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You searched for subject:(spectral spatial neural networks). Showing records 1 – 30 of 38899 total matches.

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University of Waterloo

1. Zhong, Zilong. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.

Degree: 2019, University of Waterloo

 Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and… (more)

Subjects/Keywords: hyperspectral image classification; spectral-spatial neural networks; generative adversarial networks; probabilistic graphical models

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APA (6th Edition):

Zhong, Z. (2019). Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14893

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhong, Zilong. “Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.” 2019. Thesis, University of Waterloo. Accessed December 06, 2019. http://hdl.handle.net/10012/14893.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhong, Zilong. “Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.” 2019. Web. 06 Dec 2019.

Vancouver:

Zhong Z. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10012/14893.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhong Z. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14893

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade Nova

2. Henriques, Roberto André Pereira. Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

Degree: 2011, Universidade Nova

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

The size and dimensionality of available… (more)

Subjects/Keywords: Geocomputation; Geovisualization; Neural Networks; Self-organizing Maps; Spatial Clustering

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APA (6th Edition):

Henriques, R. A. P. (2011). Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/5723

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Henriques, Roberto André Pereira. “Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.” 2011. Thesis, Universidade Nova. Accessed December 06, 2019. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/5723.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Henriques, Roberto André Pereira. “Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.” 2011. Web. 06 Dec 2019.

Vancouver:

Henriques RAP. Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science. [Internet] [Thesis]. Universidade Nova; 2011. [cited 2019 Dec 06]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/5723.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Henriques RAP. Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science. [Thesis]. Universidade Nova; 2011. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/5723

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Kansas State University

3. Boumaaza, Bouharket. 3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas.

Degree: MS, Department of Geology, 2017, Kansas State University

 This work focuses on the use of advanced seismically driven technologies to estimate the distribution of key reservoir properties which mainly includes porosity and hydrocarbon… (more)

Subjects/Keywords: Seismic attributes; Stochastic inversion; Sequential geological modelling; Volumetric curvature; Spectral attributes; Neural networks

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APA (6th Edition):

Boumaaza, B. (2017). 3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/35510

Chicago Manual of Style (16th Edition):

Boumaaza, Bouharket. “3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas.” 2017. Masters Thesis, Kansas State University. Accessed December 06, 2019. http://hdl.handle.net/2097/35510.

MLA Handbook (7th Edition):

Boumaaza, Bouharket. “3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas.” 2017. Web. 06 Dec 2019.

Vancouver:

Boumaaza B. 3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas. [Internet] [Masters thesis]. Kansas State University; 2017. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/2097/35510.

Council of Science Editors:

Boumaaza B. 3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas. [Masters Thesis]. Kansas State University; 2017. Available from: http://hdl.handle.net/2097/35510


Texas A&M University

4. Belur Jana, Raghavendra. Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions.

Degree: 2011, Texas A&M University

 This dissertation focuses on the challenge of soil hydraulic parameter scaling in soil hydrology and related applications in general; and, in particular, the upscaling of… (more)

Subjects/Keywords: Soil hydraulic parameters; spatial scaling; scale; vadose zone; Bayesian neural networks; topography; remote sensing

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APA (6th Edition):

Belur Jana, R. (2011). Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Belur Jana, Raghavendra. “Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions.” 2011. Thesis, Texas A&M University. Accessed December 06, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Belur Jana, Raghavendra. “Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions.” 2011. Web. 06 Dec 2019.

Vancouver:

Belur Jana R. Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Belur Jana R. Scaling Characteristics of Soil Hydraulic Parameters at Varying Spatial Resolutions. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Alberta

5. Dupuis, Brian A. The Cognitive Science of Reorientation.

Degree: MS, Department of Psychology, 2012, University of Alberta

 This work stands as an example of “synthetic methodology” in psychological research. Synthetic methodology involves building a model, seeing what it can and cannot do… (more)

Subjects/Keywords: Neural Networks; Cognitive Science; Cognitive Modelling; Comparative Cognition; Spatial Learning; Reorientation; Synthetic Psychology

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APA (6th Edition):

Dupuis, B. A. (2012). The Cognitive Science of Reorientation. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/h128nf06h

Chicago Manual of Style (16th Edition):

Dupuis, Brian A. “The Cognitive Science of Reorientation.” 2012. Masters Thesis, University of Alberta. Accessed December 06, 2019. https://era.library.ualberta.ca/files/h128nf06h.

