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RC5 Performance Enhancement Based on Parallel Computing
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This study aims to enhance the RC5 algorithm to improve encryption and decryption speeds in devices with limited power and memory resources. These resource-constrained applications, which range in size from wearables and smart cards to microscopic sensors, frequently function in settings where traditional cryptographic techniques because of their high computational overhead and memory requirements are impracticable. The Enhanced RC5 (ERC5) algorithm integrates the PKCS#7 padding method to effectively adapt to various data sizes. Empirical investigation reveals significant improvements in encryption speed with ERC5, ranging from 50.90% to 64.18% for audio files and 46.97% to 56.84% for image files, depending on file size. A substantial improvement of 59.90% is observed for data files sized at 1500000kb. Partitioning larger files notably reduces encryption time, while smaller files experience marginal benefits. Certain file types benefit from both strategies. Evaluation metrics include encryption execution time and throughput, consistently demonstrating ERC5's superiority over the original RC5. Moreover, ERC5 exhibits reduced power consumption and heightened throughput, highlighting its multifaceted benefits in resource-constrained environments. ERC5 is developed and tested on various file types and sizes to evaluate encryption speed, power consumption, and throughput. ERC5 significantly improves encryption speed across different file types and sizes, with notable gains for audio, image, and large data files. While partitioning smaller files only slightly improves encryption time, larger files partitioning yields faster results. Future research could explore ERC5 optimizations for different computing environments, its integration into real-time encryption scenarios, and its impact on other cryptographic operations and security protocols.

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Publication Date
Fri Dec 23 2011
Journal Name
International Journal Of The Physical Sciences
Fast prediction of power transfer stability index based on radial basis function neural network
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Publication Date
Fri Jul 01 2016
Journal Name
International Journal Of Modern Trends In Engineering And Research (ijmter)
An image processing oriented optical mark reader based on modify multi-connect architecture (MMCA)
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Optical Mark Recognition (OMR) is the technology of electronically extracting intended data from marked fields, such as squareand bubbles fields, on printed forms. OMR technology is particularly useful for applications in which large numbers of hand-filled forms need to be processed quickly and with a great degree of accuracy. The technique is particularly popular with schools and universities for the reading in of multiple choice exam papers. This paper proposed OMRbased on Modify Multi-Connect Architecture (MMCA) associative memory, its work in two phases: training phase and recognition phase. The proposed method was also able to detect more than one or no selected choice. Among 800 test samples with 8 types of grid answer sheets and tota

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Publication Date
Mon Oct 01 2018
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Effluent quality assessment of Al-Diwaniyah sewage treatment plant based on wastewater quality index
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Treated effluent wastewater is considered an alternative water resource which can provide an important contribution for using it in different purposes, so, the wastewater quality is very important for knowing its suitability for different uses before discharging it into fresh water ecosystems. The wastewater quality index (WWQI) may be considered as a useful and effective tool to assess wastewater quality by indicating one value representing the overall characteristic of the wastewater. It could be used to indicate the suitability of wastewater for different uses in water quality management and decision making. The present study was conducted to evaluate the Al-Diwaniyah sewage treatment plant (STP) effluent quality based on wastewa

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Publication Date
Sun Dec 03 2017
Journal Name
Baghdad Science Journal
Network Self-Fault Management Based on Multi-Intelligent Agents and Windows Management Instrumentation (WMI)
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This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to

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Publication Date
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Microgrid Integration Based on Deep Learning NARMA-L2 Controller for Maximum Power Point Tracking
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

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Publication Date
Wed Jan 01 2014
Journal Name
International Journal Of Computer Applications
Mobile Position Estimation based on Three Angles of Arrival using an Interpolative Neural Network
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In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

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Publication Date
Fri Jan 01 2016
Journal Name
Modern Applied Science
Hybrid Methodology for Image Segmentation Based on Active Contour Module and Alpha-Shape Theory
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The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present s

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Publication Date
Fri Nov 04 2022
Journal Name
Journal Of Optics
Coreless optical fiber for hemoglobin (HB) sensing with bilayer based on surface plasmon resonance
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In this work, an optical fiber biomedical sensor for detecting the ratio of the hemoglobin in the blood is presented. A surface plasmon resonance (SPR)-based coreless optical fiber was developed and implemented using single- and multi-mode optical fibers. The sensor is also utilized to evaluate refractive indices and concentrations of hemoglobin in blood samples, with 40 nm thickness of (20 nm Au and 20 nm Ag) to increase the sensitivity. It is found in practice that when the sensitive refractive index increases, the resonant wavelength increases due to the decrease in energy.

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Computational And Theoretical Nanoscience
Solution for Multi-Objective Optimisation Master Production Scheduling Problems Based on Swarm Intelligence Algorithms
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The emphasis of Master Production Scheduling (MPS) or tactic planning is on time and spatial disintegration of the cumulative planning targets and forecasts, along with the provision and forecast of the required resources. This procedure eventually becomes considerably difficult and slow as the number of resources, products and periods considered increases. A number of studies have been carried out to understand these impediments and formulate algorithms to optimise the production planning problem, or more specifically the master production scheduling (MPS) problem. These algorithms include an Evolutionary Algorithm called Genetic Algorithm, a Swarm Intelligence methodology called Gravitational Search Algorithm (GSA), Bat Algorithm (BAT), T

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Publication Date
Fri Dec 01 2023
Journal Name
Iraqi Journal Of Physics
Gas Sensor for Hazardous Nitrogen Dioxide Based on TiO2 Nanotube Synthesis via Electrochemical Method
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Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO2) materials with various crystal phase shapes, such as rutile TiO2 (R-TiO2NTs) and anatase TiO2 (A-TiO2NTs). In this work, we have studied the effect of voltage on preparing TiO2 nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectro

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