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Relevant bibliographies by topics / Fully connected Neural Network / Dissertations / Theses

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Dissertations / Theses on the topic 'Fully connected Neural Network'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hussain, Saed. "Fault tolerant flight control : an application of the fully connected cascade neural network." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/12123/.

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The endurance of an aircraft can be increased in the presence of failures by utilising flight control systems that are tolerant to failures. Such systems are known as fault tolerant flight control systems (FTFCS). FTFCS can be implemented by developing failure detection, identification and accommodation (FDIA) schemes. Two of the major types of failures in an aircraft system are the sensor and actuator failures. In this research, a sensor failure detection, identification and accommodation (SFDIA); and an actuator failure detection, identification and accommodation (AFDIA) schemes are develope

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2

Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.

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Hensman, Paulina. "Intra-prediction for Video Coding with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224197.

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Intra-prediction is a method for coding standalone frames in video coding. Until now, this has mainly been done using linear formulae. Using an Artificial Neural Network (ANN) may improve the prediction accuracy, leading to improved coding efficiency. In this degree project, Fully Connected Networks (FCN) and Convolutional Neural Networks (CNN) were used for intra-prediction. Experiments were done on samples from different image sizes, block sizes, and block contents, and their effect on the results were compared and discussed. The results show that ANN methods have the potential to perform be

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Cun, Xiao Dong. "Image splicing localization via semi-global network and fully connected conditional random fields." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950634.

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Liu, Jin. "Fully parallel learning neural network chip for real-time control." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/22214.

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Bingham, Philip R. "The effect of message length distribution on the performance of fully connected switches." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15389.

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Shishani, Basel. "Segmentation of connected text using constrained neural networks." Thesis, Queensland University of Technology, 1997.

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8

Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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9

Phillips, Adon. "Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36929.

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Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastati

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Yuan, Yuchen. "Advanced Visual Computing for Image Saliency Detection." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17039.

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Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations abov

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Li, Jiakai. "AI-WSN: Adaptive and Intelligent Wireless Sensor Networks." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341258416.

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Fischer, Christian [Verfasser], and Barbara [Akademischer Betreuer] Conradt. "Computational analysis of mitochondria in Caenorhabditis elegans using a Fully Convolutional Neural Network and common feature detectors / Christian Fischer ; Betreuer: Barbara Conradt." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1225682924/34.

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Aljamal, Mohammad Abdulraheem. "Real-Time Estimation of Traffic Stream Density using Connected Vehicle Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/100149.

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The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops mod

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Kanaparthi, Pradeep Kumar. "Detection and Recognition of U.S. Speed Signs from Grayscale Images for Intelligent Vehicles." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352934398.

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Lang, Matěj. "Detekce vad vláknitého materiálu užitím metod strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400649.

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Cílem této diplomové práce je automatizace detekce vad ve vláknitých materiálech. Firma SILON se již přes padesát let zabývá výrobou jemné vaty z recyklovaných PET lahví. Tato vata se následně používá ve stavebnictví, automobilovém průmyslu, ale nejčastěji v dámských hygienických potřebách a dětských plenách. Cílem firmy je produkovat co nejkvalitnější výrobek a proto je každá dávka testována v laboratoři s několika přísnými kritérii. Jednám z testů je i množství vadných vláken, jako jsou zacuchané smotky vláken, nebo nevydloužená vlákna, která jsou tvrdá a snadno se lámou. Navrhovaný systém s

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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more

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Kherroubi, Zine el abidine. "Novel off-board decision-making strategy for connected and autonomous vehicles (Use case highway : on-ramp merging)." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1331.

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L'insertion sur autoroute est un défi pour réaliser une conduite entièrement automatisée (Niveau 4 de conduite autonome). La combinaison des technologies de communication et de conduite autonome, qui sous-tend la notion de Connected Autonomous Vehicles (CAV), peut améliorer considérablement les performances de sécurité lors de l'insertion sur autoroute. Cependant, même avec l'émergence des véhicules CAVs, certaines contraintes clés doivent être prises en compte afin de réaliser une insertion sécurisée sur autoroute. Tout d'abord, les véhicules conduits par des conducteurs humains seront toujou

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Sundman, Tobias. "Noise Reduction in Flash X-ray Imaging Using Deep Learning." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355731.

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Recent improvements in deep learning architectures, combined with the strength of modern computing hardware such as graphics processing units, has lead to significant results in the field of image analysis. In this thesis work, locally connected architectures are employed to reduce noise in flash X-ray diffraction images. The layers in these architectures use convolutional kernels, but without shared weights. This combines the benefits of lower model memory footprint in convolutional networks with the higher model capacity of fully connected networks. Since the camera used to capture the diffr

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Salem, Mostafa. "Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/668990.

