arXiv subjects

Bahadır Utku Kesgin

Publications and source records attributed to Bahadır Utku Kesgin.

8 recordsLinked to original sources

arXiv2026

Low-cost passive single-shot ultrafast imaging at 685 Gfps

Capturing ultrafast transient phenomena conventionally requires streak cameras or computational imaging based on compressed sensing, which lead to complex and costly systems. In this Letter, we demonstrate, to the best of our knowledge, the first fully passive single-shot ultrafast imaging architecture assembled entirely from off-the-shelf, low-cost components. A commercial microlens array combined with a stack of standard microscope cover glasses maps temporal information into multiple spatial channels, and a consumer-grade CMOS image sensor records all delayed replicas within a single camera exposure. The proposed system has a total hardware cost below US\$500 and captures the evolution of a picosecond laser pulse with a temporal sampling interval of 1.46~ps, an effective frame rate of 685~Gfps, and a sequence depth of ten frames. The temporal fidelity of the system is verified by recovering the expected Gaussian pulse profile, and the spatial resolution is characterized through a point-source measurement with a point spread function of 1.86 and 1.62 pixels full width at half maximum along the horizontal and vertical directions, respectively. The proposed architecture presents an alternative approach to single-shot ultrafast imaging with a simple, low-cost, computation-free, and fully passive design.

physics.optics↗

arXiv2026

Recurrent neural networks implemented through spatiotemporal light propagation in optical fibers

Recurrent neural networks excel at temporal tasks and video processing but require energy-intensive sequential memory operations. We demonstrate that multimode optical fibers naturally implement spatiotemporal recurrent computation through passive light propagation. Video frames are encoded onto separate optical beams with controlled time delays; these beams combine and recirculate through a fiber loop where interference and nonlinear propagation generate high-dimensional states encoding both current inputs and fading memory. Remarkably, the entire optical system remains fixed with no trainable parameters or electronic feedback, yet this single physical configuration achieves competitive performance across diverse temporal and spatiotemporal learning tasks: chaotic time-series forecasting, human action recognition, steering angle prediction, and surgical skill assessment. Our results show that recurrent temporal processing can emerge directly from spatiotemporal wave dynamics. This paradigm shift from algorithmic to physical recurrence offers an energy-efficient pathway to temporal artificial intelligence by leveraging intrinsic spatiotemporal optical nonlinearities within multimode fibers.

physics.optics↗

arXiv2026

Multimode fiber laser cavities as nonlinear optical processors

Optical computing provides a promising path toward energy-efficient machine learning, yet implementing nonlinear transformations without complex electronics or high-power sources remains challenging. Here, we demonstrate that continuous-wave multimode fiber laser cavities can function as nonlinear optical processors. Input images encoded as phase patterns on a spatial light modulator undergo high-dimensional transformation through the interplay of multimode interference and gain saturation dynamics. The cavity maps input data into spatially stable, class-separable intensity distributions, enabling a simple linear classifier to achieve accuracies of 85--99\% across diverse benchmarks -- including medical imaging and remote sensing -- with orders of magnitude fewer trainable parameters than deep neural networks. Our results establish multimode fiber lasers as compact, low-power physical processors for scalable optical machine learning.

physics.optics↗

arXiv2025

Optical Spiking Neural Networks via Rogue-Wave Statistics

Optical computing could reduce the energy cost of artificial intelligence by leveraging the parallelism and propagation speed of light. However, implementing nonlinear activation, essential for machine learning, remains challenging in low-power optical systems dominated by linear wave physics. Here, we introduce an optical spiking neural network that uses optical rogue-wave statistics as a programmable firing mechanism. By establishing a homomorphism between free-space diffraction and neuronal integration, we demonstrate that phase-engineered caustics enable robust, passive thresholding: sparse spatial spikes emerge when the local intensity exceeds a significant-intensity rogue-wave criterion. Using a physics-informed digital twin, we optimize granular phase masks to deterministically concentrate energy into targeted detector regions, enabling end-to-end co-design of the optical transformation and a lightweight electronic readout. We experimentally validate the approach on BreastMNIST and Olivetti Faces, achieving accuracies of 82.45\% and 95.00\%, respectively, competitive with standard digital baselines. These results demonstrate that extreme-wave phenomena, often treated as deleterious fluctuations, can be harnessed as structural nonlinearity for scalable, energy-efficient neuromorphic photonic inference.

physics.optics↗

arXiv2025

Real-time surrogate modeling of nonlinear pulse evolution in multimode fibers

Modeling nonlinear pulse propagation in multimode fibers is challenging due to the large number of interacting modes and the resulting spatiotemporal complexity. Traditional optimization methods often become intractable, while learning-based approaches, such as recurrent neural networks, suffer from high computational cost and long inference times. We present a U-Net architecture as a fast, accurate surrogate for modeling nonlinear pulse propagation in multimode fibers. This approach overcomes the intractability of traditional methods while offering low computational cost. Trained on data generated by beam propagation method, our approach achieves an $\sim$88\% average structural similarity index with simulations. The model can generalize to untrained propagation distances, demonstrating convolutional architectures as efficient tools for simulating complex spatiotemporal dynamics in multimode fibers and offering potential for applications like mode decomposition.

physics.optics↗

arXiv2025

Fiber-based diffractive deep neural network

Optical computing has reemerged as a promising alternative computing paradigm for providing energy-efficient information processing in the age of artificial intelligence. Among various photonic neural network platforms, diffractive optical processing systems in free space proved high-performance computing with high parallelism. Here, we report fiber-based diffractive deep neural networks by optimizing the linear coupling of the waveguide modes. Our approach demonstrated high performance in various machine learning tasks such as biomedical disease, fashion, and geospatial classification with a simple readout layer and all-optically. Operating on linear optics, our architecture performs on par with neural networks even in complex datasets where the data cannot be separated using linear operations. These results will enable efficient and scalable diffractive information processing with waveguides for real-life computing, telecommunications, and imaging applications.

physics.optics↗

arXiv2024

Photonic neural networks with spatiotemporal chaos in multimode fibers

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency. Optical systems offer promising alternatives to digital neural networks by exploiting light's parallelism. This study explores a photonic neural network design using spatiotemporal chaos within grad-ed-index multimode fibers to improve machine learning performance. Through numerical simulations and experiments, we show that chaotic light propagation in multimode fibers enhances data classification accu-racy across domains, including biomedical imaging, fashion, and satellite geospatial analysis. This chaotic optical approach enables high-dimensional transformations, amplifying data separability and differentiation for greater accuracy. Fine-tuning parameters such as pulse peak power optimizes the reservoir's chaotic properties, highlighting the need for careful calibration. These findings underscore the potential of chaos-based nonlinear photonic neural networks to advance optical computing in machine learning, paving the way for efficient, scalable architectures.

physics.optics↗

arXiv2023

Machine Learning with Chaotic Strange Attractors

Machine learning studies need colossal power to process massive datasets and train neural networks to reach high accuracies, which have become gradually unsustainable. Limited by the von Neumann bottleneck, current computing architectures and methods fuel this high power consumption. Here, we present an analog computing method that harnesses chaotic nonlinear attractors to perform machine learning tasks with low power consumption. Inspired by neuromorphic computing, our model is a programmable, versatile, and generalized platform for machine learning tasks. Our mode provides exceptional performance in clustering by utilizing chaotic attractors' nonlinear mapping and sensitivity to initial conditions. When deployed as a simple analog device, it only requires milliwatt-scale power levels while being on par with current machine learning techniques. We demonstrate low errors and high accuracies with our model for regression and classification-based learning tasks.

cs.LG↗