MLA Handbook (7th Edition):

Dupuis, Brian A. “The Cognitive Science of Reorientation.” 2012. Web. 06 Dec 2019.

Vancouver:

Dupuis BA. The Cognitive Science of Reorientation. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2019 Dec 06]. Available from: https://era.library.ualberta.ca/files/h128nf06h.

Council of Science Editors:

Dupuis BA. The Cognitive Science of Reorientation. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/h128nf06h


University of Oxford

6. Walters, Daniel Matthew. The computational neuroscience of head direction cells.

Degree: PhD, 2011, University of Oxford

 Head direction cells signal the orientation of the head in the horizontal plane. This thesis shows how some of the known head direction cell response… (more)

Subjects/Keywords: 612.8; Computational Neuroscience; Theoretical Neuroscience; Head Direction Cells; Neural Networks; Spatial Processing

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APA (6th Edition):

Walters, D. M. (2011). The computational neuroscience of head direction cells. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658381

Chicago Manual of Style (16th Edition):

Walters, Daniel Matthew. “The computational neuroscience of head direction cells.” 2011. Doctoral Dissertation, University of Oxford. Accessed December 06, 2019. http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658381.

MLA Handbook (7th Edition):

Walters, Daniel Matthew. “The computational neuroscience of head direction cells.” 2011. Web. 06 Dec 2019.

Vancouver:

Walters DM. The computational neuroscience of head direction cells. [Internet] [Doctoral dissertation]. University of Oxford; 2011. [cited 2019 Dec 06]. Available from: http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658381.

Council of Science Editors:

Walters DM. The computational neuroscience of head direction cells. [Doctoral Dissertation]. University of Oxford; 2011. Available from: http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658381

7. Freitas, Luciana Paro Scarin [UNESP]. Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais.

Degree: 2016, Universidade Estadual Paulista

O dióxido de carbono (CO2) é considerado um dos principais gases do efeito estufa adicional e contribui significativamente para as mudanças climáticas globais. Áreas agrícolas… (more)

Subjects/Keywords: Artificial neural networks; Forecasting models; Redes neurais artificiais; Variabilidade espacial; Modelos de previsão; Spatial variability

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APA (6th Edition):

Freitas, L. P. S. [. (2016). Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais. (Thesis). Universidade Estadual Paulista. Retrieved from http://hdl.handle.net/11449/143894

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Freitas, Luciana Paro Scarin [UNESP]. “Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais.” 2016. Thesis, Universidade Estadual Paulista. Accessed December 06, 2019. http://hdl.handle.net/11449/143894.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Freitas, Luciana Paro Scarin [UNESP]. “Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais.” 2016. Web. 06 Dec 2019.

Vancouver:

Freitas LPS[. Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais. [Internet] [Thesis]. Universidade Estadual Paulista; 2016. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/11449/143894.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Freitas LPS[. Previsão da Variabilidade da Emissão de CO2 do Solo em Áreas de Cana-de-Açúcar Utilizando Redes Neurais Artificiais. [Thesis]. Universidade Estadual Paulista; 2016. Available from: http://hdl.handle.net/11449/143894

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

8. Mnatzaganian, James W. A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning.

Degree: MS, Computer Engineering, 2016, Rochester Institute of Technology

  Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The… (more)

Subjects/Keywords: Hierarchical temporal memory; Machine learning; Neural networks; Self-organizing feature maps; Spatial pooler; Unsupervised learning

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APA (6th Edition):

Mnatzaganian, J. W. (2016). A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9012

Chicago Manual of Style (16th Edition):

Mnatzaganian, James W. “A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed December 06, 2019. https://scholarworks.rit.edu/theses/9012.

MLA Handbook (7th Edition):

Mnatzaganian, James W. “A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning.” 2016. Web. 06 Dec 2019.

Vancouver:

Mnatzaganian JW. A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2019 Dec 06]. Available from: https://scholarworks.rit.edu/theses/9012.

Council of Science Editors:

Mnatzaganian JW. A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9012


University of Waterloo

9. Khan, Ahmed Faraz. Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation.

Degree: 2018, University of Waterloo

 Neurobiologically-plausible learning algorithms for recurrent neural networks that can perform supervised learning are a neglected area of study. Equilibrium propagation is a recent synthesis of… (more)

Subjects/Keywords: neural networks

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APA (6th Edition):

Khan, A. F. (2018). Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13957

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Khan, Ahmed Faraz. “Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation.” 2018. Thesis, University of Waterloo. Accessed December 06, 2019. http://hdl.handle.net/10012/13957.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khan, Ahmed Faraz. “Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation.” 2018. Web. 06 Dec 2019.

