Gotowe bibliografie tematyczne / Additive Gaussian noise

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Gotowa bibliografia na temat „Additive Gaussian noise”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Additive Gaussian noise"

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Guo, Yong-Feng, Ya-Jun Shen, Bei Xi, and Jian-Guo Tan. "Colored correlated multiplicative and additive Gaussian colored noises-induced transition of a piecewise nonlinear bistable model." Modern Physics Letters B 31, no. 28 (2017): 1750256. http://dx.doi.org/10.1142/s0217984917502566.

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In this paper, we investigate the steady-state properties of a piecewise nonlinear bistable model driven by multiplicative and additive Gaussian colored noises with colored cross-correlation. Using the unified colored noise approximation, we derive the analytical expression of the steady-state probability density (SPD) function. Then the effects of colored correlated Gaussian colored noises on SPD are presented. According to the research results, it is found that there appear some new nonlinear phenomena in this system. The multiplicative colored noise intensity, the additive colored noise int
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Wang, Kang-Kang, Hui Ye, Ya-Jun Wang, and Ping-Xin Wang. "Time delay and non-Gaussian noise-induced stochastic stability and stochastic resonance for a metapopulation system subjected to a multiplicative periodic signal." Modern Physics Letters B 32, no. 27 (2018): 1850327. http://dx.doi.org/10.1142/s021798491850327x.

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In this paper, the stable state transformation and the effect of the stochastic resonance (SR) for a metapopulation system are investigated, which is disturbed by time delay, the multiplicative non-Gaussian noise, the additive colored Gaussian noise and a multiplicative periodic signal. By use of the fast descent method, the approximation of the unified colored noise and the SR theory, the dynamical behaviors for the steady-state probability function and the SNR are analyzed. It is found that non-Gaussian noise, the colored Gaussian noise and time delay can all reduce the stability of the biol
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Idel, Martin, and Robert Konig. "On quantum additive Gaussian noise channels." Quantum Information and Computation 17, no. 3&4 (2017): 283–302. http://dx.doi.org/10.26421/qic17.3-4-6.

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We give necessary and sufficient conditions for a Gaussian quantum channel to have a dilation involving a passive, i.e., number-preserving unitary. We then establish a normal form of such channels: any passively dilatable channel is the result of applying passive unitaries to the input and output of a Gaussian additive channel. The latter combine the state of the system with that of the environment by means of a multi-mode beamsplitter.
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CHAPEAU-BLONDEAU, FRANÇOIS, and DAVID ROUSSEAU. "CONSTRUCTIVE ACTION OF ADDITIVE NOISE IN OPTIMAL DETECTION." International Journal of Bifurcation and Chaos 15, no. 09 (2005): 2985–94. http://dx.doi.org/10.1142/s0218127405013824.

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The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise mixture, when a threshold quantization is performed prior to the optimal detection, another form of improvement by noise can be obtained, with subthreshold signals and Gaussian noise. Optimization of the quantization threshold shows that even in
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Haynes, Mark S. "Homodyned-K Distribution With Additive Gaussian Noise." IEEE Transactions on Aerospace and Electronic Systems 55, no. 6 (2019): 2992–3002. http://dx.doi.org/10.1109/taes.2019.2895711.

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Ding, L., H. N. Wang, J. Chen, and Z. H. Guan. "Tracking under additive white Gaussian noise effect." IET Control Theory & Applications 4, no. 11 (2010): 2471–78. http://dx.doi.org/10.1049/iet-cta.2009.0449.

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Zhou, Yuqian, Jianbo Jiao, Haibin Huang, Jue Wang, and Thomas Huang. "Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10085–86. http://dx.doi.org/10.1609/aaai.v33i01.330110085.

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Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches.
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Wang, Kang-Kang, Hui Ye, Ya-Jun Wang, and Ping-Xin Wang. "Impact of Time Delay and Non-Gaussian Noise on Stochastic Resonance and Stability for a Stochastic Metapopulation System Driven by a Multiplicative Periodic Signal." Fluctuation and Noise Letters 18, no. 03 (2019): 1950017. http://dx.doi.org/10.1142/s0219477519500172.

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In the present paper, the stability of the population system and the phenomena of the stochastic resonance (SR) for a metapopulation system induced by the terms of time delay, the multiplicative non-Gaussian noise, the additive colored Gaussian noise and a multiplicative periodic signal are investigated in detail. By applying the fast descent method, the unified colored noise approximation and the SR theory, the expressions of the steady-state probability function and the SNR are derived. It is shown that multiplicative non-Gaussian noise, the additive Gaussian noise and time delay can all wea
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Li, Yongsong, Zhengzhou Li, Kai Wei, Weiqi Xiong, Jiangpeng Yu, and Bo Qi. "Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation." Sensors 19, no. 2 (2019): 339. http://dx.doi.org/10.3390/s19020339.

