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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
URL: http://hdl.handle.net/10012/14893 
► 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)
▼ 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 semantic segmentation, but also possesses inherent differences. The research surrounding HSI classification sheds light on an approach to bridge computer vision and remote sensing. Modern deep neural networks dominate and repeatedly set new records in all image recognition challenges, largely due to their excellence in extracting discriminative features through multi-layer nonlinear transformation. However, three challenges hinder the direct adoption of convolutional neural networks (CNNs) for HSI classification. First, typical HSIs contain hundreds of spectral channels that encode abundant pixel-wise spectral information, leading to the curse of dimensionality. Second, HSIs usually have relatively small numbers of annotated pixels for training along with large numbers of unlabeled pixels, resulting in the problem of generalization. Third, the scarcity of annotations and the complexity of HSI data induce noisy classification maps, which are a common issue in various types of remotely sensed data interpretation.
Recent studies show that taking the data attributes into the designing of fundamental components of deep neural networks can improve their representational capacity and then facilitates these models to achieve better recognition performance. To the best of our knowledge, no research has exploited this finding or proposed corresponding models for supervised HSI classification given enough labeled HSI data. In cases of limited labeled HSI samples for training, conditional random fields (CRFs) are an effective graph model to impose data-agnostic constraints upon the intermediate outputs of trained discriminators. Although CRFs have been widely used to enhance HSI classification performance, the integration of deep learning and probabilistic graph models in the framework of semi-supervised learning remains an open question.
To this end, this thesis presents supervised spectral-spatial residual networks (SSRNs) and semi-supervised generative adversarial network (GAN) -based models that account for the characteristics of HSIs and make three main contributions. First, spectral and spatial convolution layers are introduced to learn representative HSI features for supervised learning models. Second, generative adversarial networks (GANs) composed of spectral/spatial convolution and transposed-convolution layers are proposed to take advantage of adversarial training using limited amounts of labeled data for semi-supervised learning. Third, fully-connected CRFs are adopted to impose smoothness constraints on the predictions of the trained discriminators of GANs to enhance HSI classification performance. Empirical evidence acquired by experimental comparison to state-of-the-art models validates the effectiveness and generalizability of SSRN, SS-GAN, and GAN-CRF models.
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
URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/5723
► 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)
▼ 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 geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data.
Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...)
Advisors/Committee Members: Bacao, F., Lobo, Víctor.
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
URL: http://hdl.handle.net/2097/35510
► 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)
▼ 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 reservoir
pay. These reservoir properties were estimated by using a multitude
of seismic attributes derived from post-stack high resolution
inversions,
spectral imaging and volumetric curvature.
A pay model
of the reservoir in the Wierman field in Ness County, Kansas is
proposed. The proposed geological model is validated based on
comparison with findings of one blind well. The model will be
useful in determining future drilling prospects, which should
improve the drilling success over previous efforts, which resulted
in only few of the 14 wells in the area being productive. The rock
properties that were modeled were porosity and Gamma ray. Water
saturation and permeability were considered, but the data needed
were not available.
Sequential geological modeling approach uses
multiple seismic attributes as a building block to estimate in a
sequential manner dependent petrophysical properties such as gamma
ray, and porosity. The sequential modelling first determines the
reservoir property that has the ability to be the primary property
controlling most of the other subsequent reservoir properties. In
this study, the gamma ray was chosen as the primary reservoir
property. Hence, the first geologic model built using
neural
networks was a volume of gamma ray constrained by all the available
seismic attributes.
The geological modeling included post-stack
seismic data and the five wells with available well logs. The
post-stack seismic data was enhanced by
spectral whitening to gain
as much resolution as possible. Volumetric curvature was then
calculated to determine where major faults were located. Several
inversions for acoustic impedance were then applied to the
post-stack seismic data to gain as much information as possible
about the acoustic impedance.
Spectral attributes were also
extracted from the post-stack seismic data.
After the most
appropriate gamma ray and porosity models were chosen, pay zone
maps were constructed, which were based on the overlap of a certain
range of gamma ray values with a certain range of porosity values.
These pay zone maps coupled with the porosity and gamma ray models
explain the performance of previously drilled wells.
Advisors/Committee Members: Abdelmoneam RaefMatthew W. Totten.
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
URL: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-8015
► 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)
▼ 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 these parameters to provide effective values at coarse scales. Soil hydraulic properties are required for many hydrological and ecological models at their representative scales. Prediction accuracy of these models is highly dependent on the quality of the model input parameters. However, measurement of parameter data at all such required scales is impractical as that would entail huge outlays of finance, time and effort. Hence, alternate methods of estimating the soil hydraulic parameters at the scales of interest are necessary.