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This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework for the detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF). Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MR images. The model was trained end-to-end and simultaneously learned both the DFs

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(8784458), Qin He. "Learning Lighting Models with Shader-Based Neural Networks." Thesis, 2020.

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<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on me

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BISHT, KULDEEP SINGH. "STUDY AND DESIGN OF FPGA BASED FULLY CONNECTED NEURAL NETWORKS." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19153.

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The project's goal is to design a field programmable gate array (FPGA) based fully connected neural network for handwritten digit recognition. The project involves training the network utilizing the modified national institute of standards and technology dataset (MNIST), as well as designing and simulating a fully linked deep neural network (DNN). Initially, small modules such as memory and activation functions were created independently to test the fundamental functionality of neurons. A multi-layer neural network was created to improve the neural network's overall accuracy by perf

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Ji, Kai-Wun, and 紀凱文. "The FPGA Implementation of Fully-connected Layers of Convolutional Neural Networks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/56148831921633954040.

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Liu, Chih-Hung, and 劉志宏. "Modulation Classification Using Fully Connected Deep Neural Networks with Convolutional Long Short-Term Memory." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/p3y3q4.

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碩士<br>國立中央大學<br>通訊工程學系在職專班<br>106<br>Modulation classiﬁcation is usually the first step of a major communications problem with military applications. We have to know the modulation types before we decode the signals and get the content. As the modulation types increase rapidly, automatic modulation recognition becomes an important topic which is worth researching into. Traditionally, we use manual feature selection to get the features and do the classiﬁcation. In this article, we aim to use of DL to learn from data, extract features and classify signals automatically. We will concentrate on

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Ting, YI-SIANG, and 丁弈翔. "Real-time Driver’s Eyes Tracking System using Semantics-based Vague Image Representation and Fully Connected Neural Network on Single Chip." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4c3zjh.

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碩士<br>國立中正大學<br>電機工程研究所<br>107<br>Abstract For a smart vehicle system design, driver’s attention monitoring system is essential to advanced driver assistance system (ADAS). Such technology can be achieved by using face tracking to detect distraction of driver. The computing process involves complicated feature extraction and pattern recognition so that design concepts of small dimension, high computing performance, and low power consumption are required in order to be implement in a vehicle. For this, this study focuses on the way to realize an efficient driving eyes tracking algorithm on a si

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Janke, Jonathan Gabriel Martin. "Analysis of the proficiency of fully-connected neural networks in the process of classifying digital images : benchmark of different classification algorithms on high-level image features from convolutional layers." Master's thesis, 2019. http://hdl.handle.net/10362/62422.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Over the course of research on Convolutional Neural Network (CNN) architectures, little modifications have been done to the fully-connected layers at the end of the networks. In image classification, these neural network layers are responsible for creating the final classification results based on the output of the last layer of high-level image filters. Before the breakthrough of CNNs, these image filters were handcrafted, and any classification algorithm was appli

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Monteiro, Nelson Rodrigo Carvalho. "End-to-End Deep Learning Approach for Drug-Target Interaction Prediction." Master's thesis, 2019. http://hdl.handle.net/10316/87296.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>A descoberta de potenciais Interações Fármaco-Alvo é uma etapa determinante no processo de descoberta e reposicionamento de fármacos, uma vez que a eficácia do tratamento antibiótico disponível está a diminuir, provocado pelo aumento da sua utilização indevida. Apesar dos esforços colocados nos métodos tradicionais in vivo ou in vitro, o investimento financeiro farmacêutico foi reduzido ao longo dos anos. Desta forma, estabelecer métodos computacionais eficazes, é decisivo para

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Hsieh, Po-Wei, and 謝柏維. "Chinese Character Segmentation via Fully Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9b52n2.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>The important information conveyed by texts and artificial symbols in natural scenes, so capturing text context from images has many potential applications. However, the current methods are almost based on the processing of phonetic text, and the methods for morpheme text such as Chinese are still improved. This study attempts to propose a Chinese character text detection mechanism of semantic segmentation for natural scene images, with marking the label for each individual Chinese character. The proposed method is divided into two stages: in the first stage, w

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(8088461), Shakti Nagnath Wadekar. "LOCALLY CONNECTED NEURAL NETWORKS FOR IMAGE RECOGNITION." Thesis, 2019.

