564 research outputs found
Geomorphology, stratigraphy and hydrogeology of the Doha-Damour area and hinterland - by Hasan M. Awad
No full textThesis (M.S.)--Dept. of Geology, American University of Beirut, 1983.Bibliography: leaves i-v
Toggleable transparency states in thermally-shifted multiMRR cascaded filters
No full textWe showcase and analyze a control scheme to leverage thermal-induced transparency in cascaded multistage
microring resonator (MRR) structures. MRRs are one of the basic building blocks in integrated photonics,
typically used to implement high Q-factor filters and frequency-dependent circuits. While traditionally single-
order rings are reserved for straightforward and simple applications, higher-order cascaded structures can be
arranged to introduce and implement advanced filters, showcasing larger flat-top bandwidth and allowing precise
tailoring of the response. Thermally induced phase delay between the MRRs can be used to achieve precise control
of the response, while also allowing a transparent switching state obtained through destructive interference of
the different ring responses. In our work, we investigate how this effect can be leveraged to turn off the filtering
structure, introducing a toggleable state, while validating the theoretical uses and limitations of the control
scheme by implementing the structure through programmable integrated photonic circuit
2026: Ruth Awad
No full textRuth Awad is a Lebanese-American disabled poet, a 2021 NEA Poetry fellow, and the author of Outside the Joy (Third Man Books, 2024) and Set to Music a Wildfire (Southern Indiana Review Press, 2017), winner of the 2016 Michael Waters Poetry Prize and the 2018 Ohioana Book Award for Poetry. Her work can be found in The Atlantic, AGNI, Poetry, Poem-a-Day, The Believer, The New Republic, and elsewhere. She has an MGA in poetry from Southern Illinois University Carbondale, and she lives and writes in Columbus, Ohio.https://thekeep.eiu.edu/lionsinwinter_writers/1055/thumbnail.jp
Environmental Sensing and Detection based on State of Polarization Monitoring in Terrestrial Optical Data Networks
No full textTelecommunications networks based on optical fiber communication have been vastly deployed in the last years to cope with the increasing traffic demands. They cover wide terrestrial areas with thousands of kilometers of available fiber cables, arranged in meshed, rings or festoon network topologies. Moreover, their operation is becoming more and more software-defined thanks to the definition of open interfaces and data structures, transforming the infrastructure into a crucial commodity able to offer several network services. Recently, the idea of using existing telecommunications fiber networks as a wide smart grid for environmental sensing is gaining momentum, since optical fiber can be used as an excellent mechanical stress sensors, as several physical effects are impacted by external stress. Distributed acoustic sensing (DAS) techniques deliver extremely accurate and spatially resolved measurements which are the state of the art, for example, in earthquake detection. However, its high cost, need for dark fibers and physical limitations prevent its wide deployment in telecom infrastructure.
In this context, sensing based on state of polarization (SOP) monitoring of optical signals is an attractive solution. SOP is alredy monitored on optical coherent channel receivers for data recovery, although access to this data is usually closed by transceiver vendors. However, it is potentially accessible on cheaper intensity modulated optical data channels, still widespread in optical networks, especially in the access segment. Also, it can be monitored using dedicated signals which can be transmitted alongside typical data channels. Moreover, SOP sensing does not require bidirectional transmission onto the same fibers and can extend its reach farther than DAS as it supports optical amplifiers, thus improving the compatibility between data and sensing services. On the downside, SOP sensing loses DAS spatial resolution, as it provides an integrated measurements over an entire fiber span and extraction of significant event information is complicated by the randomness of fiber birefringence. However, terrestrial networks can offer several SOP sensing sites which can be implemented with far cheaper equipment with respect to DAS or interferometry. In this work we explore the possibility for wide sensing grids with fiber length scale spatial resolution, which can integrate the information provided by traditional seismic stations networks. In particular, while developed areas may leverage on seismic stations networks, SOP sensing represents a cost effective solutio in emerging economies where telecom infrastructure is already deployed. Another key aspect relates to the development of effective techniques to detect the environmental events of interest features, such as the earthquakes P/S waves, from the SOP time series. Indeed, especially in the terrestrial networks scenario, anthropic activities act as noise on the monitored SOP evolutions. To this aim, detection based on machine learning techniques is promising, due to the largely vaying characteristic figures of seismic waves. Due to the lack of extensive SOP experimental observations, we have developed simulations tools able to generate SOP synthetic data from realistic strain rates and we show how they can be used to train ML models based on spatially integrated SOP time evolutions
