Готові списки джерел за темами / Physical signals

Добірка наукової літератури з теми "Physical signals"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Physical signals"

1

Bakogiannis, Konstantinos, Spyros Polychronopoulos, Dimitra Marini, and Georgios Kouroupetroglou. "Audio Enhancement of Physical Models of Musical Instruments Using Optimal Correction Factors: The Recorder Case." Applied Sciences 11, no. 14 (July 12, 2021): 6426. http://dx.doi.org/10.3390/app11146426.

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A simulation of a musical instrument is considered to be a successful one when there is a good resemblance between the model’s synthesized sound and the real instrument’s sound. In this work, we propose the integration of physical modeling (PM) methods with an optimization process to regulate a generated digital signal. Its goal is to find a new set of values of the PM’s parameters’ that would lead to a synthesized signal matching as much as possible to reference signals corresponding to the physical musical instrument. The reference signals can be: (a) described by their acoustic characteristics (e.g., fundamental frequencies, inharmonicity, etc.) and/or (b) the signals themselves (e.g., impedances, recordings, etc.). We put this method into practice for a commercial recorder, simulated using the digital waveguides’ PM technique. The reference signals, in our case, are the recorded signals of the physical instrument. The degree of similarity between the synthesized (PM) and the recorded signal (musical instrument) is calculated by the signals’ linear cross-correlation. Our results show that the adoption of the optimization process resulted in more realistic synthesized signals by (a) enhancing the degree of similarity between the synthesized and the recorded signal (the average absolute Pearson Correlation Coefficient increased from 0.13 to 0.67), (b) resolving mistuning issues (the average absolute deviation of the synthesized from the recorded signals’ pitches reduced from 40 cents to the non-noticeable level of 2 cents) and (c) similar sound color characteristics and matched overtones (the average absolute deviation of the synthesized from the recorded signals’ first five partials reduced from 41 cents to 2 cents).
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2

Ren, Bao-Zhen, and Wei Qian. "Signal perception during plant-bacteria interactions: from chemicals to physical signals." Science China Life Sciences 63, no. 2 (December 20, 2019): 305–7. http://dx.doi.org/10.1007/s11427-019-1594-2.

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3

Arslan Tuncer, Seda, and Turgay Kaya. "True Random Number Generation from Bioelectrical and Physical Signals." Computational and Mathematical Methods in Medicine 2018 (July 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/3579275.

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It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.
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4

Cao, Xiao-Qin, Jia Zeng, and Hong Yan. "Physical signals for protein–DNA recognition." Physical Biology 6, no. 3 (June 5, 2009): 036012. http://dx.doi.org/10.1088/1478-3975/6/3/036012.

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5

MOON, MARY ANN. "Poor Physical Function Signals Dementia Onset." Caring for the Ages 7, no. 11 (November 2006): 11. http://dx.doi.org/10.1016/s1526-4114(06)60293-x.

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6

Freund, Friedemann. "Pre-earthquake signals: Underlying physical processes." Journal of Asian Earth Sciences 41, no. 4-5 (June 2011): 383–400. http://dx.doi.org/10.1016/j.jseaes.2010.03.009.

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7

Bocharov, Yu V., I. N. Gurova, O. A. Kapustina, E. I. Remizova, V. N. Reshetov, S. A. Grigoriev, M. V. Demianovich, and V. N. Novikov. "Liquid crystal sensors of physical signals." Sensors and Actuators A: Physical 28, no. 3 (August 1991): 179–83. http://dx.doi.org/10.1016/0924-4247(91)85004-8.

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8

Mohd Apandi, Ziti Fariha, Ryojun Ikeura, Soichiro Hayakawa, and Shigeyoshi Tsutsumi. "QRS Detection in Electrocardiogram Signal of Exercise Physical Activity." Journal of Physics: Conference Series 2319, no. 1 (August 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2319/1/012021.

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Abstract Accurate estimation of heart beats from electrocardiogram (ECG) signals during exercise activity is a very challenging problem. Unlike standard ECG, the signals recorded during exercise activity can affect the accuracy of QRS detection due to the noises and artifacts arise from body movements activity. However, most of the studies on QRS detection often used clean and standard data for the evaluations and assumed to reflect the overall performance of detectors. In addition, there are not many methods that evaluated using real ECG signal in their studies especially during the exercise activity. Therefore, this study was undertaken to access and evaluated the performance of QRS detection algorithms on the real ECG signal data. Three well-known QRS detection algorithms were re-implemented in this study. The ECG signal recorded under realistic movement conditions and can serving as a realistic data to assess the performance of QRS detection is used. The performance of the algorithm in real ECG signal data in sitting, walking, and jogging was then presented. The results show the algorithms capable to detect the QRS in ECG signal of exercise activity.
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9

Chang, Xiangmao, Gangkai Li, Guoliang Xing, Kun Zhu, and Linlin Tu. "DeepHeart." ACM Transactions on Sensor Networks 17, no. 2 (June 2021): 1–18. http://dx.doi.org/10.1145/3441626.

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Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user’s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).
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10

Kim, Taehun, and Chulhong Kwon. "Correlation between Physical Fatigue and Speech Signals." Phonetics and Speech Sciences 7, no. 1 (March 31, 2015): 11–17. http://dx.doi.org/10.13064/ksss.2015.7.1.011.

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Дисертації з теми "Physical signals"

1

Li, Xiang. "PHYSICAL LAYER WATERMARKING OF DIRECT SEQUENCE SPREAD SPECTRUM SIGNALS." Cleveland State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1368527408.

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2

Wong, Stephanie A. "Physical and Molecular Pathways Involved in Cellular Sensing of Mechanical Signals." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/878.

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Mechanical properties of the extracellular environment provide important cues that regulate cell behavior. Particularly, the cellular response to substrate rigidity has become an important parameter to consider in disease treatment as well as tissue engineering. The goal of this thesis is to understand how adherent cells sense and respond to external rigidity cues. It has been challenging to study the mechanism that drives the preferential migration of cells towards stiffer substrates at a rigidity border due to difficulties in capturing cells as they transiently encounter a rigidity interface. Using a model system developed for testing cellular responses at a simulated rigidity border, I find that NIH 3T3 cells preferentially localize to the rigid portion of the model system. Cells use filopodia extensions to probe substrate rigidity in front of the leading edge and use substrate strain to determine whether the filopodia protrusions retract or expand to occupy the area. Myosin II mediated contractility is necessary to generate forces for both probing the substrate and retraction in response to substrate strain. Focal adhesion kinase null (FAK -/-) cells, known to be defective in durotaxis, are able to readily cross the rigidity border, while reexpression of focal adhesion kinase (FAK) rescues rigidity sensing. The model experimental system allows efficient analyses of conditions affecting rigidity sensing of cells. The results suggest that enhanced Rho activity, likely through Rho downstream effector mDia1, may underlie many rigidity sensing defects including those caused by FAK deficiency and microtubule disassembly. Additionally, I show that probing mechanisms at the front of a cell are used not only for probing rigidity but for sensing the state of migration. Design of a new checkerboard micropattern with alternating adhesive and non-adhesive areas revealed that the appearance of new traction forces and focal adhesions at the leading edge promotes the downregulation of pre-existing traction forces and focal adhesions that lag behind. These results suggest that in migrating cells continuous protrusion and mechanical probing directly in front of existing adhesions modulates traction force build up and serves as a key mechanism for regulating mechanical output in response to physical cues.
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3

Kundu, Prantik. "Physical analysis of BOLD fMRI signals for functional brain mapping and connectomics." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648842.

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4

Wiggins, Bryan Blake. "Using Induced Signals to Develop a Position-Sensitive Microchannel Plate Detector." Thesis, Indiana University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10686059.

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A novel concept to provide position-sensitivity to a microchannel plate (MCP) is described. While several designs exist to make MCPs position sensitive, all these designs are based upon collection of the electrons. In contrast, this approach utilizes an induced signal as the electron cloud emanates from an MCP and passes a wire plane. We demonstrate the validity of the concept by constructing a device that provides single electron detection with 98 μm position resolution (FWHM) over an area of 50 mm × 50 mm. The characteristics of the detector are described through both bench-top tests and simulation. After characterization of the detector, the sense wire detector was utilized for slow-neutron radiography. Furthermore, we utilized our knowledge of position-sensitive techniques to realize a beam-imaging MCP detector useful for radioactive beam facilities.

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5

Gray, Martha Lane. "Physical regulation of epiphyseal cartilage biosynthesis : responses to electrical, mechanical, and chemical signals." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/17205.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Harvard-MIT Division of Health Sciences and Technology Program in Medical Engineering and Medical Physics, 1986.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE.
Bibliography: leaves 194-205.
by Martha Lane Gray.
Ph.D.
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6

Flodin, Mikael. "Determining upper limits on galactic ETI civilizations transmitting continuous beacon signals in the radio spectrum." Thesis, KTH, Fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266824.

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7

Succo, Kelli Fredrickson. "Acoustic Intensity of Narrowband Signals in Free-Field Environments." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/7092.

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The phase and amplitude gradient estimator (PAGE) method has proven successful in improving the accuracy of measured energy quantities over the p-p method, which has traditionally been used, in several applications. One advantage of the PAGE method is the use of phase unwrapping, which allows for increased measurement bandwidth above the spatial Nyquist frequency. However, phase unwrapping works best for broadband sources in free-field environments with high coherence. Narrowband sources often do not have coherent phase information over a sufficient bandwidth for a phase unwrapping algorithm to unwrap properly. In fact, phase unwrapping processing can cause significant error when there is no coherent signal near and above the spatial Nyquist frequency. However, for signals at any frequencies up to the spatial Nyquist frequency, the PAGE method provides correct intensity measurements regardless of the bandwidth of the signal. This is an improved bandwidth over the traditional method. For narrowband sources above the spatial Nyquist frequency, additional information is necessary for the PAGE method to provide accurate acoustic intensity. With sufficient bandwidth and a coherence of at least 0.1 at the spatial Nyquist frequency, a relatively narrowband source above the spatial Nyquist frequency can be unwrapped accurately. One way of using extra information, called the extrapolated PAGE method, uses the phase of a tone below the spatial Nyquist frequency and an assumption of a propagating field, and therefore linear phase, to extrapolate the phase above the spatial Nyquist frequency. Also, within certain angular and amplitude constraints, low-level broadband noise can be added to the field near a source emitting a narrowband signal above the spatial Nyquist frequency. The low-level additive broadband noise can then provide enough phase information for the phase to be correct at the frequencies of the narrowband signal. All of these methods have been shown to work in a free-field environment.
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8

Jackson, David. "Exploiting Rogue Signals to Attack Trust-based Cooperative Spectrum Sensing in Cognitive Radio Networks." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3072.

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Cognitive radios are currently presented as the solution to the ever-increasing spectrum shortage problem. However, their increased capabilities over traditional radios introduce a new dimension of security threats. Cooperative Spectrum Sensing (CSS) has been proposed as a means to protect cognitive radio networks from the well known security threats: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF). I demonstrate a new threat to trust-based CSS protocols, called the Rogue Signal Framing (RSF) intrusion. Rogue signals can be exploited to create the illusion of malicious sensors which leads to the framing of innocent sensors and consequently, their removal from the shared spectrum sensing. Ultimately, with fewer sensors working together, the spectrum sensing is less robust for making correct spectrum access decisions. The simulation experiments illustrate the impact of RSF intrusions which, in severe cases, shows roughly 40\% of sensors removed. To mitigate the RSF intrusion's damage to the network's trust, I introduce a new defense based on community detection from analyzing the network's Received Signal Strength (RSS) diversity. Tests show a 95\% damage reduction in terms of removed sensors from the shared spectrum sensing, thus retaining the benefits of CSS protocols.
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9

Phua, Gailene. "Estimation of geometric properties of three-component signals for condition monitoring." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT004.

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La plupart des méthodes de surveillance des systèmes sont basées sur l'analyse et la caractérisation de grandeurs physiques qui sont par nature tridimensionnelles. Tracées dans un repère euclidien à trois dimensions, ces grandeurs parcourent en fonction du temps une trajectoire dont les caractéristiques géométriques sont représentatives de l'état du système surveillé. Les techniques classiques de surveillance des systèmes étudient les grandeurs mesurées composante par composante, sans prendre en compte leur nature tridimensionnelle et les propriétés géométriques de leur trajectoire. Une part importante de l'information est ainsi ignorée. Dans le cadre de ce travail de recherche, on se propose de développer une méthode d'analyse et de traitement de grandeurs à trois composantes permettant de mettre en évidence les spécificités géométriques des données et de fournir une information complémentaire pour la surveillance des systèmes. La méthode proposée a été appliquée à deux cas différents : la surveillance des creux de tension des réseaux de puissance triphasés et la surveillance des défauts de roulement des machines électriques tournantes. Dans ces deux cas, les résultats obtenus sont prometteurs et montrent que les indicateurs géométriques estimés mènent à de l'information complémentaire qui peut être utile pour la surveillance des systèmes
Most methods for condition monitoring are based on the analysis and characterization of physical quantities that are three-dimensional in nature. Plotted in a three-dimensional Euclidean space as a function of time, these quantities follow a trajectory whose geometric characteristics are representative of the state of the monitored system. Usual techniques of condition monitoring study the measured quantities component by component, without taking into account their three-dimensional nature and the geometric properties of their trajectory. A significant part of the information is thus ignored. In this research work, we would therefore like to develop a method for the analysis and processing of three-component quantities capable of highlighting the special geometric features of such data and providing complementary information for condition monitoring. The proposed method has been applied to two different cases: voltage dips monitoring in three-phase power networks and bearing faults monitoring in rotating electrical machines. In this two cases, the results obtained are promising and show that the estimated geometric indicators lead to complementary information that can be useful for condition monitoring
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10

Viljoen, Suretha. "Analysis of crosstalk signals in a cylindrical layered volume conductor influence of the anatomy, detection system and physical properties of the tissues /." Diss., Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-08082005-113739.

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Книги з теми "Physical signals"

1

Theory and evaluation of single-molecule signals. Hackensack, NJ: World Scientific, 2008.

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2

Philippe, Müllhaupt, and SpringerLink (Online service), eds. Advances in the Theory of Control, Signals and Systems with Physical Modeling. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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3

Lévine, Jean, and Philippe Müllhaupt, eds. Advances in the Theory of Control, Signals and Systems with Physical Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16135-3.

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4

Getchell, Bud. Physical fitness: A way of life. 5th ed. Boston: Allyn and Bacon, 1998.

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5

Getchell, Bud. Physical fitness: A way of life. 4th ed. New York: Macmillan, 1992.

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6

Dawar, Niraj. Cultural universals in marketing: A study of consumers' use of brand name, price, physical appearance, and retailer reputation as signals of product quality. Fontainebleau: INSEAD, 1992.

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7

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. 2nd ed. Boston, MA: Springer US, 2013.

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8

Getchell, Bud. Acondicionamiento físico: Cómo mantenerse en forma. México, D.F: UTEHA, 1998.

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9

Sharapov, I︠U︡ I. Preobrazovanie signala bez kombinat︠s︡ionnykh chastot. Moskva: Zhurnal Radiotekhnika, 2001.

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10

Fiddy, M. A. Optical Signal Processing. Boston, MA: Springer US, 1992.

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Частини книг з теми "Physical signals"

1

Perez, André. "Downlink Physical Signals." In LTE and LTE Advanced, 149–75. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119145462.ch6.

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2

Perez, André. "Uplink Physical Signals." In LTE and LTE Advanced, 241–55. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119145462.ch8.

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3

Fischer, Walter. "Physical AV Interface Signals." In Digital Video and Audio Broadcasting Technology, 207–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32185-7_10.

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4

Engelbrecht, Jüri, Kert Tamm, and Tanel Peets. "Physical Mechanisms." In Modelling of Complex Signals in Nerves, 101–10. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75039-8_7.

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5

Madvaliew, U., V. V. Proklov, and A. M. Ashurov. "Investigations of Photoacoustic Signals in Powders." In Physical Acoustics, 485–88. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-9573-1_63.

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6

Michelsen, Axel. "Physical Aspects of Vibrational Communication." In Animal Signals and Communication, 199–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43607-3_11.

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7

de Miranda, Adriana Ramos. "Water and High Dilutions Phenomenology: Physical Characterization." In Signals and Images, 49–63. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8535-2_4.

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8

Holandino, Carla, Felipe Dias Leal, Bianca de Oliveira Barcellos, Maria Augusta Campos, Raíza Oliveira, Venício Feo da Veiga, Sheila Garcia, and Carlos Renato Zacharias. "Mechanical Versus Handmade Succussions: A Physical Chemistry Comparison." In Signals and Images, 37–48. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8535-2_3.

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9

Lauriks, W., C. Glorieux, and J. Thoen. "Depth Profiling by Fourier Analysis of Photoacoustic Signals." In Physical Acoustics, 433–39. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-9573-1_55.

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10

Bowler, Chris, Gunther Neuhaus, and Nam-Hai Chua. "Transduction of Light Signals in Plants." In Physical Stresses in Plants, 201–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61175-9_20.

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Тези доповідей конференцій з теми "Physical signals"

1

Abarbanel, H. D. I. "Chaotic signals and physical systems." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226473.

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2

Hulme, Ashley M. B. "Physical signals, events and digital streams." In 2009 IEEE AUTOTESTCON. IEEE, 2009. http://dx.doi.org/10.1109/autest.2009.5314010.

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3

Su, Jiayi, Yuqin Weng, Susan C. Schneider, and Edwin E. Yaz. "Sensor and Actuator Intrusion Detection for Cyber-Physical Systems via Adaptive Estimation Algorithm." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3245.

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Abstract In this work, a new approach to detect sensor and actuator intrusion for Cyber-Physical Systems using a bank of Kalman filters is presented. The case where the unknown type of the intrusion signal is considered first, using two Kalman filters in a bank to provide the conditional state estimates, then the unknown type of intrusion signal can be detected properly via the adaptive estimation algorithm. The case where the target (either sensor or actuator) of the intrusion signal is unknown is also considered, using four Kalman filters in a bank designed to detect if the intrusion signal is about to affect healthy sensor or actuator signal. To test these methods, a DC motor speed control system subject to attack by different types of sensor and actuator signals is simulated. Simulations show that different types of sensor and actuator intrusion signals can be detected properly without the knowledge of the nature and the type of these signals.
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4

Sokun, Hakan, Habil Kalkan, and Bayram Cetisli. "Classification of physical activities using accelerometer signals." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204506.

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5

Ouyang, Jianfei, Yonggang Yan, and Lifeng Yao. "Vital physical signals measurements using a webcam." In Photonics North 2013, edited by Pavel Cheben, Jens Schmid, Caroline Boudoux, Lawrence R. Chen, André Delâge, Siegfried Janz, Raman Kashyap, David J. Lockwood, Hans-Peter Loock, and Zetian Mi. SPIE, 2013. http://dx.doi.org/10.1117/12.2033273.

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6

Vetter, Jens. "Tangible Signals - Prototyping Interactive Physical Sound Displays." In TEI '21: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3430524.3442450.

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7

Weiss, Marc, Sundeep Chandhoke, and Hugh Melvin. "Time signals converging within cyber-physical systems." In 2015 Joint Conference of the IEEE International Frequency Control Symposium & the European Frequency and Time Forum (FCS). IEEE, 2015. http://dx.doi.org/10.1109/fcs.2015.7138935.

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8

Calore, Francesca. "Dark matter signals from the gamma-ray sky." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2016. http://dx.doi.org/10.22323/1.234.0416.

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Filip, Peter. "Searching for Exotic Signals with the NOvA Experiment." In The European Physical Society Conference on High Energy Physics. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.398.0648.

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10

Glossiotis, George N., and Ioannis A. Antoniadis. "Cyclostationary Analysis of Rolling Element Bearing Vibration Signals." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21759.

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Abstract Vibration signals resulting from rolling element bearings present a rich content of physical information, the proper analysis of which can lead among others to the identification of possible faults. Traditionally, this analysis is performed by the use of signal processing methods, which assume statistically stationary signal features. The paper proposes an alternative framework for analyzing bearing vibration signals, based on cyclostationary analysis. This framework, being able to model additionally signals with periodically varying statistics, is better able to exhibit the underlying physical concepts of the modulation mechanism, present in the vibration response of bearings. The basic concepts of the approach are demonstrated both in illustrative simulation results, as well as in experimental results and industrial measurements for two different types of bearing faults.
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Звіти організацій з теми "Physical signals"

1

Miksis-Olds, Jennifer L., and Jeffrey A. Nystuen. Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada531167.