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Weight-sharing property in convolutional neural network (CNN) is useful in reducing number of parameters in the network and also introduces regularization effect which helps to gain high performance. Non-weight-shared convolutional neural networks also known as Locally connected networks (LCNs) has potential to learn more<br>in each layer due to large number of parameters without increasing number of inference computations as compared to CNNs. This work explores the idea of where Locally connected layers can be used to gain performance benefits in terms of accuracy and computations, what are t

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JAISWAL, SHRADHA. "PERSON RE-IDENTIFICATION USING DENSELY CONNECTED CONVOLUTIONAL NEURAL NETWORK." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16706.

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A successful system for person re-identification inspired by [67], a Densely Connected Convolutional Neural Networks (DenseNet) have been developed. This architecture was proposed by Huang et el. [67] (2017) for object recognition. We are using this model for exploring the network configurations and settings for getting a better solution for person re-identification tasks. We will train and test different person re-identification datasets, search for optimal settings and other factors affecting the result. In this work, we are using two different network configurations of DenseNet model i.e.,

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Kao, Yu-Chieh, and 高鈺傑. "Partially Connected Neural Network Design for Digit Recognition on Cloud." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/86246052263146581981.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>104<br>Artificial neural networks and cloud computing are getting attention nowadays. Many breakthroughs had been made recently. Some of them are due to the progress in deep learning. Some of them are due to the thriving of Internet so that it gets easier to acquire database. Also, the quality of the database gets better. The thriving of these two makes practicing artificial neural networks appealing. The main subject of this thesis is: training partially connected neural networks on the cloud. The database used to train the neural network is MNIST (Mixed National I

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Hu, Jia-Ming, and 胡家銘. "Steganalysis in JPEG Images based on Densely Connected Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6q9gmz.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>Steganography is a technique for hiding large amounts of data in carriers such as video and video, and Steganalysis is a technique to determine whether additional information is hidden in the carrier. In our study, Deep Learning is used to train a densely connected Convolutional Neural Networks (CNN) module to design a new steganalysis architecture for JPEG image steganography. This architecture combines the design ingenuity and advantages of Inception Net, ResNet, and DenseNet.Including the inclusion of multiple scale convolution kernels in Inception Net to in

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Chen, Hung-Wei, and 陳泓瑋. "Fully Content-based Movie Recommender System with Feature Extraction Using Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/32402853221402907099.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>104<br>In recent year, movie industry is getting prosper. There are hundreds of movie released every year. However, it is difficult to notice the releasing of every movie, not to mention actually seeing it. Therefore, movie recommender system has become more and more popular as a research topic. Among a variety of movie recommender systems, content-based methods always ring a bell when it comes to recommending new movie. Content-based method use content of movie as input so that it does not suffer from “cold-start” problem. In this paper, we propose the Fully Content

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XU, RUI-GING, and 徐瑞慶. "On segmentation and recognition of connected digits based on backpropagation neural network." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/26971016980596368675.

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Chen, Li-Lin, and 陳立麟. "A Bidirectional Recurrent Neural Network for Offline Connected and Overlapped Handwritten Numeral Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8rwt48.

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碩士<br>國立東華大學<br>資訊工程學系<br>106<br>In recent years, the duty technology tended to regarding the hand-written numeral identification maturely, regarding divides the successful independent numeral (Isolated Digit) all to be able to have the very high identification rate. But the digital identification still had the very big challenging in very many situations, was likely a digital non-pair of independent numeral which we wanted in the duty to recognize, but was the Unknown Length the numstring, this let us in divide (Segment) to obtain in the correct digital integer to be more difficult; Also has

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Wang, Shyang-Yih, and 王翔毅. "Inverse Halftoning using Autoencoder based on Fully Convolutional Neural Network and Hybrid Loss Function." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/3u8t3p.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>106<br>Digital halftoning is a traditional transformation technique to obtain binary pattern from gray or color scale image, which is only black (0) and white (1). It has been widely used in many devices of limited number of colors, such as printers and electronic paper. At many instances, inverse halftoning is required to reconstruct and process printed images. The inverse halftoning is to restore a halftoning image to its original continuous tone image. However, since the inverse halftoning considers how to transform a two bits signal to 256 levels, there are count

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Chang, Wei-Lun, and 章偉倫. "SOC Estimation of Series Connected Lithium-Ion Batteries and Supercapacitors Using Hierarchical Fuzzy Neural Network." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/33481044060765875880.

