Relevant bibliographies by topics / Distributed sensing pressure sensing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Distributed sensing pressure sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Distributed sensing pressure sensing"

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Becker, Matthew, Thomas Coleman, Christopher Ciervo, Matthew Cole, and Michael Mondanos. "Fluid pressure sensing with fiber-optic distributed acoustic sensing." Leading Edge 36, no. 12 (December 2017): 1018–23. http://dx.doi.org/10.1190/tle36121018.1.

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Wang, Long, Sumit Gupta, Kenneth J. Loh, and Helen S. Koo. "Distributed Pressure Sensing Using Carbon Nanotube Fabrics." IEEE Sensors Journal 16, no. 12 (June 2016): 4663–64. http://dx.doi.org/10.1109/jsen.2016.2553045.

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Dong, Yongkang. "High-Performance Distributed Brillouin Optical Fiber Sensing." Photonic Sensors 11, no. 1 (January 22, 2021): 69–90. http://dx.doi.org/10.1007/s13320-021-0616-7.

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AbstractThis paper reviews the recent advances on the high-performance distributed Brillouin optical fiber sensing, which include the conventional distributed Brillouin optical fiber sensing based on backward stimulated Brillouin scattering and two other novel distributed sensing mechanisms based on Brillouin dynamic grating and forward stimulated Brillouin scattering, respectively. As for the conventional distributed Brillouin optical fiber sensing, the spatial resolution has been improved from meter to centimeter in the time-domain scheme and to millimeter in the correlation-domain scheme, respectively; the measurement time has been reduced from minute to millisecond and even to microsecond; the sensing range has reached more than 100 km. Brillouin dynamic grating can be used to measure the birefringence of a polarization-maintaining fiber, which has been explored to realize distributed measurement of temperature, strain, salinity, static pressure, and transverse pressure. More recently, forward stimulated Brillouin scattering has gained considerable interest because of its capacity to detect mechanical features of materials surrounding the optical fiber, and remarkable works using ingenious schemes have managed to realize distributed measurement, which opens a brand-new way to achieve position-resolved substance identification.
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Ahmadi, Mahdi, Rajesh Rajamani, Gerald Timm, and A. Serdar Sezen. "Flexible Distributed Pressure Sensing Strip for a Urethral Catheter." Journal of Microelectromechanical Systems 24, no. 6 (December 2015): 1840–47. http://dx.doi.org/10.1109/jmems.2015.2444992.

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Zhu, Xiongfeng, Tianxing Man, Xing Haw Marvin Tan, Pei-Shan Chung, Michael A. Teitell, and Pei-Yu Chiou. "Distributed colorimetric interferometer for mapping the pressure distribution in a complex microfluidics network." Lab on a Chip 21, no. 5 (2021): 942–50. http://dx.doi.org/10.1039/d0lc00960a.

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Hao, Peng, Chao Yu, Ting Feng, Zeheng Zhang, Mingliang Qin, Xin Zhao, Hua He, and X. Steve Yao. "PM fiber based sensing tapes with automated 45° birefringence axis alignment for distributed force/pressure sensing." Optics Express 28, no. 13 (June 9, 2020): 18829. http://dx.doi.org/10.1364/oe.391376.

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Sun Qizhen, 孙琪真, 汪静逸 Wang Jingyi, 张. 威. Zhang Wei, 向. 阳. Xiang Yang, 艾. 凡. Ai Fan, and 刘德明 Liu Deming. "Polymer packaged longitudinal microstructured fiber based distributed pressure sensing system." Infrared and Laser Engineering 45, no. 8 (2016): 802003. http://dx.doi.org/10.3788/irla201645.0802003.

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Levi, Alessandro, Matteo Piovanelli, Silvano Furlan, Barbara Mazzolai, and Lucia Beccai. "Soft, Transparent, Electronic Skin for Distributed and Multiple Pressure Sensing." Sensors 13, no. 5 (May 17, 2013): 6578–604. http://dx.doi.org/10.3390/s130506578.

