Machine Learning Based Linear and Nonlinear Noise Estimation

Caballero, F. J. Vaquero; Ives, D. J. G.; Laperle, Charles; Charlton, D.; Zhuge, Qunbi; O’Sullivan, Maurice; Savory, Seb J.

doi:10.1364/jocn.10.000d42

Cited by 58 publications

(23 citation statements)

References 15 publications

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“…In order to correct for the distorted multilevel optical signal caused by SPM, Shotaro and colleagues suggested a novel nonlinear equalization technique employing NN [ 215 ]. Caballero and colleagues developed a method with the ability to estimate signal-to-noise linear ratio and nonlinear ratio considering SPM assisted with an NN [ 216 ].…”

Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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“…Deep learning, as a main branch of AI, provides many neural network models to handle various optical communication problems. For example, artificial neural network (ANN) is widely utilized in nonlinear equalization [76][77][78] and optical performance monitoring [79,80], convolutional neural network (CNN) has the ability to get high accuracy of output images in MMF [81] and enabled high-spatial-density SDM framework [82]. And DNN, which is more complex than ANN, shows its excellent performance in end-to-end learning [83].…”

Section: Artificial Intelligence (Ai) Technology For Mimo Equalizationmentioning

confidence: 99%

Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review

Yang

Peng

et al. 2021

Applied Sciences

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This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMFs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMFs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMFs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.

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“…No attempts were made to de-noise the data since we were specifically interested in the performance that can be expected in a real-world scenario. Furthermore, experience in the related field of automatic speech recognition has shown that noise reduction performed before model training is often not beneficial and may reduce robustness to varying input conditions [21].…”

Section: Collection Set-upmentioning

confidence: 99%

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

Pahar,

Klopper,

Reeve

et al. 2021

Preprint

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We present first results showing that it is possible to automatically discriminate between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments in a real-world noisy environment. Our experiments are based on a dataset of cough recordings obtained in a real-world clinic setting from 16 patients confirmed to be suffering from TB and 33 patients that are suffering from respiratory conditions, confirmed as other than TB. We have trained and evaluated several machine learning classifiers, including logistic regression (LR), support vector machines (SVM), knearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN) inside a nested k-fold cross-validation and find that, although classification is possible in all cases, the best performance is achieved using the LR classifier. In combination with feature selection by sequential forward search (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the WHO as minimal requirements for community-based TB triage test. We conclude that automatic classification of cough audio sounds is promising as a viable means of low-cost easily-deployable front-line screening for TB, which will greatly benefit developing countries with a heavy TB burden.

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Machine Learning Based Linear and Nonlinear Noise Estimation

Cited by 58 publications

References 15 publications

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review

Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment

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