Two approaches to bridge this gap between the measurement and application scales for soil hydraulic parameters are presented in this dissertation. The first one is a stochastic approach, based on artificial
neural networks (ANNs) applied within a Bayesian framework. ANNs have been used before to derive soil hydraulic parameters from other more easily measured soil properties at matching scales. Here, ANNs were applied with different training and simulation scales. This concept was further extended to work within a Bayesian framework in order to provide estimates of uncertainty in such parameter estimations. Use of ancillary information such as elevation and vegetation data, in addition to the soil physical properties, were also tested. These multiscale pedotransfer function methods were successfully tested with numerical and field studies at different locations and scales.
Most upscaling efforts thus far ignore the effect of the topography on the upscaled soil hydraulic parameter values. While this flat-terrain assumption is acceptable at coarse scales of a few hundred meters, at kilometer scales and beyond, the influence of the physical features cannot be ignored. anew upscaling scheme which accounts for variations in topography within a domain was developed to upscale soil hydraulic parameters to hill-slope (kilometer) scales. The algorithm was tested on different synthetically generated topographic configurations with good results. Extending the methodology to field conditions with greater complexities also produced good results. A comparison of different recently developed scaling schemes showed that at hill-slope scales, inclusion of topographic information produced better estimates of effective soil hydraulic parameters at that scale.
Advisors/Committee Members: Mohanty, Binayak P. (advisor), Efendiev, Yalchin (committee member), Smith, Patricia K. (committee member), Datta-Gupta, Akhil (committee member).
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
URL: https://era.library.ualberta.ca/files/h128nf06h
► 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)
▼ 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 when
placed in interesting environments, comparing this behaviour to
real-world subjects for parallels and discrepancies, and then
examining the model for insight and theoretical advancement. This
methodology is employed here in the context of a common
spatial-learning “reorientation task”. Motivated by the discovery
of critical flaws in a popular model for this reorientation task,
we develop a synthetic neural network model as an alternative, and
explore its behaviour in novel tasks, as well as the mathematical
consequences of adopting such a formalism. These behaviours lead us
to question assumptions underlying normal reorientation research.
We devise a new method of collecting human data in spatial tasks,
and use this method to compare the neural network to human
subjects, in the style of comparative cognition.
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
URL: http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459
;
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658381
► 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)
▼ 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 properties might develop through learning. The research methodology employed is the computer simulation of neural network models of head direction cells that self-organize through learning. The preferred firing directions of head direction cells will change in response to the manipulation of distal visual cues, but not in response to the manipulation of proximal visual cues. Simulation results are presented of neural network models that learn to form separate representations of distal and proximal visual cues that are presented simultaneously as visual input to the network. These results demonstrate the computation required for a subpopulation of head direction cells to learn to preferentially respond to distal visual cues. Within a population of head direction cells, the angular distance between the preferred firing directions of any two cells is maintained across different environments. It is shown how a neural network model can learn to maintain the angular distance between the learned preferred firing directions of head direction cells across two different visual training environments. A population of head direction cells can update the population representation of the current head direction, in the absence of visual input, using internal idiothetic (self-generated) motion signals alone. This is called the path integration of head direction. It is important that the head direction cell system updates its internal representation of head direction at the same speed as the animal is rotating its head. Neural network models are simulated that learn to perform the path integration of head direction, using solely idiothetic signals, at the same speed as the head is rotating.
Subjects/Keywords: 612.8; Computational Neuroscience; Theoretical Neuroscience; Head Direction Cells; Neural Networks; Spatial Processing
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APA ·
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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
URL: http://hdl.handle.net/11449/143894
► 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)
▼ 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 oferecem uma oportunidade para mitigar esse efeito, uma vez que, dependendo de seu uso e manejo, são capazes de armazenar grandes quantidades de carbono, retirando-as da atmosfera. A produção de CO2 no solo é resultado de processos biológicos, como a decomposição da matéria orgânica e respiração de raízes e organismos do solo, fenômeno chamado de emissão de CO2 do solo (FCO2). O objetivo deste trabalho foi utilizar as redes neurais artificiais para estudo e previsão de padrões espaço-temporais da emissão de CO2 do solo em áreas de cana-de-açúcar em sistema de cana crua, colheita mecanizada, quando grandes quantidades de palhas são depositadas sobre a superfície do solo. Valores de FCO2 foram coletados em áreas de cultivo comercial no Sudeste do Estado de São Paulo, registrados por meio do sistema LI-8100, em gradeados amostrais para determinação da variabilidade espaçotemporal de FCO2, e atributos físicos e químicos do solo. Foram utilizados dados referentes a estudos realizados nos anos de 2008, 2010 e 2012, no período após a operação de colheita mecânica da cultura. Uma rede neural Perceptron Multi-Camadas via algoritmo backpropagation foi aplicada para estimar a emissão de FCO2 do ano de 2012, utilizando os dados referentes aos anos de 2008 e 2010 para treinamento da rede neural. A rede neural inicialmente apresentou um MAPE de 18,3852 coeficiente de determinação R2 de 0,9188. Os dados obtidos do FCO2 observado e do FCO2 estimado apresentam moderada dependência espacial, e pelos mapas do padrão espacial do fluxo de CO2 é observado que a rede neural apresentou considerável similaridade com os dados observados, identificando os pontos característicos de maior emissão como também os de menor emissão de CO2. Portanto, os resultados indicam que a rede neural artificial pode fornecer estimativas com confiabilidade para a avaliação de FCO2 a partir de dados de atributos físicos e químicos do solo, sendo capaz de caracterizar a variabilidade espaçotemporal desse atributo em áreas de cana-de-açúcar, sob o sistema de cana crua no Sudeste do Estado de São Paulo.
