Analysis of BCNS and Newhope Key-exchange Protocols

Lattice-based cryptographic primitives are believed to offer resilience against attacks by quantum computers. Following increasing interest from both companies and government agencies in building quantum computers, a number of works have proposed instantiations of practical post-quantum key-exchange protocols based on hard problems in lattices, mainly based on the Ring Learning With Errors (R-LWE) problem. In this work we present an analysis of Ring-LWE based key-exchange mechanisms and compare two implementations of Ring-LWE based key-exchange protocol: BCNS and NewHope. This is important as NewHope protocol implementation outperforms state-of-the art elliptic curve based Diffie-Hellman key-exchange X25519, thus showing that using quantum safe key-exchange is not only a viable option but also a faster one. Specifically, this thesis compares different reconciliation methods, parameter choices, noise sampling algorithms and performance

Scene illumination classification based on histogram quartering of CIE-Y component

Despite the rapidly expanding research into various aspects of illumination estimation methods, there are limited number of studies addressing illumination classification for different purposes. The increasing demand for color constancy process, wide application of it and high dependency of color constancy to illumination estimation makes this research topic challenging. Definitely, an accurate estimation of illumination in the image will provide a better platform for doing correction and finally will lead in better color constancy performance. The main purpose of any illumination estimation algorithm from any type and class is to estimate an accurate number as illumination. In scene illumination estimation dealing with large range of illumination and small variation of it is critical. Those algorithms which performed estimation carrying out lots of calculation that leads in expensive methods in terms of computing resources. There are several technical limitations in estimating an accurate number as illumination. In addition using light temperature in all previous studies leads to have complicated and computationally expensive methods. On the other hand classification is appropriate for applications like photography when most of the images have been captured in a small set of illuminants like scene illuminant. This study aims to develop a framework of image illumination classifier that is capable of classifying images under different illumination levels with an acceptable accuracy. The method will be tested on real scene images captured with illumination level is measured. This method is a combination of physic based methods and data driven (statistical) methods that categorize the images based on statistical features extracted from illumination histogram of image. The result of categorization will be validated using inherent illumination data of scene. Applying the improving algorithm for characterizing histograms (histogram quartering) handed out the advantages of high accuracy. A trained neural network which is the parameters are tuned for this specific application has taken into account in order to sort out the image into predefined groups. Finally, for performance and accuracy evaluation misclassification error percentages, Mean Square Error (MSE), regression analysis and response time are used. This developed method finally will result in a high accuracy and straightforward classification system especially for illumination concept. The results of this study strongly demonstrate that light intensity with the help of a perfectly tuned neural network can be used as the light property to establish a scene illumination classification system

Malaysia solar energy experience: intelligent fault location algorithm for unbalanced radial distribution network including PV systems

Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units

Designing the network topology of feedforward neural network for scene illumination classification

Determining the topology of network as one of the objectives of ANN systems, is not following any certain rules or algorithms but still there are several hints which help us to restrict the neural network architecture set. Hence, the process of testing structures will be the solution of finding most effective one among a limited set. This study aims to apply testing method on scene illumination classification system to find out the appropriate ANN structure. The results of this study apply on similar classification systems to avoid redoing the testing process

Short circuit power based fault location algorithm in distribution networks

This paper presents a novel accurate fault location technique for the radial unbalanced distribution systems, based on measurement of the Short Circuit Power (S/C.P) peak values at the substation. To evaluate the gathered dataset, a Multi-Layer Feed Forward Neural Network (ML-FFNN) with the tuned parameters is designed and the locations of faults are estimated in low, medium and far distances from the source. The estimated distances are compared with the real fault locations to show the accuracy of estimations. The proposed method can work with the small scale datasets and it is capable of being implemented in distribution systems with several laterals

On the fault location algorithm for distribution networks in presence of DG

Connecting distributed generation (DG) units to the distribute networks impose several impacts on it which have not been considered in conventional fault location algorithms. This paper presents an accurate fault location technique for unbalanced radial distribution networks based on evaluating measured values of short Circuit Current (S/C.C) at the source bus with a designed Multi-Layer Feed Forwarded Neural Network (ML-FFNN). The estimated locations of different fault types are compared with the actual distances and Average Difference Percentage (ADP) is calculated for each fault type. The designed neural network is able to work with small scale datasets. Hence the proposed method can be implemented in the real distribution networks

Fuzzy similarity measure based on fuzzy sets

This paper extends the notion of fuzzy similarity measure between fuzzy sets. A definite class of fuzzy similarity measures between fuzzy sets is also introduced. Using some theorems and examples, it is shown that the introduced extended similarity measures satisfy many common and desired properties, based on the common axiomatic definitions, introduced for fuzzy similarity measures. Some illustrative and practical examples from the areas of pattern recognition and approximate reasoning systems are provided in order to present the possible applications of the proposed fuzzy similarity measures

Atrous Convolution for Binary Semantic Segmentation of Lung Nodule

© 2019 IEEE. Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The Atrous convolution increases the field of view of the filters and helps to improve classification accuracy. Moreover, in order to address the significant class imbalance issue between the nodule pixels and background non-nodule pixels, a weighted loss function is proposed. We evaluate our proposed solution on the widely adopted benchmark dataset LIDC. A promising result of an average DCS of 81.24% is achieved, outperforming the state of the arts. This demonstrates the effectiveness and importance of applying the Atrous convolution and weighted loss for such problems