Design and analysis of off -grid PV/diesel system for small scale factory located in Wadi Rum, Jordan using Homer software
The fuel prices are increasing nowadays, causing a burden on the power system. From this, diesel generators that are still in use, must be hybridized with renewable energy (RE) to levelized the overall projects costs. This study aims to schedule an operation of a hybrid system photovoltaic (PV)/ diesel in a remote area which located in Wadi Rum district in Jordan to serve factory load in addition to obtain the best economical system combination that can serve the needs of a continuous electrical load without any interruption. Based on the results of the analysis using HOMER software, the configuration in the system will be a combination between PV modules, and diesel generator. The hybrid system has met the target for electricity production which is documented in upcoming sections.
A Blockchain Framework for Enhancing Selection of Optimal Parameters at NB-IOT
By 2030, wireless communications will have connected over thirty billion devices. Narrowband Internet of Things (NB-IOT) technology has grown in popularity in response to the rapid growth of the internet of things (IOT) sector. The main aim of this study is to supply overall survey of the design modifications transported in the NB-IoT standardization along with comprehensive study evolutions according to popular companies in many countries such as: Telia, Elisa, Orange, Telecom Italia, Telstra, Vodafone, On the other hand, because there is a lot of work in NB-IOT on optimizing parameters or improving optimization methods, Consequently, this work presents a Blockchain architecture that may be used to enhance security, authentication, and efficient data access while maintaining data integrity. We describe the optimization parameters for physical channel and signal transmission and reception. Using the Physical Downlink Control Channel, we devise an adaptation scheme for 200 KHz bandwidth in NB-IOT networks (PDCCH). Finally, we want to locate the following: optimal parameters: Number of frames, Doppler frequency and Diversity performance.
Supervised Neural Network Based Intrusion Detection Systems
The magnificent problem for today’s computer networks is the internet attacks which is growing rapidly. Therefore, applying security methods to prevent such attacks on computer network is very important. Network attacks are challenging because they are continuously changing their patterns and techniques. Machine learning approaches play vital roles in detecting, and preventing attacks in different types of computer networks. The security is a critical issue whereas the security mechanism may affect the way of detecting, analysing, and preventing attacks. Therefore, security methods must modify their techniques to deal with. We have proposed Supervised Neural Network Based Intrusion Detection System (SNN-based IDS) to address a critical and timely issue in computer networking - the escalating threat of internet attacks. As these attacks continue to evolve, finding effective methods to detect and prevent them is of paramount importance. this paper leverages advanced technology to tackle the complex task of intrusion detection. This demonstrates the practical application of cutting-edge methods in cybersecurity. The paper suggests that the proposed model has the potential to predict and mitigate new types of attacks, even those not present in the training dataset. This feature is crucial in a rapidly evolving threat landscape. Our SNN is trained by KDDCUP’99 dataset which include 14 attack types in the test data, with an overall number of 24 training attack types. Experiments results show that the proposed system is capable to identifying the attacks and classifying them with high accuracy and reliability, regardless of the dataset's nonlinearity, size, or incompleteness.
This work analyzes the performance and designs a rectangular Microstrip patch F antenna. 5.9 GHz is the antenna's resonance frequency range, making it suitable for Internet of Things (IOT) applications. Simulation software for this work was Computer Simulation Technology (CST) software. A rectangular Inverted F coplanar antenna array structure was used in the antenna's design. The bandwidth, gain, and return on loss of these antennas were evaluated to determine their respective performances. The main findings of this study shown that, in comparison to a conventional antenna, the optimized array-shaped antenna increased bandwidth, gain, and return on loss. Furthermore, the improved antenna attained an operating frequency of 5.9 GHz, making it appropriate for Internet of Things applications.
