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There is a rising interest in optimizing the regulation of active-reactive power control (P-Q) for a Microgrid (MG) running in grid-connected mode. This study presents the development of an optimum control strategy for active and reactive power in a three-phase grid-connected inverter inside a (MG). The suggested inverter was designed to provide consistent power and voltage to the demand load case study. This study checks Iraq's gas power plant and associated equipment and devices, indicated by a residential load of 1MW. MATLAB models a solar photovoltaic (PV) system with a battery energy storage system (BESS). The data indicate that the proposed inverter can provide constant energy to both the grid and load sides, even when demand load and solar irradiation vary. The (BESS) regulates the direct current (DC) bus voltage at a consistent level of 1500V through its control mechanism. [ABSTRACT FROM AUTHOR]

Object detection is an important issue in many fields, including robotics, self-driving cars, and surveillance. This paper describes object detection methods based on Radio Tomographic Imaging (RTI). These technologies include Frequency Modulated Continuous Wave (FMCW) and Wi-Fi with computer vision algorithms. Wi-Fi systems focus primarily on measuring the Received Signal Strength Index (RSSI), and FMCW radar systems measure parameters such as pulse frequency, time delay, and Doppler shift to extract information about targets, including their range, speed, and sometimes their direction. These technologies are becoming increasingly important because of their ability to correctly identify and describe objects using signals that computer vision can interpret, making them competitive with technologies that rely on optical and thermal vision. The methods presented in this paper are suitable for object detection tasks because they combine the advantages of radio and computer vision. The methods mentioned in processing these techniques were based on previous research in the literature for each technique. In the end, this study adds useful information in the field of computer vision and radio signals, allowing researchers to make this study a reference that extracts many methods and ideas so that future researchers can choose the most appropriate technology for their research. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is an emerging system with many applications in security, healthcare, and smart environments. HAR systems use radar features and machine learning algorithms (MLA) to automatically and instantly recognize and classify human activities. However, human activity is difficult to identify easily, and HAR suffers from requiring many manipulations to reach the target. This is why HAR is such a rich field for researchers. In this study, four MLA algorithms were proposed to be trained on a radar dataset, and then we demonstrated which algorithms achieved the highest accuracy. By comparing the proposed training models, the performance of multi-layer perceptron (MLP) was good when the Adam optimization algorithm with 1000 hidden layers, which is the ReLU activation function, was used. It achieved an accuracy of 98.2% and a classification accuracy of 95.8%. The study presented an idea using the 3D matrix of voxels as a 2D image to instantly detect human activity. Experimental investigations have shown that the proposed approach leads to satisfactory results. [ABSTRACT FROM AUTHOR]

Separating the baseline wander interface signal from data Electrocardiogram (ECG) signals is critical for a proper diagnosis of heart disease. The reducing or eliminating baseline wander is representing one of the important topic in ECG signal processing. This interface signal has a low frequency according to data ECG signal, and causing the doctor's judgment on myocardial ischemia skewed. The ECG is represented by sparse data signals, whereas low-frequency indicators represent the baseline wander. The Ant Colony Algorithm is used with efficient independent component analysis in the proposed work to improve the separation of baseline wander interface signal from raw ECG data. This work creates a new method for reducing the influence baseline wander in ECG readings by combining efficient independent analysis with the Ant Colony Algorithm. The main advantage of the suggested technique over the old method is that it isolates all the essential data without filtering it out of the raw ECG signal. The result is computed using MATLAB (Ver. 2021) and accurate data from Massachusetts Institute of Technology (MIT-BIH). Another contribution is the first time the Ant colony method has been used to evaluate ECG signal analysis with efficient independent research. The obtained results demonstrated that this method is highly effective in removing baseline wander interference signals, the signal to interface ratio is enhanced from -7.346 to -4.022 by using proposed work instead of classical Fast independent analysis for simulated data. [ABSTRACT FROM AUTHOR]

Recently, solar power represents one of the most stable and important renewable energy resources in our life, its importance increases every second because the planet will be run out of fossil fuel in the near future, but sometime uncertain fluctuations appear duo to rain, dust, clouds, and heat which prompts the issue of power supply interference. So that, in the traditional way, the solar panels have to be check from time to another to figure out if they still work correctly, and that could make either the maintenance cost will be high, or there will be loss in the performance, that could be obvious in a solar system of large number of panels. As a result a better way to check the solar cells is needed to make the performance better, especially in case of a large number of solar panels. In this paper, a study will be made about solar system consists of an array of solar panels and photovoltaic parameter in each of them. The main purpose is building a system which works on fault detection. This study depends on Smart Monitoring Device (SMD) sensors, which connected to each PV panel. These sensors collect the measurements periodically, and send them to an IoT central server, in order to detect the faults to correct them as soon as possible and improve the efficiency of solar panels. The results showed that the AC voltage from the applying the system of Solar Voltage 1when the fault occurs as the maximum is 30.62 V and average is 17.9 V while the Solar Voltage 2 as max is 31.05 V, and average is 18.15 V. The AC power consumed per unit time (0.1 seconds is 20.573 W to 0.8 seconds is 20.592 W. [ABSTRACT FROM AUTHOR]

