Thematische Bibliographien / Neural computer / Dissertationen
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Dissertationen zum Thema „Neural computer“

Autor: Grafiati
Veröffentlicht am 30. Juni 2021
Zuletzt aktualisiert am 6. Februar 2022

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1

Somers, Harriet. "A neural computer." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362021.

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2

Churcher, Stephen. "VLSI neural networks for computer vision." Thesis, University of Edinburgh, 1993. http://hdl.handle.net/1842/13397.

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Recent years have seen the rise to prominence of a powerful new computational paradigm - the so-called artificial neural network. Loosely based on the microstructure of the central nervous system, neural networks are massively parallel arrangements of simple processing elements (<i>neurons</i>) which communicate with each other through variable strength connections (<i>synapses</i>). The simplicity of such a description belies the complexity of calculations which neural networks are able to perform. Allied to this, the emergent properties of noise resistance, fault tolerance, and large data ba
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3

Khan, Altaf Hamid. "Feedforward neural networks with constrained weights." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.

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The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward-pass capability. These two differ from the conventional model in having extra constraints on their weights: the first allows its weights to take integer values in the range [-3,3] only, whereas the second restricts its synapses to the set {-1,0,1} while allowing unrestricted offsets. The benefits of the first configuration are in having weights w
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4

Kulakov, Anton. "Multiprocessing neural network simulator." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/348420/.

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Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers. A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is a
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Durrant, Simon. "Negative correlation in neural systems." Thesis, University of Sussex, 2010. http://sro.sussex.ac.uk/id/eprint/2387/.

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In our attempt to understand neural systems, it is useful to identify statistical principles that may be beneficial in neural information processing, outline how these principles may work in theory, and demonstrate the benefits through computational modelling and simulation. Negative correlation is one such principle, and is the subject of this work. The main body of the work falls into three parts. The first part demonstrates the space filling and accelerated central limit convergence benefits of negative correlation, both generally and in the specific neural context of V1 receptive fields. I
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Baker, Thomas Edward. "Implementation limits for artificial neural networks." Full text open access at:, 1990. http://content.ohsu.edu/u?/etd,268.

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7

Lam, Yiu Man. "Self-organized cortical map formation by guiding connections /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202005%20LAM.

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8

Adamu, Abdullahi S. "An empirical study towards efficient learning in artificial neural networks by neuronal diversity." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33799/.

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Artificial Neural Networks (ANN) are biologically inspired algorithms, and it is natural that it continues to inspire research in artificial neural networks. From the recent breakthrough of deep learning to the wake-sleep training routine, all have a common source of drawing inspiration: biology. The transfer functions of artificial neural networks play the important role of forming decision boundaries necessary for learning. However, there has been relatively little research on transfer function optimization compared to other aspects of neural network optimization. In this work, neuronal dive
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McMichael, Lonny D. (Lonny Dean). "A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc501018/.

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A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost any network wiring configuration may be created. While the compiler was created for the specialized ada
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Yang, Horng-Chang. "Multiresolution neural networks for image edge detection and restoration." Thesis, University of Warwick, 1994. http://wrap.warwick.ac.uk/66740/.

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One of the methods for building an automatic visual system is to borrow the properties of the human visual system (HVS). Artificial neural networks are based on this doctrine and they have been applied to image processing and computer vision. This work focused on the plausibility of using a class of Hopfield neural networks for edge detection and image restoration. To this end, a quadratic energy minimization framework is presented. Central to this framework are relaxation operations, which can be implemented using the class of Hopfield neural networks. The role of the uncertainty principle in
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Zhang, Fu. "Intelligent feature selection for neural regression : techniques and applications." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/49639/.

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Feature Selection (FS) and regression are two important technique categories in Data Mining (DM). In general, DM refers to the analysis of observational datasets to extract useful information and to summarise the data so that it can be more understandable and be used more efficiently in terms of storage and processing. FS is the technique of selecting a subset of features that are relevant to the development of learning models. Regression is the process of modelling and identifying the possible relationships between groups of features (variables). Comparing with the conventional techniques, In
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Czuchry, Andrew J. Jr. "Toward a formalism for the automation of neural network construction and processing control." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/9199.

