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Toplam kayıt 16, listelenen: 1-10
Rotten-fruit-sorting robotic arm: (Design of low complexity cnn for embedded system)
(MDPI, 2022)
Industrial Automation has revolutionized the processing industry due to its high accuracy, the time it saves, and its ability to work without tiring. Being the most fundamental part of automation machines, robotic arms are ...
PL-GAN: Path loss prediction using generative adversarial networks
(IEEE-Institute of Electrical and Electronics Engineers Inc., 2022)
Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational ...
Predicting path loss distribution of an area from satellite ımages using deep learning
(IEEE - Institute of Electrical and Electronics Engineers, Inc., 2020)
Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the ...
Path loss exponent and shadowing factor prediction from satellite images using deep learning
(Institute of Electrical and Electronics Engineers, 2019)
Optimal network planning for wireless communication systems requires the detailed knowledge of the channel parameters of the target coverage area. Channel parameters can be estimated through extensive measurements in the ...
VLCnet: Deep learning based end-to-end visible light communication system
(IEEE-Institute of Electrical and Electronics Engineers Inc, 2020)
Visible light communication is a popular research area where proposed communication methods must satisfy the lighting related requirements as well. Suggested VLC modules should not only improve communication quality such ...
SNF-CVAE: Computational method to predict drug-disease interactions using similarity network fusion and collective variational autoencoder
(Elsevier, 2021)
Drug repositioning is an emerging approach to identify novel therapeutic potentials for approved drugs and discover therapies for previously untreatable diseases. Drug repositioning has also attracted considerable attention ...
Proposing a CNN method for primary and permanent tooth detection and enumeration on pediatric dental radiographs
(NLM (Medline), 2022)
OBJECTIVE: In this paper, we aimed to evaluate the performance of a deep learning system for automated tooth detection and numbering on pediatric panoramic radiographs. STUDY DESIGN: YOLO V4, a CNN (Convolutional Neural ...
An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
(Wiley-Hindawi, 2019)
Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand ...
Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models
(MDPI, 2021)
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. ...
Regression of large-scale path loss parameters using deep neural networks
(IEEE-Institute of Electrical and Electronics Engineers Inc., 2022)
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this ...