An experimental review on deep learning architectures for time series forecasting
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …
machine learning tasks. Deep neural networks have successfully been applied to address …
Artificial neural networks-based machine learning for wireless networks: A tutorial
In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
Reservoir computing is a highly efficient network for processing temporal signals due to its
low training cost compared to standard recurrent neural networks, and generating rich …
low training cost compared to standard recurrent neural networks, and generating rich …
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
Human activity recognition systems are developed as part of a framework to enable
continuous monitoring of human behaviours in the area of ambient assisted living, sports …
continuous monitoring of human behaviours in the area of ambient assisted living, sports …
[HTML][HTML] ECG-based heartbeat classification for arrhythmia detection: A survey
An electrocardiogram (ECG) measures the electric activity of the heart and has been widely
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
Information processing using a single dynamical node as complex system
L Appeltant, MC Soriano, G Van der Sande… - Nature …, 2011 - nature.com
Novel methods for information processing are highly desired in our information-driven
society. Inspired by the brain's ability to process information, the recently introduced …
society. Inspired by the brain's ability to process information, the recently introduced …
Physical reservoir computing—an introductory perspective
K Nakajima - Japanese Journal of Applied Physics, 2020 - iopscience.iop.org
Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …
processing capability has been an active research topic for many years. Physical reservoir …
A practical guide to applying echo state networks
M Lukoševičius - Neural Networks: Tricks of the Trade: Second Edition, 2012 - Springer
Reservoir computing has emerged in the last decade as an alternative to gradient descent
methods for training recurrent neural networks. Echo State Network (ESN) is one of the key …
methods for training recurrent neural networks. Echo State Network (ESN) is one of the key …
Parallel photonic information processing at gigabyte per second data rates using transient states
The increasing demands on information processing require novel computational concepts
and true parallelism. Nevertheless, hardware realizations of unconventional computing …
and true parallelism. Nevertheless, hardware realizations of unconventional computing …