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Analog photonic computing comprises a promising candidate for accelerating the linear operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low footprint and low power consumption computing ca...

Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as reservoir computing (RC) and extreme learning machines (ELM). We propose using...

Photonic reservoir computing has been demonstrated to be able to solve various complex problems. Although training a reservoir computing system is much simpler compared to other neural network approaches, it still requir...

Photonic reservoir computing has been intensively investigated to solve machine learning tasks effectively. A simple learning procedure of output weights is used for reservoir computing. However, the lack of training of ...

Abstract This work addresses the synthesis of a multi-band frequency selective surface (FSS) through bioinspired computing and a general regression neural network (GRNN). This hybrid computational method, which utilizes ...

This paper presents an innovative parallel computational methodology employing adaptive fractional differential operators to address Monkeypox epidemiological modeling challenges. The complex dynamics of such models ofte...

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review...

Abstract Achieving highly efficient extrication of the tunnel boring machine (TBM) cutter-head driving system from the collapsed surrounding rock has become a key problem globally, and significant effort has been directe...

The possibility of arbitrary spatial control of incident wavefronts with the subwavelength resolution has driven research into dielectric optical metasurfaces in the last decade. The unit-cell based metasurface design a...

This article presents a simple model that explains significant reduction of work function in MXenes Тіn+1Сn with hydroxyl termination and demonstrates matching results to ones in the existing literature, obtained via ...

Optical logic gates play important roles in all-optical logic circuits, which lie at the heart of the next-generation optical computing technology. However, the intrinsic contradiction between compactness and robustness ...

This article identifies and describes the types of cognitive language learning strategies used by learners of Spanish. Sample consisted of 665 participants, adult learners of Spanish from 60 universities and institutions...

The constant demands that technology creates in aerospace engineering also influence education. The identification of the technologies with practical application in aerospace engineering is of current interest to decisio...

A model for computing acoustic scattering by swimbladdered fish is presented. The effect of acoustic coupling between the fish tissue and the swimbladder is included. The swimbladder is considered a gas-filled object whe...

We present HAlator, a fully automated quantum chemistry (QM) workflow for computing C–H hydricities and explore its potential in predicting the regioselectivity of electron-rich C–H functionalisation reactions. The w...

Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher ...

Abstract The growing importance of machine learning in geological data computing, as well as in the broader geoscience and energy sectors, makes it essential to evaluate the impact of data augmentation on model performan...

In this paper, the influence of signal to noise ratio in the estimation of Permutation Entropy was studied upon signals obtained by simulations of a chaoticdeterministic system. Then, using a bootstrap scheme, we applied...

In previous works, we proposed two methods for data clustering based on automatically discovered fuzzy predicates which were referred to as SOM-based Fuzzy Predicate Clustering (SFPC) [Meschino et al., Neurocomputing, 14...

ABSTRACT The growing use of neural networks in privacy‐sensitive applications necessitates architectures that inherently protect both data and model integrity. We present a model‐inversion‐resistant physical unclon...

Spatial differentiator is the key element for edge detection, which is indispensable in image processing, computer vision involving image recognition, image restoration, image compression, and so on. Spatial differentiat...

Entanglement is an essential feature of quantum theory and the core of the majority of quantum information science and technologies. Quantum computing is one of the most important fruits of quantum entanglement and requi...

Spectral reconstruction, critical for understanding sample composition, is extensively applied in fields like remote sensing, geology, and medical imaging. However, existing spectral reconstruction methods require bulky ...

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been ...