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Artykuły w czasopismach na temat "Pulse-Coupled Neural Networks"
Wang, Zhaobin, Yide Ma, Feiyan Cheng i Lizhen Yang. "Review of pulse-coupled neural networks". Image and Vision Computing 28, nr 1 (styczeń 2010): 5–13. http://dx.doi.org/10.1016/j.imavis.2009.06.007.
Pełny tekst źródłaOta, Y., i B. M. Wilamowski. "Analog implementation of pulse-coupled neural networks". IEEE Transactions on Neural Networks 10, nr 3 (maj 1999): 539–44. http://dx.doi.org/10.1109/72.761710.
Pełny tekst źródłaRanganath, H. S., i G. Kuntimad. "Object detection using pulse coupled neural networks". IEEE Transactions on Neural Networks 10, nr 3 (maj 1999): 615–20. http://dx.doi.org/10.1109/72.761720.
Pełny tekst źródłaOlmi, S., A. Politi i A. Torcini. "Collective chaos in pulse-coupled neural networks". EPL (Europhysics Letters) 92, nr 6 (1.12.2010): 60007. http://dx.doi.org/10.1209/0295-5075/92/60007.
Pełny tekst źródłaMonica Subashini, M., i Sarat Kumar Sahoo. "Pulse coupled neural networks and its applications". Expert Systems with Applications 41, nr 8 (czerwiec 2014): 3965–74. http://dx.doi.org/10.1016/j.eswa.2013.12.027.
Pełny tekst źródłaWANG, Xin, Yi-de MA, Zhi-jian XU i Lian-feng LI. "Chaos control based on pulse-coupled neural networks". Journal of Computer Applications 29, nr 12 (1.03.2010): 3277–79. http://dx.doi.org/10.3724/sp.j.1087.2009.03277.
Pełny tekst źródłaKuntimad, G., i H. S. Ranganath. "Perfect image segmentation using pulse coupled neural networks". IEEE Transactions on Neural Networks 10, nr 3 (maj 1999): 591–98. http://dx.doi.org/10.1109/72.761716.
Pełny tekst źródłaKanamaru, Takashi, i Kazuyuki Aihara. "Rewiring-Induced Chaos in Pulse-Coupled Neural Networks". Neural Computation 24, nr 4 (kwiecień 2012): 1020–46. http://dx.doi.org/10.1162/neco_a_00252.
Pełny tekst źródłaXu, Xinzheng, Guanying Wang, Shifei Ding, Yuhu Cheng i Xuesong Wang. "Pulse-coupled neural networks and parameter optimization methods". Neural Computing and Applications 28, S1 (4.06.2016): 671–81. http://dx.doi.org/10.1007/s00521-016-2397-2.
Pełny tekst źródłaHe, Changtao, Fangnian Lang i Hongliang Li. "Medical Image Registration using Cascaded Pulse Coupled Neural Networks". Information Technology Journal 10, nr 9 (15.08.2011): 1733–39. http://dx.doi.org/10.3923/itj.2011.1733.1739.
Pełny tekst źródłaRozprawy doktorskie na temat "Pulse-Coupled Neural Networks"
Swathanthira, Kumar Murali Murugavel M. "Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks". Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-dissertations/280.
Pełny tekst źródłaWise, Raydiance (Raydiance Raychele). "Optoelectronic implementations of Pulse-Coupled Neural Networks : challenges and limitations". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40539.
Pełny tekst źródłaIncludes bibliographical references (leaves 76-79).
This thesis examines Pulse Coupled Neural Networks (PCNNs) and their applications, and the feasibility of a compact, rugged, cost-efficient optoelectronic implementation. Simulation results are presented. Proposed optical architectures are discussed and analyzed. A new optoelectronic PCNN architecture is also presented. Tradeoffs of optical versus electronic implementations of PCNNs are discussed. This work combines concepts from optical information processing and pulse-coupled neural networks to examine the challenges, limitations, and opportunities of developing an optoelectronic pulse coupled neural network. The analysis finds that, despite advances in optoelectronic technology, fully electronic implementations will still outperform today's proposed optoelectronic implementations in cost, size, flexibility, and ease of implementation.
by Raydiance Wise.
S.M.
Innes, Andrew, i andrew innes@defence gov au. "Genetic Programming for Cephalometric Landmark Detection". RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080221.123310.
Pełny tekst źródłaTimoszczuk, Antonio Pedro. "Reconhecimento automático do locutor com redes neurais pulsadas". Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26102004-195250/.
