Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

Memristive devices are gaining importance in the semiconductor industry for applications in
information storage, artificial intelligence cryptography and telecommunication. Memristive …

Detection and recognition of latent fingerprints play crucial roles in identification and
security. However, the separation of sensor, memory, and processor in conventional ex-situ …

Recently, the increasing demand for data-centric applications is driving the elimination of
image sensing, memory and computing unit interface, thus promising for latency-and energy …

Abstract Machine vision systems that capture images for visual inspection and identification
tasks have to be able to perceive a scene under a range of illumination conditions. To …

Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Neuromorphic computing, a computing paradigm inspired by the human brain, enables
energy-efficient and fast artificial neural networks. To process information, neuromorphic …

With the booming growth of artificial intelligence (AI), the traditional von Neumann
computing architecture based on complementary metal oxide semiconductor devices are …

The neural network image sensor—which mimics neurobiological functions of the human
retina—has recently been demonstrated to simultaneously sense and process optical …

The visual scene in the physical world integrates multidimensional information (spatial,
temporal, polarization, spectrum and so on) and typically shows unstructured characteristics …