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13

Bragansa, John. "On the performance issues of the bidirectional associative memory." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17809.

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14

Dugan, Kier. "Non-neural computing on the SpiNNaker neuromorphic computer." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/400083/.

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Moore’s law scaling has slowed dramatically since the turn of the millennium, causing new generations of computer hardware to include more processor cores to offer more performance. Desktop computers, server machines, and even mobile phones are all multi-core devices now, and this trend has shown no signs of slowing soon. Eventually, computers will contain so many cores that they will be an abundant resource. Using this many processors requires new ways of thinking about software. Biology leads computer architecture here: mammalian brains contain billions of neurons embedded in a dense fabric
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Billings, Rachel Mae. "On Efficient Computer Vision Applications for Neural Networks." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102957.

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Since approximately the dawn of the new millennium, neural networks and other machine learning algorithms have become increasingly capable of adeptly performing difficult, dull, and dangerous work conventionally carried out by humans in times of old. As these algorithms become steadily more commonplace in everyday consumer and industry applications, the consideration of how they may be implemented on constrained hardware systems such as smartphones and Internet-of-Things (IoT) peripheral devices in a time- and power- efficient manner while also understanding the scenarios in which they fail is
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Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most
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Åström, Fredrik. "Neural Network on Compute Shader : Running and Training a Neural Network using GPGPU." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2036.

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In this thesis I look into how one can train and run an artificial neural network using Compute Shader and what kind of performance can be expected. An artificial neural network is a computational model that is inspired by biological neural networks, e.g. a brain. Finding what kind of performance can be expected was done by creating an implementation that uses Compute Shader and then compare it to the FANN library, i.e. a fast artificial neural network library written in C. The conclusion is that you can improve performance by training an artificial neural network on the compute shader as long
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Landassuri, Moreno Victor Manuel. "Evolution of modular neural networks." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3243/.

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It is well known that the human brain is highly modular, having a structural and functional organization that allows the different regions of the brain to be reused for different cognitive processes. So far, this has not been fully addressed by artificial systems, and a better understanding of when and how modules emerge is required, with a broad framework indicating how modules could be reused within neural networks. This thesis provides a deep investigation of module formation, module communication (interaction) and module reuse during evolution for a variety of classification and prediction
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Xu, Shuxiang. "Neuron-adaptive neural network models and applications /." [Campbelltown, N.S.W. : The Author], 1999. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030702.085320/index.html.

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De, Jongh Albert. "Neural network ensembles." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50035.

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Thesis (MSc)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy
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Weinstein, Randall Kenneth. "Techniques for FPGA neural modeling." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/26685.

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Thesis (Ph.D)--Bioengineering, Georgia Institute of Technology, 2007.<br>Committee Chair: Lee, Robert; Committee Member: Butera, Robert; Committee Member: DeWeerth, Steve; Committee Member: Madisetti, Vijay; Committee Member: Voit, Eberhard. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Bolt, George Ravuama. "Fault tolerance in artificial neural networks : are neural networks inherently fault tolerant?" Thesis, University of York, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317683.

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23

Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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24

Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.

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25

Sloan, Cooper Stokes. "Neural bus networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-68).<br>Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research att
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Pardoe, Andrew Charles. "Neural network image reconstruction for nondestructive testing." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/44616/.

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Conventional image reconstruction of advanced composite materials using ultrasound tomography is computationally expensive, slow and unreliable. A neural network system is proposed which would permit the inspection of large composite structures, increasingly important for the aerospace industry. It uses a tomographic arrangement, whereby a number of ultrasonic transducers are positioned along the edges of a square, referred to as the sensor array. Two configurations of the sensor array are utilized. The first contains 16 transducers, 4 of which act as receivers of ultrasound, and the second co
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Filho, Edson Costa de Barros Carvalho. "Investigation of Boolean neural networks on a novel goal-seeking neuron." Thesis, University of Kent, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277285.

