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Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single ta...

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 ...

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spike activation are two fundamental functions o...

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency...

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 ...

Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence. The implementation of optical convolutions plays a vital...

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...

Spatial computing and augmented reality are advancing rapidly, with the goal of seamlessly blending virtual and physical worlds. However, traditional depth-sensing systems are bulky and energy-intensive, limiting their u...

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 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 ...

Optical neural networks have demonstrated their potential to overcome the computational bottleneck of modern digital electronics. However, their development towards high-performing computing alternatives is hindered by o...

Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative p...

On-chip computing metasystems composed of multilayer metamaterials have the potential to become the next-generation computing hardware endowed with light-speed processing ability and low power consumption but are hindere...

Photonic computing and neuromorphic computing are attracting tremendous interests in breaking the memory wall of traditional von Neumann architecture. Photonic memristors equipped with light sensing, data storage, and in...

Abstract While multi-access edge computing (MEC) enables flexible task offloading, the inherent dynamism and complexity of real-world environments, accompanied by various uncertainties, exacerbate the challenges in offlo...

Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on ...

The intrinsic complexity of nonlinear optical phenomena offers a fundamentally new resource to analog brain-inspired computing, with the potential to address the pressing energy requirements of artificial intelligence. W...

Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in t...

Programmable feedforward photonic meshes of Mach–Zehnder interferometers are computational optical circuits that have many classical and quantum computing applications including machine learning, sensing, and telecommu...

At the present time, the reliance on computers is increasing in all aspects of life, so it is necessary to protect computer networks and computing resources from complex attacks against the network. This is done by build...

Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence, among which photonic time-delay reservoir computing (TDRC) is widely anticipated. While su...

Abstract The importance of fear level detection in affective computing using physiological signals has not been extensively explored. Accurately detecting fear responses facilitates real-time emotional monitoring and imp...

Abstract Advances in AI have transformed the traditional Internet of Things (IoT) into smarter, more versatile next-generation devices. Enhanced connectivity among sensors, actuators, and appliances boosts data availabil...

Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers...