LNR-PP: Leaf Node Count and RSSI Based Parent Prediction Scheme to Support QoS in Presence of Mobility in 6LoWPAN

2020 ◽  
Vol 150 ◽  
pp. 472-487
Author(s):  
Suganya P. ◽  
Pradeep Reddy C.H.
Keyword(s):  
Leaf Node ◽  
Prediction Scheme ◽  
Node Count

scholarly journals Prognostic impact of examined lymph node count in pT1N0M0 esophageal cancer: A population‐based study

2019 ◽  
Vol 10 (7) ◽  
pp. 1636-1643 ◽  
Author(s):  
Ying Liu ◽  
Heli Yang ◽  
Hao Fu ◽  
Meng Li ◽  
Zhen Feng ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Population Based ◽  
Prognostic Impact ◽  
Population Based Study ◽  
Lymph Node Count ◽  
Node Count

scholarly journals Using vision-based object detection for link quality prediction in 5.6-GHz channel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Riichi Kudo ◽  
Kahoko Takahashi ◽  
Takeru Inoue ◽  
Kohei Mizuno
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Wireless Lan ◽  
Autonomous Robots ◽  
Learning Technologies ◽  
Link Quality ◽  
Automated Driving ◽  
Position Information ◽  
Prediction Scheme ◽  
Accurate Perception

Abstract Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

scholarly journals Evolutionary Optimization of Case-Based Forecasting Algorithms in Chaotic Environments

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 301
Author(s):  
Alexander Musaev ◽  
Ekaterina Borovinskaya
Keyword(s):  
Chaotic Dynamics ◽  
Evolutionary Optimization ◽  
Modeling Method ◽  
Dynamic Adaptation ◽  
Evolutionary Modeling ◽  
Computational Schemes ◽  
Prediction Algorithms ◽  
Prediction Scheme ◽  
Case Based

The problem of dynamic adaptation of prediction algorithms in chaotic environments based on identification of the situations-analogs in the database of retrospective observations is considered. Under conditions of symmetrical and unsymmetrical chaotic dynamics, traditional computational schemes of precedent prediction turn out to be ineffective. In this regard, a dynamic adaptation of precedent analysis algorithms based on the method of evolutionary modeling is proposed. Implementation of the computational precedent prediction scheme for chaotic processes as well as the evolutionary modeling method are described.

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