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Articles 1 - 9 of 9
Full-Text Articles in Other Computer Engineering
Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai
Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai
Electronic Thesis and Dissertation Repository
High-Impedance Faults (HIFs) are a hazard to public safety but are difficult to detect because of their low current amplitude and diverse characteristics. Supervised machine learning techniques have shown great success in HIF detection; however, these approaches rely on resource-intensive signal processing techniques and fail in presence of non-HIF disturbances and even for scenarios not included in training data. This thesis leverages unsupervised learning and proposes a Convolutional Autoencoder framework for HIF Detection (CAE-HIFD). In CAE-HIFD, Convolutional Autoencoder learns only from HIF signals by employing cross-correlation; consequently, eliminating the need for diverse non-HIF scenarios in training. Furthermore, this thesis proposes …
A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa
A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa
Electronic Thesis and Dissertation Repository
Additively homomorphic encryption is a public-key primitive allowing a sum to be computed on encrypted values. Although limited in functionality, additive schemes have been an essential tool in the private function evaluation toolbox for decades. They are typically faster and more straightforward to implement relative to their fully homomorphic counterparts, and more efficient than garbled circuits in certain applications. This thesis presents a novel method for extending the functionality of additively homomorphic encryption to allow the private evaluation of functions of restricted domain. Provided the encrypted sum falls within the restricted domain, the function can be homomorphically evaluated “for free” …
Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat
Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat
Electronic Thesis and Dissertation Repository
The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo
Electronic Thesis and Dissertation Repository
Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian
Electronic Thesis and Dissertation Repository
Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Electronic Thesis and Dissertation Repository
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Electronic Thesis and Dissertation Repository
The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …
Development Of An Autonomous Robotic Mushroom Harvester, Nikita Alexeevich Kuchinskiy
Development Of An Autonomous Robotic Mushroom Harvester, Nikita Alexeevich Kuchinskiy
Electronic Thesis and Dissertation Repository
The process of development of a new robot is one of the modern technological arts. This process involves multiple complex steps and recursive approach. In this project, a solution for automatic harvesting of mushrooms is developed. In order to design an effective solution, it is necessary to explore and take into consideration the limitations of grasping very soft and fragile objects (particularly mushrooms). We will elaborate several strategies of picking and analyze each strategy to formulate the design requirements, develop a solution, and finally, evaluate the efficiency of the proposed solution in actual farm conditions for real mushrooms. The mushroom …
Reliability Models Applied To Smartphone Applications, Sonia Meskini
Reliability Models Applied To Smartphone Applications, Sonia Meskini
Electronic Thesis and Dissertation Repository
Smartphones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user’s needs. Therefore, instead of the operator, the user has been the main controller of the device and its applications, therefore its reliability has become an emergent requirement. As a first step, based on collected smartphone applications failure data, we investigated and evaluated the efficacy of Software Reliability Growth Models (SRGMs) when applied to these smartphone data in order to check whether they achieve the same accuracy as in the desktop/laptop area. None of the selected …