Related papers: NEMO: Neural Electro-Mechano-Optic Sensors for Mul…
We introduce MENO (''Matrix Exponential-based Neural Operator''), a hybrid surrogate modeling framework for efficiently solving stiff systems of ordinary differential equations (ODEs) that exhibit a sparse nonlinear structure. In such…
The analysis of electrophysiological data is crucial for certain surgical procedures such as deep brain stimulation, which has been adopted for the treatment of a variety of neurological disorders. During the procedure, auditory analysis of…
Optically-pumped magnetometers (OPM) -- next-generation magnetoencephalography (MEG) sensors -- may be placed directly on the head, unlike the more commonly used superconducting quantum interference device (SQUID) sensors, which must be…
Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its initial stages of development. Translating properties of…
The advent of advanced neuronal interfaces offers great promise for linking brain functions to electronics. A major bottleneck in achieving this is real-time processing of big data that imposes excessive requirements on bandwidth, energy…
Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and…
The ultimate goal of this research proposal is the creation of a micro-optomechanical intelligence. The proposal centers on the development and investigation of very large-scale integrated (VLSI) arrays of coupled M/NEMS devices as…
Abstract: Bionic learning with fused sensing, memory and processing functions outperforms artificial neural networks running on silicon chips in terms of efficiency and footprint. However, digital hardware implementation of bionic learning…
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording,…
Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for…
Active, in situ control of light at the nanoscale remains a challenge in modern physics and in nanophotonics in particular. A promising approach is to take advantage of the technological maturity of nano-electromechanical systems (NEMS) and…
Reservoir computing offers an energy-efficient alternative to deep neural networks (DNNs) by replacing complex hidden layers with a fixed nonlinear system and training only the final layer. This work investigates nanoelectromechanical…
In recent years, the progress on low temperature detector technologies has allowed design of large scale experiments aiming at pushing down the sensitivity on the neutrino mass below 1\,eV. Even with outstanding performances in both energy…
Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behavior to realize high density, low power neuromorphic computing will require huge…
The Shack-Hartmann wavefront sensor is widely employed in adaptive optics systems to measure optical aberrations. However, simultaneously achieving high sensitivity and large dynamic range is still challenging, limiting the performance of…
EMG (Electromyograph) signal based gesture recognition can prove vital for applications such as smart wearables and bio-medical neuro-prosthetic control. Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture…
Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an open challenge. This is particularly true for patients with epilepsy, 30%…
Microscopy enables detailed visualization and understanding of minute structures or processes. While cameras have significantly advanced optical, infrared, and electron microscopy, imaging nuclear magnetic resonance (NMR) signals on a…
Fingerprint analysis is a ubiquitous tool for pattern recognition with applications spanning from geolocation and DNA analysis to facial recognition and forensic identification. Central to its utility is the ability to provide accurate…
By leveraging the blur-noise trade-off, imaging with non-uniform exposures largely extends the image acquisition flexibility in harsh environments. However, the limitation of conventional cameras in perceiving intra-frame dynamic…