In this talk, we will present PhyCV: the first physics-inspired computer vision library. PhyCV is a new class of computer vision algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the first release emulate, in a metaphoric sense, the propagation of light through a physical medium with natural and engineered diffractive properties followed by coherent detection.
This talk will present a science-as-a-service application that will provide public access to a new family of computational imaging algorithms that are inspired by optical physics. These algorithms are emerging as the best-in-class tools for certain digital image processing functions such as edge and texture detection, and more. A cloud application developed in collaboration with and hosted by AWS, the application features various tools, sample data, and workflows. The cloud approach provides data security, a crucial issue in the biomedical industry and exposes the broader biomedical community to the ongoing innovations in algorithms.
KEYWORDS: Wavelength division multiplexing, Artificial intelligence, Nonlinear filtering, Machine learning, Digital signal processing, Data modeling, Binary data, Signal to noise ratio, Receivers, Performance modeling
Wavelength Division Multiplexing (WDM) is the key technology in ultra-high capacity links that form the backbone of the internet. Hundreds or more data channels each at a different wavelength travel through a single fiber resulting in aggregate data rates exceeding many Terabits per second. The fundamental limit to the data transmission rate is the optical crosstalk between channels induced by the inevitable nonlinearity of the fiber. Traditional methods for compensating for the reduction in the bit error rate caused by the crosstalk include numerical backpropagation as well as nonlinear Volterra filter, both implemented in the digital domain at the receiver. Backpropagation through the canonical nonlinear Schrodinger equation is computationally expensive and beyond the capability of today’s DSP at the data rates that optical networks operate. Volterra filters scale superlinearly with an increasing number of taps and which in turn scale with the amount dispersion in the fiber. Therefore, they are not the ideal solution for high data rates. In this talk, we report on the application of machine learning, and neural networks in particular, on the compensation of optical crosstalk in WDM communication. We compare the performance of different machine learning models such as support vector machine (SVM), decision tree, convolutional neural network (CNN) in terms of the achievable bit error rate on both binary and multilevel modulated data. We further evaluate the sensitivity of the error rate to the resolution of the analog to digital converter (ADC) and to the signal to noise ratio as well as the latency of our algorithms
KEYWORDS: Digital signal processing, Calibration, Artificial intelligence, Analog electronics, Robotic surgery, Quadrature amplitude modulation, Orthogonal frequency division multiplexing, Modulation, Evolutionary algorithms, Detection and tracking algorithms
With the advent of the 5G wireless networks, achieving tens of gigabits per second throughputs and low latency has become a reality. This level of performance will fuel numerous realtime applications where the computationally heavy tasks can be performed in the cloud. The increase in the bandwidth along with the use of dense constellations places a significant burden on the speed and accuracy of analog-to-digital converters (ADC). A popular approach to create wideband ADCs is utilizing multiple channels each operating at a lower speed in the time-interleaved pattern. However, an interleaved ADC comes with its own set of challenges. The parallel architecture is very sensitive to the inter-channel mismatch, timing jitter, clock skew between different ADC channels as well as the nonlinearity within individual channels. In this project, we utilize a deep learning algorithm to learn the complete and complex ADC behavior and to compensate for it.
Conference Committee Involvement (1)
AI and Optical Data Sciences VI
27 January 2025 | San Francisco, California, United States
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