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Relevant bibliographies by topics / Signal-noise-ratio

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Academic literature on the topic 'Signal-noise-ratio'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Signal-noise-ratio"

1

Smith, Robert C., and Robert C. Lange. "Signal to Noise Ratio." Critical Reviews in Diagnostic Imaging 42, no. 2 (January 2001): 135–40. http://dx.doi.org/10.3109/20014091086711.

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Johnson, Don. "Signal-to-noise ratio." Scholarpedia 1, no. 12 (2006): 2088. http://dx.doi.org/10.4249/scholarpedia.2088.

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Muhammad Basharat, Muhammad Basharat, Ming Ding Ming Ding, Yang Li Yang Li, Hongwei Cai Hongwei Cai, and Jiancheng Fang Jiancheng Fang. "Noise reduction and signal to noise ratio improvement in magneto-optical polarization rotation measurement." Chinese Optics Letters 16, no. 8 (2018): 081201. http://dx.doi.org/10.3788/col201816.081201.

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Davidson, Steven J. "The Signal-to-Noise Ratio." Emergency Medicine News 26, no. 8 (August 2004): 38. http://dx.doi.org/10.1097/00132981-200408000-00023.

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Jenkin, Robin. "Contrast Signal to Noise Ratio." Electronic Imaging 2021, no. 17 (January 18, 2021): 186–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.17.avm-186.

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The detection and recognition of objects is essential for the operation of autonomous vehicles and robots. Designing and predicting the performance of camera systems intended to supply information to neural networks and vision algorithms is nontrivial. Optimization has to occur across many parameters, such as focal length, f-number, pixel and sensor size, exposure regime and transmission schemes. As such numerous metrics are being explored to assist with these design choices. Detectability index (SNRI) is derived from signal detection theory as applied to imaging systems and is used to estimate the ability of a system to statistically distinguish objects [1], most notably in the medical imaging and defense fields [2]. A new metric is proposed, Contrast Signal to Noise Ratio (CSNR), which is calculated simply as mean contrast divided by the standard deviation of the contrast. This is distinct from contrast to noise ratio which uses the noise of the image as the denominator [3,4]. It is shown mathematically that the metric is proportional to the idealized observer for a cobblestone target and a constant may be calculated to estimate SNRI from CSNR, accounting for target size. Results are further compared to Contrast Detection Probability (CDP), which is a relatively new objective image quality metric proposed within IEEE P2020 to rank the performance of camera systems intended for use in autonomous vehicles [5]. CSNR is shown to generate information in illumination and contrast conditions where CDP saturates and further can be modified to provide CDP-like results.

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Cunbao Lin, Cunbao Lin, Shuhua Yan Shuhua Yan, Zhiguang Du Zhiguang Du, Guochao Wang Guochao Wang, and Chunhua Wei Chunhua Wei. "Symmetrical short-period and high signal-to-noise ratio heterodyne grating interferometer." Chinese Optics Letters 13, no. 10 (2015): 100501–5. http://dx.doi.org/10.3788/col201513.100501.

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Bosworth, B. T., W. R. Bernecky, J. D. Nickila, B. Adal, and G. C. Carter. "Estimating Signal-to-Noise Ratio (SNR)." IEEE Journal of Oceanic Engineering 33, no. 4 (October 2008): 414–18. http://dx.doi.org/10.1109/joe.2008.2001780.

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Schultz, Simon. "Signal-to-noise ratio in neuroscience." Scholarpedia 2, no. 6 (2007): 2046. http://dx.doi.org/10.4249/scholarpedia.2046.

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Redpath, T. W. "Signal-to-noise ratio in MRI." British Journal of Radiology 71, no. 847 (July 1998): 704–7. http://dx.doi.org/10.1259/bjr.71.847.9771379.

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Dhara, Asish K. "Enhancement of signal-to-noise ratio." Journal of Statistical Physics 87, no. 1-2 (April 1997): 251–71. http://dx.doi.org/10.1007/bf02181487.

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Dissertations / Theses on the topic "Signal-noise-ratio"

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Pauluzzi, David Renato. "Signal-to-noise ratio and signal-to-impairment ratio estimation in AWGN and wireless channels." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22377.pdf.

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Hamid, Syamsul Bahrin Abdul. "Enhancing signal to noise ratio for electrostatic transducers." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24250.