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28

Landry, Kenneth D. "Evolutionary neural networks." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/51904.

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To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a much larger problem. Algorithms have been developed to alter the weights slightly in order to produce the desired results. Learning algorithms such as Hebb's rule, the Delta rule and error propagation have been used, with success, to learn the appropriate weights. The major objection to this class of a
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Bailey, Scott P. "Neural network design on the SRC-6 reconfigurable computer." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FBailey.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2006.<br>Thesis Advisor(s): Douglas J. Fouts. "December 2006." Includes bibliographical references (p. 105-106). Also available in print.
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Wan, Chuen L. "Traffic representation by artificial neural system and computer vision." Thesis, Edinburgh Napier University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261024.

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31

Turega, Michael A. "A parallel computer architecture to support artificial neural networks." Thesis, University of Manchester, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316469.

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SILVA, Adenilton José da. "Artificial neural network architecture selection in a quantum computer." UNIVERSIDADE FEDERAL DE PERNAMBUCO, 2015. https://repositorio.ufpe.br/handle/123456789/15011.

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Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-01-27T17:25:47Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5)<br>Made available in DSpace on 2016-01-27T17:25:47Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5) Previous issue date: 2015-06-26<br>CNPq<br>Miniaturisa
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Llaquet, Bayo Antai. "Computer aided renal calculi detection using Convolutional Neural Networks." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-52254.

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In this thesis a novel approach is developed to detect urethral stones based on a computer-aided process. The input data is a CT scan from the patient, which is a high-resolution 3D grayscale image. The algorithm developed extracts the regions that might be stones, based on the intensity values of the pixels in the CT scan. This process includes a binarizing process of the image, finding the connected components of the resulting binary image and calculating the centroid of each of the components selected. The regions that are suspected to be stones are used as input of a CNN, a modified versio
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Bergsten, John, and Konrad Öhman. "Player Analysis in Computer Games Using Artificial Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14812.

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Star Vault AB is a video game development company that has developed the video game Mortal Online. The company has stated that they believe that players new to the game repeatedly find themselves being lost in the game. The objective of this study is to evaluate whether or not an Artificial Neural Network can be used to evaluate when a player is lost in the game Mortal Online. This is done using the free open source library Fast Artifical Neural Network Library. People are invited to a data collection event where they play a tweaked version of the game to facilitate data collection. Players sp
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Wang, Ting. "Statistical feature ordering for neural-based incremental attribute learning." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/13633/.

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In pattern recognition, better classification or regression results usually depend on highly discriminative features (also known as attributes) of datasets. Machine learning plays a significant role in the performance improvement for classification and regression. Different from the conventional machine learning approaches which train all features in one batch by some predictive algorithms like neural networks and genetic algorithms, Incremental Attribute Learning (IAL) is a novel supervised machine learning approach which gradually trains one or more features step by step. Such a strategy ena
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Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.<br>Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
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Hadjifaradji, Saeed. "Learning algorithms for restricted neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0016/NQ48102.pdf.

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Demiray, Sadettin Tuğlular Tuğkan. "Improving misuse detection with neural networks/." [s.l.]: [s.n.], 2005. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000408.pdf.

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Thesis (Master)--İzmir Institute of Technology, İzmir, 2005<br>Keywords: Neural network, back propagation networks, multilayer perceptions, computer networks security, Intrusion detection system. Includes bibliographical references (leaves 68-69)
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Goodman, Stephen D. "Temporal pattern identification in a self-organizing neural network with an application to data compression." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15794.

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Vidmark, Stefan. "Röstigenkänning med Movidius Neural Compute Stick." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151032.

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Företaget Omicron Ceti AB köpte en Intel Movidius Neural Compute Stick (NCS), som är en usb-enhet där neurala nätverk kan laddas in för att processa data. Min uppgift blev att studera hur NCS används och göra en guide med exempel. Med TensorFlow och hjälpbiblioteket TFLearn gjordes först ett testnätverk för att prova hela kedjan från träning till användning med NCS. Sedan tränades ett nätverk att kunna klassificera 14 olika ord. En mängd olika utformningar på nätverket testades, men till slut hittades ett exempel som blev en bra utgångspunkt och som efter lite justering gav en träffsäkerhet på
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Behnke, Sven. "Hierarchical neural networks for image interpretation /." Berlin [u.a.] : Springer, 2003. http://www.loc.gov/catdir/enhancements/fy0813/2003059597-d.html.

