Minor component analysis (MCA) of lower dimensional data is related to many signal processing applications. MCA strives to extract the "minor" direction in the data space where the variance of the data is minimal, identify the way for dimension reduction and data compression. In this paper neural networks are used to estimate the minor component of signal. This component is used to determine the Direction of Arrival Estimation (DOA) of incident signals. These signals are considered to be emitted from their emission sources .The neural networks knowing “Hebbian-networks” are used to estimate the minor component directions from signal subspace. Narrow band signals are considered here and strike an array composed of M sensors. Simulation results are introduced to shown the performance of the adaptive neural networks to estimate signal components, a comparison of the results obtained from classical method and MCA method, is presented which shows the performance of MCA over classical methods, to estimate exact signal direction from noise subspace.
Pile foundations are typically employed when top-soil layers are unstable and incapable of bearing super-structural pressures. Accurately modeling pile behavior is crucial for ensuring optimal structural and serviceability performance. However, traditional methods such as pregnancy testing, while highly accurate, are expensive and time-consuming. Consequently, various approaches have been developed to predict load settlement behavior, including using artificial neural networks (ANNs). ANNs offer the advantage of accurately replicating substrate behavior's nonlinear and intricate relationship without requiring prior formulation.This research aims to employ artificial neural network (ANN) modeling techniques to simulate the load-settlement relationship of drilled piles. The primary aims of this study are threefold: firstly, to assess the effectiveness of the generated ANN model by comparing its results with experimental pile load test data; secondly, to establish a validation method for ANN models; and thirdly, to conduct a sensitivity analysis to identify the significant input factors that influence the model outputs. In addition, this study undertakes a comprehensive review of prior research on using artificial neural networks for predicting pile behavior. Evaluating efficiency measurement indicators demonstrates exceptional performance, particularly concerning the agreement between the predicted and measured pile settlement. The correlation coefficient (R) and coefficient of determination (R^2) indicate a strong correlation between the predicted and measured values, with values of 0.965 and 0.938, respectively. The root mean squared error (RMSE) is 0.051, indicating a small deviation between the predicted and actual values. The mean percentage error (MPE) is 11%, and the mean absolute percentage error (MAPE) is 21.83%.
In a Mobile Ad hoc Network (MANET), routing protocols rely on asymmetric links so the received information for one connection is not useful at all for the other one. In this paper there are two approaches put under considerations; the first approach is a simulation of MANET with many nodes in one network based FTP traffic. The second approach is a simulation of the combination between WiFi and WiMax wireless technologies in one network based on the IEEE 802.11 and IEEE 802.16 standards respectively. For these two approaches, the simulation considers the situation that the MANET receives traffic from another network via a common gateway. In addition, the mobile nodes are randomly placed in the network that will provide the possibility of multihop routes from a node to another. The standard MANET’s routing protocol is Ad hoc On-demand Distance Vector routing (AODV), whose performance is evaluated in this work with respect to routing overhead, throughput and end-to-end delay. Several scenarios' simulations using WLAN technology were tested to investigate the behavior of the network performance for logical and office applications with fixed and mobile workstations. These networks are considered to operate on a single-hop or multi-hop basis where nodes in the network are able to act as intermediaries (routers) for communications of other nodes. Nodes in these networks are forced to operate with power limited batteries for power saving goal as well as the bandwidth constrained is considered.
In this work, a new key exchange protocol for IP-based mobile networks is introduced. This protocol is called KEPSOM (Key Exchange Protocol Supporting Mobility and Multihoming). The goals of designing KEPSOM are to develop key exchange protocol proposal characterized by its secrecy, simplicity, efficiency, resistivity, and its ability to support mobility and multihoming. The protocol requires only two roundtrips. The design limits the private information revealed by the initiator. An old security association (SA) can be replaced with a new one by rekeying without the need of restarting the protocol with a new session. On the other hand, the changes in IP address due to mobility or multihoming need not to restart the protocol with a new SA session. The proposed protocol can also support key exchange in hybrid wireless network, in which the mobile node can operate in both Ad Hoc and Base Station-oriented wireless network environments using different transmission modes. KEPSOM has been analyzed and proven secure. Several tests have been done to measure and evaluate the performance of the protocol. In these tests, it is found that the required time for rekeying is about 27% of the total required time for exchanging the keys. And the required time to detect and update the change in IP address, which may occur due to mobility or multihoming, is less than 10% of the total required time to establish a new SA sessions.
In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.
The city of Fallujah suffers from bad design in their network and it still dominated by the same pattern of the road and street network system that was produced by the previous stages of the development of the city, which is awaiting the necessary and appropriate solutions, which calls for planning to modernize the road network and streets in it that can accommodate the reality of the city’s condition and the proposed expansions for its subsequent urban growth. The transportation network in Fallujah city was chosen as a case study, the network was divided into roads and intersections, the evaluation included two main roads and eleven sectoral roads, eleven arterial roads, and twenty-five intersections. The network was evaluated in three stages, the first stage was traffic flow and service level, the second stage was evaluating the network in terms of road and intersections marking, while the third stage concerned with evaluating the network in terms of sustainability. The HCS 2010 program was applied to evaluate the first stage, while the second and third stages were evaluated based on the field survey. The results of the first stage showed that most parts of the network in the northern zone suffer from traffic problems and have a low level of service, while most parts of the network in the southern zone have a high service level and enjoy high traffic flow. Most parts of the network were suffered from bad marking, which causes many problems for the users of this network. Related to sustainability, we note a lack of interest on the part of designers or decision-makers. It was concluded that traffic solutions should be economically feasible for some parts of the network, which would lead to improving the network’s performance at the level of the three stages.
