Image filtering is a common technique used in digital image processing that can be used to take a picture appear differently aesthetically. Noise, also known as distracting visual artifacts, can lower the overall quality of a picture, which is why image improvement techniques are required to fix the problem. It can be utilized in a variety of ways, including smoothing, sharpening, reducing noise, and detecting borders, to name a few. In this piece, we will be using convolutional techniques to correct the images that were messed up. The first thing that needs to be done is a point-by-point multiplication of the frequency domain representation of the picture that's being entered through a black image that has a small white rectangle in the mid of it. This is the first step. Only the lowest harmonics are kept after we apply a filter that gets rid of the higher ones. Because the high frequencies in the input picture are filtered out, the special domain of the image that is produced should look like a blurrier variation of the original picture. Therefore, a greater degree of detail preservation is indicated when the white rectangle W is larger because this indicates that more high-frequency components of I have been preserved.
: Control is important to improve hardware performance. Most electronic systems are designed according to the device and then manufactured as an attached electronic device. However, if conditions change or the factory is modernized then the control device must be replaced. This is due to the complexity of the control unit represented by the program implementation algorithms, in addition to the time delay caused by digital and analog signal converters (ADC - DAC), and in this research it is replaced by deep neural networks It is a thriving field with practical and medical applications and is characterized by its ability to learn and train as it is a branch of machine learning and artificial intelligence. The results proved that the functioning of the neural networks and their performance are better than the control system where the value of the difference between the two is equal to zero.
Classically industrial systems apply a number of techniques to control their components, including the control system, which modify the relationship between input and output signals to configure the system to provide the required response. In most practical systems these signals are continuous, hence it is important to convert them into digital signals to be processed by digital systems. Despite the great development in technology, given the importance of the control system in relation to dynamic systems to achieve optimal performance, but classical control suffers from some important problems. The complexity of the control system represented by the program implementation algorithms and the loss of most information during the process of converting the system to digital and not adapting to external variables or with new updates. In this research classical control is replaced by deep neural networks, which is a thriving field with practical and medical applications and is characterized by its ability to learn and train as it is a branch of machine learning and artificial intelligence. The results proved that the functioning of the neural networks and their performance is similar to classical control systems, with the advantage of simplicity and adaptability.
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