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.

Nonlinear seismo-acoustic land mine detection and discrimination

Abstract: A novel technique for detection and discrimination of artificial objects, such as land mines, pipes, containers, etc., buried in the ground, has been developed and tested. The developed approach utilizes vibration (using seismic or airborne acoustic waves) of buried objects, remote measurements of soil surface vibration (using laser or microwave vibrometers), and processing of the measured vibration to extract mine’s “vibration signatures.” The technique does not depend upon the material from which the mine is fabricated whether it be metal, plastic, wood, or any other material. It depends upon the fact that a mine is a “container” whose purpose is to contain explosive materials and associated detonation apparatus. The mine container is in contact with the soil in which it is buried. The container is an acoustically compliant article, whose compliance is notably different from the compliance of the surrounding soil. Dynamic interaction of the compliant container and soil on top of it leads to specific linear and nonlinear effects used for mine detection and discrimination. The mass of the soil on top of a compliant container creates a classical mass–spring system with a well-defined resonance response. Besides, the connection between mass (soil) and spring (mine) is not elastic (linear) but rather nonlinear, due to the separation of the soil/mine interface in the tensile phase of applied dynamic stress. These two effects, constituting the mine’s vibration signature have been measured in numerous laboratory and field tests, which proved that the resonance and nonlinear responses of a mine/soil system can be used for detection and discrimination of buried mines. Thus, the fact that the mine is buried is turned into a detection advantage. Because the seismo-acoustic technique intrinsically detects buried containers, it can discriminate mines from noncompliant false targets such as rocks, tree roots, chunks of metal, bricks, etc. This was also confirmed experimentally in laboratory and field tests.

Factorizable Graph Convolutional Networks

Abstract: Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a bare edge that conceals such intrinsic connections. In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs, each of which represents a latent and disentangled relation among nodes. The features of the nodes are then aggregated separately in each factorized latent space to produce disentangled features, which further leads to better performances for downstream tasks. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets, and demonstrate that it yields truly encouraging results in terms of both disentangling and feature aggregation. Code is publicly available at this https URL.

Assembly of Polymer-Grafted Magnetic Nanoparticles in Polymer Melts

Abstract: Hydrophobic iron oxide nanoparticles grafted with hydrophobic polymer chains of varying molecular weights and graft densities are synthesized to underpin the role of brush entanglement and dipolar forces on creating nanostructures. Grafting density on magnetic nanoparticles is controlled in grafting-to method by changing the concentration of functionalized polymer in solution. The grafting density and brush length have varied systemically to observe the changes in nanostructures. Bridging between grafted chains and dipolar forces become effective only at low grafting density and result in long chains of particles. We demonstrate experimentally that structural transition of magnetic nanoparticles is controlled with the balance between grafted chain entanglements and dipolar forces.

SERS detection of arsenic in water: A review

Abstract: Arsenic (As) is one of the most toxic contaminants found in the environment. Development of novel detection methods for As species in water with the potential for field use has been an urgent need in recent years. In past decades, surface-enhanced Raman scattering (SERS) has gained a reputation as one of the most sensitive spectroscopic methods for chemical and biomolecular sensing. The SERS technique has emerged as an extremely promising solution for in-situ detection of arsenic species in the field, particularly when coupled with portable/handheld Raman spectrometers. In this article, the recent advances in SERS analysis of arsenic species in water media are reviewed, and the potential of this technique for fast screening and field testing of arsenic-contaminated environmental water samples is discussed. The problems that remain in the field are also discussed and an outlook for the future is featured at the end of the article.