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This Thesis describes the design, manufacture and evaluation of a Fluidically Amplified Ultrasonic Transducer (FLAUT) for an air-coupled application. The transducer utilises a pipe as an amplification mechanism to increase the output pressure; and as a dissipation mechanism to reduce inherent noise within the transducer. The new transducer design introduces the concept of matched thin plate, cavity and pipe, of which the individual geometry enhances one another. Design methodologies, which consist of analytical modelling and Finite Element (FE) Modelling, have been implemented. The analytical modelling identifies the required geometry for the FLAUT based on the desired operating resonant frequency; while FE then verifies the vibrational characteristics of the design. Through the application of FE modelling and practical analysis, FLAUT devices have been designed, developed and compared with experiment. The sensitivity analysis is utilised to realise a design and manufacturing tolerance requirements. The devices were manufactured in the operating range of 25 kHz to 85 kHz. Air-coupled pulse-echo insertion loss was found to be 61.3 dB, an improvement of 9.1 dB over the conventional cavity only design. Results from the proof of concept prototype indicate that the output of the FLAUT is maximised when the pipe radius is designed to be as large as practically possible while maintaining the matched resonant frequencies. This correlates well with theory both in term of sensitivity and noise. Furthermore, the pressure output of a FLAUT array is maximised by arranging the cell spacing to be as close as practically possible. Thus, the cells were spaced at multiples of 2.25 to the cavity radius – to reduce the risk of cell damage. An analytical method to simulate, and a technique to measure the inherent noise using a specially designed hybrid isolation vessel has been developed. From the measurement, the FLAUT noise is found to be 5.8 W, an improvement of 2.7 dB compared to the conventional cavity only design.

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Armstrong, Juliane. "Random inter stimulus interval increases signal-to-noise ratio." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/honors/29.

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Incremental improvements are continuously being made to P300-Speller BCI paradigms. Accurate classification depends on a high signal-to-noise ratio (SNR) between the target and nontarget items. Fixed presentation rates produce a large flash-evoked response that persists throughout the recording epoch, which can potentially undermine the classification of P300-responses. By introducing a random interstimulus interval (ISI) to a previously improved P300-Speller paradigm (i.e., Checkerboard Paradigm; CBP) we expect to reduce the deleterious flash-evoked responses and increase the P300 classification SNR. Data were recorded from 32 EEG locations (right mastoid referenced) from 13 subjects using the CBP with two conditions. In the Random ISI (RI) condition, ISI varied between 0 ms and 187.5 ms and averaged 93.75 ms. In the Fixed ISI (SI) condition, ISI remained static at 93.75 ms. In both conditions, participants were instructed to spell out 72 characters using an 8x9 matrix of alphanumeric characters by silently counting each target flash. The first 36 characters served as ‘calibration’ data for a stepwise linear discriminant analysis (SWLDA; 0 - 800 ms poststimulus epochs). This SWLDA classifier was then used to provide online feedback for an additional 36 character selections. Absolute amplitude of target and nontarget responses were summed across the recording epoch for each subject and averaged between Pz and Cz (maximum). Target averages were then divided by nontarget averages to create a SNR measure and compared between RI and FI conditions. The RI manipulation produced a significantly (p = .04) larger SNR (M = 5.85) than the FI condition (M =4.07).Further analysis of the averaged waveforms revealed a significantly (p = .05) greater positive peak at Cz (253 ms peak latency) for the RI condition. Classification performance measures for RI and FI conditions were high for accuracy (84 and 85%, respectively; NS) and bitrate (21 and 23 bits/min, respectively; NS). Together these results suggest that while randomizing ISI can yield higher SNR, response classification is not affected. It is possible that SWLDA is a useful classification method, in general; however, these data suggest that it does not capitalize on the additional information gained from the increase in SNR. Alternative classification techniques that can take advantage of specific subcomponents of the response may be able to utilize this additional information to improve BCI speed and accuracy.

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Cheng, Lui. "Improvement of signal-to-noise ratio in uterine EMG recordings." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/1548.

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The objective of this study is to remove or, at least, reduce the noise in uterine EMG recordings, which at their present noise level render the data unusable. Predicting when true labor will start and recognizing when labor actually starts are important for both normal and complex pregnancies. For normal pregnancy, the prognosis of labor is important for reducing unnecessary hospital costs. About 10% of the four million babies born each year in the United States are born prematurely. At $1,500 a day for neonatal intensive care, this comprises national health care expenses of well over $5 billion. Spectral analysis, filter design, and 1/3 octave analysis were applied to analyze the uterine EMG recordings. Signal-to-noise ratio was increased with IIR Butterworth bandstop filter. The spectral band between 0.25 and 0.4 Hz shows matching of the Toco belt via spectral analysis. Nevertheless, 1/3 octave analysis gives the highest correct detection percentage compare with frequency analysis and filter design.

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Cheraghi, Parisa. "Fast and accurate spectrum sensing low signal noise ratio environment." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581799.

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Opportunistic Spectrum Access (OSA) [1] promises tremendous gain in improving spectral efficiency. The main objective of OSA is to offer the ability of identifying and exploiting the under-utilised spectrum in an instantaneous manner in a wireless device, without any user intrusion. Hence, the initial requirement of any OSA device is the ability to perform spectrum sensing. Local narrow-band spectrum sensing has been quite well investigated in the literature. However, it is realised that existing schemes can hardly meet the requirements of a fast and accurate spectrum sensing particulariy in very low signal-to-noise-ratio (SNR) range without introducing high complexity to the system. Furthermore, increase in the spectrum utilisation calls for spectrum sensing techniques that adopt an architecture to simultaneously search over multiple frequency sub-bands at a time. However, the literature of sub-band spectrum sensing is rather limited at this time. The main contributions of this thesis is two-fold: First a clusterd-based differential energy detection for local sensing of multi- carrier based system is proposed. The proposed approach can form fast and reliable decision of spectrum availability even in very low SNR environment. The underlying initiative of the proposed scheme is applying order statistics on the clustered differential Energy Spectral Density (ESD) in order to exploit the channel frequency diversity inherent in high data-rate communications. Second contribution is three-fold: 1) re-defining the objective of the sub- band level spectrum sensing device to a model estimator, 2) deriving the optimal model selection estimator for sub-band level spectrum sensing for fixed and variable number of users along with a sub-optimal solution based on Bayesian statistical modelling and 3) proposing a practical model selection estimator with relaxed sample size constraint and limited system knowledge for sub-band spectrum sensing applications in Orthogonal Frequency-Division Multiple Access (OFDMA) systems. The result obtained showed that through exploitation of the channel frequency selectivity the performance of the stat-of-the-art spectrum sensing techniques can be significantly improved. Furthermore, by modelling the sub-band level spectrum sensing through model estimation allows for new spectrum sensing approach. It was proved both analytically and through simulations that the proposed approach have significantly extended to state-of-the-art spectrum sensing. Key words: Differential, energy detection, low signal-to- noise ratio (SNR), multi- carrier, opportunistic spectrum access, spectrum sensing.

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Koul, Ashish 1979. "Use of intermicrophone correlation in estimating signal to noise ratio." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29672.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaf 42).
This thesis presents the design, analysis, and simulation of a system that uses the correlation coefficient of audio inputs gathered at two spatially separate microphones to determine the signal to noise ratio in the environment. This work is motivated by past research in microphone array hearing aids, where accurate estimates of SNR were shown to improve performance. Signal to noise ratio is defined as the ratio of energy in the direct component (audio sources originating in front of a broadside array) to energy in the interference component (sources originating from the sides of the array). The design presented is a simple hypothesis testing mechanism for determining whether the SNR exceeds a fixed level. In the analysis, behavior of the system is studied theoretically under varying conditions of reverberation in the environment, and processing parameters are determined to optimize system performance. Finally, simulations test the true performance of the system to verify the validity of the theoretical analysis.
by Ashish Koul.
M.Eng.and S.B.

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Liu, Janet (Janet Kay) 1976. "Determining signal-to-noise ratio in a burst coherent demodulator." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80142.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (leaves 59-60).
by Janet Liu.
S.B.and M.Eng.

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Lie, Chin Cheong Patrick. "Iterative algorithms for fast, signal-to-noise ratio insensitive image restoration." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63767.

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Aldokhail, Abdullah M. "Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model." University of Toledo Health Science Campus / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255.

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Haboosheh, Ronette. "Diagnostic auditory brainstem response analysis : evaluation of signal-to-noise ratio criteria using signal detection theory." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31575.

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This study evaluated an online measure of signal-to-noise ratio (SNR) as a response-detection tool for threshold auditory brainstem response (ABR) testing. Threshold-ABR data were analysed for 98 infants and young children tested at BC Children's Hospital, and results were validated on an additional 10 patients. Using signal detection theory, it was possible to assess test performance for the SNR measure, with expert-clinician judgement as the gold standard. In addition, a range of SNR criteria were assessed in terms of sensitivity (the ability to accurately identify a response) and specificity (the ability to accurately reject waveforms that do not contain a response). The effect of residual noise (RN) exclusion criteria on SNR test performance, sensitivity, and specificity was also investigated. Waveforms to 500-, 2000-, and 4000-Hz air-conducted brief-tone stimuli were included in this study. Overall, SNR was found to have a test performance of A=.91, with improved performance (A=.93) when high residual-noise waveforms (RN>0.08 μV) were excluded. When low-RN data were separated by frequency, test performance for each frequency was A=.94. Results suggest that the optimal SNR criterion is slightly lower for 500-Hz recordings than for 2000- or 4000-Hz recordings. However, when high-RN recordings were excluded, a SNR criterion of 0.98 achieved a minimum specificity of 95% for each stimulus frequency, with sensitivity values ranging from 64%(for 500 Hz) to 79% (for 4000 Hz). Findings confirm the hypotheses that SNR accurately distinguishes response-present from response-absent waveform, and that quiet recordings are more easily interpreted than noisy recordings using SNR. Guidelines are provided for the clinical use of SNR as an objective response-detection tool.
Medicine, Faculty of
Audiology and Speech Sciences, School of
Graduate

