Dr. Jonathan K. George Profile

Dr. Jonathan K. George

Software Engineer, Ph.D. Student

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Publications (8)

Proceedings Article | 15 March 2023 Presentation + Paper

Michelson interferometric methods for full optical complex convolution

Haoyan Kang, Jonathan George, Behrouz Movahhed Nouri, Maria Solyanik-Gorgone, Hamed Dalir, Volker Sorger

Proceedings Volume 12438, 1243803 (2023) https://doi.org/10.1117/12.2650952

KEYWORDS: Convolution, Spatial light modulators, Neural networks, Modulation, Phase modulation, Optical computing

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Proceedings Article | 14 May 2019 Presentation

Integrated photonic FFTs for convolutional neural networks and real-time processing (Conference Presentation)

Jonathan George, Volker Sorger

Proceedings Volume 10996, 1099606 (2019) https://doi.org/10.1117/12.2518764

KEYWORDS: Convolutional neural networks, Integrated photonics, Convolution, Silicon photonics, Fourier transforms, Mach-Zehnder interferometers, Directional couplers, Phase shifts, Electrooptic modulators, Structural design

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Proceedings Article | 4 March 2019 Presentation

Information processing via photonic routing: residue number system arithmetic and reconfigurable graph processors (Conference Presentation)

Volker Sorger, Jiaxin Peng, Shuai Sun, Jonathan George, Yousra Alkabani, Tarek El-Ghazawi

Proceedings Volume 10924, 109240G (2019) https://doi.org/10.1117/12.2509887

KEYWORDS: Data processing, Integrated optics, Picosecond phenomena, Computational complexity theory, Information fusion, Convolution, Fourier transforms, Nanophotonics, Electro optics, Switches

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Proceedings Article | 4 March 2019 Presentation

Advances in photonic neuromorphic computing (Conference Presentation)

Volker Sorger, Jonathan George, Armin Mehrabian, Bhavin Shastri, Tarek El-Ghazawi, Paul Prucnal

Proceedings Volume 10922, 109220T (2019) https://doi.org/10.1117/12.2509838

KEYWORDS: Optical computing, Neural networks, Electro optics, Photonic integrated circuits, Matrix multiplication, Picosecond phenomena, Nonlinear optics, Microelectronics, Data processing, Neurons

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Proceedings Article | 17 September 2018 Presentation

Identifying mirror symmetry density with delay in spiking neural networks (Conference Presentation)

Jonathan George, Cesare Soci, Volker Sorger

Proceedings Volume 10751, 107510L (2018) https://doi.org/10.1117/12.2322083

KEYWORDS: Neural networks, Neurons, Mirrors, Laser induced fluorescence, 3D vision, Human vision and color perception, 3D visualizations, Artificial neural networks, Logic, Sensors

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The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence. Symmetry perception is a central process in human vision and may be key to human 3D visualization. While previous work in understanding neuron symmetry perception has concentrated on the neuron as an integrator, here we show how the coincidence detecting property of the spiking neuron can be used to reveal symmetry density in spatial data. We develop a method for synchronizing symmetry-identifying spiking artificial neural networks to enable layering and feedback in the network. We show a method for building a network capable of identifying symmetry density between sets of data and present a digital logic implementation demonstrating an 8x8 leaky-integrate-and-fire (LIF) symmetry detector in a field programmable gate array. Our results show that the efficiencies of spiking neural networks can be harnessed to rapidly identify symmetry in spatial data with applications in image processing, 3D computer vision, and robotics.In conclusion, we have presented a novel algorithm for finding a scalar field representing the symmetry of points in a multi-dimensional space. We have shown how time synchronization in the input values of spiking neural networks, with the appropriate choice of threshold and spike period, results in the identification of output neurons along points of high symmetry density to the network inputs. We have demonstrated an implementation of the symmetry selective LIF neural network in common hardware with a high speed, 2.8 MHz identification of symmetry points in an 8x8 Manhattan metric space. Our results show that utilizing only the delay and coincidence detecting properties of a single layer of neurons in spiking neural networks naturally lead to effective symmetry identification. A greater understanding of symmetry perception in artificial intelligences will lead to systems with more effective pattern visualization, compression, and goal setting processes.

Proceedings Article | 17 September 2018 Presentation

Towards on-chip optical FFTs for convolutional neural networks

Jonathan George, Hani Nejadriahi, Volker Sorger

Proceedings Volume 10751, 107510A (2018) https://doi.org/10.1117/12.2322067

KEYWORDS: Convolutional neural networks, Convolution, Silicon photonics, Interferometers, Fourier transforms, Directional couplers, Phase shifts, Electrooptic modulators, Structural design

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Proceedings Article | 17 September 2018 Presentation

Attojoule modulators for photonic neuromorphic algorithms (Conference Presentation)

Jonathan George, Tarek El-Ghazawi, Paul Prucnal, Volker Sorger

Proceedings Volume 10721, 107211L (2018) https://doi.org/10.1117/12.2318417

KEYWORDS: Modulators

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Proceedings Article | 31 May 2013 Paper

Polar synthetic imaging

Jonathan George

Proceedings Volume 8716, 87160X (2013) https://doi.org/10.1117/12.2014811

KEYWORDS: Modulation, Calibration, Imaging systems, Prototyping, Data modeling, Sensors, Diffraction, Staring arrays, Mirrors, Code division multiplexing

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Showing 5 of 8 publications

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