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Wenzel, Brent C. "Using neural nets to generate and improve computer graphic procedures." Virtual Press, 1992. http://liblink.bsu.edu/uhtbin/catkey/834648.

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Image compression using neural networks in the past has focused on just reducing the number of bytes that had to be stored even thought the bytes had no meaning. This study looks at a new process that reduces the number of bytes stored but also maintains meaning behind the bytes. The bytes of the compressed image will correspond to parameters of an existing graphic algorithm. After a brief review of common neural networks and graphic algorithms, the back propagation neural network was chosen to be tested for this new process. Both three layer and four layer networks were tested. The four
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Wenström, Sean, and Erik Ihrén. "Stock Trading with Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168095.

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Stock trading is increasingly done pseudo-automatically orfully automatically, using algorithms which make day-todayor even moment-to-moment decisions.This report investigates the possibility of creating a virtualstock trader, using a method used in Artificial Intelligence,called Neural Networks, to make intelligent decisions onwhen to buy and sell stocks on the stock market.We found that it might be possible to earn money overa longer period of time, although the profit is less than theaverage stock index. However, the method also performedwell in situations where the stock index is going dow
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Cheung, Ka Kit. "Neural networks for optimization." HKBU Institutional Repository, 2001. http://repository.hkbu.edu.hk/etd_ra/291.

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Wojcik, Jeremy J. "Neural Cartography: Computer Assisted Poincare Return Mappings for Biological Oscillations." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/10.

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This dissertation creates practical methods for Poincaré return mappings of individual and networked neuron models. Elliptic bursting models are found in numerous biological systems, including the external Globus Pallidus (GPe) section of the brain; the focus for studies of epileptic seizures and Parkinson's disease. However, the bifurcation structure for changes in dynamics remains incomplete. This dissertation develops computer-assisted Poincaré ́maps for mathematical and biologically relevant elliptic bursting neuron models and central pattern generators (CPGs). The first method, used for i
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Thangthai, Kwanchiva. "Computer lipreading via hybrid deep neural network hidden Markov models." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/69215/.

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Constructing a viable lipreading system is a challenge because it is claimed that only 30% of information of speech production is visible on the lips. Nevertheless, in small vocabulary tasks, there have been several reports of high accuracies. However, investigation of larger vocabulary tasks is rare. This work examines constructing a large vocabulary lipreading system using an approach based-on Deep Neural Network Hidden Markov Models (DNN-HMMs). We present the historical development of computer lipreading technology and the state-ofthe-art results in small and large vocabulary tasks. In prel
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Xie, Weidi. "Deep neural networks in computer vision and biomedical image analysis." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:5fcfb784-7b61-49cd-9561-64b5ffa5807a.

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This thesis proposes different models for a variety of applications, such as semantic segmentation, in-the-wild face recognition, microscopy cell counting and detection, standardized re-orientation of 3D ultrasound fetal brain and Magnetic Resonance (MR) cardiac video segmentation. Our approach is to employ the large-scale machine learning models, in particular deep neural networks. Expert knowledge is either mathematically modelled as a differentiable hidden layer in the Artificial Neural Networks, or we tried to break the complex tasks into several small and easy-to-solve tasks. Multi-scale
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Ahamed, Woakil Uddin. "Quantum recurrent neural networks for filtering." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.

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The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger waveequation (SWE) plays the key part. This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of the SWE. Theincorpor
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Williams, Bryn V. "Evolutionary neural networks : models and applications." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10635/.

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The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA
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Garret, Aaron Dozier Gerry V. "Neural enhancement for multiobjective optimization." Auburn, Ala., 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Dissertation/Garrett_Aaron_55.pdf.

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