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Books on the topic "Signal-noise-ratio"

1

Sivathevan, T. Signal to quantization noise ratio. London: University of East London, 1994.

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J, Curran Paul. Estimating the signal-to-noise ratio of AVIRIS data. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1989.

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J, Curran Paul. Estimating the signal-to-noise ratio of AVIRIS data. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1989.

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Curran, Paul J. Estimating the signal-to-noise ratio of AVIRIS data. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1989.

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Robinson, A. P. The relationship between vision carrier-to-noise ratio and picture signal-to-noise ratio ina system 1 television receiver. London: BBC, 1987.

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Walsh, Norman J. Bandwidth and signal-to-noise ratio enhancement of the NPS Transient Electromagnetic Scattering Laboratory. Monterey, Calif: Naval Postgraduate School, 1989.

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Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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Book chapters on the topic "Signal-noise-ratio"

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Weik, Martin H. "signal/noise ratio." In Computer Science and Communications Dictionary, 1582. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17386.

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Weik, Martin H. "signal-to-noise ratio." In Computer Science and Communications Dictionary, 1585–86. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17409.

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Zhou, Tianshou. "Signal-to-Noise Ratio." In Encyclopedia of Systems Biology, 1939–40. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_514.

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Weik, Martin H. "signal-plus-noise to noise ratio." In Computer Science and Communications Dictionary, 1583. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17391.

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Simon, Marvin K., and Samuel Dolinar. "Signal-to-Noise Ratio Estimation." In Autonomous Software-Defined Radio Receivers for Deep Space Applications, 121–92. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470087800.ch6.

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Weik, Martin H. "detector signal-to-noise ratio." In Computer Science and Communications Dictionary, 394. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_4838.

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Weik, Martin H. "postdetector signal-to-noise ratio." In Computer Science and Communications Dictionary, 1307. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_14374.

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Weik, Martin H. "photodetector signal-to-noise ratio." In Computer Science and Communications Dictionary, 1268. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_13982.

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Di Lorenzo, Renato. "Signal-to-Noise Ratio: Tradability." In Trading Systems, 215–21. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2706-0_37.

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Freye, Enno. "Optimising signal-to-noise ratio." In Cerebral Monitoring in the Operating Room and the Intensive Care Unit, 104–12. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1886-3_11.

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Conference papers on the topic "Signal-noise-ratio"

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Galleani, Lorenzo, Leon Cohen, and Douglas Nelson. "Local signal to noise ratio." In SPIE Optics + Photonics, edited by Franklin T. Luk. SPIE, 2006. http://dx.doi.org/10.1117/12.684026.

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Lu, Ning H. "A Signal-to-Noise Ratio Enhancer." In 2011 IEEE Sensors Applications Symposium (SAS). IEEE, 2011. http://dx.doi.org/10.1109/sas.2011.5739765.

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Papic, Veljko, Zeljko Djurovic, Goran Kvascev, and Predrag Tadic. "On signal-to-noise ratio estimation." In Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference. IEEE, 2010. http://dx.doi.org/10.1109/melcon.2010.5476314.

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Malbet, Fabien, Alain Chelli, and Romain G. Petrov. "AMBER performances: signal-to-noise ratio analysis." In Astronomical Telescopes and Instrumentation, edited by Pierre J. Lena and Andreas Quirrenbach. SPIE, 2000. http://dx.doi.org/10.1117/12.390213.

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Martin, David, Trevor Vent, Chloe J. Choi, Bruno Barufaldi, Raymond J. Acciavatti, and Andrew Maidment. "Signal-to-noise ratio and contrast-to-noise ratio measurements for next generation tomosynthesis." In Physics of Medical Imaging, edited by Hilde Bosmans, Wei Zhao, and Lifeng Yu. SPIE, 2021. http://dx.doi.org/10.1117/12.2582279.

