fiber optics experiments

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Science Fair Projects With Fiber Optics

How to Make Rainbows With Prisms

Fiber optics is a method of delivering light through clear, glass wires, or fibers. Light can travel through these fibers over long distances. The fiber can carry light through twists and turns just like copper wire carries electricity. Fiber optics can also use light to carry information, much like copper wires carry information in electrical current. Students can use household items to show the basic principles of fiber optics, or use fiber optic strands to demonstrate more practical fiber optic uses.

Baking Dish Fiber Optics

Young students can create a basic demonstration of how glass can transport light with a flashlight and a glass baking dish. Place a glass baking dish on a flat surface and darken the area. Shine a flashlight or laser pointer down onto one rim of the backing dish. Observe the opposite rim of the baking dish. See how the light travels down the rim of the backing dish, through the bottom of the rim and up the opposite rim.

Water Carries Light

Students can use water as a vehicle to carry light, much like fiber optic strands. Wrap a water bottle in aluminum foil; leave only the bottom and the opening of the bottle unwrapped. Fill the bottle with water, then darken the area. Shine a flashlight through the bottom of the bottle as you tip the bottle to pour out the water. The stream of water will be illuminated as it pours from the bottle.

Communicate With Light

Students can demonstrate how actual fiber optic strands can carry light directionally. Make an electrical circuit consisting of a battery, a switch and a light emitting diode (LED). Connect the electrical wiring so that the LED is illuminated when the switch is closed. Connect a fiber optic cable to the LED. Bend the cable in different ways and route it through or around obstacles, then demonstrate how the light from the LED is emitted from the end of the fiber optic cable.

Signal Degradation

Another idea for a science project is to compare fiber optic applications under different conditions. Connect an audio source with optical outputs to speakers using fiber optic cables. The cables designed for this application are called TOSLINK cables. Subject the TOSLINK cable to different heat, cold, vibration or other conditions. Compare the audio output from the experimental TOSLINK cable to audio output from a TOSLINK cable under normal operating conditions,

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• Explain That Stuff; Fiber Optics

About the Author

Michael Signal began writing professionally in 2010, with his work appearing on eHow. He has expert knowledge in aviation, computer hardware and software, elementary education and interpersonal communication. He has been an aircraft mechanic, business-to-business salesman and teacher. He holds a master's degree in education from Lesley University.

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Fiber optics

by Chris Woodford . Last updated: March 16, 2022.

T he Romans must have been particularly pleased with themselves the day they invented lead pipes around 2000 years ago. At last, they had an easy way to carry their water from one place to another. Imagine what they'd make of modern fiber-optic cables—"pipes" that can carry telephone calls and emails right around the world in a seventh of a second!

Photo: Light pipe: fiber optics means sending light beams down thin strands of plastic or glass by making them bounce repeatedly off the walls. This is a simulated image. Note that in some countries, including the UK, fiber optics is spelled "fibre optics." If you're looking for information online, it's always worth searching both spellings.

What is fiber optics?

We're used to the idea of information traveling in different ways. When we speak into a landline telephone , a wire cable carries the sounds from our voice into a socket in the wall, where another cable takes it to the local telephone exchange. Cellphones work a different way: they send and receive information using invisible radio waves—a technology called wireless because it uses no cables. Fiber optics works a third way. It sends information coded in a beam of light down a glass or plastic pipe. It was originally developed for endoscopes in the 1950s to help doctors see inside the human body without having to cut it open first. In the 1960s, engineers found a way of using the same technology to transmit telephone calls at the speed of light (normally that's 186,000 miles or 300,000 km per second in a vacuum, but slows to about two thirds this speed in a fiber-optic cable).

Optical technology

A fiber-optic cable is made up of incredibly thin strands of glass or plastic known as optical fibers; one cable can have as few as two strands or as many as several hundred. Each strand is less than a tenth as thick as a human hair and can carry something like 25,000 telephone calls, so an entire fiber-optic cable can easily carry several million calls. The current record for a "single-mode" fiber (that's explained below) is 178 terabits (trillion bits) per second—enough for 100 million Zoom sessions ( according to fiber expert Jeff Hecht )!

Fiber-optic cables carry information between two places using entirely optical (light-based) technology. Suppose you wanted to send information from your computer to a friend's house down the street using fiber optics. You could hook your computer up to a laser , which would convert electrical information from the computer into a series of light pulses. Then you'd fire the laser down the fiber-optic cable. After traveling down the cable, the light beams would emerge at the other end. Your friend would need a photoelectric cell (light-detecting component) to turn the pulses of light back into electrical information his or her computer could understand. So the whole apparatus would be like a really neat, hi-tech version of the kind of telephone you can make out of two baked-bean cans and a length of string!

Photo: A section of 144-strand fiber-optic cable. Each strand is made of optically pure glass and is thinner than a human hair. Picture by Tech. Sgt. Brian Davidson, courtesy of US Air Force .

How fiber-optics works

Light travels down a fiber-optic cable by bouncing repeatedly off the walls. Each tiny photon (particle of light) bounces down the pipe like a bobsleigh going down an ice run. Now you might expect a beam of light, traveling in a clear glass pipe, simply to leak out of the edges. But if light hits glass at a really shallow angle (less than 42 degrees), it reflects back in again—as though the glass were really a mirror . This phenomenon is called total internal reflection . It's one of the things that keeps light inside the pipe.

Photo: Fiber-optic cables are thin enough to bend, taking the light signals inside in curved paths too. Picture courtesy of NASA Glenn Research Center (NASA-GRC) and Internet Archive .

The other thing that keeps light in the pipe is the structure of the cable, which is made up of two separate parts. The main part of the cable—in the middle—is called the core and that's the bit the light travels through. Wrapped around the outside of the core is another layer of glass called the cladding . The cladding's job is to keep the light signals inside the core. It can do this because it is made of a different type of glass to the core. (More technically, the cladding has a lower refractive index .)

Artwork: Total internal reflection keeps light rays bouncing down the inside of a fiber-optic cable.

Types of fiber-optic cables

Optical fibers carry light signals down them in what are called modes . That sounds technical but it just means different ways of traveling: a mode is simply the path that a light beam follows down the fiber. One mode is to go straight down the middle of the fiber. Another is to bounce down the fiber at a shallow angle. Other modes involve bouncing down the fiber at other angles, more or less steep.

Artworks: Above: Light travels in different ways in single-mode and multi-mode fibers. Below: Inside a typical single-mode fiber cable (not drawn to scale). The thin core is surrounded by cladding roughly ten times bigger in diameter, a plastic outer coating (about twice the diameter of the cladding), some strengthening fibers made of a tough material such as Kevlar® , with a protective outer jacket on the outside.

The simplest type of optical fiber is called single-mode . It has a very thin core about 5-10 microns (millionths of a meter) in diameter. In a single-mode fiber, all signals travel straight down the middle without bouncing off the edges (yellow line in diagram). Cable TV, Internet, and telephone signals are generally carried by single-mode fibers, wrapped together into a huge bundle. Cables like this can send information over 100 km (60 miles).

Another type of fiber-optic cable is called multi-mode . Each optical fiber in a multi-mode cable is about 10 times bigger than one in a single-mode cable. This means light beams can travel through the core by following a variety of different paths (yellow, orange, blue, and cyan lines)—in other words, in multiple different modes. Multi-mode cables can send information only over relatively short distances and are used (among other things) to link computer networks together.

Even thicker fibers are used in a medical tool called a gastroscope (a type of endoscope), which doctors poke down someone's throat for detecting illnesses inside their stomach. A gastroscope is a thick fiber-optic cable consisting of many optical fibers. At the top end of a gastroscope, there is an eyepiece and a lamp. The lamp shines its light down one part of the cable into the patient's stomach. When the light reaches the stomach, it reflects off the stomach walls into a lens at the bottom of the cable. Then it travels back up another part of the cable into the doctor's eyepiece. Other types of endoscopes work the same way and can be used to inspect different parts of the body. There is also an industrial version of the tool, called a fiberscope, which can be used to examine things like inaccessible pieces of machinery in airplane engines.

Try this fiber-optic experiment!

This nice little experiment is a modern-day recreation of a famous scientific demonstration carried out by Irish physicist John Tyndall in 1870. It's best to do it in a darkened bathroom or kitchen at the sink or washbasin. You'll need an old clear, plastic drinks bottle, the brightest flashlight (torch) you can find, some aluminum foil, and some sticky tape.

• Take the plastic bottle and wrap aluminum foil tightly around the sides, leaving the top and bottom of the bottle uncovered. If you need to, hold the foil in place with sticky tape.
• Fill the bottle with water.
• Switch on the flashlight and press it against the base of the bottle so the light shines up inside the water. It works best if you press the flashlight tightly against the bottle. You need as much light to enter the bottle as possible, so use the brightest flashlight you can find.
• Standing by the sink, tilt the bottle so the water starts to pour out. Keep the flashlight pressed tight against the bottle. If the room is darkened, you should see the spout of water lighting up ever so slightly. Notice how the water carries the light, with the light beam bending as it goes! If you can't see much light in the water spout, try a brighter flashlight.

Photo: Seen from below, your water bottle should look like this when it's wrapped in aluminum foil. The foil stops light leaking out from the sides of the bottle. Don't cover the bottom of the bottle or light won't be able to get in. The black object on the right is my flashlight, just before I pressed it against the bottle. You can already see some of its light shining into the bottom of the bottle.

Photo: Working on fiber-optic cables. Picture by Nathanael Callon, courtesy of US Air Force .

Photo: Fiber-optic networks are expensive to construct (largely because it costs so much to dig up streets). Because the labor and construction costs are much more expensive than the cable itself, many network operators deliberately lay much more cable than they currently need. Picture by Chris Willis courtesy of US Air Force .

Photo: Fiber optics on the battlefield. This Enhanced Fiber-Optic Guided Missile (EFOG-M) has an infrared fiber-optic camera mounted in its nose so that the gunner firing it can see where it's going as it travels. Picture courtesy of US Army .

Who invented fiber optics?

• 1840s: Swiss physicist Daniel Colladon (1802–1893) discovered he could shine light along a water pipe. The water carried the light by internal reflection.
• 1870: An Irish physicist called John Tyndall (1820–1893) demonstrated internal reflection at London's Royal Society. He shone light into a jug of water. When he poured some of the water out from the jug, the light curved round following the water's path. This idea of "bending light" is exactly what happens in fiber optics. Although Colladon is the true grandfather of fiber-optics, Tyndall often earns the credit.
• 1930s: Heinrich Lamm and Walter Gerlach , two German students, tried to use light pipes to make a gastroscope—an instrument for looking inside someone's stomach.
• 1950s: In London, England, Indian physicist Narinder Kapany (1926–2021) and British physicist Harold Hopkins (1918–1994) managed to send a simple picture down a light pipe made from thousands of glass fibers. After publishing many scientific papers, Kapany earned a reputation as the "father of fiber optics."
• 1957: Three American scientists at the University of Michigan, Lawrence Curtiss , Basil Hirschowitz , and Wilbur Peters , successfully used fiber-optic technology to make the world's first gastroscope.
• 1960s: Chinese-born US physicist Charles Kao (1933–2018) and his colleague George Hockham realized that impure glass was no use for long-range fiber optics. Kao suggested that a fiber-optic cable made from very pure glass would be able to carry telephone signals over much longer distances and was awarded the 2009 Nobel Prize in Physics for this ground-breaking discovery.
• 1960s: Researchers at the Corning Glass Company made the first fiber-optic cable capable of carrying telephone signals.
• ~1970: Donald Keck and colleagues at Corning found ways to send signals much further (with less loss) prompting the development of the first low-loss optical fibers.
• 1977: The first fiber-optic telephone cable was laid between Long Beach and Artesia, California.
• 1988: The first transatlantic fiber-optic telephone cable, TAT8, was laid between the United States, France, and the UK.
• 2022: According to TeleGeography , there are currently around 436 fiber-optic submarine cables (carrying communications under the world's oceans), stretching a total of 1.3 million km (0.8 million miles). That's an increase on 2019's figure of 378 cables, though the total distance covered is roughly the same.

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You might like these other articles on our site covering related topics:

• History of communication

Popular science/introductions

• Understanding Fiber Optics by Jeff Hecht. Laser Light Press, 2015. A very comprehensive, clearly written overview with relatively little math.
• City of Light: The Story of Fiber Optics by Jeff Hecht. Oxford University Press, 2004. How fiber optics went from being a minor scientific curiosity to an indispensable feature of modern telecommunications, used by every single one of us, every single day!

More scholarly and technical

• Fiber-Optic Communications Systems by Govind P. Agrawal. John Wiley & Sons, 2021. A classic textbook, in print for nearly three decades.
• Nonlinear Fiber Optics by Govind P. Agrawal. Academic Press, 2019. A separate volume covers Applications of Nonlinear Fiber Optics .
• Optical Network Design and Implementation by Vivek Alwayn. Cisco Press, 2009. A comprehensive technical guide covering all aspects of fiber-optic networks.
• Optical Fibre by Charles K. Kao. P. Peregrinus, 1988. An introduction to the physics and chemistry of fiber optics by one of its major pioneers.
• Optical Fiber Systems: Technology, Design, and Applications by Charles K. Kao. McGraw-Hill, 1982. A very broad overview of fiber optics. Though it's a somewhat dated book, the general material in the first few chapters is still worth a look.

For younger readers

• The Illuminating World of Light with Max Axiom, Super Scientist by Emily Sohn and Nick Derington. Capstone, 2019. A graphic novel (comic-style) book for ages 8–14.
• Light in a Flash by Georgia Amson-Bradshaw. Franklin Watts, 2017. Simple activities make this an easier read for ages 7–9.
• A Project Guide to Light and Optics by Colleen Kessler. Mitchell Lane, 2012. Another activity-based guide for ages 9–12.
• Scientific Pathways: Light by Chris Woodford. Rosen, 2012/Blackbirch, 2004. One of my own books, this one charts how scientists have tried to understand light from ancient times to the present day. Suitable for ages 9–12.

Popular articles

• Narinder S. Kapany, 'Father of Fiber Optics,' Dies at 94 by Clay Risen, The New York Times, 8 January 2021. Celebrating the life of the Indian-born pioneer of fiber optics.
• 100 Million Zoom Sessions Over a Single Optical Fiber by Jeff Hecht, IEEE Spectrum, 27 August 2020. Researchers at University College London set a new lab record for fiber bandwidth.
• Remembering the Remarkable Foresight of Charles Kao by Jeff Hecht, IEEE Spectrum, 25 September 2018. Looking back at the life and work of the fiber-optic pioneer.
• Is Keck's Law Coming to an End? by Jeff Hecht, IEEE Spectrum, 26 January 2017. What are the limits for fiber-optic bandwidth and are we approaching them?
• How Charles Kao Beat Bell Labs to the Fiber-Optic Revolution by Jeff Hecht. IEEE Spectrum, 15 July 2016. The author of a popular book about fiber-optic history describes how Charles Kao figured out the theory of modern fiber-optic communications a half century ago.
• New Mode of Transmission May Double Fiber Optic Capacity by Charles Q. Choi, IEEE Spectrum, 25 June 2015. A new way of sending data could boost the capacity (or range) of a fiber-optic cable by 2–4 times.
• Is fibre optic cable key to Africa's economic growth? by Gabriella Mulligan, BBC News, 31 March 2015. Will fiber-optic networks help African countries to grow economically? Or are satellite and wireless communication a better bet?
• Laser puts record data rate through fibre by Jason Palmer, BBC News, 23 May 2011. Scientists explore new ways to send data down fiber-optic cables at higher rates.
• Nobel Prize in Physics 2009: Masters of Light : Explains the important contribution made by fiber-optic pioneer Charles Kao to our modern world of digital information.
• Nature's 'fibre optics' experts by Matt Walker, BBC News, 10 November 2008. How sea sponges funnel light using a similar technique to fiber optics.
• Fiber optic cables: How they work : Engineerguy Bill Hammack carries out a slightly more sophisticated version of the experiment up above.
• Understanding Lasers and Fiber Optics : MIT's Professor Shaoul Ezekiel gives us a much more detailed introduction to lasers and fiber-optics. Suitable for undergraduates (and perhaps advanced high school students).

Other useful websites

• Optics for Kids : Educational activities for children from the Optical Society of America.

Technical articles

• Dielectric fiber surface waveguides for optical frequencies by K. Charles Kao and George Hockham. Proceedings IEE, July 1966. Charles Kao's original paper on fiber-optic communication. [PDF]
• Fiber optics by Narinder S. Kapany, Scientific American, Vol. 203, No. 5 (November 1960), pp. 72–81. The fiber-optic pioneer explains the basic physics.

Text copyright © Chris Woodford 2006, 2020. All rights reserved. Full copyright notice and terms of use .

