This paper presents a meta-heuristic swarm based optimization technique for solving robot path planning. The natural activities of actual ants inspire which named Ant Colony Optimization. (ACO) has been proposed in this work to find the shortest and safest path for a mobile robot in different static environments with different complexities. A nonzero size for the mobile robot has been considered in the project by taking a tolerance around the obstacle to account for the actual size of the mobile robot. A new concept was added to standard Ant Colony Optimization (ACO) for further modifications. Simulations results, which carried out using MATLAB 2015(a) environment, prove that the suggested algorithm outperforms the standard version of AC

As an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

Blockchain has garnered the most attention as the most important new technology that supports recent digital transactions via e-government. The most critical challenge for public e-government systems is reducing bureaucracy and increasing the efficiency and performance of administrative processes in these systems since blockchain technology can play a role in a decentralized environment and execute a high level of security transactions and transparency. So, the main objectives of this work are to survey different proposed models for e-government system architecture based on blockchain technology implementation and how these models are validated.  This work studies and analyzes some research trends focused on blockchain

Photonic Crystal Fiber (PCF) based on the Surface Plasmon Resonance (SPR) effect has been proposed to detect polluted water samples. The sensing characteristics are illustrated using the finite element method. The right hole of the right side of PCF core has been coated with chemically stable gold material to achieve the practical sensing approach. The performance parameter of the proposed sensor is investigated in terms of wavelength sensitivity, amplitude sensitivity, sensor resolution, and linearity of the resonant wavelength with the variation of refractive index of analyte. In the sensing range of 1.33 to 1.3624, maximum sensitivities of 1360.2 nm ∕ RIU and 184 RIU⁻¹ are achieved with the high sensor resolutions of 7

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance pena

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

Electrospun nanofiber membranes are employed in a variety of applications due to its unique features. the nanofibers' characterizations are effected by the polymer solution. The used solvent for dissolving the polymer powder is critical in preparing the precursor solution. In this paper, the Polyacrylonitrile (PAN)-based nanofibers were prepared in a concentration of 10 wt.% using various solvents (NMP, DMF, and DMSO). The surface morphology, porosity, and the mechanical strength of the three prepared 10 wt.% PAN-based nanofibers membranes (PAN/NMP, PAN/DMF, and PAN/DMSO) were characterized using the Scanning Electron Microscopy (SEM), Dry-wet Weights method, and Dynamic Mechanical Analyzer (DMA). Using DMF as a solvent resulted in a lon