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Urey, Hakan, William T. Rhodes, H. John Caulfield, and Zafer Urey. "High-signal-to-noise-ratio image processing in low-signal-to-bias-ratio environments." In Optical Science, Engineering and Instrumentation '97, edited by Bahram Javidi and Demetri Psaltis. SPIE, 1997. http://dx.doi.org/10.1117/12.284198.

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Fauss, M., K. G. Nagananda, A. M. Zoubir, and H. V. Poor. "Sequential joint signal detection and signal-to-noise ratio estimation." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953029.

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Cegla, Frederic, and Balint Herdovics. "Coded excitation, motion and signal-to-noise ratio." In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018. http://dx.doi.org/10.1109/iscas.2018.8351579.

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Rojas, A. J. "Robust signal-to-noise ratio constrained feedback control." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6858755.

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Brekke, Edmund, Oddvar Hallingstad, and John Glattetre. "The signal-to-noise ratio of human divers." In OCEANS 2010 IEEE - Sydney. IEEE, 2010. http://dx.doi.org/10.1109/oceanssyd.2010.5603530.

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Reports on the topic "Signal-noise-ratio"

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Doerry, Armin Walter, and Brandeis Marquette. Radar antenna pointing for optimized signal to noise ratio. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1088061.

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Rakuljic, George A. Holographic Crosstalk and Signal-to-Noise Ratio in Orthogonal Data Storage. Fort Belvoir, VA: Defense Technical Information Center, February 1992. http://dx.doi.org/10.21236/ada250146.

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Hippenstiel, R. Signal to Noise Ratio Improvement Using Wavelet and Frequency Domain Based Processing. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada404025.

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Kirsteins, I. P. On the Probability Density of Signal-to-Noise Ratio in an Improved Detector. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada152529.

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Feng, Y. P., I. McNulty, Z. Xu, and E. Gluskin. Signal-to-noise ratio of intensity interferometry experiments with highly asymmetric x-ray sources. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/510394.

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Feng, Y. P., I. McNulty, Z. Xu, and E. Gluskin. Signal-to-noise ratio of intensity interferometry experiments with highly asymmetric x-ray sources. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/461284.

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Khatri, C. G., C. Radhakrishna Rao, and Y. N. Sun. Tables for Obtaining Confidence Bounds for Realized Signal to Noise Ratio with an Estimated Discriminant Function. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada166059.

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Quinn, Meghan. Geotechnical effects on fiber optic distributed acoustic sensing performance. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41325.

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Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and an optic analyzer called an interrogator. The oil and gas industry has used DAS for over a decade to monitor infrastructure such as pipelines for leaks, and in recent years changes in DAS performance over time have been observed for DAS arrays that are buried in the ground. This dissertation investigates the effect that soil type, soil temperature, soil moisture, time in-situ, and vehicle loading have on DAS performance for fiber optic cables buried in soil. This was accomplished through a field testing program involving two newly installed DAS arrays. For the first installation, a new portion of DAS array was added to an existing DAS array installed a decade prior. The new portion of the DAS array was installed in four different soil types: native fill, sand, gravel, and an excavatable flowable fill. Soil moisture and temperature sensors were buried adjacent to the fiber optic cable to monitor seasonal environmental changes over time. Periodic impact testing was performed at set locations along the DAS array for over one year. A second, temporary DAS array was installed to test the effect of vehicle loading on DAS performance. Signal to Noise Ratio (SNR) of the DAS response was used for all the tests to evaluate the system performance. The results of the impact testing program indicated that the portions of the array in gravel performed more consistently over time. Changes in soil moisture or soil temperature did not appear to affect DAS performance. The results also indicated that time DAS performance does change somewhat over time. Performance variance increased in new portions of array in all material types through time. The SNR in portions of the DAS array in native silty sand material dropped slightly, while the SNR in portions of the array in sand fill and flowable fill material decreased significantly over time. This significant change in performance occurred while testing halted from March 2020 to August 2020 due to the Covid-19 pandemic. These significant changes in performance were observed in the new portion of test bed, while the performance of the prior installation remained consistent. It may be that, after some time in-situ, SNR in a DAS array will reach a steady state. Though it is unfortunate that testing was on pause while changes in DAS performance developed, the observed changes emphasize the potential of DAS to be used for infrastructure change-detection monitoring. In the temporary test bed, increasing vehicle loads were observed to increase DAS performance, although there was considerable variability in the measured SNR. The significant variation in DAS response is likely due to various industrial activities on-site and some disturbance to the array while on-boarding and off-boarding vehicles. The results of this experiment indicated that the presence of load on less than 10% of an array channel length may improve DAS performance. Overall, this dissertation provides guidance that can help inform the civil engineering community with respect to installation design recommendations related to DAS used for infrastructure monitoring.

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