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Yu, Li, Steven Parker, Haifeng Xuan, Yujing Zhang, Shan Jiang, Maryam Tousi, Majid Manteghi, Anbo Wang, and Xiaoting Jia. "Flexible Multi‐Material Fibers for Distributed Pressure and Temperature Sensing." Advanced Functional Materials 30, no. 9 (January 3, 2020): 1908915. http://dx.doi.org/10.1002/adfm.201908915.

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Chen, Tong, Qingqing Wang, Rongzhang Chen, Botao Zhang, Charles Jewart, Kevin P. Chen, Mokhtar Maklad, and Phillip R. Swinehart. "Distributed high-temperature pressure sensing using air-hole microstructural fibers." Optics Letters 37, no. 6 (March 12, 2012): 1064. http://dx.doi.org/10.1364/ol.37.001064.

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Dissertations / Theses on the topic "Distributed sensing pressure sensing"

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Wang, Jing. "Distributed Pressure and Temperature Sensing Based on Stimulated Brillouin Scattering." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/78066.

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Brillouin scattering has been verified to be an effective mechanism in temperature and strain sensing. This kind of sensors can be applied to civil structural monitoring of pipelines, railroads, and other industries for disaster prevention. This thesis first presents a novel fiber sensing scheme for long-span fully-distributed pressure measurement based on Brillouin scattering in a side-hole fiber. After that, it demonstrates that Brillouin frequency keeps linear relation with temperature up to 1000°C; Brillouin scattering is a promising mechanism in high temperature distributed sensing. A side-hole fiber has two longitudinal air holes in the fiber cladding. When a pressure is applied on the fiber, the two principal axes of the fiber birefringence yield different Brillouin frequency shifts in the Brillouin scattering. The differential Brillouin scattering continuously along the fiber thus permits distributed pressure measurement. Our sensor system was designed to analyze the Brillouin scattering in the two principal axes of a side-hole fiber in time domain. The developed system was tested under pressure from 0 to 10,000 psi for 100m and 600m side-hole fibers, respectively. Experimental results show fibers with side holes of different sizes possess different pressure sensitivities. The highest sensitivity of the measured pressure induced differential Brillouin frequency shift is 0.0012MHz/psi. The demonstrated spatial resolution is 2m, which maybe further improved by using shorter light pulses.
Master of Science
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Dusek, Jeff Ernest. "Development of bio-inspired distributed pressure sensor arrays for hydrodynamic sensing applications." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103496.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 277-284).
The performance of marine vehicles is largely influenced by interactions with the flow around their hull, both self-generated and environmentally driven. To improve performance through flow control, a detailed, real-time measurement of the near-field flow is necessary, yet such sensing capability is presently unavailable. Looking to nature for inspiration, fish employ the distributed pressure and velocity sensing capability of their lateral line sensory organ to mediate navigation and control behaviors that, if replicated, could benefit engineered systems. Through a series of towing tank and field experiments, it was found that while distributed pressure measurements on marine vehicles enabled the detection of near-body flow phenomena, the size, cost, and mounting requirements of commercial sensors lead to sparse arrays and substantial gaps in the characterization of the flow field. To address the challenges associated with obtaining spatially-dense pressure measurements on curved surfaces in marine environments, a new waterproof and conformal pressure sensor array was developed based on a closed-cell piezo resistive foam composed of carbon black-doped-silicone composite (CBPDMS foam). The response of the CBPDMS foam sensor arrays was characterized using periodic hydrodynamic pressure stimuli from vertical plunging and water waves, and a piecewise polynomial calibration was developed to describe the sensor response. The sensitivity and frequency response of the sensor arrays was also documented through a series of biologically-inspired hydrodynamic stimuli, including the flow from a dipole source, and the Karmin vortical wake flow behind a circular cylinder. The CBPDMS foam sensor arrays have significant advantages over existing commercial sensors for distributed flow reconstruction and control. They are found to have sensitivity on the order of 5 Pascal, frequency range of 0.5-35 Hertz, are contained in a waterproof and completely flexible package, and have material cost less than $10 per sensor.
by Jeff Ernest Dusek.
Ph. D.
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大岡, 昌博, Masahiro OHKA, 行宏 毛利, Yukihiro MOURI, 徳宏 杉浦, Tokuhiro SUGIURA, 保永 三矢, Yasunaga MITSUYA, 浩嗣 古賀, and Hiroshi KOGA. "分布圧覚ディスプレイ装置による仮想形状呈示." 日本機械学会, 2002. http://hdl.handle.net/2237/9060.

