Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Open Standards and the Digital Age: History, Ideology, and Networks

Abstract: How did openness become a foundational value for the networks of the twenty-first century? Open Standards and the Digital Age answers this question through an interdisciplinary history of information networks that pays close attention to the politics of standardization. For much of the twentieth century, information networks such as the monopoly Bell System and the American military's Arpanet were closed systems subject to centralized control. In the 1970s and 1980s, however, engineers in the United States and Europe experimented with design strategies to create new digital networks. In the process, they embraced discourses of "openness" to describe their ideological commitments to entrepreneurship, technological innovation, and participatory democracy. The rhetoric of openness has flourished - for example, in movements for open government, open source software, and open access publishing - but such rhetoric also obscures the ways the Internet and other "open" systems still depend heavily on hierarchical forms of control.

Distributed Adaptive Quantization for Wireless Sensor Networks: From Delta Modulation to Maximum Likelihood

Abstract: We consider distributed parameter estimation using quantized observations in wireless sensor networks (WSNs) where, due to bandwidth constraint, each sensor quantizes its local observation into one bit of information. A conventional fixed quantization (FQ) approach, which employs a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To address this difficulty, we propose a distributed adaptive quantization (AQ) approach, which, with sensors sequentially broadcasting their quantized data, allows each sensor to adaptively adjust its quantization threshold. Three AQ schemes are presented: (1) AQ-FS that involves distributed delta modulation (DM) with a fixed stepsize, (2) AQ-VS that employs DM with a variable stepsize, and (3) AQ-ML that adjusts the threshold through a maximum likelihood (ML) estimation process. The ML estimators associated with the three AQ schemes are developed and their corresponding Cramer-Rao bounds (CRBs) are analyzed. We show that our 1-bit AQ approach is asymptotically optimum, yielding an asymptotic CRB that is only pi/2 times that of the clairvoyant sample-mean estimator using unquantized observations.

Structural optimization using multi-objective modified adaptive symbiotic organisms search

Abstract: Multiple objective structural optimization is a challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is planned along with a normal line method as an archiving technique for designing of structures. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation simultaneously. As search advances, a stability between the global search and a local search has a significant effect on the solutions. Therefore, an adaptive mutualism phase is added to the offer MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost since the majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS. For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions, elemental stresses are considered as behavior constraints and (discrete) elemental sections are considered as side constraints. Five truss optimization problems validate the applicability of the considered meta-heuristics to solve complex engineering. Also, four constrained benchmark engineering design problems are solved to demonstrate the effectiveness of MOMASOS. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal line method as an archiving technique provide superior and competitive results than the former obtained results.