Is the refrigerant of the important factors affecting the cutting process as the use of fluids and in different proportions with water used in the cutting process has a clear influence on both the roughness of the surface of the metal to be used and the age of many because of that importance was the use of neural networks to predict the impact of the proportion of mixtures of different and find the best rate of mixing terms of access to the best surface roughness and longer life for many
This research project focused on examining and (rehabilitation) redesigning water networks in a city using the GIS-EPANET program in hydraulic network analysis. Due to the availability of outline data about the study area from the municipality's water distribution system (WDS), this study dealt with four cases. From a statistical calculation, the last case was best optimized, which resulted in a high pressure and an acceptable velocity as a result of high mean pressure (13.58) m, logical mean velocity (0.43) m/s, and accurate standard deviations of 1.214 and 0.48 for pressure and velocity, respectively. The study found that the network had a shortfall in pressure, estimated at 40%, due to the lack of expansion to accommodate the growing population. However, after conducting the analysis and identifying the problem, it was found that all regions were receiving adequate amounts of water. Nevertheless, the water speed in the pipelines throughout the network was deficient, below the recommended rate, with a minimum velocity of 0.02 m/s in the pipe (p3) but a minimum pressure of 7.02 m at the junction (607), indicating that the network design was ineffective. Comparing the results obtained with the real-world situation, it was discovered that the network has many violations and disruptions, causing water loss and resulting in low pressure reaching the customers. While the study found that the pressure inside the network was within acceptable modeling limits of (7–12) m, there was a reduction in the pressure charge due to the frequent use of water pumps inside the houses, especially as the circulated area was pumped further away. The error between the model and the real problem may be attributed to water leaks and disruptions from trees, gardens, landscaping, and livestock grazing, as well as the absence of a counter to calculate the water discharge volume to consumers
Cosmetic surgery is more prevalent in the world in recent years. A beautiful and flawless face is everyone's dream. Aging, environmental factors, disease, or poor diet are among the factors that influence body wrinkles. Various methods are used to reduce these lines. It can be said that the simplest and most effective solution is to inject cosmetic fluids into these areas. But, due to the increase in facial injections using cosmetic fluids, which are considered toxins, the risk of injury to the surrounding facial nerves and injury to one of the main facial nerves is increasing, creating a catastrophe or deformation in the face irreversibly. Deep learning algorithms have been used to determine whether cosmetic fluids are injected or not. Deep Convolutional Neural Networks (CNNs), VGG16, ResNet....etc deep learning algorithms have demonstrated excellent performance in terms of object detection, picture classification, and semantic segmentation. all the suggested approach consists of three stages: feature extraction, training, and testing/validation. Deep learning technology is used to train and test the system with before and after photographs. Numerous investigations have been carried out using various deep learning algorithms and databases the main goal is to attain maximum accuracy to ensure that injected cosmetic fluids by specialists have been injected in safe areas in addition to facial recognition and determining whether or not the person received an injection. The most used databases are IIITD plastic surgery and HDA_Plastic surgery.
Designing an integrated communications system with efficient features is important to researchers and designers. This paper deals with a review of the most important technologies and applications that combine solar cells and communication systems such as Li-Fi technology and its principle of operation, which is a wireless system in which the optical signal is used as a carrier signal as an alternative to the traditional radio frequencies used in Wi-Fi networks, where Li-Fi relies on LED to transmit data, and at high speeds that exceed Wi-Fi technology. Solar Power Satellite (SPS) technology where the satellite is placed in a geostationary orbit in the equatorial plane. As well as the application of photovoltaic solar cells in the SOLPLANT planar antenna, and the replacement of the radiating element of the antenna with a solar cell. The solar cell can transmit and receive electromagnetic signals as well as generate direct current and can be used as antennas either as a single solar cell or group cells and has wide applications in wireless, mobile, Bluetooth and satellite systems. The solar cell has also been applied in Micro strip antenna called Solan , where the solar cell antenna can be considered as a platform for many communication applications and can also be adopted as a radio frequency transmitter and receiver. As well as the design of many antennas integrated with solar cells and compatible with the 5G communication system , in addition to the presence of many applications that combined smart phones and solar cells. This study showed that these technologies and applications provided clean, safe, high-efficiency, high-speed, data-transferring communication systems with low cost.
The detection of faults in electronic circuits is crucial to ensure the proper performance and reliability of electronic applications that utilize these devices. This work discovers, for the first time, that a direct tester board for fault diagnosis can be used not only for the intended measurement of current and voltage but also for studying the potential development of these magnitudes in inaccessible locations, as it detects register transfer level signals through oscilloscopes with low acquisition speeds. The experimental analysis carried out combines the use of commercial software with spatial distribution tracking and the exploitation of the sizes of network links in their computer graphical representation. The proper detection of malfunctions in electronic systems is crucial for enhancing their performance and reliability. We intend to explore the troubleshooting of analog electronic systems, for which we use wide-band direct tester boards. To evaluate its performance in routine practice, we perform experimentation using two different analog circuits designed. They consist of conventional operational amplifiers and element modeling based on equivalent resistance-capacitance networks. Given the procedure followed, commercial programs were used. Special mention should be made of the conclusion matrix, which is interesting when selecting suitable diagnostic parameters. The effectiveness of direct measurement based on integrated probes in the two projects, which allowed for fault insertion, was also confirmed. The results and discussions were enriched by the summarized experimental test report. The work concludes with a reflection on the relationship between this work and the existing state of the art, as well as the new challenges posed by international researchers.