Vancouver:

Khan AF. Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10012/13957.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khan AF. Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13957

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


California State Polytechnic University – Pomona

10. Priya, Renita. A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks.

Degree: MS, Department of Computer Science, 2018, California State Polytechnic University – Pomona

 Deep Learning is an emerging field in Artificial Intelligence that uses biologically inspired neural networks to recognize patterns in the natural world. These neural networks(more)

Subjects/Keywords: neural networks

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APA (6th Edition):

Priya, R. (2018). A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/206684

Chicago Manual of Style (16th Edition):

Priya, Renita. “A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks.” 2018. Masters Thesis, California State Polytechnic University – Pomona. Accessed December 06, 2019. http://hdl.handle.net/10211.3/206684.

MLA Handbook (7th Edition):

Priya, Renita. “A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks.” 2018. Web. 06 Dec 2019.

Vancouver:

Priya R. A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10211.3/206684.

Council of Science Editors:

Priya R. A Deep Dive Into Automatic Code Generation Using Character Based Recurrent Neural Networks. [Masters Thesis]. California State Polytechnic University – Pomona; 2018. Available from: http://hdl.handle.net/10211.3/206684


University of Maine

11. Neville, François. Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models.

Degree: PhD, Spatial Information Science and Engineering, 2015, University of Maine

  As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system… (more)

Subjects/Keywords: artificial neural networks; wireless sensor networks; data approximation; spatiotemporal partitioning; data clustering; Numerical Analysis and Scientific Computing; Remote Sensing; Spatial Science

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APA (6th Edition):

Neville, F. (2015). Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models. (Doctoral Dissertation). University of Maine. Retrieved from https://digitalcommons.library.umaine.edu/etd/2332

Chicago Manual of Style (16th Edition):

Neville, François. “Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models.” 2015. Doctoral Dissertation, University of Maine. Accessed December 06, 2019. https://digitalcommons.library.umaine.edu/etd/2332.

MLA Handbook (7th Edition):

Neville, François. “Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models.” 2015. Web. 06 Dec 2019.

Vancouver:

Neville F. Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models. [Internet] [Doctoral dissertation]. University of Maine; 2015. [cited 2019 Dec 06]. Available from: https://digitalcommons.library.umaine.edu/etd/2332.

Council of Science Editors:

Neville F. Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models. [Doctoral Dissertation]. University of Maine; 2015. Available from: https://digitalcommons.library.umaine.edu/etd/2332


Mississippi State University

12. Inakollu, Prasanthi. A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra.

Degree: MS, Electrical and Computer Engineering, 2003, Mississippi State University

 Laser-induced breakdown spectroscopy (LIBS) is an advanced data analysis technique for spectral analysis based on the direct measurement of the spectrum of optical emission from… (more)

Subjects/Keywords: artificial neural networks; LIBS; spectral analysis; elemental concentrations

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APA (6th Edition):

Inakollu, P. (2003). A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152003-190916/ ;

Chicago Manual of Style (16th Edition):

Inakollu, Prasanthi. “A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra.” 2003. Masters Thesis, Mississippi State University. Accessed December 06, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152003-190916/ ;.

MLA Handbook (7th Edition):

Inakollu, Prasanthi. “A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra.” 2003. Web. 06 Dec 2019.

Vancouver:

Inakollu P. A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra. [Internet] [Masters thesis]. Mississippi State University; 2003. [cited 2019 Dec 06]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152003-190916/ ;.

Council of Science Editors:

Inakollu P. A study of the Effectiveness of Neural Networks for Elemental Concentration from LIBS Spectra. [Masters Thesis]. Mississippi State University; 2003. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152003-190916/ ;


University of Lund

13. Brynolfsson, Johan. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.

Degree: 2019, University of Lund

 This thesis deals with estimation and classification problems of non-stationary processes in a few special cases.In paper A and paper D we make strong assumptions… (more)

Subjects/Keywords: Signalbehandling; Time-Frequency Estimation; Parameter Estimation; Reassignment method; Non-Stationary Processes; Smooth spectral estimation; Neural Networks

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APA (6th Edition):

Brynolfsson, J. (2019). Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; http://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf

Chicago Manual of Style (16th Edition):

Brynolfsson, Johan. “Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.” 2019. Doctoral Dissertation, University of Lund. Accessed December 06, 2019. http://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; http://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf.