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2

Miksis-Olds, Jennifier L. Cumulative and Synergistic Effects of Physical, biological, and Acoustic Signals on Marine Mammal Habitat Use. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada576394.

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3

Nystuen, Jeffrey A., and Jennifer L. Miksis-Olds. Cumulative and Synergistic Effects of Physical, Biological and Acoustic Signals on Marine Mammal Habitat Use. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada598906.

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4

Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoelastic variables. This yields ensemble realizations of signal arrivals at specified locations where statistical properties of the signals can be estimated. Such ensemble predictions can be useful for preliminary site characterization, for military applications, and risk analysis for remote or inaccessible locations for which no data can be acquired. Comparison with experiments revealed that measured signals are not always within the predicted ranges of variability. Variance-based global sensitivity analysis has shown that the most significant parameters for signal amplitude predictions in the developed stochastic model are the uncertainty in the shear quality factor and the Poisson ratio above the water table depth.
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5

Miksis-Olds, Jennifer L., and Jeffrey A. Nystuen. Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use (PSU). Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada505100.

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6

Nystuen, Jeffrey A. Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use Physical Oceanography Component: Soundscapes Under Sea Ice: Can We Listen for Open Water? Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598970.

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7

Nafakh, Abdullah Jalal, Yunchang Zhang, Sarah Hubbard, and Jon D. Fricker. Assessment of a Displaced Pedestrian Crossing for Multilane Arterials. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317318.

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This research explores the benefits of a pedestrian crosswalk that is physically displaced from the intersection, using simulation software to estimate the benefits in terms of delay and pedestrian travel time. In many cases, the displaced pedestrian crossing may provide benefits such as reduced vehicle delay, reduced crossing distance, increased opportunity for signal progression, and reduced conflicts with turning vehicles. The concurrent pedestrian service that is traditionally used presents potential conflicts between pedestrians and three vehicular movements: right turns, permissive left turns, and right turns on red. The findings of this research suggest that a displaced pedestrian crossing should be considered as an option by designers when serving pedestrians crossing multi-lane arterials. In addition to reduced delay, pedestrian safety may be improved due to the shorter crossing distance, the elimination of conflicts with turning vehicles, and the potential for high driver compliance rates associated with signals, such as pedestrian hybrid beacons.
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8

Silva, J. P. Qualitative signals of new physics in B{anti B} mixing. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/753299.

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9

Casazza, Peter G. Frames in Compressive Sensing and Approximate Signal Recovery Pertaining to Physical Sensing Matrices. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada613503.

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10

Wilson, D., Daniel Breton, Lauren Waldrop, Danney Glaser, Ross Alter, Carl Hart, Wesley Barnes, et al. Signal propagation modeling in complex, three-dimensional environments. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40321.

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The Signal Physics Representation in Uncertain and Complex Environments (SPRUCE) work unit, part of the U.S. Army Engineer Research and Development Center (ERDC) Army Terrestrial-Environmental Modeling and Intelligence System (ARTEMIS) work package, focused on the creation of a suite of three-dimensional (3D) signal and sensor performance modeling capabilities that realistically capture propagation physics in urban, mountainous, forested, and other complex terrain environments. This report describes many of the developed technical capabilities. Particular highlights are (1) creation of a Java environmental data abstraction layer for 3D representation of the atmosphere and inhomogeneous terrain that ingests data from many common weather forecast models and terrain data formats, (2) extensions to the Environmental Awareness for Sensor and Emitter Employment (EASEE) software to enable 3D signal propagation modeling, (3) modeling of transmitter and receiver directivity functions in 3D including rotations of the transmitter and receiver platforms, (4) an Extensible Markup Language/JavaScript Object Notation (XML/JSON) interface to facilitate deployment of web services, (5) signal feature definitions and other support for infrasound modeling and for radio-frequency (RF) modeling in the very high frequency (VHF), ultra-high frequency (UHF), and super-high frequency (SHF) frequency ranges, and (6) probabilistic calculations for line-of-sight in complex terrain and vegetation.
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