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: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Incremental Learning From Stream Data

Abstract: Recent years have witnessed an incredibly increasing interest in the topic of incremental learning Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance Detailed system level architecture and design strategies are presented in this paper Simulation results over several real-world data sets are used to validate the effectiveness of this method

Synthesis of reliable networks - a survey

Abstract: In contrast to the usual probabilistic model for network reliability, one can use a deterministic model which is called network vulnerability. Many different vulnerability criteria and the related synthesis results are reviewed. These synthesis problems are all graph external questions. Certain reliability synthesis problems can be converted to a vulnerability question. Several open problems and conjectures are presented.

A three-dimensional simulation of the Hudson-Raritan Estuary. Part I: description of the model and model simulations.

Abstract: A time-dependent, three-dimensional, finite difference simulation of the Hudson‐Raritan estuary is presented. The calculation covers July–September 1980. The model estuary is forced by time-dependent observed winds, tidal elevation at open boundaries, and river and sewage discharges. Turbulence mixing coefficients in the estuary are calculated according to a second-moment, turbulence-closure submodel. Horizontal diffusivities are zero in the simulation and small-scale eddies produced by the interaction of unsteady, three-dimensional velocity and salinity fields with coastline and bottom bathymetry were resolved by the model. These eddies are important physical elements in shear dispersion processes in an estuary. Model results show unstably stratified water columns produced by advection of waters of different densities. These instabilities produce intense mixing with vertical eddy diffusivities reaching 2–3 times their neutral values. They occur most frequently at slack currents, during initial s...

Spin-Orbit Coupling and Other Relativistic Effects in Atoms and Molecules

Abstract: Publisher Summary This chapter focuses on the recommended method for the inclusion of spin-orbit coupling and other relativistic effects for molecules containing heavy elements, considering computational complexity and accuracy factors that is one based on ab initio REPS. The calculation of accurate wave functions for systems containing heavy elements requires addressing the difficulties of the treatment of large numbers of electrons and the subtleties of electron correlation. This is not intended as a general review of relativity in chemistry or quantum mechanics, nor even of effective potential procedures, but is rather a critical discussion, including a limited number of applications, of the background, approximations, and implications of techniques developed by the present authors and collaborators and colleagues. The underlying assumption behind all methods for defining effective core potentials (EP) is the frozen core approximation. That is, the intrinsic reliability of core-valence separability. However, substantial savings are not realized by this approximation alone because of the radical oscillations of the valence orbitals in the region near the nuclei. An accurate procedure for performing calculations that incorporate spinorbit and other relativistic effects, and that represents intermediate coupling states for molecules containing heavy atoms, is based on A-S coupling in conjunction with the use of the ab initio REP-based spin-orbit operator and extended configuration interaction.

Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions

Abstract: Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.