Abstract Portable communication devices such as WLAN, WiMAX, LTE, ISM, and 5G utilize one or more of the triple bands at (2.32.7 GHz,3.4–3.6GHz,and5–6GHz)andsufferfromtheeffectofmultipathproblemsbecausetheyareusedinurbanregions.To date, no one has performed a review of the antennas used for these types of wireless communications. This study reviewed two types of microstrip antennas (slot and fractal) that have been reported by researchers (as a single element) using a survey that included the evaluation of several important specifications of the antennas in previous research, such as operating bandwidth, gain, efficiency, axial ratio bandwidth (ARBW), and size. The weaknesses in the design of all antennas were carefully identified to de

A new method for determination of allopurinol in microgram level depending on its ability to reduce the yellow absorption spectrum of (I-3) at maximum wavelength ( ?max 350nm) . The optimum conditions such as "concentration of reactant materials , time of sitting and order of addition were studied to get a high sensitivity ( ? = 27229 l.mole-1.cm-1) sandal sensitivity : 0.0053 µg cm-2 ,with wide range of calibration curve ( 1 – 9 µg.ml-1 ) good stability (more then24 hr.) and repeatability ( RSD % : 2.1 -2.6 % ) , the Recovery % : ( 98.17 – 100.5 % ) , the Erel % ( 0.50 -1.83 % ) and the interference's of Xanthine , Cystein , Creatinine , Urea and the Glucose in 20 , 40 , 60 fold of analyate were also studied .

Accounting profession has been survived for long time and expected to survive in future, with out basing on theory in the field of practical practice . But there are numerous problems which emerge from the practical practice, their solution needs to be based on generally accepted accounting theory. The results of the researches in this field since the beginning of the last century unfolded the difficulty of formulating theory in accounting , but possible to formulate several accounting

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images. So, this study aimed at testing the system performance at poo

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images.

So, this study aimed at testing the system performance at poor s

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.