High frequency (HF) communications have an important role in long distances wireless communications. This frequency band is more important than VHF and UHF, as HF frequencies can cut longer distance with a single hopping. It has a low operation cost because it offers over-the-horizon communications without repeaters, therefore it can be used as a backup for satellite communications in emergency conditions. One of the main problems in HF communications is the prediction of the propagation direction and the frequency of optimum transmission (FOT) that must be used at a certain time. This paper introduces a new technique based on Oblique Ionosonde Station (OIS) to overcome this problem with a low cost and an easier way. This technique uses the

Objectives: Hyperglycosylated human chorionic gonadotropin (hCG) is a variant of hCG. In addition, it has a different oligosaccharide structure compared to the regular hCG and promotes the invasion and differentiation of peripheral cytotrophoblast. This study aimed to measure hyperglycosylated hCG as a predictor in the diagnosis of placenta accreta. Materials and Methods: In general, 90 pregnant women were involved in this case-control study among which, 30 ladies (control group) were pregnant within the gestational age of ≥36 weeks with at least one previous caesarean section and a normal sited placenta in transabdominal ultrasound (TAU). The other 60 pregnant women (case

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Urban land uses of all kinds are the constituent elements of the urban spatial structure. Because of the influence of economic and social factors, cities in general are characterized by the dynamic state of their elements over time. Urban functions occur in a certain way with different spatial patterns. Hence, urban planners and the relevant urban management teams should understand the future spatial pattern of these changes by resorting to quantitative models in spatial planning. This is to ensure that future predictions are made with a high level of accuracy so that appropriate strategies can be used to address the problems arising from such changes. The Markov chain method is one of the quantitative models used in spatial planning to ana

A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. This paper proposes an algorithm based on hybrid transform (stationary wavelet transform proceeding by slantlet transform); The slantlet transform is applied to the approximation subband of the stationary wavelet transform. BlockShrink thresholding technique is applied to the hybrid transform coefficients. This technique can decide the optimal block size and thresholding for every wavelet subband by risk estimate (SURE). The proposed algorithm was executed by using MATLAB R2010aminimizing Stein’s unbiased with natural images contaminated by white Gaussian noise. Numerical results show that our algorithm co

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

The aesthetic expression and its orders are important for steel structures forming. Steel structures are a compilation of structural elements, where its shapes have standard dimensions and pre-fabricated. As the steel construction systems not only aim to achieve the functional requirements for users, but must also have the symbolic aesthetics which provides visually and cognitive expression for viewers. In this sense the research interested in expressional aesthetics in these systems and highlights the importance of attention as structural items. Therefore the visual items which related with steel structures contain some of the most powerful forms of modern architecture, steel structures with a glass cladding, agility an