Carbon dioxide (CO2) is considered one of the main gases additional greenhouse effect and contributes significantly to global climate change. Agriculture areas offer an opportunity to mitigate this effect, since, depending on its use and handling, are capable of storing large amounts of carbon, removing them from the atmosphere. The CO2 production in soil is the result of biological processes such as the decomposition of organic matter and breathing roots and soil organisms, a phenomenon called soil CO2 emissions (FCO2). The aim of this study was to use artificial neural networks to study and forecast patterns spatiotemporal of soil CO2 emission in areas of sugarcane in raw cane system, mechanical harvesting, when large amounts of straw are deposited on soil surface. FCO2 values were…
Advisors/Committee Members: Lotufo, Anna Diva Plasencia [UNESP], Universidade Estadual Paulista (UNESP).
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
URL: https://scholarworks.rit.edu/theses/9012
► 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)
▼ Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, consists of two primary components, namely the
spatial pooler (SP) and the temporal memory (TM). The SP is utilized to map similar inputs into generalized sparse distributed representations (SDRs). Those SDRs are then utilized by the TM, which performs sequence learning and prediction. One challenge with HTM is ensuring that proper SDRs are generated from the SP. If the SDRs are not generalizable, the TM will not be able to make proper predictions.
This work focuses on the SP and its corresponding output SDRs. A single unifying mathematical framework was created for the SP. The primary learning mechanism was explored, where a maximum likelihood estimator for determining the degree of permanence update was proposed. The boosting mechanisms were studied and found to only be relevant during the initial few iterations of the network. Observations were made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods were provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verified that given the proper parameterizations, the SP may be used for feature learning.
Similarity metrics were created for scoring the SDRs produced by the SP. The overlap metric proved that the SP is extremely robust to noise. The SP was able to produce similar outputs for a given input, provided the noise did not cause the input to change classes. This overlap metric was further utilized to create a classifier for novelty detection. The SP proved to be able to withstand more noise than the well-known support vector machine (SVM).
Advisors/Committee Members: Dhireesha Kudithipudi, Ernest Fokoue, Andreas Savakis.
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
URL: http://hdl.handle.net/10012/13957
► 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)
▼ 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 several ideas in biological and artificial neural network research that uses a continuous-time, energy-based neural model with a local learning rule. However, despite dealing with recurrent networks, equilibrium propagation has only been applied to discriminative categorization tasks. This thesis generalizes equilibrium propagation to bidirectional learning with asymmetric weights. Simultaneously learning the discriminative as well as generative transformations for a set of data points and their corresponding category labels, bidirectional equilibrium propagation utilizes recurrence and weight asymmetry to share related but non-identical representations within the network. Experiments on an artificial dataset demonstrate the ability to learn both transformations, as well as the ability for asymmetric-weight networks to generalize their discriminative training to the untrained generative task.
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
URL: http://hdl.handle.net/10211.3/206684
► 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)
▼ 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 have an amazing ability to process large amounts of data and learn from them. Recurrent
Neural Networks (RNN) are used in applications involving natural language processing like text translations and text generation. This research evaluates the effectiveness of a RNN to be able to automatically generate programming code. Programming languages are different from natural languages in that they have unique structure and syntax. The goal for this research is to conduct experiments on a character RNN model with for three programming languages; Java, Python and C#, and evaluate the results by testing and analyzing the ability for the RNN to automatically produce code that is able to compile.
Advisors/Committee Members: Sun, Yu (advisor), Husain, Mohammad (committee member).
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
URL: https://digitalcommons.library.umaine.edu/etd/2332
► 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)
▼ 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 must account for potential data loss due to a variety of natural and technological causes. Modeling a natural
spatial region can be problematic due to
spatial nonstationarities in environmental variables, and as particular regions may be
subject to specific influences at different
spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity.
This dissertation evaluates the use of multilayer perceptron
neural network models in the context of sensor
networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old.
We demonstrate accuracy results for the MLP generally on par with those of
spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for
spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of
neural models using both
spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by
k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models.
Advisors/Committee Members: Kate Beard-Tisdale, Neal Pettigrew, Phillippe Tissot.
