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.

CALIPSO/CALIOP Cloud Phase Discrimination Algorithm

Abstract: The current cloud thermodynamic phase discrimination by Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) is based on the depolarization of backscattered light measured by its lidar [Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)]. It assumes that backscattered light from ice crystals is depolarizing, whereas water clouds, being spherical, result in minimal depolarization. However, because of the relationship between the CALIOP field of view (FOV) and the large distance between the satellite and clouds and because of the frequent presence of oriented ice crystals, there is often a weak correlation between measured depolarization and phase, which thereby creates significant uncertainties in the current CALIOP phase retrieval. For water clouds, the CALIOP-measured depolarization can be large because of multiple scattering, whereas horizontally oriented ice particles depolarize only weakly and behave similarly to water clouds. Because of the nonunique depolarization–cloud ph...

Refining the search for project success factors: a multivariate, typological approach

Abstract: Although the causes for project success and failure have been the subject of many studies, no conclusive evidence or common agreement has been achieved so far. One criticism involves the universalistic approach used often in project management studies, according to which all projects are assumed to be similar. A second problem is the issue of subjectiveness, and sometimes weakly defined success measures; yet another concern is the limited number of managerial variables examined by previous research. In the present study we use a project-specific typological approach, a multidimensional criteria for assessing project success, and a multivariate statistical analysis method. According to our typology projects were classified according to their technological uncertainty at project initiation and their system scope which is their location on a hierarchical ladder of systems and subsystems. For each of the 127 projects in our study that were executed in Israel, we recorded 360 managerial variables and 13 success measures. The use of a very detailed data and multivariate methods such as canonical correlation and eigenvector analysis enables us to account for all the interactions between managerial and success variables and to address a handful of perspectives, often left unanalyzed by previous research. Assessing the variants of managerial variables and their impact on project success for various types of projects, serves also a step toward the establishment of a typological theory of projects. Although some success factors are common to all projects, our study identified project-specific lists of factors, indicating for example, that high-uncertainty projects must be managed differently than low-uncertainty projects, and high-scope projects differently than low-scope projects.

Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

Abstract: We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.