Pseudo-random number generator (PRNG) are a key component in the design of modern cryptographic mechanisms and are regarded as a backbone element of many modern cryptographic applications. However and in spite of their robustness, quantum computers could crack down PNGR-based systems. Quantum walks, a universal model of quantum computation, have nonlinear properties that make them a robust candidate to produce PRNG. In this paper, we utilize controlled alternate quantum walk (CAQW) to create PRNG. Moreover, we use the presented PRNG mechanism as a component of a new quantum color image

Vehicular Ad Hoc Networks (VANETs) are considered as a promising approach for facilitating road safety, traffic management, and infotainment dissemination for drivers and passengers. However, they are subject to an attack that has a severe impact on their security. This attack is called the Sybil attack, and it is considered as one of the most serious attacks to VANETs, and a threat to lives of drivers and passengers. In this paper, we propose a detection scheme for the Sybil attack. The idea is based on public key cryptography and aims to ensure privacy preservation, confidentiality, and non

Magnifying micro changes in motion and brightness of videos that are unnoticeable by the human visual system have recently been an interesting area to explore. In this paper, we explore this technique in 3D facial video identification, we utilize this technique to identify 3D objects from 2D images. We present a Complex Wavelet Transform CWT, 2D-Dual CWT based technique, to calculate any changes between subsequent video frames of CWT sub-bands at different spatial locations. In this technique, a gradient based method is proposed to determine the orientation of each CWT sub band in addition to

A distributed data collection algorithm to accurately store and forward information obtained by wireless sensor networks is proposed. The proposed algorithm does not depend on the sensor network topology, routing tables, or geographic locations of sensor nodes, but rather makes use of uniformly distributed storage nodes. Analytical and simulation results for this algorithm show that, with high probability, the data disseminated by the sensor nodes can be precisely collected by querying any small set of storage nodes.

In this work, we investigate the effect of using spatio-tepmoral features on a regional basis on the liver focal lesions classification performance in the multiphase CT images. Texture, Density, and temporal feature set and their different combinations along spatial partitioned ROI were investigated to better characterizing five hepatic pathologies from multiphase contrast-enhanced CT scans. Embedded feature selection followed by decision tree ensembles classification with ten folds cross-validation were employed to classify a total of 180 ROI includes normal tissues, cyst, haemangioma

In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs and showed the efficiency of feature selection on the CAD system. The proposed method has been implemented in four stages: (a) the region of interest (ROI) selection of 32x32 pixels size which identifies clusters of microcalcifications, (b) the feature extraction stage is based on the wavelet decomposition of locally processed image (region of

To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from

The development of quantum computers and quantum algorithms conveys a challenging scenario for several cryptographic protocols due to the mathematical scaffolding upon which those protocols have been built. Quantum walks constitute a universal quantum computational model which is widely used in various fields, including quantum algorithms and cryptography. Quantum walks can be utilized as a powerful tool for the development of modern chaos-based cryptographic applications due to their nonlinear dynamical behavior and high sensitivity to initial conditions. In this paper, we propose new

In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. The goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. Then we

Background: Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the