Water scarcity has become the most important challenge facing the world and a source of fear to the global community from the spread of famines due to the lack of agricultural production. For this reason, researchers seek to optimize the use of food resources, including water wealth. This project contributes to the legalization of the use of water resources. One of these methods is to solve the problem of a decrease in water resources, is drip irrigation systems. An affordable system was developed using soil moisture FC-28 hygrometer sensor compatible with arduino uno R3 and sending electrical signals based on voltage difference due to increased or decreased water in the soil to the monitor through the arduino for decision to operate irrigation pumps. The system is controlled by a software that allows the user to know the current state of the soil to choose the type of plant to determine the amount of water and the possibility of adding new types of plants to the program library and other features.
In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.
Image restoration is one of the digital image processing techniques used to repaint or restore image information. One common problem encountered during restoration is image blurring. To solve this problem, a new de-blurring technique was proposed to reduce or remove image blurring. The proposed filter was designed using the least square interpolation calculation controlled by the neural network to select the blurred pixels. The wavelet decomposition technique was used to improve the performance of the proposed filter, which showed good results for fully and partially blurred regions in images.
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