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks
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GOSWAMI, GURUPADA, PRADIP MAJEE, and BIDHAN CHANDRA BAG. "ESCAPE THROUGH A FLUCTUATING ENERGY BARRIER IN THE PRESENCE OF NON-GAUSSIAN NOISE." Fluctuation and Noise Letters 07, no. 02 (2007): L151—L161. http://dx.doi.org/10.1142/s0219477507003799.

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In this paper we have studied how barrier crossing dynamics is affected by colored additive non-Gaussian noise if the barrier fluctuates deterministically. Our investigation indicates that resonant activation(RA) is either enhanced or it becomes robust if noise characteristic is deviated from the Gaussian behavior. We find that additive colored non Gaussian noise can induce the RA-like phenomenon. Another interesting observation is that the turnover behavior persists even in presence of barrier fluctuations at finite rate. Finally, it is observed that mean first passage time(MFPT) decreases wi
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Rozprawy doktorskie na temat "Additive Gaussian noise"

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Shu, Li 1970. "A power interval perspective on additive white Gaussian noise (AWGN) channels." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9118.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (leaves 215-216).<br>We present a new perspective on additive white Gaussian noise (AWGN) channels that separates user and channel attributes. Following this separation, various questions concerning achievability and successive decoding can be reformulated as properties of the set of user attributes, which can be determined independently of the actual channel noise. To obtain these properties directly, we introduce a graphical framework calle
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Chen, Brian. "Efficient communication over additive white Gaussian noise and intersymbol interference channels using chaotic sequences." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40151.

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Huang, Weizheng. "Investigation on Digital Fountain Codes over Erasure Channels and Additive White Gaussian Noise Channels." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1336067205.

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Ni, Li. "Non-equiprobable multi-level coding for the additive white Gaussian noise channel with Tikhonov phase error." Online access for everyone, 2005. http://www.dissertations.wsu.edu/Dissertations/Fall2005/l%5Fni%5F120905.pdf.

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DeRieux, David A. "Investigation of spectral-based techniques for classification of wideband transient signals in additive white Gaussian noise." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA282954.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, March 1994.<br>Thesis advisor(s): Ralph Hippenstiel, Monique P. Fargues. "March 1994." Includes bibliographical references. Also available online.
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Ng, Jimmy Hon-yuen. "Estimation of error rates and fade distributions on a Rayleigh fading channel with additive white Gaussian noise." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26318.

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Several characteristics of the Rayleigh fading channel are examined. A digital Rayleigh fading simulator is used to generate the (fading) signal envelope from which various statistics are derived. Based on the simulation results, a simple model is proposed in order to estimate the block error rate of a block of N data bits transmitted over the Rayleigh fading channel in the presence of additive white Gaussian noise. This model gives an average estimation error of about 4 % over the range of blocksizes N = 63, 127, 255, 511, 1023, 2047 (bits), average signal-to-noise ratios 70 = 5 to 35 (dB) a
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Argyriou, Andreas. "Probability of symbol error for coherent and non-coherent detection of M-ary frequency-shift keyed (MFSK) signals affected by co-channel interference and additive white Gaussian noise (AWGN) in a fading channel." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA376826.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, March 2000.<br>Thesis advisor(s): Lebaric, Jovan; Robertson, Clark. Includes bibliographical references (p. 289). Also available online.
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Fougias, Nikolaos. "High speed network access to the last-mile using fixed broadband wireless." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FFougias.pdf.

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Thesis (M.S. in Information Technology Management and M.S. in Computer Science)--Naval Postgraduate School, March 2004.<br>Thesis advisor(s): Burt Lundy. Includes bibliographical references (p. 99-100). Also available online.
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Erdogan, Ahmet Yasin. "Analysis of the effects of phase noise and frequency offset in orthogonal frequency division multiplexing (OFDM) systems." Thesis, Monterey California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1712.

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Approved for public release, distribution is unlimited<br>Orthogonal frequency division multiplexing (OFDM) is being successfully used in numerous applications. It was chosen for IEEE 802.11a wireless local area network (WLAN) standard, and it is being considered for the fourthgeneration mobile communication systems. Along with its many attractive features, OFDM has some principal drawbacks. Sensitivity to frequency errors is the most dominant of these drawbacks. In this thesis, the frequency offset and phase noise effects on OFDM based communication systems are investigated under a variety of
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Chen, Jui-Chun. "A virtual RSNS direction finding antenna system." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FChen%5FJui.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, Dec. 2004.<br>Thesis Advisor(s): David C. Jenn, Phillip E. Pace. Includes bibliographical references (p. 69-70). Also available online.
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Książki na temat "Additive Gaussian noise"

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Tudor, Ciprian A. Stochastic Partial Differential Equations with Additive Gaussian Noise: Analysis and Inference. World Scientific Publishing Co Pte Ltd, 2022.

