347 citations
- North Carolina State UniversityUS67 papers
- Columbia UniversityUS12 papers
- Goddard Space Flight CenterUS8 papers
- University of California, BerkeleyUS8 papers
- Los Alamos National LaboratoryUS7 papers
- California Institute of TechnologyUS6 papers
- McGill UniversityCA6 papers
- Technical University of DenmarkDK6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Chungbuk National UniversityKR5 papers
- Jet Propulsion LaboratoryUS5 papers
- University of ManitobaCA5 papers
7 papers · 1 filter
Improving the Representation of Energy Efficiency in an Energy System Optimization Model
Neha Patankar, Harrison G. Fell, Anderson Rodrigo de Queiroz +2
Energy system optimization models (ESOMs) are designed to examine the potential effects of a proposed policy, but often represent energy-efficient technologies and policies in an o…
A Framework for Developing Algorithms for Estimating Propagation Parameters from Measurements
Akbar Sayeed, Peter Vouras, Camillo Gentile +13
A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by sounders at millimeter-wave (…
Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach
Chethan K. Anjinappa, Ismail Guvenc
The utilization of millimeter-wave (mmWave) bands in 5G networks poses new challenges to network planning. Vulnerability to blockages at mmWave bands can cause coverage holes (CHs)…
Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors
Ender Ozturk, Chethan Kumar Anjinappa, Fatih Erden +3
Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplex…
PlaneTR: Structure-Guided Transformers for 3D Plane Recovery
Bin Tan, Nan Xue, Song Bai +2
This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image. Different from previous methods,…
Markov Equivalence of Max-Linear Bayesian Networks
Carlos Améndola, Ben Hollering, Seth Sullivant +1
Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian ne…