MLA Handbook (7th Edition):

Brynolfsson, Johan. “Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.” 2019. Web. 06 Dec 2019.

Vancouver:

Brynolfsson J. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. [Internet] [Doctoral dissertation]. University of Lund; 2019. [cited 2019 Dec 06]. Available from: http://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; http://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf.

Council of Science Editors:

Brynolfsson J. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. [Doctoral Dissertation]. University of Lund; 2019. Available from: http://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; http://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf


University of Waterloo

14. Bekolay, Trevor. Learning in large-scale spiking neural networks.

Degree: 2011, University of Waterloo

 Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides low-level descriptions of… (more)

Subjects/Keywords: neuroplasticity; learning; neural networks; spiking neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bekolay, T. (2011). Learning in large-scale spiking neural networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6195

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bekolay, Trevor. “Learning in large-scale spiking neural networks.” 2011. Thesis, University of Waterloo. Accessed December 06, 2019. http://hdl.handle.net/10012/6195.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bekolay, Trevor. “Learning in large-scale spiking neural networks.” 2011. Web. 06 Dec 2019.

Vancouver:

Bekolay T. Learning in large-scale spiking neural networks. [Internet] [Thesis]. University of Waterloo; 2011. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10012/6195.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bekolay T. Learning in large-scale spiking neural networks. [Thesis]. University of Waterloo; 2011. Available from: http://hdl.handle.net/10012/6195

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Johannesburg

15. De Wet, Anton Petrus Christiaan. An incremental learning system for artificial neural networks.

Degree: 2014, University of Johannesburg

M.Ing. (Electrical And Electronic Engineering)

This dissertation describes the development of a system of Artificial Neural Networks that enables the incremental training of feed forward… (more)

Subjects/Keywords: Neural networks (Computer science); Artificial Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

De Wet, A. P. C. (2014). An incremental learning system for artificial neural networks. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/12024

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

De Wet, Anton Petrus Christiaan. “An incremental learning system for artificial neural networks.” 2014. Thesis, University of Johannesburg. Accessed December 06, 2019. http://hdl.handle.net/10210/12024.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

De Wet, Anton Petrus Christiaan. “An incremental learning system for artificial neural networks.” 2014. Web. 06 Dec 2019.

Vancouver:

De Wet APC. An incremental learning system for artificial neural networks. [Internet] [Thesis]. University of Johannesburg; 2014. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10210/12024.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

De Wet APC. An incremental learning system for artificial neural networks. [Thesis]. University of Johannesburg; 2014. Available from: http://hdl.handle.net/10210/12024

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Montana State University

16. Glassy, Louis. SIERRA : an octree-based spatial data system for neural data.

Degree: College of Engineering, 1998, Montana State University

Subjects/Keywords: Spatial systems.; Medical informatics.; Neural networks (Computer science)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Glassy, . L. (1998). SIERRA : an octree-based spatial data system for neural data. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/8507

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Glassy, Louis. “SIERRA : an octree-based spatial data system for neural data.” 1998. Thesis, Montana State University. Accessed December 06, 2019. https://scholarworks.montana.edu/xmlui/handle/1/8507.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Glassy, Louis. “SIERRA : an octree-based spatial data system for neural data.” 1998. Web. 06 Dec 2019.

Vancouver:

Glassy L. SIERRA : an octree-based spatial data system for neural data. [Internet] [Thesis]. Montana State University; 1998. [cited 2019 Dec 06]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/8507.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Glassy L. SIERRA : an octree-based spatial data system for neural data. [Thesis]. Montana State University; 1998. Available from: https://scholarworks.montana.edu/xmlui/handle/1/8507

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Georgia

17. Crowell, Kevin Lee. Precipitation prediction using artificial neural networks.

Degree: MS, Artificial Intelligence, 2008, University of Georgia

 Precipitation, in meteorology, is defined as any product, liquid or solid, of atmospheric water vapor that is accumulated onto the earth’s surface. Water, and thus… (more)

Subjects/Keywords: Artificial Neural Networks

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APA (6th Edition):

Crowell, K. L. (2008). Precipitation prediction using artificial neural networks. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms

Chicago Manual of Style (16th Edition):

Crowell, Kevin Lee. “Precipitation prediction using artificial neural networks.” 2008. Masters Thesis, University of Georgia. Accessed December 06, 2019. http://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms.