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• Published: 10 July 2019

Field and lab experimental demonstration of nonlinear impairment compensation using neural networks

• Shaoliang Zhang 1 ,
• Fatih Yaman 1 ,
• Kohei Nakamura 2 ,
• Takanori Inoue 2 ,
• Valey Kamalov 3 ,
• Ljupcho Jovanovski 3 ,
• Vijay Vusirikala 3 ,
• Eduardo Mateo 2 ,
• Yoshihisa Inada 2 &
• Ting Wang 1  

Nature Communications volume  10 , Article number:  3033 ( 2019 ) Cite this article

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• Electrical and electronic engineering
• Fibre optics and optical communications

Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. Nonlinear impairments are determined by the signal pattern and the transmission system parameters. Deterministic algorithms based on approximating the nonlinear Schrodinger equation through digital back propagation, or a single step approach based on perturbation methods have been demonstrated, however, their implementation demands excessive signal processing resources, and accurate knowledge of the transmission system. A completely different approach uses machine learning algorithms to learn from the received data itself to figure out the nonlinear impairment. In this work, a single-step, system agnostic nonlinearity compensation algorithm based on a neural network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6 dB Q improvement after 2800 km standard single-mode fiber transmission using 32 Gbaud signal. Without prior knowledge of the transmission system, the neural network tensor weights are constructed from training data thanks to the intra-channel cross-phase modulation and intra-channel four-wave mixing triplets used as input features.

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Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning

Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications

Single-ended recovery of optical fiber transmission matrices using neural networks

Introduction.

Capacity of optical transmission systems are bound by fundamental limits in both linear and nonlinear regime 1 . Recent experiments demonstrated capacities approaching the Shannon limit in the linear regime 2 , 3 , 4 . This leaves the Kerr nonlinearity as one of the major limitations to increasing capacity per fiber. Nonlinear compensation (NLC) algorithms were introduced in digital coherent receivers to compensate for the pattern-dependent and deterministic Kerr nonlinearity 5 , 6 . These digital signal processing (DSP) NLC algorithms were based on solving the nonlinear Schrödinger equation (NLSE) which governs the propagation of optical field in the fiber 7 in multiple steps such as digital back propagation (DBP) 5 , 6 . A variety of techniques were developed to reduce the complexity of NLC for a given amount of improvement, some of which reduced the required complexity by orders of magnitude such as the filtered DBP 8 . Compared to full-step DBP, at least 0.2 steps per span (SpS) and 0.6 SpS are found to be sufficient for QPSK 8 and 16QAM 9 , respectively. The first-order linear perturbation of NLSE has led to the single-step perturbation-based pre/post-distortion (PPD) algorithm for Gaussian 10 and root-raised cosine (RRC) pulses 11 , 12 , 13 . PPD was demonstrated over transoceanic distances to achieve only slightly less NLC gain than filtered DBP at 0.5 SpS 12 . Furthermore, after the initial success of filtered DBP and PPD in reducing the complexity, further reduction of complexity proved difficult to achieve. The common thread among these algorithms was that they were based on equalizing the nonlinearity based on a deterministic model of the impairment.

Recently, a different approach was taken using machine learning algorithms 14 , 15 , 16 , 17 . These algorithms aim to equalize nonlinear impairments directly by learning from data, rather than through emulating NLSE. These attempts remained limited in success in particular for dispersion-uncompensated links, or in scope until it was demonstrated recently through a field trial on a live-traffic cable that a simple neural network (NN) can provide NLC if it was supplemented with nonlinear impairment features 18 . A second approach 19 showed through simulations that, NN can also be used to obtain NLC by treating the DBP steps as NN layers. However, by design, this approach requires operating on at least two samples per symbol, which induces additional complexity.

In this paper it is shown that the NN architecture demonstrated in ref. 18 can be simplified further so that it can achieve NLC gain at a complexity lower than filtered DBP algorithms that are based on solving NLSE, especially at the most critical regime where the available DSP resources become scarce. Furthermore, NN not only learns from the received data and generates a black-box model of the transmission, it also can guide us how to reduce the complexity through the weights by distinguishing the terms that contribute significantly from the ones that do not. Another advantage of the proposed algorithm is that since it relies only on received data to emulate the transmission model, it works without prior knowledge of the link parameters. Since the algorithm becomes free from specifics of the link design, it can be applied universally to all fiber optical communication links whether they are short-haul, long-haul, terrestrial, submarine, or whether they are legacy systems or the state-of the art. It is also shown that the algorithm is versatile and robust enough that while the training can be performed at the receiver side which is the most practical case, the equalization can be performed on the transmitter side. NLC at the transmitter has the benefit of achieving slightly better Q improvement, but more importantly the possibility of a further reduction of complexity by calculating nonlinearity features with look up tables (LUT) rather than real multiplications. Performance of the proposed universal NN-NLC algorithm is demonstrated in both lab experiment over 2800 km standard single-mode fiber (SSMF) loop and field trial over 11,017 km straight-line FASTER cable. Compared with single-step filtered DBP algorithm, NN-NLC algorithm is capable of achieving ~0.35 dB Q-factor improvement in the 2800 km SSMF transmission and attaining ~0.08 b/s/Hz higher generalized mutual information (GMI) after 11,017 km submarine distance. The results show that NN-NLC has more potential to outperform filtered-DBP especially in the regime where the degree of computational complexity is limited.

Input features

Even though the NN algorithm needs only data to achieve a working model of the nonlinear impairment, it was found that providing the NN with nonlinear impairment features was necessary 18 . These features are provided to the NN by first calculating the intra-channel cross-phase modulation (IXPM) and intra-channel four-wave mixing (IFWM) triplets from the received symbols 10 , 20 . The triplets originated from the first-order perturbation of the NLSE that describes the evolution of the optical field as follows 7 :

where u x / y ( t , z ) is the optical field of x and y polarization, respectively, β 2 is the group velocity dispersion, and γ is the nonlinear coefficient. In the first-order perturbation theory, the solution to Eq. ( 1 ) consists of both linear u 0, x / y ( t , z ) and nonlinear perturbation Δ u x / y ( t , z ) terms 10 , 21 . Assuming much larger accumulated dispersion than symbol duration, the nonlinear perturbation terms for the symbol at t  = 0 can be approximated as 22

where P 0 , H m and V m , and C m , n are, respectively, the launch power, symbol sequences for the x - and y -polarization, and nonlinear perturbation coefficients, m and n are symbol indices with respect to the symbol of interest H 0 and V 0 . The triplet is defined as \(T \equiv H_nH_{m + n}^ \ast H_m + V_nV_{m + n}^ \ast H_m\) in this paper. The nonlinear perturbation coefficients C m , n can be analytically computed given the link parameters and signal pulse duration/shaping factors 10 , whereas the triplets do not depend on the link and can be calculated directly from the received symbols.

The proposed NN-NLC algorithm is divided into two stages: training and execution stages. In the training stage, the NN learns from the training data and generates a black-box model of the transmission link. In the execution stage, the nonlinear impairment is calculated based on the model, and the impairment is removed from the data.

Training stage

During the training stage it is necessary to have sufficient nonlinearity therefore the launch power P 0 should be close to, or larger than the optimum channel power. One drawback of data-driven modeling is that the received data is corrupted not only by nonlinear impairments but also by amplified spontaneous emission (ASE) noise. This can be easily taken care of by transmitting the same training data repeatedly and averaging out the pattern-independent noise such as ASE, and inter-channel nonlinearities, while the intra-channel nonlinear impairments can be retained. NN-NLC operates on the soft data output from carrier phase recovery block shown in DSP flowchart of Fig.  1a . At the training stage NN-NLC needs to be implemented at the receiver side whereas the execution stage can be implemented at either the transmitter or receiver side, see Fig.  1 . As the training does not have to operate at the data rate unlike the execution stage, and the amount of training data is not excessive, it can be performed offline to save computation cost. To test the NN-NLC algorithm with experimental data, single-channel 32 Gbaud dual-polarization (DP)-16QAM with RRC 0.01 pulse shaping as shown in Fig.  2 is generated using 64 Gsa/s DAC and transmitted over a recirculating loop testbed consisting of five spans of 80 km SSMF with 0.2 dB/km loss and 17 ps/nm/km dispersion. A digital coherent receiver running at 50-GSa/s with analog bandwidth of 20 GHz downsamples the optical waveforms for offline DSP outlined in Fig.  1a to recover the transmitted symbols. In addition, 50% chromatic dispersion compensation (CDC) is applied at the transmitter to reduce the interaction length between symbols 13 . Three uncorrelated datasets each with ~115 k symbols are generated for training, cross-validation (CV) and testing. The data pattern used in the training, CV and test datasets is measured to have maximum 0.6% normalized cross-correlation to ensure data independence.

DSP flowchart with NN-NLC. a Receiver side, and b transmitter side

System setup of the transmitter and transmission loop. DAC: digital-to-analog converter, ECL: external cavity laser, GEQ: gain equalizer, PS: polarization scrambler

As the training pattern is fixed at the transmitter side and the ASE noise is an independent additive Gaussian noise, the recovered symbols can be easily synchronized using a framer in practice to align the symbols in the same order such that the ASE noise can be reduced after averaging, while keeping the nonlinear interaction intact. Multiple waveform acquisition is processed, and the recovered soft symbols after carrier phase recovery are aligned to average out the additive noise. Figure  3 plots the impact of the number of acquired waveforms on the Q-factor and constellation of the training dataset received at ~2 dB higher channel power than the optimum after 2800 km transmission. About 1.6 dB Q-factor improvement is observed after averaging over just 5 acquired waveforms. The saturation curves show that the resulting cleaner constellation in Fig.  3c is able to more accurately represent the nonlinear noise than the one in Fig.  3b .

