scholarly journals Radiance Caching with Environment Maps

2013 ◽  
Author(s):  
Michael Buerli
2021 ◽  
Vol 11 (7) ◽  
pp. 2910
Author(s):  
Paweł Kaniewski ◽  
Janusz Romanik ◽  
Edward Golan ◽  
Krzysztof Zubel

In this paper, we present the concept of the Radio Environment Map (REM) designed to ensure electromagnetic situational awareness of cognitive radio networks. The map construction techniques based on spatial statistics are presented. The results of field tests done for Ultra High Frequency (UHF) range with different numbers of sensors are shown. Exemplary maps with selected interpolation techniques are presented. Control points where the signal from licensed users is correctly estimated are identified. Finally, the map quality is assessed, and the most promising interpolation techniques are selected.


Author(s):  
David R. Walton ◽  
Diego Thomas ◽  
Anthony Steed ◽  
Akihiro Sugimoto

Author(s):  
Vinay K Sriram ◽  
Wesley Griffin

We have developed a utility to both stitch cube maps into other types of texture maps (equirectangular,dual paraboloid, and octahedral), and stitch those other types back into cube maps. The utility allows for flexibility in the image size of the conversion - the user can specify the desired image width, and the height is computed (cube, paraboloid, and octahedral mappings are square, and spherical maps are generated to have 16:9 aspect ratio). Moreover, the utility is sampling-agnostic, so the user can select whether to use uniform or jittered sampling over the pixels, as well as the number of samples to use per pixel. The rest of this paper discusses the mathematical framework for projecting from cube maps to equirectangular, dual paraboloid, and octahedral environment maps, as well as the mathematical framework for the inverse projections. We also describe two sampling techniques: uniform sampling and correlated multi-jittered sampling. We perform an evaluation of the sampling techniques and a comparative analysis of the different projections using objective image quality assessment metrics.


2014 ◽  
Vol 53 ◽  
pp. 62-72 ◽  
Author(s):  
Liljana Gavrilovska ◽  
Jaap van de Beek ◽  
Yong Xie ◽  
Erik Lidström ◽  
Janne Riihijärvi ◽  
...  

2021 ◽  
Vol 40 (4) ◽  
pp. 1-16
Author(s):  
Thomas Müller ◽  
Fabrice Rousselle ◽  
Jan Novák ◽  
Alexander Keller

2020 ◽  
Vol 10 (9) ◽  
pp. 3219 ◽  
Author(s):  
Sung-Hyeon Joo ◽  
Sumaira Manzoor ◽  
Yuri Goncalves Rocha ◽  
Sang-Hyeon Bae ◽  
Kwang-Hee Lee ◽  
...  

Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.


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