MLA Handbook (7th Edition):

Crowell, Kevin Lee. “Precipitation prediction using artificial neural networks.” 2008. Web. 06 Dec 2019.

Vancouver:

Crowell KL. Precipitation prediction using artificial neural networks. [Internet] [Masters thesis]. University of Georgia; 2008. [cited 2019 Dec 06]. Available from: http://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms.

Council of Science Editors:

Crowell KL. Precipitation prediction using artificial neural networks. [Masters Thesis]. University of Georgia; 2008. Available from: http://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms


University of Georgia

18. Martin, Charles Maxwell. Crop yield prediction using artificial neural networks and genetic algorithms.

Degree: MS, Artificial Intelligence, 2009, University of Georgia

 Previous research has established that large-scale climatological phenomena influence local weather conditions in various parts of the world. These weather conditions have a direct effect… (more)

Subjects/Keywords: Artificial Neural Networks

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APA (6th Edition):

Martin, C. M. (2009). Crop yield prediction using artificial neural networks and genetic algorithms. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms

Chicago Manual of Style (16th Edition):

Martin, Charles Maxwell. “Crop yield prediction using artificial neural networks and genetic algorithms.” 2009. Masters Thesis, University of Georgia. Accessed December 06, 2019. http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms.

MLA Handbook (7th Edition):

Martin, Charles Maxwell. “Crop yield prediction using artificial neural networks and genetic algorithms.” 2009. Web. 06 Dec 2019.

Vancouver:

Martin CM. Crop yield prediction using artificial neural networks and genetic algorithms. [Internet] [Masters thesis]. University of Georgia; 2009. [cited 2019 Dec 06]. Available from: http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms.

Council of Science Editors:

Martin CM. Crop yield prediction using artificial neural networks and genetic algorithms. [Masters Thesis]. University of Georgia; 2009. Available from: http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms


University of Waterloo

19. Caterini, Anthony. A Novel Mathematical Framework for the Analysis of Neural Networks.

Degree: 2017, University of Waterloo

 Over the past decade, Deep Neural Networks (DNNs) have become very popular models for processing large amounts of data because of their successful application in… (more)

Subjects/Keywords: Neural Networks; Convolutional Neural Networks; Deep Neural Networks; Machine Learning; Recurrent Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Caterini, A. (2017). A Novel Mathematical Framework for the Analysis of Neural Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12173

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Caterini, Anthony. “A Novel Mathematical Framework for the Analysis of Neural Networks.” 2017. Thesis, University of Waterloo. Accessed December 06, 2019. http://hdl.handle.net/10012/12173.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Caterini, Anthony. “A Novel Mathematical Framework for the Analysis of Neural Networks.” 2017. Web. 06 Dec 2019.

Vancouver:

Caterini A. A Novel Mathematical Framework for the Analysis of Neural Networks. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10012/12173.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Caterini A. A Novel Mathematical Framework for the Analysis of Neural Networks. [Thesis]. University of Waterloo; 2017. Available from: http://hdl.handle.net/10012/12173

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

20. Comsa, Iulia-Maria. Tracking brain dynamics across transitions of consciousness .

Degree: 2019, University of Cambridge

 How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity.… (more)

Subjects/Keywords: consciousness; neuroscience of consciousness; states of consciousness; levels of consciousness; impaired consciousness; onset of sleep; sedation; coma; EEG; EEG microstates; brain connectivity; frontoparietal connectivity; temporal brain dynamics; graph theory; Lempel-Ziv complexity; neural complexity; neural integration; brain networks; spectral power; spectral connectivity; weighted phase lag index

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APA (6th Edition):

Comsa, I. (2019). Tracking brain dynamics across transitions of consciousness . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/290496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Comsa, Iulia-Maria. “Tracking brain dynamics across transitions of consciousness .” 2019. Thesis, University of Cambridge. Accessed December 06, 2019. https://www.repository.cam.ac.uk/handle/1810/290496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Comsa, Iulia-Maria. “Tracking brain dynamics across transitions of consciousness .” 2019. Web. 06 Dec 2019.