Denoising. a The impact of de-noising by  averaging over the training datasets on the Q-factor at SNR = 18.4 dB after 2800 km. b Received constellation before de-noising, and c after de-noising

Parameter optimization of ML model

There are various ML models from simple linear regression to sophisticated deep-learning models used for solving a variety of problems. Fully connected neuron network is selected to demonstrate the effectiveness of NN for NLC at a similar or even lower complexity than existing DSP algorithm. The optimized feed-forward NN model shown in Fig.  4 is constructed from an input layer with 2 N t triplets nodes, 2 hidden layers consisting of 2 and 10 nodes, respectively, and two output nodes corresponding to the real and imaginary parts of the estimated nonlinearity. The optimization of the number of hidden layers and the number of nodes in each layer is carried out to simplify the complexity of the NN architecture without degrading the BER performance of the derived models on the CV datasets. Note that the triplets are separated into real and imaginary parts before being fed into the NN model. Although Eq. ( 2 ) describes linear relationships among these IFWM/IXPM triplets due to the first-order perturbation, nonlinear activation function in the neuron nodes is found in our study to achieve better performance than linear function. The impact of activation function is explained in the next section. A dropout layer with probability of 0.5 is placed after the 2nd hidden layer during training only to avoid overfitting. Applying Adam learning algorithm 23 with a learning rate of 0.001 and batch size of B  = 100, the network is trained by transmitting known but randomly generated patterns, and searching for the best node tensor parameters that minimize the mean square error (MSE) between the transmitted and received symbols after NN-NLC, i.e.,

where \(\hat H_i\) and \(\hat H_{i,{\mathrm{NL}}}\) , respectively, are the received symbols and estimated nonlinearity for pol-x. Although the model is trained for x-polarization data, same weights can be used to obtain a similar performance improvement for the y-polarization data too. Note that the training can be done at much slower pace than data rate to allow deep-learning algorithm to locate the appropriate NN models and compute the optimum tensor weights prior to the execution stage.

The block diagram of the proposed NN-NLC. Illustrated for pol-x only. The diagram in the dashed box describes the optimized NN architecture with two hidden layers used in the paper

The impact of activation function

A single-channel 32Gbaud DP-16QAM is simulated over 40 × 80 km SSMF with 50% pre-CDC to compare the performance of four different activation functions plotted in Fig.  5a , namely SELU, ReLU, Leaky ReLU, and linear, on both CV and test datasets with N t  = 2065 triplets. Note that linear function represents just the linear regression. As shown in Fig.  5b , the training process converges faster when applying nonlinear activation function as they help alleviating the vanishing gradients problems 23 . In addition, it is found that the Leaky ReLU activation function is the optimum among these four varieties. As shown in Fig.  5b , the linear combination of these triplets shown in the linear regression curve is not as good as the cases with nonlinear activation using SELU and Leaky ReLU. It may be caused by the nonlinear interaction between these triplets in these nonlinear activation functions to account for even higher-order nonlinearities, such as 6th-order interaction between symbols. These observations are verified in simulation shown in Fig.  5c to further demonstrate the advantage using Leaky ReLU activation function as the gap between the linear regression and RELU grows at higher powers. Based on the study results, Leaky ReLU is used in our experiments to maximize the NLC gain.

The impact of activation function on the NN-NLC algorithm. a Plots of different activation functions. b The BER optimization trace of DNN over the CV dataset in the training with different activation functions. c The Q-factor of DNN with different activation functions as a function of channel power. d The Q-factor improvement of PPD and NN-NLC with Leaky ReLU w.r.t. the optimum CDC Q

To exclude the impact of the uncertainties of the experimental setup on the performance, simulation results are used to compare the NLC performance between PPD and NN-NLC algorithm as a function of the number of triplets. The calculation of perturbation coefficients are based on ref. 13 . The Q improvement over the CDC is plotted in Fig.  5d as a function of the number of triplets at the optimum channel power of 1 dBm. The NN-NLC with Leaky-ReLU outperforms PPD by ~0.15 dB at ~2000 triplets thanks to the nonlinear activation function in the neuron nodes.

Triplets selection

After cleaning up the ASE noises in the received training dataset, IXPM & IFWM triplets are calculated according to Eq. ( 2 ). In systems with large dispersion, symbols can overlap and interact with thousands of neighboring symbols. In order to keep the complexity low, the number of triplets should be kept low by only including the ones that contribute the most. This requires establishing a selection criterion 10 , 13 . In previous work 18 , nonlinear perturbation coefficients C m , n were used as a way to estimate which triplets contributed the most. Only the triplets that satisfied the criterion C m , n  >  κ was retained where κ was a free parameter to adjust the trade off between complexity and performance. Even though C m , n were not used in the execution stage, their computation during the training stage still required accurate link and transmission system parameters 18 . Considering that the C m , n has a hyperbolic dependence on m and n , we propose to choose all the triplets with index pairs m , and n that satisfies the following criterion that is independent of link parameters

where min{ ⋅ } takes the minimum of its arguments, \(\left\lceil \cdot \right\rceil\) stands for the ceiling function, L determines the largest value of m , and n , and ρ controls the maximum of the m , n product. The values required for L and ρ are not too restrictive as long as a sufficiently large number of triplets are chosen to initialize the training. Through iterative trimming during the training stage, as discussed below, the excess triplets can be removed before the execution stage.

Execution stage

During the training stage, the performance of the model is checked against the CV dataset only to optimize the NN model parameters. Afterwards the learned model is applied to the uncorrelated test dataset for all channel powers in the execution stage. The block diagram of the proposed NN-NLC is shown in Fig.  4 . Given the symbol of interest \(\hat H_0\) centered at the middle of pattern length L , the IXPM and IFWM terms are calculated and fed into the NN model described in the dashed box of Fig.  4 to estimate the nonlinearity. The estimated \(\hat H_{0,{\mathrm{NL}}}\) is then scaled by the channel power ( P ch ) of the test dataset with respect to the reference channel power ( P ref ) of the training data used for deriving the model, i.e.,

The estimated \(\hat H_{0,{\mathrm{NL}}}\) is subtracted from the original symbol of interest before being sent to next DSP block, for instance, the FEC decoding in Fig.  1a .

Since the complexity of real multiplications could be four times as much as addition operation 24 , only real multiplication is taken into account when comparing the complexity of the NLC algorithm. The NN model shown in the dashed box of Fig.  4 requires 2 N t  × 2 + 2 × 10 + 10 × 2 = 4 N t  + 40 real multiplications because of three cross-layer tensor interaction. Note that the activation function Leaky ReLU() in the hidden nodes and IXPM/IFWM triplets computation are assumed to be implemented in LUT. After scaling the estimated nonlinearity term, the number of real multiplication per symbol for the proposed NN-NLC shown in Fig.  4 can be summarized as

Therefore, reducing the number of triplets N t is the most effective way to lower the complexity of the NN-NLC algorithm in our model.

As shown in Fig.  6a , with the initial N t  = 1929 triplets, some of the input tensor weights W m , n in the trained model show much smaller contribution to the signal nonlinearity than the center ones. As a result, the number of triplets N t can be further reduced by only keeping those weights larger than a specified threshold κ , i.e., W m , n  >  κ . After trimming off the weights W m , n that are less than κ  = −22 dB, the remaining 615 triplets are re-trained in the NN model and the new density plot of the input tensor weights W m , n is shown in Fig.  6b . Figure  7 plots the impact of the trimming threshold κ on the performance improvement of the NN-NLC as a function of received SNR after 2800 km transmission. At the optimum received SNR, the NN-NLC algorithm at trimming threshold κ  < −15 dB achieves >0.5 dB Q improvement over CDC. Larger Q improvement at the highest received SNR further confirms the NN model is able to accurately predict the signal nonlinearity. At the optimum received SNR, Q value improvement is adjusted from 0.2 to 0.4 dB by varying the complexity through adjusting κ from −35 to −15 dB, as shown in Fig.  7 . Training the NN is more practical at the receiver side, however, computation of triplets at the receiver side requires the use of soft symbols. To reduce complexity further, it is better to execute the NN-NLC block at the transmitter side and use a LUT to store the triplet values rather than calculate them online. The LUT size could scale as large as M 3 where M is the constellation size. Figure  1b shows the DSP block diagram of the NN-NLC at the transmitter side. Figure  8a shows the constellation generated after applying pre-distortion calculated by the NN at the transmitter side with κ set to −22 dB.