Positioning within indoor environments is one of the major challenges facing many different applications. It has become necessary to find algorithms and systems capable of locating a specific object within an environment that contains many objects. In this paper, the researchers have made a detailed study on many methods of positioning within different environments. Some methods rely on digital images and others rely on identification systems by radio waves and other methods. Each system has a number of advantages that qualify it to be the best choice in certain applications. On the other hand, there are drawbacks to each system that prevent it from working within certain environments. Clear comparisons are made in this paper showing the different systems with the pros and cons of each one. [ABSTRACT FROM AUTHOR]

The 2.45 GHz area is used to demonstrate the operation of a penetrating windmill-like surface with wireless energy harvesting that is comparable to the polarization-independent collecting of electromagnetic waves (Wifi). An improved wireless energy harvester's efficiency can be achieved by reducing the susceptibility of the reception to polarization and wave angle. Four identical metal wires that have been 45 degrees rotated make form the meta-surface unit cell. A wide-angle, polarization-insensitive behavior, exceptionally high absorption coefficients, and extreme compactness characterize the simulated unit cell. An 86% harvesting efficiency at 2.45 GHz is achieved by building and simulating a three-dimensional metamaterial harvester. For applications requiring great efficiency, the suggested meta-material design may be particularly intriguing. [ABSTRACT FROM AUTHOR]

Meeting the increasing global demand for energy has been a significant challenge for the world, and photovoltaic (PV) energy has made tremendous progress in recent years. However, designing and implementing solar systems still poses significant challenges due to the conversion of a substantial fraction from the solar irradiation that strikes photovoltaic cells to heat. Maintaining a consistent temperature is crucial for solar photovoltaics to achieve maximum efficiency, especially in areas with extremely high average annual temperatures. Several PV module cooling methods, such as using water or air, are used for thermal regulation, often cycled by pumps, fans, and blowers. Unfortunately, these simple PV cooling technologies have a few limitations, including poor heat dissipation in passive mode and a need for external power sources, which lower system efficiency and increase repair and maintenance costs. However, the combination of photovoltaic modules with phase change materials (PCMs) can effectively lower solar cell temperatures and increase their output power without requiring an external power source. In this study, we evaluated the use of two locally manufactured PCMs, Paraffin wax and Petroleum jelly (Vaseline), which are readily available in the Iraqi market at low costs. Our outdoor experiment aimed to examine the effects of a mixture of these PCMs on the temperature and electrical performance of PV panels. The findings reveal that the mixed PCM increased the electrical efficiency of the PV panel by up to 13.1% and reduced its temperature by 6.1 °C, and the average temperature by 5.29 °C. [ABSTRACT FROM AUTHOR]

With the introduction of 5th generation (5G) networks, the demand for new services and applications is driving a rapid increase in data traffic, which poses new challenges for wireless networks. Unmanned aerial vehicles (UAVs) or (Drone) can be used to support wireless networks during peak hours, especially when there is a lot of traffic because users are moving around in space and time. In this work, the problems with mobile network congestion and the biggest problems that come with the launch of the next generation are looked at. An overview of applications that help UAVs with the help of networks that are affected by the flow of users in time and space is also given. [ABSTRACT FROM AUTHOR]

As next-generation wireless communication technologies advance and spectrum resources become scarcer, Cognitive Radio Networks (CRN) must incorporate learning and reasoning capabilities. Spectrum Sensing (SS) in particular has emerged as the primary target for investigation to solve the problems of CRN. Deep learning techniques have been used to add new aspects to SS. This paper is based on a statistical analysis of the classic energy detection scheme, which is solely dependent on the number of samples and secondary users' signal-to-noise ratio. The detection of channel occupancy is based on well-established analytical techniques such as Maximum Ratio Combining (MRC) and AND/OR rules. Furthermore, the deep learning Long Short Term Memory (LSTM) algorithm is used for SS and compared to analytic techniques. the results demonstrate that the LSTM technique presents a better trade-off between the probability of false alarms and the probability of missed especially for fading channels with accurate detection of 87.4%. [ABSTRACT FROM AUTHOR]