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Andries, Mihai. "Localisation et suivi d'humains et d'objets, et contrôle de robots au travers d'un sol sensible." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0293.

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Cette thèse explore les capacités d’une intelligence ambiante équipée d’un réseau de capteurs de pression au sol. Elle traite le problème de la perception d’un environnement au travers un réseau de capteurs de basse résolution. Les difficultés incluent l’interpretation des poids dispersés pour des objets avec multiples supports, l’ambiguïté de poids entre des objets, la variation du poids des personnes pendant les activités dynamiques, etc. Nous introduisons des nouvelles techniques, partiellement inspirées du domaine de la vision par l’ordinateur, pour la détection, le suivi et la reconnaissance des entités qui se trouvent sur le sol. Nous introduisons également des nouveaux modes d’interaction entre les environnements équipés de tels capteurs aux sols, et les robots qui évoluent dans ces environnements. Ceci permet l’interprétation non-intrusive des événements qui ont lieu dans des environnements dotés d’une intelligence ambiante, avec des applications dans l’assistance automatisée à domicile, l’aide aux personnes âgées, le diagnostic continu de la santé, la sécurité, et la navigation robotique
This thesis explores the capabilities of an ambient intelligence equipped with a load-sensing floor. It deals with the problem of perceiving the environment through a network of low-resolution sensors. Challenges include the interpretation of spread loads for objects with multiple points of support, weight ambiguities between objects, variation of persons’ weight during dynamic activities, etc. We introduce new techniques, partly inspired from the field of computer vision, for detecting, tracking and recognizing the entities located on the floor. We also introduce new modes of interaction between environments equipped with such floor sensors and robots evolving inside them. This enables non-intrusive interpretation of events happening inside environments with embedded ambient intelligence, with applications in assisted living, senile care, continuous health diagnosis, home security, and robotic navigation
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Sundman, Dennis. "Greedy Algorithms for Distributed Compressed Sensing." Doctoral thesis, KTH, Kommunikationsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-144907.

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Compressed sensing (CS) is a recently invented sub-sampling technique that utilizes sparsity in full signals. Most natural signals possess this sparsity property. From a sub-sampled vector, some CS reconstruction algorithm is used to recover the full signal. One class of reconstruction algorithms is formed by the greedy pursuit, or simply greedy, algorithms, which is popular due to low complexity and good performance. Meanwhile, in sensor networks, sensor nodes monitor natural data for estimation or detection. One application of sensor networking is in cognitive radio networks, where sensor nodes want to estimate a power spectral density. The data measured by different sensors in such networks are typically correlated. Another type are multiple processor networks of computational nodes that cooperate to solve problems too difficult for the nodes to solve individually. In this thesis, we mainly consider greedy algorithms for distributed CS. To this end, we begin with a review of current knowledge in the field. Here, we also introduce signal models to model correlation and network models for simulation of network. We proceed by considering two applications; power spectrum density estimation and distributed reconstruction algorithms for multiple processor networks. Then, we delve deeper into the greedy algorithms with the objective to improve reconstruction performance; this naturally comes at the expense of increased computational complexity. The main objective of the thesis is to design greedy algorithms for distributed CS that exploit data correlation in sensor networks to improve performance. We develop several such algorithms, where a key element is to use intuitive democratic voting principles. Finally, we show the merit of such voting principles by probabilistic analysis based on a new input/output system model of greedy algorithms in CS. By comparing the new single sensor algorithms to well known greedy pursuit algorithms already present in the literature, we see that the goal of improved performance is achieved. We compare complexity using big-O analysis where the increased complexity is characterized. Using simulations we verify the performance and confirm complexity claims. The complexity of distributed algorithms is typically harder to analyze since it depends on the specific problem and network topology. However, when analysis is not possible, we provide extensive simulation results. No distributed algorithms based on the signal-models used in this thesis were so far available in the literature. Therefore, we compare our algorithms to standard single-sensor algorithms, and our results can then easily be used as benchmarks for future research. Compared to the stand-alone case, the new distributed algorithms provide significant performance gains. Throughout the thesis, we strive to present the work in a smooth flow of algorithm design, simulation results and analysis.
Compressed sensing (CS) är en nyutvecklad teknik som utnyttjar gleshet i stora undersamplade signaler. Många intressanta signaler besitter dessa glesa egenskaper. Utifrån en undersamplad vektor återskapar CS-algoritmer hela den sökta signalen. En klass av rekonstruktionsalgoritmer är de så kallade giriga algoritmerna, som blivit populära tack vare låg komplexitet och god prestanda. CS kan användas i vissa typer av nätverk för att detektera eller estimera stora signaler. En typ av nätverk där detta kan göras är i sensornätverk för kognitiv radio, där man använder sensorer för att estimera effektspektrum. Datan som samplas av de olika sensorerna i sådana nätverk är typiskt korrelerad. En annan typ av nätverk är multiprocessornätverk bestående av distribuerade beräkningsnoder, där noderna genom samarbete kan lösa svårare problem än de kan göra ensamma. Avhandlingen kommer främst att behandla giriga algoritmer för distribuerade CS-problem. Vi börjar med en överblick av nuvarande kunskap inom området. Här introducerar vi signalmodeller för korrelation och nätverksmodeller som används för simulering i nätverk. Vi fortsätter med att studera två tillämpningar; estimering av effektspektrum och en distribuerad återskapningsalgoritm för multiprocessornätverk. Därefter tar vi ett djupare steg i studien av giriga algoritmer, där vi utvecklar nya algoritmer med förbättrad prestanda, detta till priset av ökad beräkningskomplexitet. Huvudmålet med avhandlingen är giriga algoritmer för distribuerad CS, där algoritmerna utnyttjar datakorrelationen i sensornätverk. Vi utvecklar flera sådana algoritmer, där en huvudingrediens är att använda demokratiska röstningsalgoritmer. Vi analyserar sedan denna typ av röstningsalgoritmer genom att introducera en ingång/utgångs modell. Analysen visar att algoritmerna ger bra resultat. Genom att jämföra algoritmer för enskilda sensorer med redan befintliga algoritmer i litteraturen ser vi att målet med ökad prestanda uppnås. Vi karaktäriserar också komplexiteten. Genom simulationer verifierar vi både prestandan och komplexiteten. Att analysera komplexitet hos distribuerade algoritmer är generellt svårare eftersom den beror på specifik signalrealisation, nätverkstopologi och andra parametrar. I de fall där vi inte kan göra analys presenterar vi istället genomgående simuleringsresultat. Vi jämför våra algoritmer med de vanligaste algoritmerna för enskilda sensorsystem, och våra resultat kan därför enkelt användas som referens för framtida forskning. Jämfört med prestandan för enskilda sensorer visar de nya distribuerade algoritmerna markant förbättring.
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Kelly, Devin WW. "A Practical Distributed Spectrum Sensing System." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/378.

