211 research outputs found
Optical Companding
We introduce a new nonlinear analog optical computing concept that compresses
the signal's dynamic range and realizes non-uniform quantization that reshapes
and improves the signal-to-noise ratio in the digital domain
A new stochastic differential equation approach for waves in a random medium
We present a mathematical approach that simplifies the theoretical treatment
of electromagnetic localization in random media and leads to closed form
analytical solutions. Starting with the assumption that the dielectric
permittivity of the medium has delta-correlated spatial fluctuations, and using
the Ito lemma, we derive a linear stochastic differential equation for a one
dimensional random medium. The equation leads to localized wave solutions. The
localized wave solutions have a localization length that scales inversely with
the square of the frequency of the wave in the low frequency regime, whereas in
the high frequency regime, this length varies inversely with the frequency to
the power of two thirds
Low Latency Computing for Time Stretch Instruments
Time stretch instruments have been exceptionally successful in discovering
single-shot ultrafast phenomena such as optical rogue waves and have led to
record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the
ultrafast events into the spectrum of a femtosecond pulse and then dilate the
time scale of the data using group velocity dispersion. Generating as much as
Tbit per second of data, they are ideal partners for deep learning networks
which by their inherent complexity, require large datasets for training.
However, the inference time scale of neural networks in the millisecond regime
is orders of magnitude longer than the data acquisition rate of time stretch
instruments. This underscores the need to explore means where some of the
lower-level computational tasks can be done while the data is still in the
optical domain. The Nonlinear Schr\"{o}dinger Kernel computing addresses this
predicament. It utilizes optical nonlinearities to map the data onto a new
domain in which classification accuracy is enhanced, without increasing the
data dimensions. One limitation of this technique is the fixed optical transfer
function, which prevents training and generalizability. Here we show that the
optical kernel can be effectively tuned and trained by utilizing digital phase
encoding of the femtosecond laser pulse leading to a reduction of the error
rate in data classification
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