Deep Photonic Reservoir Computer for Speech Recognition.

Speech recognition is a critical task in the field of artificial intelligence (AI) and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing (RC), is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared with more resource-intensive machine learning algorithms. In this work, we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep RC (DRC).

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers.

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines.

Photonic reservoir computer based on frequency multiplexing.

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.

Photonic extreme learning machine based on frequency multiplexing.

The optical domain is a promising field for the physical implementation of neural networks, due to the speed and parallelism of optics. Extreme learning machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup.

Scanless optical feedback imaging principle by single-pixel compressed sensing.

Optical feedback in lasers is being used for unconventional imaging of fluid dynamics, pressure fields, material properties, and free-carrier distribution, especially in spectral regions where two-dimensional detectors are not yet available. As this technique requires scanning the laser spot across the target, the resulting image contrast is often hampered by the speckle effect. Compressed sensing is becoming a workhorse technique for signal analysis, allowing the reconstruction of complex images from a relatively small number of integrated (single-pixel) measurements, and is being efficiently adapted to a number of single-pixel detector cameras.

Correlation plenoptic imaging between arbitrary planes.

We propose a novel method to perform plenoptic imaging at the diffraction limit by measuring second-order correlations of light between two reference planes, arbitrarily chosen, within the tridimensional scene of interest. We show that for both chaotic light and entangled-photon illumination, the protocol enables to change the focused planes, in post-processing, and to achieve an unprecedented combination of image resolution and depth of field. In particular, the depth of field results larger by a factor 3 with respect to previous correlation plenoptic imaging protocols, and by an order of magnitude with respect to standard imaging, while the resolution is kept at the diffraction limit.

Upper limb motor improvement in chronic stroke after combining botulinum toxin A injection and multi-joints robot-assisted therapy: a case report.

Spasticity is one of the major complications after stroke. Botulinum toxin type A (BoNT-A) injection is commonly used to manage focal spasticity. However, it is uncertain whether BoNT-A can improve activities of daily living function of paretic arm.