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As the demand for wireless communication systems grows, the need for spectrum grows accordingly. However, a large portion of the usable spectrum has already been exclusively licensed to various entities. This exclusive allocation method encourages spectrum to be left unused if the licensee has no need for that spectrum. In order to better utilize spectrum and formulate new approaches for greater spectrum use efficiency, it is imperative to possess a thorough understanding about how wireless spectrum behaves over time, frequency, and space. In this thesis, a practical, scalable, and low-cost wideband distributed spectrum sensing system is designed, implemented, and tested. The proposed system is made up of a collection of nodes that use general purpose, off-the-shelf computer hardware as well as a collection of inexpensive software-defined radio (SDR) equipment in order to collect and analyze spectrum data that varies across time, frequency, and space. The spectrum data the proposed system collects is the power present at a given frequency. The tools needed to analyze the gathered data are also created, including a periodogram and spectrogram function, which visualize average spectrum use over a period of time and as spectrum use varies with time, respectively. The proposed system also facilitates the testing of a spatio-spectrum characterization method using real data. This method has only been simulated up to this point. The characterization technique allows for spatially varying spectrum measurements to be visualized using heat maps.
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Feced, Ricardo. "Nonlinear techniques for distributed optical fibre sensing." Thesis, King's College London (University of London), 1998. https://kclpure.kcl.ac.uk/portal/en/theses/nonlinear-techniques-for-distributed-optical-fibre-sensing(48661ada-da47-4da7-b6db-fc995f840603).html.

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Dhliwayo, Jabulani. "Stimulated Brillouin scattering for distributed temperature sensing." Thesis, University of Kent, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242858.

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Frazier, Janay Amber Wright. "High-Definition Raman-based Distributed Temperature Sensing." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/95934.

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Distributed Temperature Sensing (DTS) has been used in a variety of different applications. Its ability to detect temperature fluctuations along fiber optic lines that stretch for several kilometers has made it a popular topic in various fields of science, engineering, and technology. From pre-fire detection to ecological monitoring, DTS has taken a vital role in scientific research. DTS uses the principle of backscattering by three different spectral components, e.g., Rayleigh scattering, Brillouin scattering, and Raman scattering. Although there have been various improvements to DTS, its slow response time and poor spatial resolution have been hard to overcome. Its repetition rate is low because the pulse must travel the distance of the fiber optic line and return to the detector to record the temperature change along the fiber. A spatial resolution of 7.4 cm with a response time as low as 1 second and a temperature resolution of the 0.196 ℃ is achieved from the current Raman-based DTS system. This research proves that high-spatial resolution can be obtained with the use of a Silicon Avalanche Photodetector with a 1 GHz bandwidth.
MS
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Reyda, Caitlin J. (Caitlin Jilaine). "Design of a pressure sensing laparoscopic grasper." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68854.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 34).
With smaller incisions, laparoscopic, or minimally invasive, surgery is considered safer for patients than open surgery. However, the safety of current laparoscopic grasping instruments can still be improved. Current devices provide surgeons with limited tactile feedback, and the current alligator-style jaws create pinch points that can lead to torn or damaged tissue. Additionally, the angled jaws can result in excessive grasping forces, due to the uneven pressure distribution along the jaws, or slippage when grasping larger organs. Tissue trauma, in the form of mechanical injury (crushing), ischemia (cut off blood supply), or perforation, can occur. A new design uses a symmetric, 10-bar linkage to keep the grasping jaws parallel, creating a uniform pressure distribution along the length of the jaws. A pressure sensor, located near the trigger in the handle, can detect when the grasper jaws are applying too much force on an object. When the force is above a given threshold, a vibration motor in the handle activates, warning the surgeon. This improved tactile feedback can help surgeons control pressures applied during grasping. The grasper design is further enhanced through an ergonomic pistol-grip handle, which also includes a turning wheel to rotate the grasper and a locking mechanism to fix the jaws in place. A working lx scale prototype was built to verify the feasibility of the design.
by Caitlin J. Reyda.
S.B.
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Books on the topic "Distributed sensing pressure sensing"

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Jonathan, E. Optical pressure sensing. Manchester: UMIST, 1995.

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Coluccia, Giulio, Chiara Ravazzi, and Enrico Magli. Compressed Sensing for Distributed Systems. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-390-3.

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Mazzeo, Pier Luigi, Paolo Spagnolo, and Thomas B. Moeslund, eds. Activity Monitoring by Multiple Distributed Sensing. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13323-2.

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Sniatala, Pawel, M. Hadi Amini, and Kianoosh G. Boroojeni. Fundamentals of Brooks–Iyengar Distributed Sensing Algorithm. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33132-0.

