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.

Dark matter constraints from a joint analysis of dwarf Spheroidal galaxy observations with VERITAS

Abstract: We present constraints on the annihilation cross section of weakly interacting massive particles dark matter based on the joint statistical analysis of four dwarf galaxies with VERITAS. These results are derived from an optimized photon weighting statistical technique that improves on standard imaging atmospheric Cherenkov telescope (IACT) analyses by utilizing the spectral and spatial properties of individual photon events. We report on the results of ∼230 hours of observations of five dwarf galaxies and the joint statistical analysis of four of the dwarf galaxies. We find no evidence of gamma-ray emission from any individual dwarf nor in the joint analysis. The derived upper limit on the dark matter annihilation cross section from the joint analysis is 1.35×10-23 cm3 s-1 at 1 TeV for the bottom quark (bb) final state, 2.85×10-24 cm3 s-1 at 1 TeV for the tau lepton (τ+τ-) final state and 1.32×10-25 cm3 s-1 at 1 TeV for the gauge boson (γγ) final state.

Autonomous robot calibration using vision technology

Abstract: Unlike the traditional robot calibration methods, which need external expensive calibration apparatus and elaborate setups to measure the 3D feature points in the reference frame, a vision-based self-calibration method for a serial robot manipulator, which only requires a ground-truth scale in the reference frame, is proposed in this paper. The proposed algorithm assumes that the camera is rigidly attached to the robot end-effector, which makes it possible to obtain the pose of the manipulator with the pose of the camera. By designing a manipulator movement trajectory, the camera poses can be estimated up to a scale factor at each configuration with the factorization method, where a nonlinear least-square algorithm is applied to improve its robustness. An efficient approach is proposed to estimate this scale factor. The great advantage of this self-calibration method is that only image sequences of a calibration object and a ground-truth length are needed, which makes the robot calibration procedure more autonomous in a dynamic manufacturing environment. Simulations and experimental studies on a PUMA 560 robot reveal the convenience and effectiveness of the proposed robot self-calibration approach.

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Abstract: Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.