IoT data encryption algorithm for security

This research project is about encryption simulation for IoT data. It is important to enhance the security system when sending and receiving the IoT data. Some of these data, especially the health information for a particular person is very sensitive. Therefore, there is a need to encrypt and protect the data from malicious attack. The technique proposed in this research is using Hash function and encryption method to protect the data. To show the working of the encryption, a simulation is performed. The simulation used is a MATLAB coding. By inserting the number of bit and size of the data with random plain text, the system is able to encrypt the data. The simulation results showing that the encrypted data is completely different from the original data or the data haven't encrypted. Upon encrypted, the data being protected and will be unknown to the malicious. At the end of this research project, the result concluded that the waveforms will show the encryption process

Crystallization in suspensions of hard spheres: A Monte Carlo and Molecular Dynamics simulation study

The crystallization of a metastable melt is one of the most important non equilibrium phenomena in condensed matter physics, and hard sphere colloidal model systems have been used for several decades to investigate this process by experimental observation and computer simulation. Nevertheless, there is still an unexplained discrepancy between simulation data and experimental nucleation rate densities. In this paper we examine the nucleation process in hard spheres using molecular dynamics and Monte Carlo simulation. We show that the crystallization process is mediated by precursors of low orientational bond-order and that our simulation data fairly match the experimental data sets

Policy Advice Derived From Simulation Models

When advising policy we face the fundamental problem that economic processes are connected with uncertainty and thus policy can err. In this paper we show how the use of simulation models can reduce policy errors. We suggest that policy is best based on so-called abductive simulation models, which help to better understand how policy measures can influence economic processes. We show that abductive simulation models use a combination of theoretical and empirical analysis based on different data sets. This helps inferring empirically reliable and meaningful statements about how policy measures influence economic processes. By way of example we show how research subsidies by the government influence the likelihood that a regional cluster emerges.Policy Advice, Simulation Models, Uncertainty, Methodology

An algorithm for the multivariate group lasso with covariance estimation

We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to alternative estimators. We illustrate the method on a time series data set of gene expressions

Thermoluminescence energy response of TLD-100 subjected to photon irradiation using Monte Carlo N-particle transport code version 5

Useful TL properties of TLD-100 that is an excellent candidate for using in TL dosimetry of ionizing radiation are demonstrated. This study is focused on response of TLD-100 subjected to photon irradiation. The thermoluminescence (TL) response of TLD-100 subject to various photon energy, ranging from 20 keV to 6 MeV, was investigated as energy absorbed in the TL material using Monte Carlo N-Particle transport code version 5 (MCNP5). The input parameters included in this study are experimental geometry specification, source information, material information, and tallies. Tally F6 is used in this simulation. The results from MCNP5 simulation show good agreement with previous experimental data. However, the data obtained from the simulation are greater than the experimental data especially in lower energy ranges

Elementary simulation of tethered Brownian motion

We describe a simple numerical simulation, suitable for an undergraduate project (or graduate problem set), of the Brownian motion of a particle in a Hooke-law potential well. Understanding this physical situation is a practical necessity in many experimental contexts, for instance in single molecule biophysics; and its simulation helps the student to appreciate the dynamical character of thermal equilibrium. We show that the simulation succeeds in capturing behavior seen in experimental data on tethered particle motion.Comment: Submitted to American Journal of Physic