scholarly journals Static-Priority Scheduling of Sporadic Messages on a Wireless Channel

2006 ◽  
pp. 322-333 ◽  
Author(s):  
Björn Andersson ◽  
Eduardo Tovar
Keyword(s):  
Wireless Channel ◽  
Priority Scheduling

Analysis of Wireless Channel Capacity in RoF-DAS over WDM-PON System

2013 ◽  
Vol E96.C (2) ◽  
pp. 171-179 ◽  
Author(s):  
Tatsuhiko IWAKUNI ◽  
Kenji MIYAMOTO ◽  
Takeshi HIGASHINO ◽  
Katsutoshi TSUKAMOTO ◽  
Shozo KOMAKI ◽  
...  
Keyword(s):  
Channel Capacity ◽  
Wireless Channel ◽  
Wdm Pon

Current Issues and Trends in Wireless Channel Modeling and Simulation

2009 ◽  
Vol 2 (3) ◽  
pp. 166-177
Author(s):  
Konstantinos Baltzis
Keyword(s):  
Modeling And Simulation ◽  
Channel Modeling ◽  
Wireless Channel ◽  
Wireless Channel Modeling

scholarly journals Ray Tracing Modeling of Electromagnetic Propagation for On-Chip Wireless Optical Communications

2018 ◽  
Vol 8 (4) ◽  
pp. 39 ◽  
Author(s):  
Franco Fuschini ◽  
Marina Barbiroli ◽  
Marco Zoli ◽  
Gaetano Bellanca ◽  
Giovanna Calò ◽  
...  
Keyword(s):  
Ray Tracing ◽  
Path Loss ◽  
Wireless Channel ◽  
Electromagnetic Propagation ◽  
Networks On Chip ◽  
Spatial Properties ◽  
Radiation Properties ◽  
Wireless Optical Communications ◽  
On Chip ◽  
The Relationship

Multi-core processors are likely to be a point of no return to meet the unending demand for increasing computational power. Nevertheless, the physical interconnection of many cores might currently represent the bottleneck toward kilo-core architectures. Optical wireless networks on-chip are therefore being considered as promising solutions to overcome the technological limits of wired interconnects. In this work, the spatial properties of the on-chip wireless channel are investigated through a ray tracing approach applied to a layered representation of the chip structure, highlighting the relationship between path loss, antenna positions and radiation properties.

scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

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