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
URL: http://sun.library.msstate.edu/ETD-db/theses/available/etd-05152003-190916/
;
► 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)
▼ 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 a laser-induced plasma. Assignment of different atomic and ionic lines, which are signatures of a particular element, is the basis of a qualitative identification of the species present in plasma. The relative intensities of these atomic and ionic lines can be used for the quantitative determination of the corresponding elements present in different samples. Calibration curve based on absolute intensity is the statistical method of determining concentrations of elements in different samples. Since we need an exact knowledge of the sample composition to build the proper calibration curve, this method has some limitations in the case of samples of unknown composition. The current research is to investigate the usefulness of ANN for the determination of the element concentrations from
spectral data. From the study it is shown that
neural networks predict elemental concentrations that are at least as good as the results obtained from traditional analysis. Also by automating the analysis process, we have achieved a vast saving in the time required for the data analysis.
Advisors/Committee Members: Dr. Thomas Philip (chair), Dr. J. P. Singh (chair), Dr. Yul Chu (committee member).
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
URL: http://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da
;
http://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf
► 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)
▼ 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
about the observed signal, where a specific model is assumed and
the parameters of the model are estimated.In Paper B, Paper C, and
Paper E more general assumptions about the structure of the
observed processes are made, and the methods in these papers may be
applied to a wider range of parameter estimation and classification
scenarios.All papers handle non-stationary signals where the
spectral power distribution may change with respect to time. Here,
we are interested in finding time-frequency representations (TFR)
of the signal which can depict how the frequencies and
corresponding amplitudes change.In Paper A, we consider the
estimation of the shape parameter detailing time- and frequency
translated Gaussian bell functions.The algorithm is based on the
scaled reassigned spectrogram, where the spectrogram is calculated
using a unit norm Gaussian window.The spectrogram is then
reassigned using a large set of candidate scaling factors.For the
correct scaling factor, with regards to the shape parameter, the
reassigned spectrogram of a Gaussian function will be perfectly
localized into one single point.In Paper B, we expand on the
concept in Paper A, and allow it to be applied to any twice
differentiable transient function in any dimension.Given that the
matched window function is used when calculating the spectrogram,
we prove that all energy is reassigned to one single point in the
time-frequency domain if scaled reassignment is applied.Given a
parametric model of an observed signal, one may tune the
parameter(s) to minimize the entropy of the matched reassigned
spectrogram.We also present a classification scheme, where one may
apply multiple different parametric models and evaluate which one
of the models that best fit the data. In Paper C, we consider the
problem of estimating the spectral content of signals where the
spectrum is assumed to have a smooth structure.By dividing the
spectral representation into a coarse grid and assuming that the
spectrum within each segment may be well approximated as linear, a
smooth version of the Fourier transform is derived.Using this, we
minimize the least squares norm of the difference between the
sample covariance matrix of an observed signal and any covariance
matrix belonging to a piece-wise linear spectrum.Additionally, we
allow for adding constraints that make the solution obey common
assumptions of spectral representations.We apply the algorithm to
stationary signals in one and two dimensions, as well as to
one-dimensional non-stationary processes. In Paper D we consider
the problem of estimating the parameters of a multi-component chirp
signal, where a harmonic structure may be imposed.The algorithm is
based on a group sparsity with sparse groups framework where a
large dictionary of candidate parameters is constructed.An
optimization scheme is formulated such as to find harmonic groups
of chirps that also…
Subjects/Keywords: Signalbehandling; Time-Frequency Estimation; Parameter Estimation; Reassignment method; Non-Stationary Processes; Smooth spectral estimation; Neural Networks
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Export
to Zotero / EndNote / Reference
Manager
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
URL: http://hdl.handle.net/10012/6195
► 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)
▼ 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 how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologically plausible spiking neural networks with experimentally supported learning rules.
We present three novel neural models that contribute to the understanding of how the brain might solve the three main problems posed by machine learning: supervised learning, in which the rational agent has a fine-grained feedback signal, reinforcement learning, in which the agent gets sparse feedback, and unsupervised learning, in which the agents has no explicit environmental feedback.
In supervised learning, we argue that previous models of supervised learning in spiking neural networks solve a problem that is less general than the supervised learning problem posed by machine learning. We use an existing learning rule to solve the general supervised learning problem with a spiking neural network. We show that the learning rule can be mapped onto the well-known backpropagation rule used in artificial neural networks.
In reinforcement learning, we augment an existing model of the basal ganglia to implement a simple actor-critic model that has a direct mapping to brain areas. The model is used to recreate behavioural and neural results from an experimental study of rats performing a simple reinforcement learning task.
In unsupervised learning, we show that the BCM rule, a common learning rule used in unsupervised learning with rate-based neurons, can be adapted to a spiking neural network. We recreate the effects of STDP, a learning rule with strict time dependencies, using BCM, which does not explicitly remember the times of previous spikes. The simulations suggest that BCM is a more general rule than STDP.
