AIMC Topic: Neural Networks, Computer

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LSTM Attention Neural-Network-Based Signal Detection for Hybrid Modulated Faster-Than-Nyquist Optical Wireless Communications.

Sensors (Basel, Switzerland)
In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC...

A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection.

Sensors (Basel, Switzerland)
This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wave...

A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm.

Sensors (Basel, Switzerland)
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and ...

Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen.

Biomolecules
Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is o...

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Ultrasonics
Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arr...

A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis. The strategies employed to investigate their theoretical properties mainly rely on Euclide...

Forgetting memristor based STDP learning circuit for neural networks.

Neural networks : the official journal of the International Neural Network Society
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. ...

Strictly intermittent quantized control for fixed/predefined-time cluster lag synchronization of stochastic multi-weighted complex networks.

Neural networks : the official journal of the International Neural Network Society
This article addresses the fixed-time (F-T) and predefined-time (P-T) cluster lag synchronization of stochastic multi-weighted complex networks (SMWCNs) via strictly intermittent quantized control (SIQC). Firstly, by exploiting mathematical induction...

A unified deep semi-supervised graph learning scheme based on nodes re-weighting and manifold regularization.

Neural networks : the official journal of the International Neural Network Society
In recent years, semi-supervised learning on graphs has gained importance in many fields and applications. The goal is to use both partially labeled data (labeled examples) and a large amount of unlabeled data to build more effective predictive model...

RGBD Salient Object Detection, Based on Specific Object Imaging.

Sensors (Basel, Switzerland)
RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can m...