Damping And Vibrations Experiment (DAVE): On-Orbit Performance of a CubeSat Particle Damper

The Damping And Vibrations Experiment (DAVE) is a 1U CubeSat designed to study the performance of particle damping technology in the space environment. Particle dampers rely on the free movement and collision of particles and, as such, are influenced significantly by gravitational effects on Earth. Damper performance was characterized using a single degree of freedom cantilever beam experiment. Beams were equipped with particle dampers and then excited to produce a response at various input amplitudes and frequencies. The on-orbit response of the system was compared to a theoretical model of particle damping as well as ground and ZERO-G flight test data in order to ascertain the degree of non-linearity of the system

Understanging Information Use in the Multidisciplinary Field: A Local Citation Analysis of Neuroscience Research

Assessing the information needs of a multidisciplinary academic community presents challenges to librarians managing journal collections. This case study analyzed the literature used by the neuroscience community at the University of Maryland to determine the following about the publications they cited: their type, their discipline, and how recent they were relative to the citing publication. The authors searched the ISI Science Citation Index and Social Sciences Citation Index to identify the publishing, citing, and coauthoring patterns of both faculty and graduate students to inform library decisions about collecting journals and other types of literature

Building Oblivious Transfer on Channel Delays

In the information-theoretic setting, where adversaries have unlimited computational power, the fundamental cryptographic primitive Oblivious Transfer (OT) cannot be securely achieved if the parties are communicating over a clear channel. To preserve secrecy and security, the players have to rely on noise in the communication. Noisy channels are therefore a useful tool to model noise behavior and build protocols implementing OT. This paper explores a source of errors that is inherently present in practically any transmission medium, but has been scarcely studied in this context: delays in the communication. In order to have a model for the delays that is both general and comparable to the channels usually used for OT – such as the Binary Symmetric Channel (BSC) – we introduce a new noisy channel, the Binary Discrete-time Delaying Channel (BDDC). We show that such a channel realistically reproduces real-life communication scenarios where delays are hard to predict and we propose a protocol for achieving oblivious transfer over the BDDC. We analyze the security of our construction in the semi-honest setting, showing that our realization of OT substantially decreases the protocol sensitivity to the user’s knowledge of the channel compared to solutions relying on other channel properties, and is very efficient for wide ranges of delay probabilities. The flexibility and generality of the model opens the way for future implementation in media where delays are a fundamental characteristic

An analytic framework for modeling and detecting access layer misbehavior in wireless networks

The widespread deployment of wireless networks and hot spots that employ the IEEE 802.11 technology has forced network designers to put emphasis on the importance of ensuring efficient and fair use of network resources. In this work we propose a novel framework for detection of intelligent adaptive adversaries in the IEEE 802.11 MAC by addressing the problem of detection of the worst-case scenario attacks. Utilizing the nature of this protocol we employ sequential detection methods for detecting greedy behavior and illustrate their performance for detection of least favorable attacks. By using robust statistics in our problem formulation, we attempt to utilize the precision given by parametric tests, while avoiding the specification of the adversarial distribution. This approach establishes the lowest performance bound of a given Intrusion Detection System (IDS) in terms of detection delay and is applicable in online detection systems where users who pay for their services want to obtain the information about the best and the worst case scenarios and performance bounds of the system. This framework is meaningful for studying misbehavior due to the fact that it does not focus on specific adversarial strategies and therefore is applicable to a wide class of adversarial strategies