Vancouver:

Comsa I. Tracking brain dynamics across transitions of consciousness . [Internet] [Thesis]. University of Cambridge; 2019. [cited 2019 Dec 06]. Available from: https://www.repository.cam.ac.uk/handle/1810/290496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Comsa I. Tracking brain dynamics across transitions of consciousness . [Thesis]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/290496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cambridge

21. Comsa, Iulia-Maria. Tracking brain dynamics across transitions of consciousness.

Degree: PhD, 2019, University of Cambridge

 How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity.… (more)

Subjects/Keywords: consciousness; neuroscience of consciousness; states of consciousness; levels of consciousness; impaired consciousness; onset of sleep; sedation; coma; EEG; EEG microstates; brain connectivity; frontoparietal connectivity; temporal brain dynamics; graph theory; Lempel-Ziv complexity; neural complexity; neural integration; brain networks; spectral power; spectral connectivity; weighted phase lag index

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APA (6th Edition):

Comsa, I. (2019). Tracking brain dynamics across transitions of consciousness. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/290496 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774595

Chicago Manual of Style (16th Edition):

Comsa, Iulia-Maria. “Tracking brain dynamics across transitions of consciousness.” 2019. Doctoral Dissertation, University of Cambridge. Accessed December 06, 2019. https://www.repository.cam.ac.uk/handle/1810/290496 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774595.

MLA Handbook (7th Edition):

Comsa, Iulia-Maria. “Tracking brain dynamics across transitions of consciousness.” 2019. Web. 06 Dec 2019.

Vancouver:

Comsa I. Tracking brain dynamics across transitions of consciousness. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2019 Dec 06]. Available from: https://www.repository.cam.ac.uk/handle/1810/290496 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774595.

Council of Science Editors:

Comsa I. Tracking brain dynamics across transitions of consciousness. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/290496 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774595


ETH Zürich

22. Neil, Daniel. Deep Neural Networks and Hardware Systems for Event-driven Data.

Degree: 2017, ETH Zürich

 Event-based sensors, built with biological inspiration, differ greatly from traditional sensor types. A standard vision sensor uses a pixel array to produce a frame containing… (more)

Subjects/Keywords: Deep Neural Networks; Event-driven sensors; Deep neural networks (DNNs); Spiking deep neural networks; Recurrent Neural Networks; Convolutional neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Neil, D. (2017). Deep Neural Networks and Hardware Systems for Event-driven Data. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/168865

Chicago Manual of Style (16th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Doctoral Dissertation, ETH Zürich. Accessed December 06, 2019. http://hdl.handle.net/20.500.11850/168865.

MLA Handbook (7th Edition):

Neil, Daniel. “Deep Neural Networks and Hardware Systems for Event-driven Data.” 2017. Web. 06 Dec 2019.

Vancouver:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/20.500.11850/168865.

Council of Science Editors:

Neil D. Deep Neural Networks and Hardware Systems for Event-driven Data. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/168865

23. Rasheed, Farah. Artificial Neural Network Circuit for Spectral Pattern Recognition.

Degree: 2013, Texas A&M University

 Artificial Neural Networks (ANNs) are a massively parallel network of a large number of interconnected neurons similar to the structure of biological neurons in the… (more)

Subjects/Keywords: Verilog; Circuit; Artificial Neural Networks; Spectral Pattern Recognition

…number of input features more efficiently [1]. The motivation behind neural networks… …networks and conventional identification methodologies," Artificial Neural Networks, Fifth… …the results obtained for the ANNs. 12 2. SOFTWARE MODEL FOR TRAINING NEURAL NETWORKS In… …circuit. 21 3. HARDWARE IMPLEMENTATION OF NEURAL NETWORKS FOR PREDICTION The most important… …4 Figure 2: Feedforward Artificial Neural Network Representation (Multi Layer… 

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APA (6th Edition):

Rasheed, F. (2013). Artificial Neural Network Circuit for Spectral Pattern Recognition. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151635

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rasheed, Farah. “Artificial Neural Network Circuit for Spectral Pattern Recognition.” 2013. Thesis, Texas A&M University. Accessed December 06, 2019. http://hdl.handle.net/1969.1/151635.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rasheed, Farah. “Artificial Neural Network Circuit for Spectral Pattern Recognition.” 2013. Web. 06 Dec 2019.

