Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays

1998 ◽  
Vol 46 (12) ◽  
pp. 1891-1893 ◽  
Author(s):  
A.H.El. Zooghby ◽  
C.G. Christodoulou ◽  
M. Georgiopoulos
Keyword(s):  
Neural Network ◽  
Antenna Arrays ◽  
Adaptive Beamforming ◽  
Two Dimensional

Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition

2016 ◽  
Author(s):  
Bo Li ◽  
Tara N. Sainath ◽  
Ron J. Weiss ◽  
Kevin W. Wilson ◽  
Michiel Bacchiani
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Adaptive Beamforming

scholarly journals Heat induction in two-dimensional graphene–Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling

RSC Advances ◽  
2021 ◽  
Vol 11 (35) ◽  
pp. 21702-21715
Author(s):  
M. S. Dar ◽  
Khush Bakhat Akram ◽  
Ayesha Sohail ◽  
Fatima Arif ◽  
Fatemeh Zabihi ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Artificial Neural Network ◽  
Network Modeling ◽  
Magnetic Hyperthermia ◽  
Alternating Magnetic Field ◽  
Neural Network Modeling ◽  
Two Dimensional ◽  
Artificial Neural Network Modeling ◽  
Heat Induction

Synthesis of Fe3O4–graphene (FG) nanohybrids and magnetothermal measurements of FxG100–x (x = 0, 25, 45, 65, 75, 85, 100) nanohybrids (25 mg each) at a 633 kHz alternating magnetic field of strength 9.1 mT.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

Convolutional Neural Network Based Symbol Detector for Two-dimensional Magnetic Recording

2020 ◽  
pp. 1-1
Author(s):  
Jinlu Shen ◽  
Benjamin J. Belzer ◽  
Krishnamoorthy Sivakumar ◽  
Kheong Sann Chan ◽  
Ashish James
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Magnetic Recording ◽  
Two Dimensional ◽  
Two Dimensional Magnetic Recording

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

2021 ◽  
pp. 1-7
Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phased Array ◽  
Weight Vector ◽  
Adaptive Beamforming ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Array Antennas ◽  
Optimum Weight ◽  
High Computational Complexity

Abstract Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

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