ملخص البحث :

In this search, an important methodology has been presented for communicated information rectification utilizing advanced channel windowing approach. The modern data communication technologies are ensured with numerous challenges because of their unpredictability and arrangement. Various digital transmission topologies in 4G can't fulfill the requirements in future arrangements, therefore, alternative multicarrier modulation (MCM) becoming the nominated approaches among all other data transmission techniques. Wherein prototype filter configuration is a fundamental system based on which the synthesis and analysis filters are derived. This paper presents a complete review on the ongoing advances of finite impulse response (FIR) filter plan procedures in MCM based correspondence frameworks. Initially, the essential issues are tried, taking into consideration the presentation of available data signal applicants and the FIR filter design concept. At that point the techniques for FIR filter configuration are summed up in subtleties and are center around the accompanying three group’s recurrence testing strategies, windowing based strategies and advancement-based techniques. At last, the exhibitions of different FIR structure strategies are assessed and measured by power spectral density (PSD) and bit error rate (BER), and variable MCM plots as well as their potential prototype filters are examined.

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In this study, two techniques are introduced for image steganography in the spatial domain. These systems employ chaos theory to track the addresses of shuffled bits in steganography. The first system is based on the well-known LSB technique, while the second system is based on a recent approach that searches for the identical bits between the secret message and the cover image. A modified logistic map is employed in the chaotic map to generate integer chaotic series to extract the shuffled addresses bits. Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis are used for testing and evaluating the new levels of security for the proposed techniques. The results show that the proposed methods outperform existing systems.

ملخص البحث :

Bézier curve of the first rank is a simple equation in terms of form, but it is characterized by the nature of private transactions making it difficult to use in image encryption because the dispersion of color values is not enough, this results in an encrypted image that gives clear references to the original image. This weakness in the equation does not exist in the case of text encryption where enough to change the numerical values of the components of the text to get a digital matrix representing the encrypted text.Through this algorithm we have used the Bézier curve technique from the first order of image coding we used a new method to generate the coefficients of the equation where we simulated the Bazier equation where it became as follows: · y=x_1*(t-1)+x_2*t · Where 0

• سنة النشر : 2020
• تصنيف البحث : scopus
• تحميل

ملخص البحث :

E-learning has become a popular learning method used in many local and international universities and in many educational institutions. The initial achievements of e-learning platforms and the online learning environment demonstrated outstanding advantages in distance education. However, it is necessary to conduct an evaluation of the educational process, and in particular an effective assessment of the learning environment via online-based e-learning platforms. Where this study aimed to identify the evaluation of the effectiveness of e-learning from the point of view of the teaching staff in the Technical Institute of Babel and the Technical Institute Al-Mussaib. To achieve the objectives of the study, the researchers prepared a questionnaire containing (32) questions, after verifying the tools of reliability and validity. The results of the study revealed that the evaluation of the effectiveness of e-learning was average and above average in some paragraphs of the questionnaire. The percentage (84,070) of faculty members use computers and smart phones to publish academic content through the use of the home internet, at a rate of (95,575) in the form of creating educational content in several forms, including video, audio and text at the same time.

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Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one of the mostly prominent techniques used today for its reliability and ability to generate rapid results. The system was trained on a set of X-Ray images taken of the chest area of infected and uninfected people. The CNN structure gave accuracy, Precision, Recall and F-Measure 98%. This model is characterized by its ability to distinguish efficiently and adapt to different cases.

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An edge is where an image’s intensity values rapidly change from low to high-intensity values or vice versa. The edge itself is at the midpoint of this change. Edge detection remains a challenge in computer vision despite recent advances. It cannot be applied to an image with excessive brightness and contrast. This paper produces a new method based on the standard deviation histogram feature to reduce the onerousness. The proposed method aims to prepare the input image for the edge detection approaches by performing a histogram feature extraction. The main characteristics of the proposed approach are simplicity and functionality. The authors utilize twenty MATLAB standard images as well as ADNI brain images. The authors use the Canny edge detection method to defect edges from the proposed method. The authors use edge detection evaluation metrics such as Figure of Merit (FOM), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) measures for evaluating and justifying edge quality. The experimental results show that the proposed method performs better in visual and statistical edge quality than both classical and fractional-order edge detection methods.

ملخص البحث :

One of the most common image operation analyses is the edge detection technique. Edge detection is used for shaping the edge of an image. Also, it is used for enhancing images. This paper presents a new approach to detecting the edge of color image using the Mamdani fuzzy inference classifier based on the Fuzzy Set Membership Function (FSMF). Here, Gaussian Curve Membership Function (GCMF) is used as a FSMF. GCMF is used for each class to assign that class to each pixel. In this approach, two windows/filters are used in size (1x2) and (2x1). Several standard color images are used to test our proposed algorithm (City, Jelly_cc11, Baboon, Lena, and Peppers). In order to parametric evaluation of selected images, Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE) are considered. However, the performance of our proposed algorithm compared with other well-known approaches (Canny, Prewitt, and Sobel) is somehow very similar but significantly faster.

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Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM). We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier.

ملخص البحث :

Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%)

ملخص البحث :

Due to increased information security concerns, biometric recognition technology has become more important. Unimodal biometrics still work effectively, but they struggle with noise sensitivity and spoof attack susceptibility since they rely on a single data source. This paper uses advances in deep learning and machine learning to propose new unimodal systems for the palm, face, and iris. These models use deep wavelet transform networks (WTN) for face and iris identification and deep convolutional neural networks (CNNs) for palmprint identification. In addition, we introduce a novel multimodal biometric system based on unimodal systems. We get 98.29% for face, 98.86% for palmprint, and 95.59% for iris in individual unimodal systems with Support Vector Machines (SVM). This is done by using the new property MULB dataset, which has many biometric features. The multimodal system achieves 99.88% accuracy and a 0.0186 equal error rate, underscoring the relevance of several biometric features and the superior performance of the identification system