Comparing Artificial Bees Colony Algorithm and Firefly Algorithm to Achieve Optimization in Route Selection Processing Time in VANETs

In achieving Intelligent Transportation System (ITS), a new trending area in networking is Vehicular Ad-hoc Networks (VANETs). It is a sub branch of Mobile Ad-hoc Networks (MANETs) where all the nodes or devices are interlinked and interconnected wirelessly. The challenges that a VANET faces while communicating within a network are signal fading, routing decision making and connectivity hitch. All these three mentioned problems have already been discussed and well researched in history by using conventional techniques as well as intelligent techniques based on artificial intelligence. Amongst all these three issues we are focusing on mitigating decision making problem for optimal route selection. In order to solve this issue, researchers have found that meta-heuristic intelligent algorithms such as, Artificial Bees Colony (ABC) and Firefly Algorithm (FFA) are more efficient algorithms as compared to traditional approach. In this paper we compared ABC and FFA to check which algorithm runs faster in order to achieve routing decision in lesser time

3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.

Electron spin resonance signal of human nails: increase after irradiation

We report the phenomenon whereby the electron spin resonance signal of human nails intensify after irradiation. Forty nail samples from three donors were divided into three groups (X-ray irradiated, γ-ray irradiated, and control). The extent of signal intensified after irradiation was proportional to the given dose under 11% humidity at 20 °C. This result seemed to contradict previous studies, reporting signal fading. Based on literature and our experimental results, we constructed a model taking both the decay and increase into consideration. Our investigation revealed that rapid deterioration in the signal intensity obscured the increase at humidity levels exceeding 40%.

Minimizing the Probability of Bit Error in a Multi-Stage MIMO Relay System

This research is devoted to the investigation of the relay multi-stage relay MIMO (Multiple-Input Multiple-Output) of cellular communication with antenna arrays at each stage of transmission, in which parallel independent subchannels for data transmission are formed, are considered. The possibility of minimizing the error probability per bit of transmitted information is investigated while using the method of separate optimization of each transmission stage and the threshold method for selecting energetically strong subchannels. The presented simulation results in the case of a multipath channel with Rayleigh signal fading confirm the high efficiency of the proposed approach.

Estimation of signal reception probability characteristics using models of fading transionosphere channels

Statistical models of narrow-band transionosphere channels, which determine the occurrence of signal fading due to random temporal and spatial fluctuations of the electron density of ionospheric irregularities, are considered. Fading models are based on the use of empirical relationships with respect to the distribution density of the amplitude of the signals at the output of the transionosphere channel as a random process. The parameters of empirical distribution densities (lognormal distribution, m-Nakagami distribution, Rician distribution) are related to the scintillation coefficient of signal fading. Using the considered models of signal fading, a technique is presented for estimating the probability of erroneous reception of digital signals with phase shift keying during reception. Using this technique, estimates of energy losses with respect to propagation in free space were made, reaching 0.001 for a scintillation coefficient value of 0.5 to 3 dB for signals with 2- and 4-phase shift keying.

The Potential Use of Car Windscreens for Post-Accident Dose Reconstruction in the Periphery of Nuclear Installations

Cars of a variety of brands are usually parked at a fixed but increasing distance in the periphery of nuclear installations. Herein we focus on the potential use of car windscreens for post-accident dose reconstruction from unplanned nuclear events and natural disasters, also in regard to unexpected events arising during large-scale use of radioactive and nuclear materials. The situation requires identification of analytical techniques that could both readily and reliably be used to assess absorbed dose, sufficient to prompt remedial action where necessary. Samples from three widely used car brands—Honda, Toyota and Proton—are studied in respect of their thermoluminescence (TL) yield. Key TL dosimetric features in the gamma-ray dose range of 1–100 Gy are examined. An ERESCO model 200 MF4-RW X-ray machine has also been used for energy response studies; a Harshaw 3500 TLD reader equipped with WinREMS software was used for the luminescence measurements. All brands exhibit linearity of TL yield versus dose, the samples from Honda showing the greatest response followed by that of the Toyota and Proton brands. The marked energy dependence reflects the effect of the strongly Z-dependent photoelectric effect. Signal fading was investigated over a period of 28 days, the Toyota and Proton brand windshield glass showing a relatively low loss at 52.1% and 52.6% respectively compared to a 56.7% loss for that of the Honda samples. This work forms the first such demonstration of the potential of car windshield glass as a retrospective accident dosimeter.