Low-complexity sampling point selection in OFDM receiver with fractional sampling

2010 ◽  
Author(s):  
Eisuke Sakai ◽  
Haruki Nishimura ◽  
Mamiko Inamori ◽  
Yukitosi Sanada ◽  
Mohammad Ghavami
Keyword(s):  
Low Complexity ◽  
Sampling Point ◽  
Point Selection ◽  
Fractional Sampling

scholarly journals Beehive Air Sampling and Sensing Device Operation in Apicultural Applications—Methodological and Technical Aspects

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4019
Author(s):  
Andrzej Szczurek ◽  
Monika Maciejewska
Keyword(s):  
Gas Sensing ◽  
Operation Mode ◽  
Air Sampling ◽  
Sampling Point ◽  
Sampling System ◽  
Point Selection ◽  
Bee Colony ◽  
Temporal Aspects ◽  
High Quality Information ◽  
Device Operation

The basis of effective beekeeping is the information about the state of the bee colony. A rich source of respective information is beehive air. This source may be explored by applying gas sensing. It allows for classifying bee colony states based on beehive air measurements. In this work, we discussed the essential aspects of beehive air sampling and sensing device operation in apicultural applications. They are the sampling method (diffusive vs. dynamic, temporal aspects), sampling system (sample probe, sampling point selection, sample conditioning unit and sample delivery system) and device operation mode (‘exposure-cleaning’ operation). It was demonstrated how factors associated with the beehive, bee colony and ambient environment define prerequisites for these elements of the measuring instrument. These requirements have to be respected in order to assure high accuracy of measurement and high-quality information. The presented results are primarily based on the field measurement study performed in summer 2020, in three apiaries, in various meteorological conditions. Two exemplars of a prototype gas sensing device were used. These sensor devices were constructed according to our original concept.

scholarly journals A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data

2020 ◽  
Vol 9 (11) ◽  
pp. 668
Author(s):  
Zhenwu Wang ◽  
Benting Wan ◽  
Mengjie Han
Keyword(s):  
Three Dimensional ◽  
Pso Algorithm ◽  
Sampling Point ◽  
Compute Unified Device Architecture ◽  
Point Selection ◽  
Triangulated Irregular Network ◽  
Device Architecture ◽  
Ground Penetrating ◽  
Geometry Method ◽  
Difficult Issue

The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment.

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