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.

A new model for optimal routing and wavelength assignment in wavelength division multiplexed optical networks

Abstract: We consider the problem of routing and assignment of wavelength (RAW) in optical networks. Given a set of requests for all-optical connections (or lightpaths), the problem is to (a) find routes from the source nodes to their respective destination nodes, and (b) assign wavelengths to these routes. Since the number of wavelengths is limited, lightpaths cannot be established between every pair of access nodes. In this paper we first consider the dynamic RAW problem where lightpath requests arrive randomly with exponentially distributed call holding times. Then, the static RAW problem is considered which assumes that all the lightpaths that are to be set-up in the network are known initially. Several heuristic algorithms have already been proposed for establishing a maximum number of lightpaths out of a given set of requests. However most of these algorithms are based an the traditional model of circuit-switched networks where routing and wavelength assignment steps are decoupled. In this paper a new graph-theoretic formulation of the RAW problem, dubbed as layered-graph, has been proposed which provides an efficient tool for solving dynamic as well as static RAW problems. The layered-graph model also provides a framework for obtaining exact optimal solution for the number of requested lightpaths as well as far the throughput that a given network can support. A dynamic and two static RAW schemes are proposed which are based on the layered-graph model. Layered-graph-based RAW schemes are shown to perform better than the existing ones.

Why Do We Trust? Moving Beyond Individual to Dyadic Perceptions

Abstract: Trust between coworkers is critical to the success of organizations and teams. This is especially true for those who are geographically dispersed and who must interact virtually. The authors studied the factors related to the development of trust between pairs of coworkers (dyads) in a new product development team. Some of the members were colocated, and others worked virtually. Using the actor-partner interdependence model, the authors found reciprocal effects for propensity to trust and trust in dyads. They found that propensity has greater influence on trust for virtual dyads and that trust has less influence on organizational citizenship when partners are virtual. Trustworthiness was shown to fully mediate the influence of trusting predisposition on trust.

Ultra-efficient frequency conversion in quasi-phase-matched lithium niobate microrings

Abstract: We demonstrate quasi-phase-matched frequency conversion in a chip-integrated lithium niobate microring resonator, whose normalized efficiency reaches 230,000%/W or 10−6 per single photon.

Super-Resolution Compressed Sensing for Line Spectral Estimation: An Iterative Reweighted Approach

Abstract: Conventional compressed sensing theory assumes signals have sparse representations in a known dictionary. Nevertheless, in many practical applications such as line spectral estimation, the sparsifying dictionary is usually characterized by a set of unknown parameters in a continuous domain. To apply the conventional compressed sensing technique to such applications, the continuous parameter space has to be discretized to a finite set of grid points, based on which a “nominal dictionary” is constructed for sparse signal recovery. Discretization, however, inevitably incurs errors since the true parameters do not necessarily lie on the discretized grid. This error, also referred to as grid mismatch, leads to deteriorated recovery performance. In this paper, we consider the line spectral estimation problem and propose an iterative reweighted method which jointly estimates the sparse signals and the unknown parameters associated with the true dictionary. The proposed algorithm is developed by iteratively decreasing a surrogate function majorizing a given log-sum objective function, leading to a gradual and interweaved iterative process to refine the unknown parameters and the sparse signal. A simple yet effective scheme is developed for adaptively updating the regularization parameter that controls the tradeoff between the sparsity of the solution and the data fitting error. Theoretical analysis is conducted to justify the proposed method. Simulation results show that the proposed algorithm achieves super resolution and outperforms other state-of-the-art methods in many cases of practical interest.