Finally, we propose a novel learning rule that can be used in all three of these simulations. The existence of such a rule suggests that the three types of learning examined separately in machine learning may not be implemented with separate processes in the brain.
Subjects/Keywords: neuroplasticity; learning; neural networks; spiking neural networks
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Export
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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
URL: http://hdl.handle.net/10210/12024
► 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)
▼ 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 neural networks using supervised training algorithms such as back propagation. It is argued that incremental learning is fundamental to the adaptive learning behavior observed in human intelligence and constitutes an imperative step towards artificial cognition. The importance of developing incremental learning as a system of ANNs is stressed before the complete system is presented. Details of the development and implementation of the system is complemented by the description of two case studies. In conclusion the role of the incremental learning system as basis for further development of fundamental elements of cognition is projected.
Subjects/Keywords: Neural networks (Computer science); Artificial Neural Networks
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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
URL: https://scholarworks.montana.edu/xmlui/handle/1/8507
Subjects/Keywords: Spatial systems.; Medical informatics.; Neural networks (Computer science)
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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
URL: http://purl.galileo.usg.edu/uga_etd/crowell_kevin_l_200812_ms
► 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)
▼ 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 precipitation, has a major impact on our daily livelihood. As such, the uncertainty of both the future occurrence and amount of precipitation can have a negative impact on many sectors of our economy, especially agriculture. There is, therefore, a need to use innovative computer technologies such as artificial intelligence to improve the accuracy of precipitation predictions. Artificial
neural networks have been shown to be useful as an aid for the prediction of weather variables. The goal of this study was to develop artificial
neural network models for the purpose of predicting both the Probability of Precipitation and quantitative precipitation over a 24-hour period beginning and ending at midnight.
Advisors/Committee Members: Gerrit Hoogenboom.
Subjects/Keywords: Artificial Neural Networks
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APA ·
Chicago ·
MLA ·
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Export
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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
URL: http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms
► 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)
▼ 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 on crop yield. Consequently, much research has been done exploring the connections between large-scale climatological phenomena and crop yield. Artificial
neural networks have been demonstrated to be powerful tools for modeling and prediction, and can be combined with genetic algorithms to increase their effectiveness. The goal of the research presented in this thesis was to develop artificial
neural network models using genetic algorithm-selected inputs in order to predict southeastern US maize yield at various points throughout the year.
Advisors/Committee Members: Gerrit Hoogenboom.
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
URL: http://hdl.handle.net/10012/12173
► 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)
▼ 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 a wide variety of fields. These models are layered, often containing parametrized linear and non-linear transformations at each layer in the network. At this point, however, we do not rigorously understand why DNNs are so effective. In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks, and demonstrate how this framework can be used to represent specific neural network architectures.
In chapter 1, we start by exploring mathematical contributions to neural networks. We can rigorously explain some properties of DNNs, but these results fail to fully describe the mechanics of a generic neural network. We also note that most approaches to describing neural networks rely upon breaking down the parameters and inputs into scalars, as opposed to referencing their underlying vector spaces, which adds some awkwardness into their analysis. Our framework strictly operates over these spaces, affording a more natural description of DNNs once the mathematical objects that we use are well-defined and understood.
We then develop the generic framework in chapter 3. We are able to describe an algorithm for calculating one step of gradient descent directly over the inner product space in which the parameters are defined. Also, we can represent the error backpropagation step in a concise and compact form. Besides a standard squared loss or cross-entropy loss, we also demonstrate that our framework, including gradient calculation, extends to a more complex loss function involving the first derivative of the network.
After developing the generic framework, we apply it to three specific network examples in chapter 4. We start with the Multilayer Perceptron, the simplest type of DNN, and show how to generate a gradient descent step for it. We then represent the Convolutional Neural Network (CNN), which contains more complicated input spaces, parameter spaces, and transformations at each layer. The CNN, however, still fits into the generic framework. The last structure that we consider is the Deep Auto-Encoder, which has parameters that are not completely independent at each layer. We are able to extend the generic framework to handle this case as well.
In chapter 5, we use some of the results from the previous chapters to develop a framework for Recurrent Neural Networks (RNNs), the sequence-parsing DNN architecture. The parameters are shared across all layers of the network, and thus we require some additional machinery to describe RNNs. We describe a generic RNN first, and then the specific case of the vanilla RNN. We again compute gradients directly over inner product spaces.
Subjects/Keywords: Neural Networks; Convolutional Neural Networks; Deep Neural Networks; Machine Learning; Recurrent Neural Networks
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APA ·
Chicago ·
MLA ·
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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
URL: https://www.repository.cam.ac.uk/handle/1810/290496
► 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)
▼ 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. In this
thesis, I investigate computational measures applicable to the electroencephalogram to
quantify the loss and recovery of consciousness from the perspective of modern theoretical
frameworks. I examine three different transitions of consciousness caused by natural,
pharmacological and pathological factors: sleep, sedation and coma.
