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.

Single-Step Direct Fabrication of Pillar-on-Pore Hybrid Nanostructures in Anodizing Aluminum for Superior Superhydrophobic Efficiency

Abstract: Conventional electrochemical anodizing processes of metals such as aluminum typically produce planar and homogeneous nanopore structures. If hydrophobically treated, such 2D planar and interconnected pore structures typically result in lower contact angle and larger contact angle hysteresis than 3D disconnected pillar structures and, hence, exhibit inferior superhydrophobic efficiency. In this study, we demonstrate for the first time that the anodizing parameters can be engineered to design novel pillar-on-pore (POP) hybrid nanostructures directly in a simple one-step fabrication process so that superior surface superhydrophobicity can also be realized effectively from the electrochemical anodization process. On the basis of the characteristic of forming a self-ordered porous morphology in a hexagonal array, the modulation of anodizing voltage and duration enabled the formulation of the hybrid-type nanostructures having controlled pillar morphology on top of a porous layer in both mild and hard anodizatio...

A Parametric Moving Target Detector for Distributed MIMO Radar in Non-Homogeneous Environment

Abstract: This paper considers moving target detection (MTD) with distributed multi-input multi-output (MIMO) radars in non-homogeneous environments, where the received disturbance signal (clutter and noise) exhibits non-homogeneity in not only power but also covariance structure from one transmit-receive (TX-RX) antenna pair to another as well as across different test cells. To address this problem, we introduce a parametric approach by employing a set of distinctive auto-regressive (AR) models, one for each TX-RX pair, to model the non-homogeneous disturbance signals. We develop a parametric generalized likelihood ratio test (PGLRT), referred to as the MIMO-PGLRT detector, for MTD in distributed MIMO radars. The MIMO-PGLRT detector, which consists of local adaptive subspace detection, non-coherent combining using local decision variables, and a global threshold comparison, is shown to asymptotically achieve constant false alarm rate (CFAR). We also investigate the target velocity estimation problem, an integral part of MTD, and develop its maximum likelihood estimator. The Cramer-Rao bound, in both the exact and asymptotic forms, respectively, is examined to shed additional light to the problem. Numerical results are presented to demonstrate the effectiveness of the proposed methods.

Standardization and research in cognitive and dynamic spectrum access networks: IEEE SCC41 efforts and other activities

Abstract: Spectrum crowding, spectrum management, quality of service, and user support are the topics of vigorous research in the cognitive and dynamic spectrum access network communities. As research matures, standardization provides a bridge between research results, implementation, and widespread deployment of such networks. This article reports recent developments within the IEEE Standardization Coordinating Committee 41, "Dynamic Spectrum Access Networks." It outlines possible future standardization topics for IEEE SCC41, in the framework of other related standardization activities, and discusses open research issues that present future challenges for the standardization community.