Density plots. The density plot of the input layer weights of the NN model at a initial N t  = 1929 and b N t  = 615 triplets after iterative trimming ( κ  = −22 dB). Colorbar shows the magnitude of the weights

Trimming threshold. The impact of trimming threshold κ on the NN-NLC with Leaky ReLU at the receiver side after 2800 km transmission

Constellations. a Pre-distorted symbols at transmitter side; recovered constellation at the receiver b with and c without Tx-side NN-NLC. Received SNR = 18.4 dB after 2800 km transmission

Performance of the transmitter side compensation is compared with the receiver side compensation in Fig.  9a . Even though the NN is trained at the receiver side, the transmitter side compensation performs better in all the ranges but especially at the high complexity end. This improvement is expected considering that at the transmitter side the NN model works on the clean transmitted symbols. Moreover, once the nonlinearity is mitigated at the transmitter, the receiver DSP algorithm works on the signals with reduced nonlinearity and cycle slip rate 25 . Tx-side NN-NLC outperforms PPD by more than 0.4 dB, which is attributed to the learning features and nonlinear Leaky ReLU() activation functions. Compared to the recovered constellation without NN-NLC algorithm shown in Fig.  8c , the transmitter-side NN-NLC can significantly improve the constellation quality as plotted in Fig.  8b .

The measured performance of the NN-NLC algorithm at 2800 km. a The comparison of Q-factor improvement between Rx/Tx-side NN-NLC and Rx-side PPD. b The comparison of Q-factor improvement between Tx-side NN-NLC and filtered-DBP versus the number of multiplications

One of the most important criteria for a practical NLC algorithm is its low complexity while providing a significant Q improvement. Complexity of the NN-NLC was compared with NLC algorithms based on perturbation approach whose complexity also scales with the number of triplets 18 . To establish the performance of the NN-NLC with respect to existing NLC algorithms in terms of the performance-complexity trade off, the comparison is extended to the filtered-DBP technique. Filtered-DBP is chosen for comparison because it is a well-established, low-complexity 8 , 9 technique where the complexity versus improvement trade-off can be easily adjusted and it provides gain as long as it has sufficient number of steps. In filtered-DBP, the number of steps can be reduced by filtering the intensity waveforms by a low-pass filter (LPF) at the nonlinear phase rotation stage. The optimal bandwidth for the Gaussian-shaped LPF is found to be 5, 1, 1, and 0.5 GHz for 1, 5, 7, and 35 spans per step (SpS). The optimum scaling factor ξ used to de-rotate the signal’s phase is ~0.7 for all cases. The Q performance improvement over CDC is plotted in Fig.  9b to show that Tx-side NN-NLC is capable of matching the performance of the filtered-DBP even at higher complexity than 2000 real multiplications per symbol while still keeping the performance advantage at lower complexity over filtered-DBP. More importantly, NN-NLC performs significantly better at the lowest complexity end. The complexity of filtered-DBP is calculated based on Eq. (9) in ref. 9 by assuming FFT size of 4086. The performance of NN-NLC is further tested on an 11,017 km commercial FASTER submarine cable together with live traffic. Digital subcarrier modulation (DSM) 4 × 12.25Gbaud capacity-approaching probabilistic-shaped (PS) 64QAM 26 , 27 at RRC 0.01 with 50 MHz guard band carrying in total 300 Gb/s bit rate is used as the probe signal in 50 GHz WDM configuration. The details of the system setup and optical spectra can be found in 18 . After applying de-noising through averaging over ASE approach, the received PS-64QAM constellation at 2 dB channel pre-emphasis is shown in Fig.  10a . Note that generalized mutual information (GMI) 28 is used for accurately measuring the gain of NN-NLC for PS-64QAM format. Figure  10c compares the performance of NN-NLC and filtered-DBP with respect to the CDC only as a function of computation complexity. Note that the received PS-64QAM soft symbols are first hard-decoded into 64 symbols in order to avoid the multiplication involved in computing triplets. Once again it is found that NN-NLC performs better than filtered-DBP when the complexity is less than ~500 real multiplications per symbol. It is expected that Tx-side NN-NLC is likely to further improve the performance gain. Figure  10b plots the density map of the input-layer nodes weights after training with 240 triplets.

Undersea cable performance. a The received PS-64QAM constellation after de-noising; b the density map of the input layer tensors; c GMI performance of Rx-side NN-NLC and filtered-DBP

Even though NN-NLC lowers the complexity in terms of required number of multipliers, a comparison of this algorithm with existing algorithms in term of detailed circuit design is not studied. Nevertheless, being a feed-forward algorithm, NN-NLC is highly parallelizable as it would be required for high-speed transponders. The proposed NN-NLC is experimentally demonstrated in both lab testbed and field cables to show the system-agnostic performance without prior knowledge of the transmission link parameters such as dispersion, fiber nonlinearity, and fiber length.

Data availability

The datasets generated during the current study are not publicly available due to restrictions from commercial privilege, but portions of the data are available from the corresponding author on reasonable request.

Code availability

Example code is included for calculating the triplets, and performing the training and testing stages at http://www.nec-labs.com/~ons/NICNN/

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S.Z. and F.Y. formulated the problem and developed the algorithms, S.Z., F.Y., K.N. and T.I. took the lab and field data, V.K., L.J., V.V., K.N., T.I., E.M. and Y.I. managed the field trial, all the co-authors contributed to the development of the concept, setting the direction, and writing the manuscript.

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Zhang, S., Yaman, F., Nakamura, K. et al. Field and lab experimental demonstration of nonlinear impairment compensation using neural networks. Nat Commun 10 , 3033 (2019). https://doi.org/10.1038/s41467-019-10911-9

