Medium Access Control Layer Implementation on Field Programmable Gate Array Board for Wireless Networks

Triple play services are playing an important role in modern telecommunications systems. Nowadays, more researchers are engaged in investigating the most efficient approaches to integrate these services at a reduced level of operation costs. Field Programmable Gate Array (FPGA) boards have been found as the most suitable platform to test new protocols as they offer high levels of flexibility and customization. This thesis focuses on implementing a framework for the Triple Play Time Division Multiple Access (TP-TDMA) protocol using the Xilinx FPGA Virtex-5 board. This flexible framework design offers network systems engineers a reconfigiirable platform for triple-play systems development. In this work, MicorBlaze is used to perform memory and connectivity tests aiming to ensure the establishment of the connectivity as well as board’s processor stability. Two different approaches are followed to achieve TP-TDMA implementa­tion: systematic and conceptual. In the systematic approach, a bottom-to-top design is chosen where four subsystems are built with various components. Each component is then tested individually to investigate its response. On the other hand, the concep­tual approach is designed with only two components, in which one of them is created with the help of Xilinx Integrated Software Environment (ISE) Core Generator. The system is integrated and then tested to check its overall response. In summary, the work of this thesis is divided into three sections. The first section presents a testing method for Virtex-5 board using MicroBlaze soft processor. The following two sections concentrate on implementing the TP-TDMA protocol on the board by using two design approaches: one based on designing each component from scratch, while the other one focuses more on the system’s broader picture

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%

Safety of pregnancy in uterine fibroids

Uterine fibroid is one of the most common intrauterine masses among females at the reproductive age. Pregnancy and uterine fibroids are highly correlated. Pregnancy-related hormones influence the size of uterine fibroids, and fibroids have many impacts on pregnancy. Although most if the uterine fibroids are asymptomatic during pregnancy, serious complications may occur. The main complications include abortion, premature rupture of membranes, premature labor, abruptio placentae, peripartum hemorrhage, fetal malpresentation, fetal intrauterine growth retardation, small for gestational age infants, and fetal anomalies. The main risk factors for complications are related to the fibroid number, size, volume, location, and type. Large, multiple, retroplacental, submucosal, subserosal, pedunculated, or low-lying fibroids carries the highest risk for complications during pregnancy. This review will address the prevalence of uterine fibroids during pregnancy, its effects, and complications

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images

© 2020 Elsevier Ltd Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

Evaluation of the shear bond strength of a tricalcium silicate-based material to four self-adhering glass ionomer materials: an in vitro study

BackgroundThis study aimed to evaluate and compare the shear bond strength (SBS) of EQUIA Forte HT with that of other restorative materials, including EQUIA Forte, glass ionomer cement (GIC), and resin-modified glass ionomer cement (RMGIC) when bonded to NeoMTA 2.Materials and methodsA total of 120 holes were created in Teflon molds and filled with NeoMTA 2. The restorative materials were immediately applied using customized silicone molds. The samples were randomly divided into two main groups: one to measure the immediate SBS and the other to measure the delayed SBS. These two main groups were further divided into four subgroups based on the restorative material used: EQUIA Forte HT, EQUIA Forte, GIC, and RMGIC.ResultsThe study groups showed statistically significant differences in the mean SBS (p < 0.0001). The immediate SBS of the RMGIC group (mean ± SD: 5.43 ± 1.22) was significantly higher than those of the GIC and EQUIA Forte groups, with no significant difference found compared to the SBS of EQUIA Forte HT. In the delayed SBS, both the RMGIC and EQUIA Forte HT groups (4.98 ± 0.67 and 4.93 ± 0.60, respectively) demonstrated significantly higher bond strengths than the GIC and EQUIA Forte groups (3.81 ± 0.57 and 4.2 ± 0.63, respectively). However, there were no statistically significant differences between the RMGIC and EQUIA Forte HT groups or between the GIC and EQUIA Forte groups.ConclusionBased on our findings, EQUIA Forte HT has shown promising outcomes when used as a restorative material following pulpotomies, with results comparable to those of RMGIC

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

Wearable metamaterial dual-polarized high isolation UWB MIMO Vivaldi antenna for 5G and satellite communications

A low-profile Multiple Input Multiple Output (MIMO) antenna showing dual polarization, low mutual coupling, and acceptable diversity gain is presented by this paper. The antenna introduces the requirements of fifth generation (5G) and the satellite communications. A horizontally (4.8–31 GHz) and vertically polarized (7.6–37 GHz) modified antipodal Vivaldi antennas are simulated, fabricated, and integrated, and then their characteristics are examined. An ultra-wideband (UWB) at working bandwidths of 3.7–3.85 GHz and 5–40 GHz are achieved. Low mutual coupling of less than −22 dB is achieved after loading the antenna with cross-curves, staircase meander line, and integration of the metamaterial elements. The antennas are designed on a denim textile substrate with = 1.4 and h= 0.5 mm. A conductive textile called ShieldIt is utilized as conductor with conductivity of 1.8 × 10⁴. After optimizing the proposed UWB-MIMO antenna’s characteristics, it is increased to four elements positioned at the four corners of a denim textile substrate to be employed as a UWB-MIMO antenna for handset communications, 5G, Ka and Ku band, and satellite communications (X-band). The proposed eight port UWB-MIMO antenna has a maximum gain of 10.7 dBi, 98% radiation efficiency, less than 0.01 ECC, and acceptable diversity gain. Afterwards, the eight-ports antenna performance is examined on a simulated real voxel hand and chest. Then, it is evaluated and compared on physical hand and chest of body. Evidently, the simulated and measured results show good agreement between them. The proposed UWB-MIMO antenna offers a compact and flexible design, which is suitably wearable for 5G and satellite communications applications