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碩士<br>輔仁大學<br>電機工程學系<br>99<br>The main object of this paper is using hierarchical fuzzy neural network (HFNN) to estimate the state-of-charge (SOC) of lithium-ion batteries and supercapacitors (SC). The lithium-ion battery is the one of the most widely used energy storage products in commercial and industrial applications. Lithium cell aging is a significant issue in industrial application. We consider the aging affect and then add a direct current internal resistance parameter for the training and testing of HFNN SOC estimating scheme. Supercapacitors, which are another storage device, offer

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Shrestra, Sanjeevan. "Improved fully convolutional network with conditional random field for building extraction." Master's thesis, 2018. http://hdl.handle.net/10362/33652.

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Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies<br>Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases and several other geospatial applications. Several published contributions are dedicated to the applications of Deep Convolutional Neural Network (DCNN) for building extraction using aerial/satellite imagery exists; however, in all these contributions a good accuracy is always paid at the price of extremely

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Chuang, Yue-Che, and 莊育棋. "An Adaptive Approach to Implement the Collective Communication of MPI on Distributed Memory Machine with Fully-connected Interconnection Network." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/91260684713582820697.

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Lee, Yih_Ling, and 李一麟. "A Dynamic Learning Back-Propagation (DLBP) Neural Network Approachfor Target Classification Using Airborne Fully Polarimetric SAR." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/71720919630575975796.

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碩士<br>國立成功大學<br>測量工程學系碩博士班<br>92<br>Because fully polarimetric SAR (POLSAR) provides more information about the scattering characteristics of the earth’s surface, it enables a more accurate classification than single-channel and single-polarization SAR do. POLSAR gives all polarimetric properties in the covariance matrix. Due to speckle noise of POLSAR data, the fine pattern is difficult to be identified and the true value of back-scattering is contaminated by noise. Hence, the speckle noise filter approach is utilized to reduce noise disturbance. Moreover, the fuzzy approach is utilized to ta

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Lopes, Ana Patrícia Ribeiro. "Study of Deep Neural Network architectures for medical image segmentation." Master's thesis, 2020. http://hdl.handle.net/1822/69850.

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Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)<br>Medical image segmentation plays a crucial role in the medical field, since it allows performing quantitative analyses used for screening, monitoring and planning the treatment of numerous pathologies. Manual segmentation is time-consuming and prone to inter-rater variability. Thus, several automatic approaches have been proposed for medical image segmentation and most are based on Deep Learning. These approaches became specially relevant after the development of the Fully Convolution

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Hsieh, Ching-Chung, and 謝清中. "Combining Neural Network and Genetic Algorithms in the Optimum Design of Noise Cancellation Mufflers with Multiply Connected Tubes." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/45373188185942039972.

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碩士<br>大同大學<br>機械工程學系(所)<br>96<br>The thesis combines the optimum methods of Taguchi methods, Neural Network System and Genetic Algorithm (GA), and uses the Boundary Element Method to the optimum design of muffler by the software of acoustic analysis, “SYSNOISE”. The thesis is composed of 2 parts: (1) The performance of noise cancellation mufflers: the route is separated into two or more, and converge the entire routes to reducing noise. The setting parameters of dimension included the diameter of the straight pipe (d1), the diameter of curve pipe (d2), the distance from curve pipe to straight

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Liu, Chien-Chang, and 劉建章. "Development of Genetic Algorithm, Fuzzy Logic, and Neural Network Based Controllers for a Piping System with Series Connected Pumps." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/79694515135497445024.

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碩士<br>華梵大學<br>機電工程研究所<br>92<br>The object of this thesis is to develop controllers based upon genetic algorithm, fuzzy logic, and neural network for series-connected pump systems. Also, control strategies for systems with a single pump or with series-connected multi-pumps are studied as well. In addition, strategies of implementing inverters to control the water pumping speed are discussed as to prevent or eliminate transient surges or water-hammer effects, which might cause serious vibration problems onto the piping systems. The research scope of this study includes controllers

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Santos, Ângelo Emanuel Neves dos. "Design and simulation of a smart bottle with fill-level sensing based on oxide TFT technology." Master's thesis, 2016. http://hdl.handle.net/10362/19593.

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Packaging is an important element responsible for brand growth and one of the main rea-sons for producers to gain competitive advantages through technological innovation. In this re-gard, the aim of this work is to design a fully autonomous electronic system for a smart bottle packaging, being integrated in a European project named ROLL-OUT. The desired application for the smart bottle is to act as a fill-level sensor system in order to determine the liquid content level that exists inside an opaque bottle, so the consumer can exactly know the remaining quantity of the product inside. An in-h

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