82 citations · 150 across the 20 of their papers we have counts for
21 papers
Path-Integrated Polarization Rotation Signatures in Subsea Networks: Cable Geometry Effects in 2023 Turkey Earthquakes
Mohammad M. Hosseini, Miquel Masanas, Giuseppe Parisi +5
We analyze polarization data streamed from a commercial coherent transponder, used in MedNautilus link, during 2023 Turkey earthquakes. We show how earthquake features and cable ge…
Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our m…
DPD-KAN: Kolmogorov-Arnold Networks for Low Complexity Digital Predistortion in 5G Analog Radio-over-Fiber Systems
Bilal Khalid, Fabio Cavaliere, Luca Giorgi +3
We demonstrate the first KAN-based DPD model for 5G analog RoF fronthaul link, achieving a 24.2% lower EVM than multi-layer perceptron and 29.6% lower than Volterra-based GMP at eq…
Dual Line Coherent Detection
Nelson Castro, Yiming Li, Mohammed Patel +3
We experimentally demonstrate dual-line coherent detection using an optical frequency comb local oscillator, enabling large frequency offset tolerance with minimal additional signa…
Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Luca Nogueira Calçado, Sergei K. Turitsyn, Egor Manuylovich
Photonic neural networks promise ultrafast inference, yet most architectures rely on linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical…
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
Bilal Khalid, Pedro Freire, Sergei K. Turitsyn +1
Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises significant con…
Low-complexity neural network equalization for long-haul coherent transmission with cascaded semiconductor optical amplifiers
S. Bogdanov, S. Sygletos, O. Sidelnikov +3
In this letter, we numerically investigate a long-haul coherent data transmission system with a cascade of semiconductor optical amplifiers (SOAs). We exploit low-complexity neural…
Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4
We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventio…
PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging
Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn +1
Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded…
From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
Machine learning-based failure management in optical networks has gained significant attention in recent years. However, severe class imbalance, where normal instances vastly outnu…
Dual Path Learning -- learning from noise and context for medical image denoising
Jitindra Fartiyal, Pedro Freire, Yasmeen Whayeb +3
Medical imaging plays a critical role in modern healthcare, enabling clinicians to accurately diagnose diseases and develop effective treatment plans. However, noise, often introdu…
Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo +5
We compare pre-, in-, and post-processing techniques for class imbalance mitigation in optical network failure detection. Threshold Adjustment achieves the highest F1 gain (15.3%),…
Reverse energy flows: the physical mechanism underling dramatic reduction of loss in hollow-core fibers
Andrey Pryamikov, Sergei Turitsyn
Hollow-core fibers (HCFs) with claddings composed of silica glass capillaries have recently attracted a great deal of attention following the demonstration of optical loss levels l…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform
Egor Manuylovich, Dmitrii Stoliarov, David Saad +1
Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as Reservoir Computing (RC) and Extreme L…
FPGA Implementation of Low-Power Multiplierless Pre-Processing Free Chromatic Dispersion Equalizer
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
We present a novel time-domain chromatic dispersion equalizer, implemented on FPGA, eliminating pre-processing and multipliers, achieving up to 54.3% energy savings over 80-1280 km…
FPGA Implementation of Complex Value-based Clustering Filter for Chromatic Dispersion Compensation in Coherent Metro Links with Ultra-low Power Consumption
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
This paper introduces a new machine learning-assisted chromatic dispersion compensation filter, demonstrating its superior power efficiency compared to conventional FFT-based filte…
Geometric Clustering for Hardware-Efficient Implementation of Chromatic Dispersion Compensation
Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky +1
Power efficiency remains a significant challenge in modern optical fiber communication systems, driving efforts to reduce the computational complexity of digital signal processing,…
Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations
Alice Duque, Pedro Freire, Egor Manuylovich +3
This work tackles the critical challenge of mitigating "hardware noise" in deep analog neural networks, a major obstacle in advancing analog signal processing devices. We propose a…
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky +1
This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer scie…
Computational Complexity Evaluation of Neural Network Applications in Signal Processing
Pedro Freire, Sasipim Srivallapanondh, Antonio Napoli +2
In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link…