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Fiber Tutorial by Telephone Company Senior Engineer/Director of Research & Development - derived from years of presentations, contains information voted most interesting by students, teachers and adults. In this tutorial, we will discover how optical fibers the size of a human hair are manufactured; how they are installed, spliced and tested; and how they impact your daily life for phone calls, text messages, tweets and web browsing. In each lesson you will perform experiments to illustrate key points, followed by a short quiz (not to worry, this is an "open web test").  NOTE- For safety purposes, all experiments require adult supervision!   The Experiments are all great fun and a highlight of each Lesson.  You will see a picture of your own voice, compare your hair to an optical fiber, simulate the manufacture of optical fibers by creating a long, thin string of cheese, create models and much more!  We even issue a challenge to teachers and professors. (click here) If you enjoyed this tutorial, please tell 10 friends about us! To aide in Lesson Experiments, you can order sample fibers from a real telephone cable by clicking the ORDER FIBER or using the button on the top right of the this page.  Prices as low as $1.00 per fiber.  Great way to earn extra credits in your next Technical Presentation, Science Fair or Merit Badge or just impress your friends! share this! May 3, 2023 This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source Experiment demonstrates continuously operating optical fiber made of thin air by University of Maryland Researchers at the University of Maryland (UMD) have demonstrated a continuously operating optical fiber made of thin air. The most common optical fibers are strands of glass that tightly confine light over long distances. However, these fibers are not well-suited for guiding extremely high-power laser beams due to glass damage and scattering of laser energy out of the fiber. Additionally, the need for a physical support structure means that glass fiber must be laid down long in advance of light signal transmission or collection. Howard Milchberg and his group in UMD's Departments of Physics and Electrical & Computer Engineering and Institute for Research in Electronics & Applied Physics have demonstrated an optical guiding method that beats both limitations, using auxiliary ultrashort laser pulses to sculpt fiber optic waveguides in the air itself. These short pulses form a ring of high-intensity light structures called "filaments," which heat the air molecules to form an extended ring of low-density heated air surrounding a central undisturbed region; this is exactly the refractive index structure of an optical fiber. With air itself as the fiber, very high average powers can potentially be guided. 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More information Privacy policy Donate and enjoy an ad-free experience We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account. Kitchen Science Experiments to Try at Home Electricity Kitchen Science Experiments Water Fibre Optics Part of the show meteorites, satellites and avoiding asteroids, fibre_optic.jpg. Ingredients A Plastic bottle Something to make a hole in it with A torch (flashlight if you must) A Source of water Instructions Make a small round (~5mm) hole in the side of the bottle near the base. It is probably best to use a drill to do this as the bottle will be very slippery. Put your finger over the hole and fill the bottle up with water. Shine the torch through the bottle at the back of the hole Remove your finger from the hole and move it down the stream of water. You should notice a spot of light on your hand while it is in the stream of water even though it must have gone around a corner to get there. It tends to work best when the water comes out quite slowly. Explanation To understand what is going on here it helps to do another experiment. Fill a transparent bowl with water, put something in the bowl and then look upwards at the bottom of water. If you look at the bowl from the top you can see the spoon at the bottom. Looking upwards in the bowl of water you see a reflection of the spoon at the bottom of the bowl in the surface. The water is behaving like a mirror. So light will reflect really well off the inside surface of water at a relatively small angle. This means that if you shine the light into a tube of water whenever it meets the side it is reflected so the light stays within the water until it hits your hand lighting it up. This happens even if the water goes around a corner. What has this got to do with fibre optics? If instead of making the tube out of water you use very very pure glass and pull it to a thin flexible fibre, when you shine light in at one end it will come out of the other. By getting the right design of fibre the light can travel through up to 50km of fibre and still be detectable. You can then send signals through the fibre by flashing the light on and off again a bit like morse code, because you can flash the light very fast you can transfer huge amounts of information. The record is now over 1000 GB per second down a single optical fibre. Because they are so good at transmitting data optic fibres move most of the data around the world (internet traffic, phone calls etc.)  and  you are almost certainly reading this via one. If you make the tube out of plastic rather than glass it is more flexible and safer, and you can use it to make the artificial Christmas trees with the tiny pin pricks of light. Why do you get such a good reflection from the surface of the water? Light goes more slowly in water than in air and whenever light changes materials and the speed changes it will be bent (refracted). When it moves from a slow material (like water) to a faster one (like air) it is bent towards the surface. If light leaves water at an angle it is refracted closer to the surface. Some light is reflected back into the water but not very much. The smaller the angle the light meets the surface at, the bigger the change in angle. At a certain point the refracted light should be inside the water. Light must leave the water to refract so this is impossible, so all  the light is reflected. This is known as total internal reflection. Previous Images from a Magnifying Glass Next Racing Jam Jars Related Content Fibre optics rev up, can we use light to store information, would sea level rise if we all went swimming, how does one telephone wire transfer all of that data, mars solar conjunction, great''''really enjoyed it.... great - really enjoyed it....thanks so much Add a comment Support us, forum discussions. Lesson Plans Science Lesson Plans Experiments Understanding Fiber Optics I got the idea for this week's experiment while reading about fiber optics. Instead of using electricity through wires, fiber optic cables use light traveling through a clear fiber to carry phone signals, etc. Even though the fiber is clear, the light stays inside until it reaches the end. Then it emerges from the fiber, even if it has twisted and turned along the way. How can it do that? To find out, you will need: a small mirror a container of clear water at least 5 or 6 inches deep Hold the mirror so that it is about 3 inches under the surface of the water, with the shiny side facing upwards. Hold it flat, so that you can look down into the mirror, through the water. You should see your reflection in the mirror, staring up at you. Stick your finger into the water, so that you can see its reflection in the mirror. You should see the reflection of your entire finger. You can see the part that is under the water and the part that is above the surface. Slowly tilt the mirror. As the angle increases, suddenly, the part of your finger that is above the water will disappear. You can still see the part that is under the surface, but it looks as if it is sticking through a mirror. If you lift your finger, it will seem to vanish into the mirrored surface. If you are a fan of the science fiction show Stargate, this will look very familiar. I wonder if the person that designed the animation for their special effects ever played with this experiment. Why does the surface of the water suddenly change from transparent to a mirror? It has to do with the way that light bends as it moves from one substance to another. Fill a clear glass with water. Looking from the side, stick your finger into the water. It seems to be broken, with the part below the water moved to the side. It looks that way because as light passes from the water to the glass, and from the glass to the air, it is bent. That bending of the light moves the image of your finger to the side. If the light is coming straight through, the bending does not have a big impact, but if the light is coming from an angle, then the bending is more important. When the angle is small enough, then the light is bent enough so that it is directed away from the surface, reflecting back into the water. The point where this happens is called the critical angle. When you turn the mirror to the proper angle, the light that is being reflected from it is at a sharp enough angle that it does not pass through the surface. When your finger is in the water, you can see it, but when it is above the water you cannot. This is the basic idea of fiber optics. As the light goes through the clear fiber, its critical angle keeps reflecting the light back from the sides. Almost 100% of the beam of light remains inside the fiber, even if it is bent. At the end of the fiber the light is moving directly towards the surface, making an angle greater than the critical angle, so it passes through easily. Have a wonder filled week. All lessons are brought to you by The Teacher's Corner and Robert Krampf's Science Education Company. Robert Krampf's Science Shows thehappyscientist.com We are currently working on making the site load faster, and work better on mobile & touch devices. 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Seventh International Conference on Education and Training in Optics and Photonics Technical Digest Series (Optica Publishing Group, 2001), paper PDP464 Undergraduate Experiments in Optics Employing a Fiber Optic Version of the Mach-Zehnder Interferometer Adonis Flores, Mario Flores, Kees Karremans, and Ben Zuidberg Author Affiliations Optoelectronics Laboratory, Department of Physics, University of San Carlos, Cebu City 6000, Philippines K Karremans Singapore Singapore 26–30 November 2001 From the session Post-Deadline Papers (PDP) Seventh International Conference on Education and Training in Optics and Photonics , Technical Digest Series (Optica Publishing Group, 2001), paper PDP464.%0Ahttps://opg.optica.org/abstract.cfm?URI=ETOP-2001-PDP464%0A---------------------------------------------------------------------------%0AThis is sent to you as an email notification feature from Optica Publishing Group: https://opg.optica.org"> Email Share with Facebook X Share on X Post on reddit Share with LinkedIn Add to Mendeley Share with WeChat Endnote (RIS) Save article Index measurements Interferometry Mach Zehnder interferometers Optical elements Refractive index Single mode fibers References ( 6 ) Back to Top Interferometry (the use of interference phenomena) provides ample opportunities for measurements in various areas of physics, particularly in optics. In an interferometer, light from a single source is split into two beams that travel along different paths. The beams are recombined to produce an interference pattern that can be used to detect changes in the optical path length in one of the two arms. Here we report about the use of a fiber optic version of the Mach-Zehnder interferometer in measurements of the index of refraction of water and air. The open air version of the Mach-Zehnder interferometer employs two beam splitters and two highly reflective mirrors. This open air version is difficult to align and sensitive to environmental disturbance. In our fiber optic version we have replaced one beamsplitter and two mirrors by a bidirectional coupler supplied with single mode fibers. This replacement greatly simplified the operation of the interferometer. A stable interference pattern could quite easily be obtained. The simplified operation allowed the introduction of the instrument in our BS program. This year two students performed highly accurate measurements on the index of refraction of various fluids (water, air) for their graduate project. Recently the instrument has been introduced in the regular laboratory classes. © 2001 Optical Society of America Ping Hua, Kenji Kawaguchi, and James S. Wilkinson MG1_6 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2001 Marco Fiorentino, Jay E. Sharping, Paul Voss, Prem Kumar, Dmitry Levandovsky, and Michael Vasilyev QMC7 Quantum Electronics and Laser Science Conference (CLEO:FS) 2001 Chai-Ming Li, Chen-Wei Chan, Jing-Shyang Horng, Jui-Ming Hsu, and Cheng-Ling Lee TuPL_7 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2013 Qianfan Xu, Yi Dong, Minyu Yao, Wenshan Cai, and Jianfeng Zhang MB6 Optical Fiber Communication Conference (OFC) 2001 P. Yvernault, D. Mechin, E. Goyat, L. Brilland, and D. Pureur WDD92 Optical Fiber Communication Conference (OFC) 2001 Education and Training in Optics and Photonics 2001 From the session  Post-Deadline Papers (PDP) Confirm Citation Alert Field error. 