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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]

The conventional grid in Iraq has many issues, such as substantial network damage and a significant deficit in the generated power due to the country's circumstances over the last three decades, the load consumption and the carbon emissions is increasing, and the load demand is not uniform. The traditional power system without smart grid faces supply shortage problems that lead to a large number of outages that may cause frequent power outages. Therefore, it becomes necessary to interact renewable energy plants with traditional power system for switching to low carbon energy sources and employ loading management technique which must be made through peak load periods. Smart grid refers to an energy network which employs digitized and other modern technologies to monitor and supervise the flow of power from each generation sources to meet the various electricity requirements of consumers. Hence, this study proposes a MATLAB smart grid simulation model that incorporates renewable energy sources, a traditional power system, grid parameter monitoring and the priority control strategy. In the load prority strategy, daily energy allocation is formulated and mathematically optimized to maximize total energy hours delivered. The load with the highest priority receives more power hours under this technique, followed by the load with the lowest priority. As the control level has been raised, the results show the correct operation of the proposed system by maintaining 100% continuity for the essential loads. [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]

The transmission process of sending data across carrier media whether it's text, audio, and graphics or any other source of data is influenced by the environment in which it's sent, and the transmitted signals are distorted as a result. Blind source separation BSS is a popular technique for recovering signals from separate sources that are isolated from the environment. In this paper, three poplars algorithms based on BSS techniques are used to identify sources of interference in an electromagnetic environment. Audio, text, and image data are utilized across networks with interference and noise, where the original data and another two types of Electromagnetic and Crosstalk noises were created; then merged and delivered over a network through Ethernet cable. The stone algorithm, Joint Approximate Diagonalization of Eigenmatrices JADE Algorithm, and the Enhanced Fast Independent Component Analysis EFICA algorithm are employed to exposing the information and the data passing through the cable for detecting the harmful impacts of interference sources over the electromagnetic environment. As a result, subjective and objective evaluation is done based on the popular criteria of signal to noise ratio SNR where the stone algorithm has best value of SNR over the others with 33 while JADE algorithm has 25, and EFICA algorithm has 4. [ABSTRACT FROM AUTHOR]

When sending data across carrier media, whether it is text, audio, graphics, or any other sort of data, the transmission process is influenced by the environment in which it is sent, and the transmitted signals are distorted as a result. Blind source separation (BSS) is a popular method for recovering signals from separate sources that are isolated from the environment. In this paper, an algorithm based completely on BSS blind source separation techniques will be created in our current research to identify sources of interference in an electromagnetic environment. An algorithm based completely on BSS blind source separation techniques will be created in our current research to identify sources of interference in an electromagnetic environment. Where aodui data will be utilized to transport them across networks with interference and noise, where the original data was created using two types of electromagnetic noise, and Crosstalk noise, and then merged and delivered over a network through Ethernet cable. The stone algorithm, the JADE algorithm, and the EFICA algorithm were employed as BSS algorithms, exposing the information and data passing through the cable to the harmful impacts of the electromagnetic environment and determining one of these algorithms is better than the other. [ABSTRACT FROM AUTHOR]