlike most general aviation aircraft, the ultralight lacks sufficient design criteria and more importantly it lacks sufficient certification enforcement. The Airmass Sunburst ultralight that is currently being tested is responsible for over a dozen deaths. It is believed that had there been a more stringent criteria and certification process, this might have been prevented. Our attempt is to show that the failing loads of the aircraft in question are so far below that of the current design criteria, that the laws need to be changed

Attentional control of spatial scale: effects on self-organized motion patterns

AbstractPrior to the presentation of a test stimulus, subjects’ attentional state was either narrowly focused on a particular location or broadly spread over a large spatial region. In previous studies, it was found that broadly spread attention enhances the sensitivity of relatively large spatial filters (increasing the perceiver’s spatial scale), thereby diminishing spatial resolution and enhancing sensitivity to global stimulus structure. In this study it is shown that attentional spread also affects the self-organization of unidirectional versus oscillatory motion patterns for the directionally ambiguous, counterphase presentation of rows of evenly-spaced visual elements (lines segments; dots); i.e. qualitatively different motion patterns can be formed for the same stimulus at different spatial scales. Although the degree to which attention is spread along a spatial axis can be controlled by the perceiver, the effects of spread attention are not limited to a single axis. These results, as well as previously observed effects of attentional spread on spatial resolution, are accounted for by a neural model involving large, foveally-centered receptive fields with co-operatively interacting subunits (probably at the level of MST or higher)

Paper Session III-B - Development of a Microgravity-Rated Hydroponic Plant Culture Apparatus

Porous tubes provide a novel means of growing plants hydroponically under conditions of microgravity. The experimental strategy for a spaceflight experiment utilizing this technology (anticipated in the 2000-2001 timeframe) is presented. The primary question to be addressed relates to the control of optimal rates of water provision, and how it can be expected to differ between the spaceflight and earth-based environments

Timely Target Tracking in Cognitive Radar Networks

We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period. The solution for the selection problem at the fusion center is not well suited for sequential learning frameworks. We derive an Age of Information-inspired track sensitive metric to inform node selection in such a network and compare it against less-informed techniques.Comment: 6 pages, 6 figure

Bostonia. Volume 5

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Bring Back My Daddy To Me

https://digitalcommons.library.umaine.edu/mmb-vp/1122/thumbnail.jp

Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance