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Статті в журналах з теми "GAUSSIAN GAIN"
Iparraguirre, I., and T. del Río Gaztelurrutia. "Analytic solutions for Gaussian gain profile resonators." Optics Communications 255, no. 4-6 (November 2005): 241–47. http://dx.doi.org/10.1016/j.optcom.2005.06.020.
Повний текст джерелаHartman, Eric, and James D. Keeler. "Predicting the Future: Advantages of Semilocal Units." Neural Computation 3, no. 4 (December 1991): 566–78. http://dx.doi.org/10.1162/neco.1991.3.4.566.
Повний текст джерелаHolevo, A. S. "Gaussian classical-quantum channels: Gain from entanglement-assistance." Problems of Information Transmission 50, no. 1 (January 2014): 1–14. http://dx.doi.org/10.1134/s0032946014010013.
Повний текст джерелаPyatakhin, M. V. "Gaussian beams in media with transverse gain inhomogeneity." Journal of Russian Laser Research 18, no. 5 (September 1997): 445–63. http://dx.doi.org/10.1007/bf02559669.
Повний текст джерелаJirauschek, Christian, and Franz X. Kärtner. "Gaussian pulse dynamics in gain media with Kerr nonlinearity." Journal of the Optical Society of America B 23, no. 9 (September 1, 2006): 1776. http://dx.doi.org/10.1364/josab.23.001776.
Повний текст джерелаIbison, M. C., and D. C. Hanna. "Analysis of Raman gain for focussed Gaussian pump beams." Applied Physics B Photophysics and Laser Chemistry 45, no. 1 (January 1988): 37–44. http://dx.doi.org/10.1007/bf00692339.
Повний текст джерелаChang, R. J. "Optimal Linear Feedback Control for a Class of Nonlinear Nonquadratic Non-Gaussian Problems." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (December 1, 1991): 568–74. http://dx.doi.org/10.1115/1.2896459.
Повний текст джерелаMolnár, Etele, and Dan Stutman. "Direct Laser-Driven Electron Acceleration and Energy Gain in Helical Beams." Laser and Particle Beams 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/6645668.
Повний текст джерелаMaes, Carl F., and Ewan M. Wright. "Mode properties of an external-cavity laser with Gaussian gain." Optics Letters 29, no. 3 (February 1, 2004): 229. http://dx.doi.org/10.1364/ol.29.000229.
Повний текст джерелаFilippov, Sergey, and Alena Termanova. "Superior Resilience of Non-Gaussian Entanglement against Local Gaussian Noises." Entropy 25, no. 1 (December 30, 2022): 75. http://dx.doi.org/10.3390/e25010075.
Повний текст джерелаДисертації з теми "GAUSSIAN GAIN"
Maes, Carl F. "Transverse mode properties of lasers with Gaussian gain." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/289894.
Повний текст джерелаOrndorff, Josh. "Amplified Total Internal Reflection at the Surface of Gain Medium." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1365156891.
Повний текст джерелаSun, Yifan. "Theory of mode-locked lasers based on non-conventional cavity modes." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASP003.
Повний текст джерелаThis PhD thesis mainly addresses the dynamics and the robustness of a novel concept of mode locking in ultracompact semiconductor nanolasers. Such a nanolaser exhibits Hermite-Gaussian modes created by a harmonic photonic cavity to confine light. This maps the optical cavity into quantum mechanical harmonic oscillator, with evenly spaced eigenfrequencies, an essential requirement for mode locking. The possible nonlinear regimes are described by the Gross-Pitaevskii equation with a parabolic potential and nonlinear terms describing gain and absorption. To investigate these dynamical behaviors, direct numerical simulations are mainly implemented. Continuation calculations are also performed using pde2path.First, the mode competition for gain among Hermite-Gaussian modes in the absence of saturable absorption is investigated and shown to be very different from usual resonators.Second, mode locking is predicted to occur with instantaneous saturation of gain and absorption over a broad range of parameters, corresponding to the emergence of dissipative soliton and multisoliton solutions. The mode locking period is controlled by the design of the photonic potential, and not by the cavity length. The dissipative soliton is well described by the coherent state of a quantum mechanical oscillator, namely a Gaussian envelope oscillating without deformation.Third, in the regime of noninstantaneous gain and absorption saturation, different dynamical behaviors of the nanolaser are obtained by varying the gain and the absorption. These different regimes, including Q-switching, Q-switched mode locking, and CW mode locking, are described in detail, illustrating the rich physics of this nonlinear system. The influence of the Henry factor on the mode locking is also discussed. Moreover, similar dynamical behaviors using spatially separated gain and absorber sections inside the cavity are obtained.Fourth, the robustness of mode locking of the Hermite-Gaussian modes to the disorder of the harmonic cavity is investigated in details. It includes the effect of non-parabolicity of the potential and the random errors in the shape of the potential
Jaskowak, Daniel Joseph. "Detecting Transient Changes in Gait Using Fractal Scaling of Gait Variability in Conjunction with Gaussian Continuous Wavelet Transform." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87393.
Повний текст джерелаMaster of Science
Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.
Saad, Ali. "Detection of Freezing of Gait in Parkinson's disease." Thesis, Le Havre, 2016. http://www.theses.fr/2016LEHA0029/document.
Повний текст джерелаFreezing of Gait (FoG) is an episodic phenomenon that is a common symptom of Parkinson's disease (PD). This research is headed toward implementing a detection, diagnosis and correction system that prevents FoG episodes using a multi-sensor device. This particular study aims to detect/diagnose FoG using different machine learning approaches. In this study we validate the choice of integrating multiple sensors to detect FoG with better performance. Our first level of contribution is introducing new types of sensors for the detection of FoG (telemeter and goniometer). An advantage in our work is that due to the inconsistency of FoG events, the extracted features from all sensors are combined using the Principal Component Analysis technique. The second level of contribution is implementing a new detection algorithm in the field of FoG detection, which is the Gaussian Neural Network algorithm. The third level of contribution is developing a probabilistic modeling approach based on Bayesian Belief Networks that is able to diagnosis the behavioral walking change of patients before, during and after a freezing event. Our final level of contribution is utilizing tree-structured Bayesian Networks to build a global model that links and diagnoses multiple Parkinson's disease symptoms such as FoG, handwriting, and speech. To achieve our goals, clinical data are acquired from patients diagnosed with PD. The acquired data are subjected to effective time and frequency feature extraction then introduced to the different detection/diagnosis approaches. The used detection methods are able to detect 100% of the present appearances of FoG episodes. The classification performances of our approaches are studied thoroughly and the accuracy of all methodologies is considered carefully and evaluated
Narozny, Michel. "Analyse en composantes indépendantes et compression de données." Paris 11, 2005. http://www.theses.fr/2005PA112268.
Повний текст джерелаIn this thesis we are interested in the performances of independent component analysis (ICA) when it is used for data compression. First we show that the ICA transformations yield poor performances compared to the Karhunen-Loeve transform (KIT) for the coding of some continuous-tone images and a musical signal, but can outperform the KTL on some synthetic signals. In medium-to-high (resp. Low) bit rate coding, the bit-rate measured is the empirical first (resp. Second, fourth and ninth) order entropy. The mean square error between the original signal and that reconstructed is used for the evaluation of the distortion. Then we show that for non Gaussian signals the problem of finding the optimal linear transform in transform coding is equivalent to finding the solution of a modified ICA problem. Two new algorithms, GCGsup and ICAorth, are then proposed to compute the optimal linear transform and the optimal orthogonal transform, respectively. In our simulations, we show that GCGsup and ICAorth can outperform the KLT or some continuous-tone images and some synthetic signals. Finally, we are also interested in a multicomponent images coding scheme which employs a wavelet transform for reducing the spatial redundancy and the transformations returned by GCGsup et ICAorth for reducing the spectral redundancy. In this case, further work has to be done in order to find some images whose compression using the new transforms is significantly better than that obtained with the TKL
Rönnby, Karl. "Quantum Chemical Feasibility Study of Methylamines as Nitrogen Precursors in Chemical Vapor Deposition." Thesis, Linköpings universitet, Kemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132812.
Повний текст джерелаSHARMA, MONIKA. "AUTOMATED TEXTURE DEFECT DETECTION USING THE NON-EXTENSIVE ENTROPY WITH GAUSSIAN GAIN." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14881.
Повний текст джерелаKouh, Minjoon, and Tomaso Poggio. "A general mechanism for tuning: Gain control circuits and synapses underlie tuning of cortical neurons." 2004. http://hdl.handle.net/1721.1/30512.
Повний текст джерелаSafari, Majid. "Relay-Assisted Free-Space Optical Communications." Thesis, 2011. http://hdl.handle.net/10012/5752.
Повний текст джерелаЧастини книг з теми "GAUSSIAN GAIN"
Tevar, Niraj, Vedvyas Dwivedi, and Prarthan Mehta. "Multi-object Optimization of Corrugated Horn Antenna with Gaussian Profile Using Genetic Algorithm with High Gain for W Band." In Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, 727–39. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6407-6_62.
Повний текст джерелаÁlvarez, Lorena, Enrique Alexandre, Cosme Llerena, Roberto Gil-Pita, and Lucas Cuadra. "Speech Enhancement in Noisy Environments in Hearing Aids Driven by a Tailored Gain Function Based on a Gaussian Mixture Model." In Artificial Intelligence and Soft Computing, 503–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38658-9_45.
