Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You only look once”) neural network algorithm, which is an efficient real-time object identification algorithm, an intelligent system was developed in this thesis to distinguish which faces are wearing a mask and who is not wearing a wrong mask. The proposed system was developed based on data preparation, preprocessing, and adding a multi-layer neural network, followed by extracting the detection algorithm to improve the accuracy of the system. Two global data sets were used to train and test the proposed system and worked on it in three models, where the first contains the AIZOO data set, the second contains the MoLa RGB CovSurv data set, and the third model contains a combined data set for the two in order to provide cases that are difficult to identify and the accuracy results that were obtained. obtained from the merging datasets showed that the face mask (0.953) and the face recognition system were the most accurate in detecting them (0.916).
Various document types play an influential role in a lot of our lives activities today; hence preserving their integrity is an important matter. Such documents have various forms, including texts, videos, sounds, and images. The latter types' authentication will be our concern here in this paper. Images can be handled spatially by doing the proper modification directly on their pixel values or spectrally through conducting some adjustments to some of the addressed coefficients. Due to spectral (frequency) domain flexibility in handling data, the domain coefficients are utilized for the watermark embedding purpose. The integer wavelet transform (IWT), which is a wavelet transform based on the lifting scheme,
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Optical fiber biomedical sensor based on surface plasmon resonance for measuring and sensing the concentration and the refractive index of sugar in blood serum is designed and implemented during this work. Performance properties such as signal to noise ratio (SNR), sensitivity, resolution and the figure of merit were evaluated for the fabricated sensor. It was found that the sensitivity of the optical fiber-based SPR sensor with 40 nm thick and 10 mm long Au metal film of the exposed sensing region is 7.5µm/RIU, SNR is 0.697, figure of merit is 87.2 and resolution is 0.00026. The sort of optical fiber utilized in this work is plastic optical fiber with a core diameter of 980 µm, a cladding of 20μm, and a numerical aperture of 0.
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Glaucoma is one of the most dangerous eye diseases. It occurs as a result of an imbalance in the drainage and flow of the retinal fluid. Consequently, intraocular pressure is generated, which is a significant risk factor for glaucoma. Intraocular pressure causes progressive damage to the optic nerve head, thus leading to vision loss in the advanced stages. Glaucoma does not give any signs of disease in the early stages, so it is called "the Silent Thief of Sight". Therefore, early diagnosis and treatment of retinal eye disease is extremely important to prevent vision loss. Many articles aim to analyze fundus retinal images and diagnose glaucoma. This review can be used as a guideline to help diagnose glaucoma. It presents 63 artic
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In this paper, a fast lossless image compression method is introduced for compressing medical images, it is based on splitting the image blocks according to its nature along with using the polynomial approximation to decompose image signal followed by applying run length coding on the residue part of the image, which represents the error caused by applying polynomial approximation. Then, Huffman coding is applied as a last stage to encode the polynomial coefficients and run length coding. The test results indicate that the suggested method can lead to promising performance.
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It is known that images differ from texts in many aspects, such as high repetition and correlation, local structure, capacitance characteristics and frequency. As a result, traditional encryption methods can not be applied to images. In this paper we present a method for designing a simple and efficient messy system using a difference in the output sequence. To meet the requirements of image encryption, we create a new coding system for linear and nonlinear structures based on the generation of a new key based on chaotic maps.
The design uses a kind of chaotic maps including the Chebyshev 1D map, depending on the parameters, for a good random appearance. The output is a test in several measurements, including the complexity of th
... Show MoreA fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted direct
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Graphene (Gr) decorated with silver nanoparticles (Ag NPs) were used to fabricate a wideband range photodetector. Silicon (Si) and porous silicon (PS) were used as a substrate to deposit Gr /Ag NPs by drop-casting technique. Silver nanoparticles (Ag NPs) were prepared using the chemical method. As well as the dispersion of silver NPs is achieved by a simple chemistry process on the surface of Gr.
The optical, structure and electrical characteristics of AgNPs and Gr decorated with Ag NPs were characterized by ultraviolet-visible spectroscopy (UV-Vis), x-ray diffraction (XRD). The X-ray diffraction (XRD) spectrum of Ag NPs exhibited 2θ values (38.1o, 44.3 o, 64.5 o and 77.7
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Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discover
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye
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