Vancouver:

Rasheed F. Artificial Neural Network Circuit for Spectral Pattern Recognition. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/1969.1/151635.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rasheed F. Artificial Neural Network Circuit for Spectral Pattern Recognition. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151635

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

24. Giannakos, Apostolos. Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα.

Degree: 2013, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

 The present study aims at examining the potential of developing rainfall estimation schemes using the enhanced spectral resolution of the Meteosat Second Generation (MSG).Initially, the… (more)

Subjects/Keywords: Εκτίμηση βροχής; Φασματικοί παράμετροι; Παράμετροι υφής; Σωρειτόμορφη βροχόπτωση; Στρατόμορφη βροχόπτωση; Δορυφόρος Meteosat; Νευρωνικά δίκτυα; Rainfall estimation; Spectral parameters; Textural parameters; Convective rain; Stratiform rain; Meteosat satellite; Seviri; Neural networks

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APA (6th Edition):

Giannakos, A. (2013). Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα. (Thesis). Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Retrieved from http://hdl.handle.net/10442/hedi/35628

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Giannakos, Apostolos. “Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα.” 2013. Thesis, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Accessed December 06, 2019. http://hdl.handle.net/10442/hedi/35628.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Giannakos, Apostolos. “Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα.” 2013. Web. 06 Dec 2019.

Vancouver:

Giannakos A. Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα. [Internet] [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2013. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10442/hedi/35628.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Giannakos A. Συμβολή στην ανάπτυξη τεχνικής για την εκτίμηση της βροχόπτωσης από πολυφασματικά δορυφορικά δεδομένα. [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2013. Available from: http://hdl.handle.net/10442/hedi/35628

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Case Western Reserve University

25. Lin, Chii-Wann. Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis.

Degree: PhD, Biomedical Engineering, 1993, Case Western Reserve University

 Compartmental distribution of protons and associated regulation mechanisms are important aspects of brain functions. The dynamic regulation of proton concentration in brain tissue is essential… (more)

Subjects/Keywords: Optical measurement intracellular pH brain tissue quantitative application artificial neural networks spectral analysis

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APA (6th Edition):

Lin, C. (1993). Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis. (Doctoral Dissertation). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1056659116

Chicago Manual of Style (16th Edition):

Lin, Chii-Wann. “Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis.” 1993. Doctoral Dissertation, Case Western Reserve University. Accessed December 06, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1056659116.

MLA Handbook (7th Edition):

Lin, Chii-Wann. “Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis.” 1993. Web. 06 Dec 2019.

Vancouver:

Lin C. Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis. [Internet] [Doctoral dissertation]. Case Western Reserve University; 1993. [cited 2019 Dec 06]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1056659116.

Council of Science Editors:

Lin C. Optical measurement of intracellular pH in brain tissue and the quantitative application of artificial neural networks to spectral analysis. [Doctoral Dissertation]. Case Western Reserve University; 1993. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1056659116


University of Stirling

26. Varley, A L. Bridging the capability gap in environmental gamma-ray spectrometry.

Degree: PhD, 2015, University of Stirling

 Environmental gamma-ray spectroscopy provides a powerful tool that can be used in environmental monitoring given that it offers a compromise between measurement time and accuracy… (more)

Subjects/Keywords: Radioactivity; Contaminated land; Artificial intelligence; Neural networks; Spectral processing; Support vector machines; gamma-ray spectroscopy; Gamma ray spectrometry; Artificial intelligence; Support vector machines; Radioactivity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Varley, A. L. (2015). Bridging the capability gap in environmental gamma-ray spectrometry. (Doctoral Dissertation). University of Stirling. Retrieved from http://hdl.handle.net/1893/23320

Chicago Manual of Style (16th Edition):

Varley, A L. “Bridging the capability gap in environmental gamma-ray spectrometry.” 2015. Doctoral Dissertation, University of Stirling. Accessed December 06, 2019. http://hdl.handle.net/1893/23320.

MLA Handbook (7th Edition):

Varley, A L. “Bridging the capability gap in environmental gamma-ray spectrometry.” 2015. Web. 06 Dec 2019.

Vancouver:

Varley AL. Bridging the capability gap in environmental gamma-ray spectrometry. [Internet] [Doctoral dissertation]. University of Stirling; 2015. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/1893/23320.