First, I investigate the neural dynamics of falling asleep. By combining the established
methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects,
a unique microstate is identified, whose increased duration predicts behavioural
unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely
captures an increase in frontoparietal theta connectivity, a putative marker of the loss of
consciousness prior to sleep onset.
I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild
and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute
signal complexity and symbolic mutual information to assess information integration. An
intriguing dissociation between responsiveness and drug level in blood during sedation is
revealed: responsiveness is best predicted by the temporal complexity of the signal at single-
channel and low-frequency integration, whereas drug level is best predicted by the
complexity of spatial patterns and high-frequency integration.
Finally, I investigate brain connectivity in the overnight EEG recordings of a group of
patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find
that increased variability in delta network integration early after injury predicts the eventual
coma recovery score. A case study is also described where the re-emergence of frontoparietal
connectivity predicted a full recovery long before behavioural improvement.
The findings of this thesis inform prospective clinical applications for tracking states of
consciousness and advance our understanding of the slow and fast brain dynamics
underlying its transitions. Collating these findings under a common theoretical framework, I
argue that the diversity of dynamical states, in particular in temporal domain, and
information integration across brain networks are fundamental in sustaining consciousness.
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 ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
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
URL: https://www.repository.cam.ac.uk/handle/1810/290496
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774595
► 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)
▼ 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. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.
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 ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
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
URL: http://hdl.handle.net/20.500.11850/168865
► 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)
▼ 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 the light intensity at every pixel whenever the sensor is sampled; a standard audio sensor produces a waveform of sound amplitude over time. Event-based sensors, on the other hand, are typically substantially sparser in their output, producing output events that occur upon informative changes in the scene, usually with low latency and accurate timing, and are data-driven rather than sampled.
The outputs produced by these novel sensor types differ radically from traditional sensors. Unfortunately, these differences make it hard to apply standard data analysis techniques to event-based data, despite the advanced state of computational techniques for image understanding and acoustic processing. Machine learning especially has made great strides in recent years towards scene understanding, and particularly in the area of deep learning.
The goal of this thesis is to study how to make use of these novel sensors to draw from the state-of-the-art in machine learning while maintaining the advantages of event-based sensors. This thesis takes the view that frame-based, traditional data has limited the scope of discovery for new kinds of machine learning algorithms. While machine learning algorithms have reached great success, their achievements pale in comparison to biological reasoning, and perhaps this arises from the fundamental assumptions about what is processed in addition to how. That is, by relaxing expectations on the kinds of data that will be processed, perhaps even better algorithms can be discovered that not only work with biologically-inspired event-based sensors but also outperform traditional machine learning algorithms.
This thesis is studied at multiple levels of abstraction. In Chapter 2, custom hardware platforms are introduced that prototype an existing machine learning algorithm in hardware. That work aims to ensure that the advantages of both state-of-the-art machine learning and the novel sensor types are maintained at the most fundamental hardware level and to understand the limitations of the algorithms better. Indeed, this revealed that the most significant bottleneck when combining both is the accuracy loss compared to traditional machine learning algorithms, and motivates the work in Chapter 3 that dramatically increases the accuracy of event-driven
neural networks for fixed, unchanging scenes (e.g., image analysis, perhaps the most well-studied problem in deep learning currently). With that primary limitation addressed, Chapter 4 explores advantages that are unavailable to traditional deep learning but are available to event-driven deep
networks.
Chapter 5 forms perhaps the key contribution of this thesis by introducing a novel algorithm, Phased LSTM, that natively works with event-driven sensors observing dynamic and changing scenes. Indeed, as hypothesized above, Phased LSTM offers significant…
Advisors/Committee Members: Liu, Shih-Chii, Delbruck, Tobi, Lee, Daniel, Martin, Kevan A.C..
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
URL: http://hdl.handle.net/1969.1/151635
► 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)
▼ 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 human brain. ANNs find applications in a large number of fields, from pattern classification problems in Computer Science like handwriting recognition to cancer classification problems in Biomedical Engineering.
The parallelism inherent in
neural networks makes hardware a good choice to implement ANNs compared to software implementations. The ANNs implemented in this thesis have feedforward architecture and are trained using backpropagation learning algorithm. Different
neural network models are trained offline using software and the prediction algorithms are implemented using Verilog and compared with the software models.
The circuit implementation of feedforward
neural networks is found to be much faster than its software counterpart because of the parallel and pipelined structure as well as the presence of a large number of computations that makes the software simulations slower in comparison. The time taken from input to output by the circuit implementing the feedforward prediction algorithm is measured from the waveform diagram, and it is seen that the circuit implementation of the ANNs provides an increase of over 90% in processing speeds obtained via post-synthesis simulation compared to the software implementation.
