An enhanced Blowfish Algorithm based on cylindrical coordinate system and dynamic permutation box

The Blowfish Algorithm (BA) is a symmetric block cipher that uses Feistel network to iterate simple encryption and decryption functions. BA key varies from 32 to 448 bits to ensure a high level of security. However, the substitution box (S-Box) in BA occupies a high percentage of memory and has problems in security, specifically in randomness of output with text and image files that have large strings of identical bytes. Thus, the objective of this research is to enhance the BA to overcome these problems. The research involved three phases, algorithm design, implementation, and evaluation. In the design phase, a dynamic 3D S-Box, a dynamic permutation box (P-Box), and a Feistal Function (F-Function) were improved. The improvement involved integrating Cylindrical Coordinate System (CCS) and dynamic P-Box. The enhanced BA is known as Ramlan Ashwak Faudziah (RAF) algorithm. The implementation phase involved performing key expansion, data encryption, and data decryption. The evaluation phase involved measuring the algorithm in terms of memory and security. In terms of memory, the results showed that the RAF occupied 256 bytes, which is less than the BA (4096 bytes). In terms of randomness of text and image files that have large strings of identical bytes, the average rate of randomness for 188 statistical tests obtained values of more than 96%. This means that the RAF has high randomness indicating that it is more secured. Thus, the results showed that the RAF algorithm that integrates the CCS and dynamic P-Box serves as an effective approach that can consume less memory and strengthen security

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

Year 2010 Issues on Cryptographic Algorithms

In the financial sector, cryptographic algorithms are used as fundamental techniques for assuring confidentiality and integrity of data used in financial transactions and for authenticating entities involved in the transactions. Currently, the most widely used algorithms appear to be two-key triple DES and RC4 for symmetric ciphers, RSA with a 1024-bit key for an asymmetric cipher and a digital signature, and SHA-1 for a hash function according to international standards and guidelines related to the financial transactions. However, according to academic papers and reports regarding the security evaluation for such algorithms, it is difficult to ensure enough security by using the algorithms for a long time period, such as 10 or 15 years, due to advances in cryptanalysis techniques, improvement of computing power, and so on. To enhance the transition to more secure ones, National Institute of Standards and Technology (NIST) of the United States describes in various guidelines that NIST will no longer approve two-key triple DES, RSA with a 1024-bit key, and SHA-1 as the algorithms suitable for IT systems of the U.S. Federal Government after 2010. It is an important issue how to advance the transition of the algorithms in the financial sector. This paper refers to issues regarding the transition as Year 2010 issues in cryptographic algorithms. To successfully complete the transition by 2010, the deadline set by NIST, it is necessary for financial institutions to begin discussing the issues at the earliest possible date. This paper summarizes security evaluation results of the current algorithms, and describes Year 2010 issues, their impact on the financial industry, and the transition plan announced by NIST. This paper also shows several points to be discussed when dealing with Year 2010 issues.Cryptographic algorithm; Symmetric cipher; Asymmetric cipher; Security; Year 2010 issues; Hash function

