The role of machine learning algorithms in materials science: A state of art review on industry 4.0
A Choudhury - Archives of Computational Methods in Engineering, 2021 - Springer
The 21st century has witnessed a rapid convergence of manufacturing technology, computer
science and information technology. This has led to a paradigm of 4.0. The hitherto known …
science and information technology. This has led to a paradigm of 4.0. The hitherto known …
A spatiotemporal energy model based on spiking neurons for human motion perception
Inspired by the motion processing pathway, this paper proposes a bio-inspired feedforward
spiking network model based on Hodgkin–Huxley neurons for human motion perception …
spiking network model based on Hodgkin–Huxley neurons for human motion perception …
Implementation of the canny edge detector using a spiking neural network
KV Vemuru - Future Internet, 2022 - mdpi.com
Edge detectors are widely used in computer vision applications to locate sharp intensity
changes and find object boundaries in an image. The Canny edge detector is the most …
changes and find object boundaries in an image. The Canny edge detector is the most …
Image interpolation based on spiking neural network model
MO İncetaş - Applied Sciences, 2023 - mdpi.com
Image interpolation is used in many areas of image processing. It is seen that many
techniques developed to date have been successful in both protecting edges and increasing …
techniques developed to date have been successful in both protecting edges and increasing …
Efficient multispike learning for spiking neural networks using probability-modulated timing method
Error functions are normally based on the distance between output spikes and target spikes
in supervised learning algorithms for spiking neural networks (SNNs). Due to the …
in supervised learning algorithms for spiking neural networks (SNNs). Due to the …
Image edge detector with Gabor type filters using a spiking neural network of biologically inspired neurons
KV Vemuru - Algorithms, 2020 - mdpi.com
We report the design of a Spiking Neural Network (SNN) edge detector with biologically
inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model …
inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model …
Adaptive threshold selection of anisotropic diffusion filters using spiking neural network model
M Kılıçaslan - Signal, Image and Video Processing, 2024 - Springer
Image denoising takes place as the first step in many image processing operations.
Anisotropic diffusion filters (ADFs) have remained popular for years because of their ability …
Anisotropic diffusion filters (ADFs) have remained popular for years because of their ability …
Anisotropic diffusion filter based on spiking neural network model
MO İncetaş - Arabian Journal for Science and Engineering, 2022 - Springer
Image denoising is one of the most important steps in image processing. Anisotropic
diffusion filters (ADFs), which are quite popular, stand out with their edge preservation …
diffusion filters (ADFs), which are quite popular, stand out with their edge preservation …
Single-compartment models of retinal ganglion cells with different electrophysiologies
W Qin, A Hadjinicolaou, DB Grayden… - … in Neural Systems, 2017 - Taylor & Francis
There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian
retina. To model responses of RGCs to electrical stimulation, we use single-compartment …
retina. To model responses of RGCs to electrical stimulation, we use single-compartment …
Neuromorphic computing spiking neural network edge detection model for content based image retrieval
Ambuj, R Machavaram - Network: Computation in Neural Systems, 2024 - Taylor & Francis
In contemporary times, content-based image retrieval (CBIR) techniques have gained
widespread acceptance as a means for end-users to discern and extract specific image …
widespread acceptance as a means for end-users to discern and extract specific image …