The ANN models developed in this thesis are plant disease classification, soil clay content classification and handwriting recognition for digits. The accuracy of the ANN model is found to be 75% to 97% for the three different problems. The results obtained from the circuit implementation show a < 1% decrease in accuracy compared with the software simulations because of the use of fixed-point representation for the real numbers. Fixed-point representation of numbers is used instead of floating-point representation for faster computational speed and better resource utilization.
Advisors/Committee Members: Hu, Jiang (advisor), Ge, Yufeng (committee member), Palermo, Samuel (committee member), Li, Peng (committee member).
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 ·
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MLA ·
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Export
to Zotero / EndNote / Reference
Manager
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)
URL: http://hdl.handle.net/10442/hedi/35628
► 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)
▼ 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 possibility of developing precipitating cloud detection schemes was investigated, using the enhanced thermal infrared spectral resolution of the Meteosat Second Generation (MSG). Two different classification methodologies were proposed that use the spectral parameters along with textural parameters derived from the thermal infrared MSG channels to delineate rain from no rain clouds. The first is an algorithm based on the probability of rain (Probability of Rainfall, PΟR) for each pixel of the thermal infrared MSG satellite data and the second is an Artificial Neural Network (Neural Network Μultilayer Perceptron, MLP) model. Both schemes were trained using as rain information spatially and temporally matched gauge data from 88 stations in Greece, for 30 rainy cases covering the period from March 2008 to February 2009. Both schemes were evaluated against an independent sample of rain gauge data for ten rainy days. During the training phase, POR1 model based on spectral parameters showed the best performance among all the rain area delineation models while MLP2 model exhibited the lowest performance. When evaluating against the independent dataset, the MLP1 model provides the best results among all the rain area discrimination techniques and POR2 algorithm produces the worst results. From the validation results, it was found that the introduction of textural parameters does not improve the discrimination between rain and no rain clouds. All algorithms overestimate the rain occurrences detected by the rain stations network.The next step of the rainfall estimation methodology was the development of two convective and stratiform rain delineation schemes based on the high spectral resolution of the MSG. Two different classification methods were proposed that use spectral cloud parameters along with textural cloud parameters. The first model is an empirical algorithm based on the estimation of the probability of convective rainfall on a pixel basis (Probability of Convective Rainfall, PCR) for the satellite infrared dataset and the second is an Artificial Neural Network model (MLP) for convective/stratiform rain classification that is based on the correlation between spectral and textural parameters and convective and stratiform classes of surface rainfall. The rain delineation algorithms were trained using different rainy cases with high convective activity covering the same period from March 2008 to February 2009. The PCR2 algorithm that is based on both spectral and textural measures, during the training phase provided the best results among all the rain classification models and MLP1 algorithm exhibited the worst scores. When evaluating against the independent dataset, the MLP2 model based on both spectral and textural parameters showed the best performance among all the convective/stratiform rain discrimination schemes and algorithm PCR1…
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
URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1056659116
► 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)
▼ 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 for maintaining normal metabolic and electrophysiological
activities. Two optical methods are used because of their superior
spatial and temporal resolution and the potential capability for
measurement of multiple ionic species. Neutral red (NR) and
carboxy-seminaphthorhodaflur-1 (SNARF-1) are used to measure
intracellular pH in hippocampal brain slices and in vivo brain. The
evidence suggests that these two dyes locate in different
compartments. NR may enter both neuronal and glial compartments
while SNARF-1 predominantly stains the neuronal compartment. The
different baseline pH i reading observed by using these two dyes
also suggest that different pH regulation schemes are used in these
two compartments. The effect of the
Na
+/H
+ exchanger
blockers, amiloride and its analogs, are tested on the recovery
slope of NH
4Cl acid-loading technique. The
different responses to the amiloride suggest that different set
point for the activation of
Na
+/H
+ exchanger in
these two compartments may operate in the slice pr eparation.
Quantitative application of artificial
neural network is
demonstrated with the
spectral recognition for pH value output. A
working network can be trained with a set of teaching spectra from
a small random connection weight matrix or from one with previous
experience by using generalized delta rule and back-propogation for
weight modification. The imprinting of principal components of the
teaching patterns is distributively stored within the connection
weight matrix of the input to hidden layers. A calibration curve
needs to be constructed to translate the actual output values of
the network to pH values after the convergence with training
patterns. The quantitative output during performing phase is the
inner product of weight matrix and the input vectors (unknown
patterns). This method can thus achieve the real-time quantitative
application with learning from example spectra.
Advisors/Committee Members: LaManna, Joseph (Advisor).
Subjects/Keywords: Optical measurement intracellular pH brain tissue
quantitative application artificial neural networks spectral
analysis
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APA ·
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CSE |
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Manager
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
URL: http://hdl.handle.net/1893/23320
► 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)
▼ 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 allowing for large areas to be surveyed quickly and relatively inexpensively. Depending on monitoring objectives, spectral information can then be analysed in real-time or post survey to characterise contamination and identify potential anomalies.