Council of Science Editors:

Varley AL. Bridging the capability gap in environmental gamma-ray spectrometry. [Doctoral Dissertation]. University of Stirling; 2015. Available from: http://hdl.handle.net/1893/23320


Michigan State University

27. Kavdir, Ismail. Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging.

Degree: PhD, Department of Agricultural Engineering, 2000, Michigan State University

Subjects/Keywords: Apples – Inspection; Sorting devices; Neural networks (Computer science); Spectral reflectance

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APA (6th Edition):

Kavdir, I. (2000). Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:28657

Chicago Manual of Style (16th Edition):

Kavdir, Ismail. “Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging.” 2000. Doctoral Dissertation, Michigan State University. Accessed December 06, 2019. http://etd.lib.msu.edu/islandora/object/etd:28657.

MLA Handbook (7th Edition):

Kavdir, Ismail. “Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging.” 2000. Web. 06 Dec 2019.

Vancouver:

Kavdir I. Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging. [Internet] [Doctoral dissertation]. Michigan State University; 2000. [cited 2019 Dec 06]. Available from: http://etd.lib.msu.edu/islandora/object/etd:28657.

Council of Science Editors:

Kavdir I. Apple sorting using neural networks, statistical classifiers and spectral reflectance imaging. [Doctoral Dissertation]. Michigan State University; 2000. Available from: http://etd.lib.msu.edu/islandora/object/etd:28657


California State University – Sacramento

28. Deo, Sudarshan. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.

Degree: MS, Computer Science, 2019, California State University – Sacramento

 This project collects several experiments in Deep Learning Convolutional Neural Network for Image predictions. It makes use of Google TensorFlow and TFlearn Deep Learning libraries… (more)

Subjects/Keywords: Neural Networks; Classification; Neural networks; Convolutional neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Deo, S. (2019). Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207763

Chicago Manual of Style (16th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Masters Thesis, California State University – Sacramento. Accessed December 06, 2019. http://hdl.handle.net/10211.3/207763.

MLA Handbook (7th Edition):

Deo, Sudarshan. “Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization.” 2019. Web. 06 Dec 2019.

Vancouver:

Deo S. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10211.3/207763.

Council of Science Editors:

Deo S. Deep learning with convolutional neural networks for image recognition: step-by-step process from preparation to generalization. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207763


University of Victoria

29. Edwards, Roderick. Neural networks and neural fields: discrete and continuous space neural models.

Degree: Department of Mathematics and Statistics, 2018, University of Victoria

 'Attractor' neural network models have useful properties, but biology suggests that more varied dynamics may be significant. Even the equations of the Hopfield network, without… (more)

Subjects/Keywords: Neural circuitry; Neural networks (Computer science)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Edwards, R. (2018). Neural networks and neural fields: discrete and continuous space neural models. (Thesis). University of Victoria. Retrieved from https://dspace.library.uvic.ca//handle/1828/9682

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Edwards, Roderick. “Neural networks and neural fields: discrete and continuous space neural models.” 2018. Thesis, University of Victoria. Accessed December 06, 2019. https://dspace.library.uvic.ca//handle/1828/9682.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Edwards, Roderick. “Neural networks and neural fields: discrete and continuous space neural models.” 2018. Web. 06 Dec 2019.

Vancouver:

Edwards R. Neural networks and neural fields: discrete and continuous space neural models. [Internet] [Thesis]. University of Victoria; 2018. [cited 2019 Dec 06]. Available from: https://dspace.library.uvic.ca//handle/1828/9682.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Edwards R. Neural networks and neural fields: discrete and continuous space neural models. [Thesis]. University of Victoria; 2018. Available from: https://dspace.library.uvic.ca//handle/1828/9682

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Bucknell University

30. Mukhopadhyay, Himadri. The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks.

Degree: 2010, Bucknell University

 The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the… (more)

Subjects/Keywords: Neural Signals; Neural Networks; Temporal Backpropagation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mukhopadhyay, H. (2010). The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks. (Thesis). Bucknell University. Retrieved from https://digitalcommons.bucknell.edu/masters_theses/23

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mukhopadhyay, Himadri. “The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks.” 2010. Thesis, Bucknell University. Accessed December 06, 2019. https://digitalcommons.bucknell.edu/masters_theses/23.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mukhopadhyay, Himadri. “The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks.” 2010. Web. 06 Dec 2019.

Vancouver:

Mukhopadhyay H. The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks. [Internet] [Thesis]. Bucknell University; 2010. [cited 2019 Dec 06]. Available from: https://digitalcommons.bucknell.edu/masters_theses/23.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mukhopadhyay H. The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks. [Thesis]. Bucknell University; 2010. Available from: https://digitalcommons.bucknell.edu/masters_theses/23

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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