Pełny tekst źródłaPulsed Neural Networks have received a lot of attention from researchers. This work aims to verify the capability of this neural paradigm when applied to a speaker recognition task. After a description of the automatic speaker recognition and artificial neural networks fundamentals, a spike response model of neurons is tested. A novel neural network architecture based on this neuron model is proposed and used in a speaker recognition system. Text dependent and independent tests were performed using the Speaker Recognition v1.0 database from CSLU Center for Spoken Language Understanding of Oregon Graduate Institute - U.S.A. A multilayer perceptron is used as a classifier. The Pulsed Neural Networks demonstrated its capability to deal with temporal information and the use of this neural paradigm in a speaker recognition task is promising.
Sandmann, Humberto Rodrigo. "Padrões de pulsos e computação em redes neurais com dinâmica". Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-05092012-165022/.
Pełny tekst źródłaThe signal processing done by the neural systems is highly efficient and complex, so that it attracts a large attention for research. Basically, all the signal processing functions are based on networks of neurons that send and receive spikes. Therefore, in general, the stimuli received from the sensory system by a biological neural network somehow are converted into spike trains. Here, in this thesis, we present a new architecture composed of two layers: the first layer receives streams of input stimuli and maps them on spike trains; the second layer receives these spike trains and classifies them in a sets of stimuli. In the first layer, the conversion of currents of stimuli on spike trains is made by a pulse-coupled neural network. Neurons in this context are like oscillators and have a natural frequency to shoot; when they are grouped into networks, they can be coordinated to present a global long-term dynamics. In turn, this global dynamics is also sensible to the input currents. In the second layer, the classification of spike trains in sets of stimuli is implemented by an integrate-and-re neuron. The typical behavior for this neuron is to shoot at least once every time that it receives a known spike train; otherwise, it should be in silence. The learning process of the second layer depends on the knowledge of the time interval of repetition of a spike train. Therefore, in this thesis, metrics are presented to define this time interval, thus giving autonomy to the architecture. It can be concluded on the basis of the tests developed that the architecture has a large capacity for mapping input currents on spike trains without requiring changes in its structure; moreover, the addition of the time dimension done by the first layer helps in the classification performed by the second layer. Thus, a new model to perform the encoding and decoding processes is presented, developed through a series of computational experiments and characterized by measurements of its dynamics.
Åberg, K. Magnus. "Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation". Doctoral thesis, Stockholm University, Department of Analytical Chemistry, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-283.
Pełny tekst źródłaThis thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods.
Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules.
Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments.
Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV.
Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed.
Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).
Hsiang, Hsi-Bao, i 向錫堡. "An improved image de-noise method based on pulse-coupled neural networks". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/45991084426879623188.
Pełny tekst źródła輔仁大學
資訊工程學系碩士班
102
Image de-noise is the first step in image processing. Salt and pepper noises are common image noises. There are already many de-noise algorithms, such as the non local means (NL-means), mean filtering, and median filtering. Although these filters can de-noise images, they may reduce image details at the same time, resulting image blur and distortion. To reduce image blur and distortion after de-noising, we use pulse-coupled neural network PCNN (Pulse Coupled Neural Network) to de-noise images. PCNN can effectively remove noises and preserve image details. PCNN generally uses a fixed pane size in image de-noise. It uses gray-scale values of pixels as input neurons to calculate whether pixels have noises. In this paper, dynamic sized panes are used instead of fixed sized panes. When there are no noises in a pane, our improved PCNN will automatically enlarge the size of the pane, to recalculate whether there are noises. Keywords: Image De-noising, PCNN (Pulse Coupled Neural Network), detect noise
Timme, Marc. "Collective Dynamics in Networks of Pulse-Coupled Oscillators". Doctoral thesis, 2002. http://hdl.handle.net/11858/00-1735-0000-0006-B575-5.
Pełny tekst źródłaKirst, Christoph. "Synchronization, Neuronal Excitability, and Information Flow in Networks of Neuronal Oscillators". Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-000D-F08D-2.
Pełny tekst źródłaKsiążki na temat "Pulse-Coupled Neural Networks"
Ma, Yide. Applications of Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Znajdź pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. Applications of Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-13745-7.
Pełny tekst źródła1962-, Kinser Jason M., red. Image processing using pulse-coupled neural networks. London: Springer, 1998.
Znajdź pełny tekst źródłaLindblad, Thomas, i Jason M. Kinser. Image Processing using Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36877-6.
Pełny tekst źródłaLindblad, Thomas, i Jason M. Kinser. Image Processing using Pulse-Coupled Neural Networks. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3617-0.
Pełny tekst źródłaLindblad, Thomas. Image Processing using Pulse-Coupled Neural Networks: Applications in Python. Wyd. 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Znajdź pełny tekst źródłaBrain dynamics: Synchronization and activity patterns in pulse-coupled neural nets with delays and noise. Berlin: Springer, 2002.