Smaller volume detectors are of particular worth to environmental surveys as they can be operated in the most demanding environments. However, difficulties are encountered in the selection of an appropriate detector that is robust enough for environmental surveying yet still provides a high quality signal. Furthermore, shortcomings remain with methods employed for robust spectral processing since a number of complexities need to be overcome including: the non-linearity in detector response with source burial depth, large counting uncertainties, accounting for the heterogeneity in the natural background and unreliable methods for detector calibration.
This thesis aimed to investigate the application of machine learning algorithms to environmental gamma-ray spectroscopy data to identify changes in spectral shape within large Monte Carlo calibration libraries to estimate source characteristics for unseen field results. Additionally, a 71 × 71 mm lanthanum bromide detector was tested alongside a conventional 71 × 71 mm sodium iodide to assess whether its higher energy efficiency and resolution could make it more reliable in handheld surveys.
The research presented in this thesis demonstrates that machine learning algorithms could be successfully applied to noisy spectra to produce valuable source estimates. Of note, were the novel characterisation estimates made on borehole and handheld detector measurements taken from land historically contaminated with 226Ra. Through a novel combination of noise suppression and neural networks the burial depth, activity and source extent of contamination was estimated and mapped. Furthermore, it was demonstrated that Machine Learning techniques could be operated in real-time to identify hazardous 226Ra containing hot particles with much greater confidence than current deterministic approaches such as the gross counting algorithm. It was concluded that remediation of 226Ra contaminated legacy sites could be greatly improved using the methods described in this thesis.
Finally, Neural Networks were also applied to estimate the activity distribution of 137Cs, derived from the nuclear industry, in an estuarine environment. Findings demonstrated the method to be theoretically sound, but practically inconclusive, given that much of the contamination at the site was buried beyond the detection limits of the method.
It was generally concluded that the noise posed by intrinsic counts in the 71 × 71 mm lanthanum bromide was too substantial to make any significant improvements over a comparable sodium iodide in contamination characterisation using 1 second…
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 ·
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MLA ·
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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
URL: http://etd.lib.msu.edu/islandora/object/etd:28657
Subjects/Keywords: Apples – Inspection; Sorting devices; Neural networks (Computer science); Spectral reflectance
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
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
URL: http://hdl.handle.net/10211.3/207763
► 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)
▼ 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 for computations, training, and testing of images. The project is developed in Python language on Linux Operating System. It makes use of TensorFlow on CPU and has the capability to implement on GPU as well. The scope of the project is defined for 4 different sets of data to show how Convolutional
Neural Network is architecture independent. The final step is to prepare my own dataset which can be trained and tested for Facial Recognition.
The final experiment walks through the entire process on a custom dataset ??? from image preparation, through
neural network configuration, training, and then testing for generalization.
Advisors/Committee Members: Gordon, V. Scott.
Subjects/Keywords: Neural Networks; Classification; Neural networks; Convolutional neural network
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APA ·
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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
URL: https://dspace.library.uvic.ca//handle/1828/9682
► '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)
▼ '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 the constraint of symmetry, can have complex behaviours which have been little studied. Several new ideas or approaches to
neural network theory are examined here, focussing on the distinction between discrete and continuous space
neural models. First, simple chaotic dynamical systems are examined, as candidates for more natural
neural network models, including coupled systems of Lorenz equations and a Hopfield equation model with a balance of inhibitory and excitatory neurons. Also, continuous space models with a structure like that of the Hopfield network are briefly explored, with interesting training possibilities.
The main results deal with the approximation of Hopfield network equations with a particular class of connection structures (allowing asymmetry), by a reaction-diffusion equation, using techniques borrowed from particle methods used in the numerical solution of fluid-dynamical equations. It is shown that the approximation holds rigorously only in certain
spatial regions but the small regions where it fails, namely within transition layers between regions of high and low activity, are not likely to be critical. The result serves to classify connectivities in Hopfield-type models and sheds light on the limiting behaviour of
networks as the number of neurons goes to infinity. Standard discretizations of the reaction-diffusion equations are analyzed to clarify the effects which can arise in the limiting process. The discrete space systems can have stable patterned equilibria which must be close to metastable patterns of the continuous systems.
Our results also suggest that the fine structure of
neural connections is important, and to obtain complex behaviour in the Hopfield network equations, a predominance of inhibition or wildly oscillating connection matrix entries are indicated.
Advisors/Committee Members: Illner, Reinhard (supervisor).
Subjects/Keywords: Neural circuitry; Neural networks (Computer science)
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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
URL: https://digitalcommons.bucknell.edu/masters_theses/23
► 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)
▼ 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 principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.
Subjects/Keywords: Neural Signals; Neural Networks; Temporal Backpropagation
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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
◁ [1] [2] [3] [4] [5] … [1297] ▶
. 
Open Access Theses and Dissertations