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Probability of Symbol Error for Coherent and Non-Coherent Detection of M-ary Frequency-Shift Keyed (MFSK) Signals Affected by Co-Channel Interference and Additive White Gaussian Noise (AWGN) in a. Storming Media, 2000.

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Berber, Stevan. Discrete Communication Systems. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198860792.001.0001.

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The book present essential theory and practice of the discrete communication systems design, based on the theory of discrete time stochastic processes, and their relation to the existing theory of digital communication systems. Using the notion of stochastic linear time invariant systems, in addition to the orhogonality principles, a general structure of the discrete communication system is constructed in terms of mathematical operators. Based on this structure, the MPSK, MFSK, QAM, OFDM and CDMA systems, using discrete modulation methods, are deduced as special cases. The signals are processe
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Części książek na temat "Additive Gaussian noise"

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Weik, Martin H. "additive white Gaussian noise." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_319.

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Nicola, J., and L. Jaulin. "Guaranteed Nonlinear Parameter Estimation with Additive Gaussian Noise." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31041-7_19.

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Bertero, Mario, Patrizia Boccacci, and Christine De MoI. "Statistical methods in the case of additive Gaussian noise." In Introduction to Inverse Problems in Imaging, 2nd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9781003032755-12.

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Chen, Yuan, Dingfan Zhang, and Longting Huang. "Optimum Parameter Estimation Under Additive Cauchy-Gaussian Mixture Noise." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77569-8_16.

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Lupo, C., L. Memarzadeh, and S. Mancini. "Multipartite Entangled Codewords for Gaussian Channels with Additive Noise and Memory." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11731-2_3.

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Rosnes, Eirik. "On Linear Programming Decoding on a Quantized Additive White Gaussian Noise Channel." In Cryptography and Coding. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10868-6_2.

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Singh, Bablu Kumar, and Supriya Vyas. "Performance Comparison of Minimum Shift Keying and Gaussian Minimum Shift Keying in Additive White Gaussian Noise Environment." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8820-4_26.

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Olapetan, Olawale, and Ifiok Otung. "Implementation and Performance Analysis of Trellis Coded Modulation in Additive White Gaussian Noise." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98827-6_15.

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Sun, Xiaojuan, Shujuan Lü, and Qishao Lu. "Effects of Additive Gaussian Noise on Neuronal Firings in a Heterogeneous Neuronal Network." In Advances in Cognitive Neurodynamics (II). Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9695-1_34.

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Turajlić, Emir, and Vedran Karahodzic. "An Adaptive Scheme for X-ray Medical Image Denoising using Artificial Neural Networks and Additive White Gaussian Noise Level Estimation in SVD Domain." In IFMBE Proceedings. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4166-2_7.

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Streszczenia konferencji na temat "Additive Gaussian noise"

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Panigrahi, Susant Kumar, Supratim Gupta, and Prasanna K. Sahu. "Phases under Gaussian additive noise." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754471.

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Li, Dapeng, and Naira Hovakimyan. "Noise attenuation over additive Gaussian channels." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717502.

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Dytso, Alex, Ronit Bustin, H. Vincent Poor, and Shlomo Shamai Shitz. "On additive channels with generalized Gaussian noise." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006563.

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Robinson, P. E., and Y. Roodt. "Blind deconvolution of Gaussian blurred images containing additive white Gaussian noise." In 2013 IEEE International Conference on Industrial Technology (ICIT 2013). IEEE, 2013. http://dx.doi.org/10.1109/icit.2013.6505824.

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Jenkins, James L. "Distribution functions for additive Gaussian and gamma noise." In Signal and Data Processing of Small Targets 1990. SPIE, 1990. http://dx.doi.org/10.1117/12.2321756.

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Jenkins, James L. "Distribution functions for additive Gaussian and gamma noise." In OE/LASE '90, 14-19 Jan., Los Angeles, CA, edited by Oliver E. Drummond. SPIE, 1990. http://dx.doi.org/10.1117/12.21587.

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Eckford, Andrew W., K. V. Srinivas, and Raviraj S. Adve. "The peak constrained additive inverse Gaussian noise channel." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6284105.

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Egan, Malcolm, Samir M. Perlaza, and Vyacheslav Kungurtsev. "Capacity sensitivity in additive non-Gaussian noise channels." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006561.

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Shomorony, Ilan, and A. Salman Avestimehr. "Is Gaussian noise the worst-case additive noise in wireless networks?" In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283743.

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Al-Ghaib, Huda, and Reza Adhami. "On the digital image additive white Gaussian noise estimation." In 2014 International Conference on Industrial Automation, Information and Communications Technology (IAICT). IEEE, 2014. http://dx.doi.org/10.1109/iaict.2014.6922089.

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Raporty organizacyjne na temat "Additive Gaussian noise"

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Sher, David. Optimal Likelihood Generators for Edge Detection under Gaussian Additive Noise. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada179945.

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