Background: Osteoid osteoma(OO) is a relatively common benign skeletal neoplasm of unknown etiology that is composed of osteoid and woven bone, usually seen in adolescent and young males Although, the clinical, radiological and scintigraphic features of OO have been well described, these features may be misleading or altered in the cases of lesser trochanter of the femur which is relatively uncommon location for OO with a few number of cases reported up to date. Case Presentation: We report a case of a 20-year-old man who presented with painful limpThe pain had begun six months earlier and was made worse by walking and by exercise., with normal initial X-rays .The diagnosis was made after six months when typical Computed tomography, magneti

Background: Osteoid osteoma(OO) is a relatively common benign skeletal neoplasm of unknown etiology that is composed of osteoid and woven bone, usually seen in adolescent and young males Although, the clinical, radiological and scintigraphic features of OO have been well described, these features may be misleading or altered in the cases of lesser trochanter of the femur which is relatively uncommon location for OO with a few number of cases reported up to date.Case Presentation: We report a case of a 20-year-old man who presented with painful limpThe pain had begun six months earlier and was made worse by walking and by exercise., with normal initial X-rays .The diagnosis was made after six months when typical Computed tomography, magne

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Background: Joubert syndrome (JS) is a very rare autosomal recessive disorder characterized by agenesis of cerebellar vermis, abnormal eye movements, respiratory irregularities, and delayed generalized motor development. Retinal dystrophy and cystic kidneys may also be associated with this clinical syndrome. The importance of recognizing JS is related to the outcome and its potential complications. This syndrome is difficult to diagnose clinically because of its variable phenotype. Its neuroimaging hallmarks include the characteristic molar tooth sign and bat wing-shaped fourth ventricle