Повний текст джерелаTilmanne, Joëlle, and Thierry Dutoit. "Expressive Gait Synthesis Using PCA and Gaussian Modeling." In Motion in Games, 363–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16958-8_34.
Повний текст джерелаZhang, Yuan, and Shexiang Ma. "Power Allocation for Cooperative Communication of Gaussian Channel Gains." In Lecture Notes in Electrical Engineering, 1416–22. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_189.
Повний текст джерелаCastro, Pablo A. D., and Fernando J. Von Zuben. "GAIS: A Gaussian Artificial Immune System for Continuous Optimization." In Lecture Notes in Computer Science, 171–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14547-6_14.
Повний текст джерелаLong, Yi, Zhi-jiang Du, Wei Dong, and Wei-dong Wang. "Human Gait Trajectory Learning Using Online Gaussian Process for Assistive Lower Limb Exoskeleton." In Wearable Sensors and Robots, 165–79. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2404-7_14.
Повний текст джерелаBortoletto, Roberto, Stefano Michieletto, Enrico Pagello, and Davide Piovesan. "Human Muscle-Tendon Stiffness Estimation During Normal Gait Cycle Based on Gaussian Mixture Model." In Intelligent Autonomous Systems 13, 1185–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_86.
Повний текст джерелаArora, Parul, Smriti Srivastava, and Shivank Singhal. "Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition." In Deep Learning and Neural Networks, 429–49. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch025.
Повний текст джерелаCañón-Tapia, Edgardo. "Volcano distribution and tectonics: A planetoidic perspective." In In the Footsteps of Warren B. Hamilton: New Ideas in Earth Science. Geological Society of America, 2022. http://dx.doi.org/10.1130/2021.2553(08).
Повний текст джерелаFan, Guoliang, and Xin Zhang. "Gaussian Process-based Manifold Learning for Human Motion Modeling." In Intelligent Data Analysis for Real-Life Applications, 283–308. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1806-0.ch015.
Повний текст джерелаТези доповідей конференцій з теми "GAUSSIAN GAIN"
Rusu, Corneliu, and Lacrimioara Grama. "Gain-phase relationships evaluation by Gaussian quadrature." In 2008 International Conference on Signals and Electronic Systems. IEEE, 2008. http://dx.doi.org/10.1109/icses.2008.4673347.
Повний текст джерелаLa Porta, A., and R. E. Slusher. "High-gain spatial distortion limits for squeezing and quantum measurement." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.fbb1.
Повний текст джерелаLin, Fuchun, and Frederique Oggier. "Secrecy gain of Gaussian wiretap codes from unimodular lattices." In 2011 IEEE Information Theory Workshop (ITW). IEEE, 2011. http://dx.doi.org/10.1109/itw.2011.6089529.
Повний текст джерелаFujinami, Tesshu, Junya Yamauchi, Marco Omainska, and Masayuki Fujita. "Gaussian Process-based Visual Pursuit Control with Automatic Gain Tuning." In 2022 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2022. http://dx.doi.org/10.1109/ccta49430.2022.9966033.
Повний текст джерелаDrühl, Kai J. "Solutions of the Raman wave equation for focused pump beams." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tua7.
Повний текст джерелаWei, Jing, Shifeng Ou, Suojin Shen, and Ying Gao. "Laplacian-Gaussian mixture based dual-gain wiener filter for speech enhancement." In 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, 2016. http://dx.doi.org/10.1109/siprocess.2016.7888321.
Повний текст джерелаLin, Fuchun, and Frederique Oggier. "Secrecy gain of Gaussian wiretap codes from 2- and 3-modular lattices." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283577.
Повний текст джерелаYang, Shiyong, Tao Jiang, and Yang Cao. "Capacity region of Gaussian cognitive broadcast channel in low-interference-gain regime." In 2012 1st IEEE International Conference on Communications in China (ICCC). IEEE, 2012. http://dx.doi.org/10.1109/iccchina.2012.6356885.
Повний текст джерелаBalictsis, Constantinos M. "Unified Description of Gaussian Pulse Propagation in a Lorentz-Type Gain Medium." In Frontiers in Optics. Washington, D.C.: OSA, 2012. http://dx.doi.org/10.1364/fio.2012.fw3a.4.
Повний текст джерелаTrimarchi, Biagio, Lorenzo Gentilini, Fabrizio Schiano, and Lorenzo Marconi. "Data-Driven Analytic Differentiation via High Gain Observers and Gaussian Process Priors." In 2023 American Control Conference (ACC). IEEE, 2023. http://dx.doi.org/10.23919/acc55779.2023.10156223.
Повний текст джерела