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These experiments will test Flawless Photonics, Inc.’s unique approach to solving the issue of gravity-induced defects in optical glass products manufactured on Earth. To eliminate such defects, Flawless Photonics aims to validate the company’s method for manufacturing various glass materials in space, beginning with ZBLAN. ZBLAN is a type of optical glass with many applications, such as communications, sensors, and laser technology. It can perform up to 100 times better than silica, but current terrestrial restrictions limit its full potential. “We have uncovered a new approach to manufacturing ZBLAN in space that promises to unlock its full capabilities and radically advance the optical fiber market,” said Michael Vestel, the principal investigator of the project and CTO of Flawless Photonics. “Testing our unique approach on the space station will provide crucial data to advance breakthrough materials for telecommunications, defense, medical devices, and quantum computing.” Flawless Photonics seeks to develop fiber manufacturing processes that outperform existing options. The real-world benefits of these fibers on Earth are substantial, promising to revolutionize optical communication technologies, Vestel said. Hubert Moser, senior director of engineering at the company’s Luxembourg lab, added, “This mission is a crucial milestone for Flawless Photonics. Its primary focus is deploying our cutting-edge autonomous manufacturing platform and establishing new paradigms in optical fiber technology and furthering in-space manufacturing.” Optical fiber drawn during the experiments will return on SpaceX’s 30 th Commercial Resupply Services (CRS) mission in April. The Flawless Photonics manufacturing platform will stay on the space station for future use. The NG-20 mission launched from Cape Canaveral Space Force Station on January 30 at 12:07 p.m. EST onboard a SpaceX Falcon 9 rocket. This mission included more than 20 ISS National Lab-sponsored payloads. Please visit our launch page to learn more about all ISS National Lab-sponsored research on this mission. 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The research demonstrates that quantum data in the form of entangled photons and conventional internet data sent as laser pulses can coexist in the same fiber-optic cable. Most research into building a quantum internet has focused on the need for separate infrastructure or dedicated channels for quantum data to avoid interference from "classical" data. But this new "hybrid" network could pave the way for more efficient implementation of quantum communications by enabling quantum and conventional data to share the same infrastructure. The researchers revealed their findings in a study published July 26 in the journal Science Advances . Fiber-optic cables are composed of thin strands of glass or plastic fibers that carry data as infrared light pulses. These fibers transmit data through different color channels, with each corresponding to a specific wavelength of light. Related: Fiber-optic data transfer speeds hit a rapid 301 Tbps — 1.2 million times faster than your home broadband connection Researchers have previously shown that quantum data can be transmitted through a standard fiber-optic cable, but this new experiment marks the first time that both quantum and conventional data have been transmitted together in the same color channel. Creating hybrid networks is challenging because quantum data is often transmitted through fiber-optic cables using entangled photons . Sign up for the Live Science daily newsletter now Get the world’s most fascinating discoveries delivered straight to your inbox. Entanglement occurs when two qubits — the most basic units of quantum information — are linked in such a way that information is shared between them regardless of their relationship over time or space. But entanglement is an extremely delicate state that can be easily disrupted by environmental disturbances like noise or interference from other signals. This includes any data sharing the same wavelength on a fiber-optic channel. This is known as "decoherence," and breaking this connection causes the qubits to lose their quantum state, resulting in data loss. ​​"To make the quantum internet a reality, we need to transmit entangled photons via fibre optic networks," study co-author Michael Kues , head of the Institute of Photonics at Leibniz University Hannover, said in a statement . "We also want to continue using optical fibres for conventional data transmission." — World's 'best-performing' quantum computing chip could be used in machines by 2027, scientists claim — 'Quantum-inspired' laser computing is more effective than both supercomputing and quantum computing, startup claims — New quantum computer smashes 'quantum supremacy' record by a factor of 100 — and it consumes 30,000 times less power To get around these challenges, the scientists used a technique called electro-optic phase modulation to precisely adjust the frequency of the laser pulses so that they matched the color of the entangled photons. This enabled both types of data to be transmitted in the same color channel without disrupting the quantum information held by the entangled photons. The ability to transmit quantum and conventional data in the same channel frees up other color channels in the fiber-optic cable for more data, the scientists said. This will be key to making the many applications of quantum computing , such as ultra-secure communications and quantum cryptography , more practical and scalable. "Our research is an important step to combine the conventional internet with the quantum internet," said Kues. "Our experiment shows how the practical implementation of hybrid networks can succeed." Owen Hughes is a freelance writer and editor specializing in data and digital technologies. Previously a senior editor at ZDNET, Owen has been writing about tech for more than a decade, during which time he has covered everything from AI, cybersecurity and supercomputers to programming languages and public sector IT. Owen is particularly interested in the intersection of technology, life and work ­– in his previous roles at ZDNET and TechRepublic, he wrote extensively about business leadership, digital transformation and the evolving dynamics of remote work. 'Absurdly fast' algorithm solves 70-year-old logjam — speeding up network traffic in areas from airline scheduling to the internet Scientists achieve record-breaking 402 Tbps data transmission speeds — 1.6 million times faster than home broadband 'Final parsec problem' that makes supermassive black holes impossible to explain could finally have a solution Most Popular 2 Why is a 'once-in-a-decade' Supermoon Blue Moon happening twice in 2 years? 3 'Banana apocalypse' could be averted thanks to genetic breakthrough 4 The sun might've just had a record-breaking number of visible sunspots 5 'Spectacular silver treasure' from Viking Age unearthed by college student on farm in Denmark Information Author Services Initiatives You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. 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Visit our dedicated information section to learn more about MDPI. JSmol Viewer An image processing-based correlation method for improving the characteristics of brillouin frequency shift extraction in distributed fiber optic sensors. 1. Introduction 2. the idea of the proposed method, description of metrics for comparing algorithms, 3. the simulation and its results, 4. the experiment and its results, 5. discussion, 6. conclusions. It is imperative to optimize the algorithm to eliminate duplicate operations. Of particular interest is the alignment of spectra through the use of artificial intelligence rather than the traditional approach of calculating the correlation function. The calculation function is to be transferred to the hardware part of the device. Author Contributions Data availability statement, conflicts of interest. Song, Y.; Tian, H.; Jia, Z.; Chen, W.; Zhang, W.; Wang, C.; Zhou, T.; Li, L.; Hong, D.; Wang, Y.; et al. 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Share and Cite Konstantinov, Y.; Krivosheev, A.; Barkov, F. An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors. Algorithms 2024 , 17 , 365. https://doi.org/10.3390/a17080365 Konstantinov Y, Krivosheev A, Barkov F. An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors. Algorithms . 2024; 17(8):365. https://doi.org/10.3390/a17080365 Konstantinov, Yuri, Anton Krivosheev, and Fedor Barkov. 2024. "An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors" Algorithms 17, no. 8: 365. https://doi.org/10.3390/a17080365 Article Metrics Article access statistics, further information, mdpi initiatives, follow mdpi. Subscribe to receive issue release notifications and newsletters from MDPI journals Online sensing method for transmission line conductor ice cover based on fiber optic sensing information fusion and continuous wavelet decomposition Published: 18 August 2024 Volume 56 , article number  1418 , ( 2024 ) Cite this article Boyan Jia 1 , 2 , Yanwei Xia 1 , 2 , Hongliang Liu 1 , 2 , Yabing Xu 3 & Xiaoyu Yi 1 , 2   Explore all metrics At present, the identification of icing status of transmission line conductors is mainly manual. Although the mainstream method based on intelligent vision and UAV can solve the manual problem, the specific situation of conductor icing cannot be further mastered. Based on the information fusion of optical fiber sensor and continuous wavelet decomposition, the online sensing method of transmission line icing is studied. This method uses the transmission line conductor vibration and stress signal acquisition method based on distributed optical sensing technology. After the transmission line conductor vibration and stress signals are collected online by multiple optical fiber sensors, the fusion model based on Elman neural network is used to fuse the multiple optical fiber sensing information, and then the spectrum feature extraction method of optical fiber sensing signal based on continuous wavelet decomposition is used. The spectral characteristics of fused optical fiber sensing signals are extracted as diagnostic samples of the online perception method of conductor icing based on improved logical regression, and the binary classification method is used to identify whether there is icing problem on transmission line conductors online. In the experiment, this method can accurately perceive the online perception results of rime type icing, mixed rime type icing, wet snow type icing, and has the function of accurately sensing the icing status of transmission line conductors. This is a preview of subscription content, log in via an institution to check access. Access this article Subscribe and save. Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Price includes VAT (Russian Federation) Instant access to the full article PDF. Rent this article via DeepDyve Institutional subscriptions Similar content being viewed by others Feature extraction and identification in distributed optical-fiber vibration sensing system for oil pipeline safety monitoring Identifying and Positioning Illegal Incursion of Drilling Rigs Above Subway Lines Based on Ultra-weak FBG Sensing Array Analysis of Icing on Wind Turbines by Combined Wireless and Wired Acceleration Sensor Monitoring Explore related subjects. Artificial Intelligence Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. 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Simul. 38 (1), 88–91 (2021) Download references Acknowledgements The study was supported by the Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (Grant number: kj2023-007) The study was supported by the Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (Grant number: kj2023-007). Author information Authors and affiliations. State Grid Hebei Electric Power Co., Ltd., Research Institute, Shijiazhuang, 050021, Hebei, China Boyan Jia, Yanwei Xia, Hongliang Liu & Xiaoyu Yi Hebei Technology Innovation Center of Power Transmission and Transformation, Shijiazhuang, 050021, China STATE GRID CANGZHOU POWER SUPPLY COMPANY, Cangzhou, 061000, Hebei, China You can also search for this author in PubMed   Google Scholar Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Boyan Jia, Yanwei Xia, Hongliang Liu, Yabing Xu, Xiaoyu Yi. The first draft of the manuscript was written by Boyan Jia and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Corresponding author Correspondence to Boyan Jia . Ethics declarations Competing interests. The authors declare they have no conmpeting interests. Additional information Publisher's note. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions About this article Jia, B., Xia, Y., Liu, H. et al. Online sensing method for transmission line conductor ice cover based on fiber optic sensing information fusion and continuous wavelet decomposition. Opt Quant Electron 56 , 1418 (2024). https://doi.org/10.1007/s11082-024-07308-4 Download citation Received : 19 April 2024 Accepted : 24 July 2024 Published : 18 August 2024 DOI : https://doi.org/10.1007/s11082-024-07308-4 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Fiber optic sensing Information fusion Continuous wavelet decomposition Transmission line Conductor ice cover Online sensing Find a journal Publish with us Track your research 486 IMAGES Optical Fiber_Physics Experiment optical fiber (experiment) Experiments with a Fiber-Optics-Plate added (a) Schematic of the Blend Swap Optical fiber experiment Evaluate Numerical Aperture of an Optical Fiber COMMENTS PDF Fiber Optics Lab Manual Instructor's Manual The Fiber Optic Association, Inc., Phone: 1-760-451-3655 Fax: 1-760-207-2421 Email: [email protected] Web: www.foa.org. Availability of plastic optical fiber (POF) The plastic optical fiber used in some of these experiments is available for science distributors. It is a 1000micron (1mm) POF available from several suppliers. PDF Lab 6: OPTICAL FIBERS (3 Lab Periods) A. Fiber Geometry. An optical fiber is illustrated in Fig. 6.1. It consists of a core with a refractive index. ncore and di-ameter 2a and a cladding, with a refractive index ncl and diameter d. As shown in Fig. 6.2, typical core diameters range from 4 to 8 μm for single-mode fibers, from 50 to l00 μm for multimode. Science Fair Projects With Fiber Optics Fiber optics is a method of delivering light through clear, glass wires, or fibers. Light can travel through these fibers over long distances. The fiber can carry light through twists and turns just like copper wire carries electricity. Fiber optics can also use light to carry information, much like copper wires carry information in electrical ... PDF Experiment 3: fiber optics The first is a mechanical strip with a razor. Simply scratch the plastic away from the fiber by holding the razor at a slight angle to the fiber. Another method is to chemically dissolve the plastic with a solvent like methylene chloride. Dip the section into the solvent for 3-5 minutes, remove the fiber, and rub the plastic off with a tissue. This Brilliant Experiment Shows How Fiber Optic Cables Bend Light Impossible Engineering | Thursdays at 9/8cSpecial chemicals give even water what looks like magical properties.Full Episodes Streaming FREE on Science Channe... How does fiber optics work? Try this fiber-optic experiment! This nice little experiment is a modern-day recreation of a famous scientific demonstration carried out by Irish physicist John Tyndall in 1870. It's best to do it in a darkened bathroom or kitchen at the sink or washbasin. You'll need an old clear, plastic drinks bottle, ... PDF PROJECTS IN FIBER OPTICS Tyndall's experiment showing that a stream of water will guide a beam of light. 0.01 1 966 Figure 0.3. Progress in optical fiber transmission. ... Optical fiber is also used in sensor applications, where the high sensitivity, low loss, and electromagnetic interfer-ence immunity of the fibers can be exploited. Optical fibers PDF Fiber Optic Lab Manual Fiber optic simplex receptacle 228042-1 2280421 1 Fiber optic simplex assembly 228087-1 2280871 2 1000 µm core plastic optical fiber IFCE1000 3 meters Vinyl ... The experiment will demonstrate how effective even a simple light guide is for coupling energy from a light source to a detector. You will also observe how the Field and lab experimental demonstration of nonlinear ... Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance fiber optic transmission systems. ... algorithm is demonstrated in both lab experiment over 2800 km ... PDF Experiment 2 FIBER ATTENUATION Attenuation (loss) is a logarithmic relationship between the optical output power and the optical input power in a fiber optical system. It is a measure of the decay of signal strength, or loss of light power, that occurs as light pulses propagate through the length of the fiber. The decay along the fiber is exponential and can be expressed as: A Set of Fiber Optics Experiments A set of ten experiments designed to introduce undergraduate electrical engineering students to the area of fiber optics is described. The projects include measurement of pertinent parameters of optical fibers, sources, and detectors (the major components of fiber optic systems), the construction of a simple fiber optic communication link, the use of an optical fiber as a sensor of acoustic ... A Simple Experiment Perfectly Illustrates How Fiber Optic Cables ... April 18, 2016, 12:55pm. Fiber optic cables connect the world by making communication possible. They're in our homes, workplaces, hospitals, and even at the bottom of the sea. But most of us don ... Fiber Optic Project for a Science Fair Fiber Optics Projects for Class Labs or a Science Fair Introduction. We have gotten many requests for projects involving fiber optic communications for science fairs and K-12 science class projects. ... You can duplicate this experiment for your class or science project. You need an acrylic plastic rod about 25mm (1 inch) diameter (available ... PDF Tyndall's Historical Experiment 1. Choose a flat, level table about 60 × 90 cm (2 × 3 feet) in size and 75 cm (30 inches) in height on which to place equipment. 2. Position the plastic beaker or cylinder so the valve protrudes over the table edge as shown in Figure 2. 3. Check the laser to ensure the laser beam shutter is closed. Numerical Aperture of Optical Fiber (Theory) : Laser Optics Virtual Lab An optic fiber consists of a core that is surrounded by a cladding which are normally made of silica glass or plastic. The core transmits an optical signal while the cladding guides the light within the core. Since light is guided through the fiber it is sometimes called an optical wave guide. The basic construction of an optic fiber is shown ... Fiber Optic Tutorial The Experiments are all great fun and a highlight of each Lesson. You will see a picture of your own voice, compare your hair to an optical fiber, simulate the manufacture of optical fibers by creating a long, thin string of cheese, create models and much more! We even issue a challenge to teachers and professors. Experiment: Fiber Optics Experiment: Fiber Optics. Fiber-Optics. Instructions for this lab are still delivered on paper or PDF file, available in the labs. A few things have been added, however, and the experiments will soon be converted to use fiber-optic FC connectors and some fiber-optic cables. This is being done to simplify measurements you now can do using beam ... Experiment demonstrates continuously operating optical fiber made of Credit: University of Maryland. Researchers at the University of Maryland (UMD) have demonstrated a continuously operating optical fiber made of thin air. The most common optical fibers are ... Fiber Optics Through Experiments, 2/E Fiber Optics Through Experiments, 2/E. M. R. Shenoy. Viva Books Private Limited, 2009 - Fiber optics - 216 pages. During the last forty years the science of Fiber Optics as well as its technological applications have seen a phenomenal progress so much so that, in recent years, theory and laboratory courses on various aspects of Fiber Optics ... Water Fibre Optics Instructions. Make a small round (~5mm) hole in the side of the bottle near the base. It is probably best to use a drill to do this as the bottle will be very slippery. Put your finger over the hole and fill the bottle up with water. Shine the torch through the bottle at the back of the hole. Remove your finger from the hole and move it down ... Understanding Fiber Optics Science Experiment Understanding Fiber Optics. I got the idea for this week's experiment while reading about fiber optics. Instead of using electricity through wires, fiber optic cables use light traveling through a clear fiber to carry phone signals, etc. Even though the fiber is clear, the light stays inside until it reaches the end. Undergraduate Experiments in Optics Employing a Fiber Optic Version of A. Flores, M. Flores, K. Karremans, and B. Zuidberg, "Undergraduate Experiments in Optics Employing a Fiber Optic Version of the Mach-Zehnder Interferometer," in Seventh International Conference on Education and Training in Optics and Photonics, Technical Digest Series (Optica Publishing Group, 2001), paper PDP464. Northrop Grumman CRS-20 Delivers Fiber Optic Research CAPE CANAVERAL (FL), February 14, 2024 - New fiber optics experiments sponsored by the International Space Station (ISS) National Laboratory launched on Northrop Grumman's 20 th Commercial Resupply Services (NG-20) mission. These experiments will test Flawless Photonics, Inc.'s unique approach to solving the issue of gravity-induced defects in optical glass products manufactured on Earth. Quantum data beamed alongside 'classical' data in a single fiber-optic Researchers have previously shown that quantum data can be transmitted through a standard fiber-optic cable, but this new experiment marks the first time that both quantum and conventional data ... Applying optical coherence tomography to inline quality monitoring of Wenninger M, Marschik C, Felbermayer K, et al. Optical coherence tomography - a New method for evaluating the quality of thermoplastic glass-fiber-reinforced unidirectional Tapes. In: Conference proceedings of the 37th international conference of the polymer processing society, Fukuoka, Japan, 11-15 April 2022. AIP Publishing. Algorithms A commercial Brillouin analyzer and standard telecommunication optical fiber SMF-28e were used for the experiments. The optical fiber on a split spool (free winding) was placed in a thermal chamber, where the temperature was maintained at 25 °C during the experiment to eliminate the influence of temperature variations in the laboratory on the ... Online sensing method for transmission line conductor ice ... 3.2 Utility testing of fiber optic sensors. In the experiment, in order to test the effect of the method in this paper, the calibration experiment and measurement experiment of the optical fiber sensor are carried out first, so as to simulate and test the data perception ability of the sensor in the online perception of icing. essay homework paper phd project resume review speech study