Deep Learning Based Early Earthquake Detection through Terrestrial Optical Networks
No full textWe demonstrate the use of existing terrestrial optical networks as a smart sensing grid for early earthquake detection we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data to train the model to detect Primary Waves (P waves) arrivals that precede earthquakes’ destructive Surface waves. . The main idea of our approach is to deploy a fast, accurate and reliable trained deep learning model. We evaluated the performance of LSTM and GRU models on experimentally emulated data collected from a 38 km deployed fiber link in Turin, Italy. Our results demonstrate that the GRU model consistently outperformed the LSTM model with 99% recall for P-wave detection
Seismic detection through state-of-polarization analysis in optical fiber networks
No full textThe existing optical fiber terrestrial network can be leveraged to serve as a wide distributed network of sensors, especially to detect mechanical stresses as the optical signal polarization is significantly influenced by external disturbances. Exploiting this trend, paves the way for employing the optical fiber network in environmental sensing, like detecting earthquakes or tracking anthropic activities. The purpose is to examine the changes in the state of light polarization caused by birefringence induced by seismic events. Consequently, we have developed a Python-based Waveplate Model to track state of light polarization changes in buried optical fiber cables. This model integrates real ground motion data from a 4.9 magnitude earthquake that occurred southwest Marradi city in Italy, and converts it into strain values along the fiber cable. To further investigate the effects of this particular seismic activity, we propose a centralized smart grid fiber network approach based on a neural network model with an attention mechanism for earthquake early warnings. Along with the aforementioned Waveplate Model, numerous sets of polarization evolution were produced on two distinct sensing points with different distances from the epicenter in two different cities, enabling earthquake early detection upon P-wave arrivals that precede the earthquake’s destructive surface waves and allowing for a swift initiation of emergency plans including early warning alerts and earthquake countermeasures
Experimental earthquake early detection through polarization changes in intelligent optical networks
No full textIn this paper, we replicate the dynamics of an earthquake in a laboratory experiment by employing an optical scrambler and a polarimeter. This experimental setup emulates the impact of a real earthquake-induced ground displacement values on the state of polarization of light propagating through an optical fiber, modeled using a Waveplate computer-based approach. A large dataset of polarization evolution was collected from the experiment to evaluate the performance of our pre-trained machine learning model in detecting the primary earthquake wave which precedes the destructive wave by tens of seconds. The objective is to perform a comparative analysis with our previous findings which were conducted using the same machine learning model, but applied to computer-based simulations of the Waveplate model. The results demonstrate that the model achieved over 95% of accuracy in both computer-based simulations and Laboratory-based experiment, validating the high accuracy and reliability of our system in early earthquake detection despite the inherent challenges of experimental error
Earthquake Emulation for Environmental Sensing in Terrestrial Telecom Networks
No full textThere is a huge interest in exploiting the extensive telecom fiber infrastructure for early warning of environmental disasters. We propose a method to generate synthetic earthquakes events to test their detection in terrestrial telecommunication networks
Comprehensive thermal crosstalk model of meshed MZI topologies for neuromorphic computing
No full textWe propose a comprehensive Mach-Zehnder Interferometer (MZI) model that takes into account propagation
effects, losses, thermal and optical crosstalk, and can be used to simulate the behavior of meshed MZI topologies
(e.g., in the context of neuromorphic photonic computing). The model is validated by comparing the simulated
results with power and spectral measurements of a 3x3 Silicon Photonic circuit based on cascaded MZIs. This
circuit can be used as a programmable logic gate and this application is demonstrated with the proposed mod
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