Znajdź pełny tekst źródłaWorkshop, on Virtual Intelligence/Dynamic Neural Networks (9th 1998 Stockholm Sweden). Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Academic/industrial/NASA/defense technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks--neural networks, fuzzy systems, evolutionary systems, and virtual reality/pulse coupled neural networks, 1998. Bellingham, Wash: SPIE, 1999.
Znajdź pełny tekst źródłaThomas, Lindblad, Padgett Mary Lou, Kinser Jason M. 1962-, United States. National Aeronautics and Space Administration, Society of Photo-optical Instrumentation Engineers. i IEEE Industry Applications Society, red. Proceedings, Ninth Workshop on Virtual Intelligence: Dynamic neural networks: academic/industrial/NASA/defense : technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks: neural networks, fuzzy systems, evolutionary systems and virtual reality/pulse coupled neural networks, 1998. Bellingham, Wash., USA: SPIE, 1999.
Znajdź pełny tekst źródłaImage Processing Using Pulse-Coupled Neural Networks. Wyd. 2. Springer, 2005.
Znajdź pełny tekst źródłaCzęści książek na temat "Pulse-Coupled Neural Networks"
Ma, Yide, Kun Zhan i Zhaobin Wang. "Pulse-Coupled Neural Networks". W Applications of Pulse-Coupled Neural Networks, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_1.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Image Filtering". W Applications of Pulse-Coupled Neural Networks, 11–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_2.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Image Segmentation". W Applications of Pulse-Coupled Neural Networks, 27–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_3.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Image Coding". W Applications of Pulse-Coupled Neural Networks, 43–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_4.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Image Enhancement". W Applications of Pulse-Coupled Neural Networks, 61–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_5.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Image Fusion". W Applications of Pulse-Coupled Neural Networks, 83–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_6.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Feature Extraction". W Applications of Pulse-Coupled Neural Networks, 111–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_7.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "Combinatorial Optimization". W Applications of Pulse-Coupled Neural Networks, 147–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_8.
Pełny tekst źródłaMa, Yide, Kun Zhan i Zhaobin Wang. "FPGA Implementation of PCNN Algorithm". W Applications of Pulse-Coupled Neural Networks, 167–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_9.
Pełny tekst źródłaLi, Min, Wei Cai i Zheng Tan. "Pulse Coupled Neural Network Based Image Fusion". W Advances in Neural Networks – ISNN 2005, 741–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_119.
Pełny tekst źródłaStreszczenia konferencji na temat "Pulse-Coupled Neural Networks"
Johnson, John L. "Pulse-coupled neural networks". W Critical Review Collection. SPIE, 1994. http://dx.doi.org/10.1117/12.171194.
Pełny tekst źródłaTimoszczuk, Antonio Pedro, i Euvaldo F. Cabral. "Speaker Recognition Using Pulse Coupled Neural Networks". W 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371259.
Pełny tekst źródłaJohnson, John L. "Pulse-coupled neural networks can benefit ATR". W Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, redaktorzy Thomas Lindblad, Mary Lou Padgett i Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343032.
Pełny tekst źródłaRanganath, Heggere S., Michele R. Banish, John R. Karpinsky, Rodney L. Clark, Glynn A. Germany i Philip G. Richards. "Three applications of pulse-coupled neural networks". W Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, redaktorzy Thomas Lindblad, Mary Lou Padgett i Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343055.
Pełny tekst źródłaYourui, Huang, i Wang Shuang. "Image Segmentation Using Pulse Coupled Neural Networks". W 2008 International Conference on MultiMedia and Information Technology (MMIT). IEEE, 2008. http://dx.doi.org/10.1109/mmit.2008.121.
Pełny tekst źródłaInguva, Ramarao, John L. Johnson i Marius P. Schamschula. "Multifeature fusion using pulse-coupled neural networks". W AeroSense '99, redaktor Belur V. Dasarathy. SPIE, 1999. http://dx.doi.org/10.1117/12.341357.
Pełny tekst źródłaRanganath, Heggere S., i Govindaraj Kuntimad. "Iterative segmentation using pulse-coupled neural networks". W Aerospace/Defense Sensing and Controls, redaktorzy Steven K. Rogers i Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235943.
Pełny tekst źródłaMalkani, Mohan, Mohammad Bodruzzaman, John L. Johnson i Joel Davis. "Center for Neural Engineering: applications of pulse-coupled neural networks". W Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, redaktorzy Thomas Lindblad, Mary Lou Padgett i Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343042.
Pełny tekst źródłaHisamatsu, Kozo, i Toshimichi Saito. "Delay-induced order in pulse-coupled bifurcating neurons". W 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596846.
Pełny tekst źródłaSzekely, Geza, i Thomas Lindblad. "Parameter adaptation in a simplified pulse-coupled neural network". W Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, redaktorzy Thomas Lindblad, Mary Lou Padgett i Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343046.
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