Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar

In this paper, a method based on modified adomian decomposition method for solving Seventh order integro-differential equations (MADM). The distinctive feature of the method is that it can be used to find the analytic solution without transformation of boundary value problems. To test the efficiency of the method presented two examples are solved by proposed method.

Elzaki Transform Adomian decomposition technique (ETADM), which an elegant combine, has been employed in this work to solve non-linear Riccati matrix differential equations. Solutions are presented to demonstrate the relevance of the current approach. With the use of figures, the results of the proposed strategy are displayed and evaluated. It is demonstrated that the suggested approach is effective, dependable, and simple to apply to a range of related scientific and technical problems.

يسعى المجتمع من خلال الوحدات الإقتصادية إلى الوصول إلى تحقيق أفضل الإنجازات التي لا تتمثل بالسلع والخدمات حسب وإنما بما يتحقق من مردود لكافة الاطراف المعنية بالوحدات الإقتصادية على ان لايتم ذلك على حساب قيم المجتمع وأخلاقياته وآدابه العامة. وعليه تصبح الوحدات الإقتصادية مسرحاً لصراعات قوى متعددة كل منها له قيمه الأخلاقية سواء كان فرداً أومجموعة افراد أو وحدة إقتصادية أو أي جهة أُخرى، وبحكم مسؤولياته

This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

This paper discusses a comparative study to relate parametric and non-parametric mode decomposition algorithms for response-only data. Three popular mode decomposition algorithms are included in this study: the Eigensystem Realization Algorithm with the Natural Excitation Technique (NExT-ERA) for the parametric algorithm, as well as the Principal Component Analysis (PCA) and the Independent Component Analysis (ICA) for the non-parametric algorithms. A comprehensive parametric study is provided for (i) different response types, (ii) excitation types, (iii) system damping, and (iv) sensor spatial resolution to compare the mode shapes and modal coordinates of using a 10-DOF building model. The mode decomposition results are also compared using

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.