Enhanced perception in volume visualization

Due to the nature of scientic data sets, the generation of convenient visualizations may be a difficult task, but crucial to correctly convey the relevant information of the data. When working with complex volume models, such as the anatomical ones, it is important to provide accurate representations, since a misinterpretation can lead to serious mistakes while diagnosing a disease or planning surgery. In these cases, enhancing the perception of the features of interest usually helps to properly understand the data. Throughout years, researchers have focused on different methods to improve the visualization of volume data sets. For instance, the definition of good transfer functions is a key issue in Volume Visualization, since transfer functions determine how materials are classified. Other approaches are based on simulating realistic illumination models to enhance the spatial perception, or using illustrative effects to provide the level of abstraction needed to correctly interpret the data. This thesis contributes with new approaches to enhance the visual and spatial perception in Volume Visualization. Thanks to the new computing capabilities of modern graphics hardware, the proposed algorithms are capable of modifying the illumination model and simulating illustrative motifs in real time. In order to enhance local details, which are useful to better perceive the shape and the surfaces of the volume, our first contribution is an algorithm that employs a common sharpening operator to modify the lighting applied. As a result, the overall contrast of the visualization is enhanced by brightening the salient features and darkening the deeper regions of the volume model. The enhancement of depth perception in Direct Volume Rendering is also covered in the thesis. To do this, we propose two algorithms to simulate ambient occlusion: a screen-space technique based on using depth information to estimate the amount of light occluded, and a view-independent method that uses the density values of the data set to estimate the occlusion. Additionally, depth perception is also enhanced by adding halos around the structures of interest. Maximum Intensity Projection images provide a good understanding of the high intensity features of the data, but lack any contextual information. In order to enhance the depth perception in such a case, we present a novel technique based on changing how intensity is accumulated. Furthermore, the perception of the spatial arrangement of the displayed structures is also enhanced by adding certain colour cues. The last contribution is a new manipulation tool designed for adding contextual information when cutting the volume. Based on traditional illustrative effects, this method allows the user to directly extrude structures from the cross-section of the cut. As a result, the clipped structures are displayed at different heights, preserving the information needed to correctly perceive them.Debido a la naturaleza de los datos científicos, visualizarlos correctamente puede ser una tarea complicada, pero crucial para interpretarlos de forma adecuada. Cuando se trabaja con modelos de volumen complejos, como es el caso de los modelos anatómicos, es importante generar imágenes precisas, ya que una mala interpretación de las mismas puede producir errores graves en el diagnóstico de enfermedades o en la planificación de operaciones quirúrgicas. En estos casos, mejorar la percepción de las zonas de interés, facilita la comprensión de la información inherente a los datos. Durante décadas, los investigadores se han centrado en el desarrollo de técnicas para mejorar la visualización de datos volumétricos. Por ejemplo, los métodos que permiten definir buenas funciones de transferencia son clave, ya que éstas determinan cómo se clasifican los materiales. Otros ejemplos son las técnicas que simulan modelos de iluminación realista, que permiten percibir mejor la distribución espacial de los elementos del volumen, o bien los que imitan efectos ilustrativos, que proporcionan el nivel de abstracción necesario para interpretar correctamente los datos. El trabajo presentado en esta tesis se centra en mejorar la percepción de los elementos del volumen, ya sea modificando el modelo de iluminación aplicado en la visualización, o simulando efectos ilustrativos. Aprovechando la capacidad de cálculo de los nuevos procesadores gráficos, se describen un conjunto de algoritmos que permiten obtener los resultados en tiempo real. Para mejorar la percepción de detalles locales, proponemos modificar el modelo de iluminación utilizando una conocida herramienta de procesado de imágenes (unsharp masking). Iluminando aquellos detalles que sobresalen de las superficies y oscureciendo las zonas profundas, se mejora el contraste local de la imagen, con lo que se consigue realzar los detalles de superficie. También se presentan diferentes técnicas para mejorar la percepción de la profundidad en Direct Volume Rendering. Concretamente, se propone modificar la iluminación teniendo en cuenta la oclusión ambiente de dos maneras diferentes: la primera utiliza los valores de profundidad en espacio imagen para calcular el factor de oclusión del entorno de cada pixel, mientras que la segunda utiliza los valores de densidad del volumen para aproximar dicha oclusión en cada vóxel. Además de estas dos técnicas, también se propone mejorar la percepción espacial y de la profundidad de ciertas estructuras mediante la generación de halos. La técnica conocida como Maximum Intensity Projection (MIP) permite visualizar los elementos de mayor intensidad del volumen, pero no aporta ningún tipo de información contextual. Para mejorar la percepción de la profundidad, proponemos una nueva técnica basada en cambiar la forma en la que se acumula la intensidad en MIP. También se describe un esquema de color para mejorar la percepción espacial de los elementos visualizados. La última contribución de la tesis es una herramienta de manipulación directa de los datos, que permite preservar la información contextual cuando se realizan cortes en el modelo de volumen. Basada en técnicas ilustrativas tradicionales, esta técnica permite al usuario estirar las estructuras visibles en las secciones de los cortes. Como resultado, las estructuras de interés se visualizan a diferentes alturas sobre la sección, lo que permite al observador percibirlas correctamente

An analysis and a comparative study of cryptographic algorithms used on the internet of things (IoT) based on avalanche effect

Ubiquitous computing is already weaving itself around us and it is connecting everything to the network of networks. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. Many capacity and non-capacity constrained devices, such as sensors are connecting to the Internet. These devices interact with each other through the network and provide a new experience to its users. In order to make full use of this ubiquitous paradigm, security on IoT is important. There are problems with privacy concerns regarding certain algorithms that are on IoT, particularly in the area that relates to their avalanche effect means that a small change in the plaintext or key should create a significant change in the ciphertext. The higher the significant change, the higher the security if that algorithm. If the avalanche effect of an algorithm is less than 50% then that algorithm is weak and can create security undesirability in any network. In this, case IoT. In this study, we propose to do the following: (1) Search and select existing block cryptographic algorithms (maximum of ten) used for authentication and encryption from different devices used on IoT. (2) Analyse the avalanche effect of select cryptographic algorithms and determine if they give efficient authentication on IoT. (3) Improve their avalanche effect by designing a mathematical model that improves their robustness against attacks. This is done through the usage of the initial vector XORed with plaintext and final vector XORed with cipher tect. (4) Test the new mathematical model for any enhancement on the avalanche effect of each algorithm as stated in the preceding sentences. (5) Propose future work on how to enhance security on IoT. Results show that when using the proposed method with variation of key, the avalanche effect significantly improved for seven out of ten algorithms. This means that we have managed to improve 70% of algorithms tested. Therefore indicating a substantial success rate for the proposed method as far as the avalanche effect is concerned. We propose that the seven algorithms be replaced by our improved versions in each of their implementation on IoT whenever the plaintext is varied.Electrical and Mining EngineeringM. Tech. (Electrical Engineering