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Valis, Tomas. Distributed fiber optic sensing based on counterpropagating waves. [S.l.]: [s.n.], 1989.

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Tzou, H. S. Piezoelectric Shells: Distributed Sensing and Control of Continua. Dordrecht: Springer Netherlands, 1993.

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Tzou, H. S. Piezoelectric shells: Distributed sensing and control of continua. Dordrecht: Kluwer Academic, 1993.

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National Research Council (U.S.). Committee on Distributed Remote Sensing for Naval Undersea Warfare. Distributed remote sensing for naval undersea warfare: Abbreviated version. Washington: National Academies Press, 2007.

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D'Errico, Marco. Distributed Space Missions for Earth System Monitoring. New York, NY: Springer New York, 2013.

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O'Brien, D. M. Zones of feasibility for retrieval of surface pressure from observations of absorption in the A band of oxygen. Australia: CSIRO, 1989.

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Book chapters on the topic "Distributed sensing pressure sensing"

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Streit, Roy L. "Distributed Sensing." In Poisson Point Processes, 179–200. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6923-1_7.

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Loughlin, C. "Pressure Sensing." In Sensors for Industrial Inspection, 191–96. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2730-1_9.

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Fan, Xinyu. "Distributed Rayleigh Sensing." In Handbook of Optical Fibers, 1559–607. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-7087-7_5.

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Coluccia, Giulio, Chiara Ravazzi, and Enrico Magli. "Distributed Compressed Sensing." In SpringerBriefs in Electrical and Computer Engineering, 5–16. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-390-3_2.

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Fan, Xinyu. "Distributed Rayleigh Sensing." In Handbook of Optical Fibers, 1–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-1477-2_5-1.

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Soto, Marcelo A., and Fabrizio Di Pasquale. "Distributed Raman Sensing." In Handbook of Optical Fibers, 1–55. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1477-2_6-1.

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Soto, Marcelo A., and Fabrizio Di Pasquale. "Distributed Raman Sensing." In Handbook of Optical Fibers, 1609–62. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-7087-7_6.

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Chen, Yanping, Yulong Gao, and Yongkui Ma. "Distributed Compressive Sensing Based Spectrum Sensing Method." In Machine Learning and Intelligent Communications, 239–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73564-1_24.

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Lynch, J. P., K. J. Loh, T. C. Hou, and N. Kotov. "Nanocomposite Sensing Skins for Distributed Structural Sensing." In Nanotechnology in Construction 3, 303–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00980-8_40.

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Tolk, Andreas. "Modeling Sensing." In Engineering Principles of Combat Modeling and Distributed Simulation, 127–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118180310.ch8.

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Conference papers on the topic "Distributed sensing pressure sensing"

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Assiri, Wisam, Ilkay Uzun, and Erdal Ozkan. "Distributed Pressure Sensing for Production Data Analysis." In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/194799-ms.

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Lecoy, Pierre, Abdelrafik Malki, H. Dammak, Mohamed Ketata, Olivier Latry, and R. Miry. "New pressure optical sensor for distributed sensing." In OE Fiber 91, edited by Alan D. Kersey and John P. Dakin. SPIE, 1992. http://dx.doi.org/10.1117/12.56511.

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Sah, Sripati, and Robert X. Gao. "An Embedded Pressure Sensing Approach for Stamping Process Monitoring." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42190.

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This paper investigates the interactions between sheet metal stamping tool and workpiece through finite element simulation and experiments using an array of force sensors embedded into a customized stamping test fixture. The study is aimed at developing a more effective means for stamping process monitoring as compared to the conventionally used tonnage sensing technique. An analytical framework for processing data acquired from the sensor array is established, and the spatial significance of the sensor positions has been incorporated into the analysis. The utility of two numeric surface generation schemes has been investigated to interpolate the pressure distribution at any point across the tooling surface, based on measurements from limited, spatially distributed sensing points. The suitability of the surface schemes has been evaluated using finite element models established for the customized test fixture. The feasibility of embedded pressure sensing for defect detection in stamping operations has been demonstrated.
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Cellucci, Daniel, Nicholas Cramer, and Sean S. M. Swei. "Distributed Pressure Sensing for Enabling Self-Aware Autonomous Aerial Vehicles." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593664.

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Dang, Fengying, and Feitian Zhang. "DMD-Based Distributed Flow Sensing for Bio-Inspired Autonomous Underwater Robots." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9113.

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This paper presents a novel flow sensing method for autonomous underwater robots using distributed pressure measurements. The proposed flow sensing method harnesses a Bayesian filter and a dynamic mode decomposition (DMD)-based reduced-order flow model to estimate the dynamic flow environments. This data-driven estimation method does not rely on any analytical flow models and is applicable to many and various dynamic flow fields for arbitrarily shaped underwater robots. To demonstrate the effectiveness of the proposed distributed flow sensing approach, a simulation study with a Joukowski-foil-shaped underwater robot is presented.
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Laurence, Roger J., Brian Argrow, and Eric W. Frew. "Development of Wind Sensing from Small UAS with Distributed Pressure Sensors." In 8th AIAA Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4199.

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Zhang, Zeheng, Ting Feng, Xichen Wang, Yanling Shang, Mingming Wang, and X. Steve Yao. "Demonstration of Liquid Pressure Fiber Sensing Based on Distributed Polarization Crosstalk Analysis." In 2018 Asia Communications and Photonics Conference (ACP). IEEE, 2018. http://dx.doi.org/10.1109/acp.2018.8595992.

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Schenato, Luca, Alessandro Pasuto, Andrea Galtarossa, and Luca Palmieri. "An optical fibre cable for distributed pressure sensing: a proof of concept." In Seventh European Workshop on Optical Fibre Sensors (EWOFS 2019), edited by Kyriacos Kalli, Gilberto Brambilla, and Sinead O. O'Keeffe. SPIE, 2019. http://dx.doi.org/10.1117/12.2539832.

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Kahn, Jeff C., Brooke E. Flammang, and James L. Tangorra. "Hover kinematics and distributed pressure sensing for force control of biorobotic fins." In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012). IEEE, 2012. http://dx.doi.org/10.1109/iros.2012.6386066.

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Zhang, Li, Zhisheng Yang, Łukasz Szostkiewicz, Krzysztof Markiewicz, Tomasz Nasilowski, and Luc Thévenaz. "Fully distributed pressure sensing with ultra-high-sensitivity using side-hole fibers." In Optical Fiber Sensors. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/ofs.2018.wf13.

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Reports on the topic "Distributed sensing pressure sensing"

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Baron, Dror, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, and Richard G. Baraniuk. Distributed Compressive Sensing. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada521228.

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Hurtado, John E. Distributed Sensing & Cooperative Control for Plume Tracing. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada410645.

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Moura, Jose M. Distributed Sensing and Processing: A Graphical Model Approach. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada455686.

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Walmsley, Ian. Scalable Quantum Networks for Distributed Computing and Sensing. Fort Belvoir, VA: Defense Technical Information Center, April 2016. http://dx.doi.org/10.21236/ad1007637.

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Juntao Wu. Distributed Fiber Optic Gas Sensing for Harsh Environment. Office of Scientific and Technical Information (OSTI), March 2008. http://dx.doi.org/10.2172/938805.

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Levchuk, Georgiy, Andres Ortiz, and John-Collonna Romano. Distributed Sensing and Processing Adaptive Collaboration Environment (D-SPACE). Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada608436.

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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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Shepard, R. L., and L. H. Thacker. Evaluation of pressure sensing concepts: A technology assessment. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10190000.

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Mecimore, Ivan, Chuck D. Creusere, and Bion John Merchant. Distributed video coding for arrays of remote sensing nodes : final report. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/992327.

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Freifeld, B., and S. Finsterle. Imaging Fluid Flow in Geothermal Wells Using Distributed Thermal Perturbation Sensing. Office of Scientific and Technical Information (OSTI), December 2010. http://dx.doi.org/10.2172/1016576.

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