Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Investigation of an Adaptable Crash Energy Management System to Enhance Vehicle Crashworthiness

The crashworthiness enhancement of vehicle structures is a very challenging task during the vehicle design process due to complicated nature of vehicle design structures that need to comply with different conflicting design task requirements. Although different safety agencies have issued and modified standardized crash tests to guarantee structural integrity and occupant survivability, there is continued rise of fatalities in vehicle crashes especially the passenger cars. This dissertation research explores the applicability of a crash energy management system of providing variable energy absorbing properties as a function of the impact speed to achieve enhanced occupant safety. The study employs an optimal crash pulse to seek designs of effective energy absorption mechanisms for reducing the occupant impact severity. The study is conducted in four different phases, where the performance potentials of different concepts in add-on energy absorbing/dissipating elements are investigated in the initial phase using a simple lumped-parameter model. For this purpose, a number of performance measures related to crash safety are defined, particular those directly related to occupant deceleration and compartment intrusion. Moreover, the effects of the linear, quadratic and cubic damping properties of the add-on elements are investigated in view of structure deformation and occupant`s Head Injury Criteria (HIC). In the second phase of this study, optimal design parameters of the proposed add-on energy absorber concept are identified through solutions of single- and weighted multi-objective minimization functions using different methods, namely sequential quadratic programming (SQP), genetic algorithms (GA) and hybrid genetic algorithms. The solutions obtained suggest that conducting multiobjective optimization of conflicting functions via genetic algorithms could yield an improved design compromise over a wider range of impact speeds. The effectiveness of the optimal add-on energy absorber configurations are subsequently investigated through its integration to a full-scale vehicle model in the third phase. The elasto-plastic stress-strain and force-deflection properties of different substructures are incorporated in the full-scale vehicle model integrating the absorber concept. A scaling method is further proposed to adapt the vehicle model to sizes of current automobile models. The influences of different design parameters on the crash energy management safety performance measures are studied through a comprehensive sensitivity analysis. In the final phase, the proposed add-on absorber concept is implemented in a high fidelity nonlinear finite element (FE) model of a small passenger car in the LS-DYNA platform. The simulation results of the model with add-on system, obtained at different impact speeds, are compared with those of the baseline model to illustrate the crashworthiness enhancement and energy management properties of the proposed concept. The results show that vehicle crashworthiness can be greatly enhanced using the proposed add-on crash energy management system, which can be implemented in conjunction with the crush elements

Modelling, simulation and experimental investigation of the effects of material microstructure on the micro-endmiling process

Recently it has been revealed that workpiece microstructure has dominant effects on the performance of the micro-machining process. However, so far, there has been no detailed study of these effects on micro-endmilling. In this research, the influence of the microstructure on the matters such as cutting regime, tool wear and surface quality has been investigated. Initially, an experimental investigation has been carried out to identify the machining response of materials metallurgically and mechanically modified at the micro-scale. Tests have been conducted that involved micro-milling slots in coarse-grained (CG) Cu99.9E with an average grain size of 30 μm and ultrafine-grained (UFG) Cu99.9E with an average grain size of 200 nm. Then, a method of assessing the homogeneity of the material microstructure has been proposed based on Atomic Force Microscope (AFM) measurements of the coefficient of friction at the atomic scale, enabling a comparative evaluation of the modified microstructures. The investigation has shown that, by refining the material microstructure, the minimum chip thickness can be reduced and a better surface finish can be achieved. Also, the homogeneity of the microstructure can be improved which in turn reduces surface defects. Furthermore, a new model to simulate the surface generation process during micro- endmilling of dual-phase materials has been developed. The proposed model considers the effects of the following factors: the geometry of the cutting tool, the feed rate, and the workpiece microstructure. In particular, variations of the minimum chip thickness at phase boundaries are considered by feeding maps of the microstructure into the model. Thus, the model takes into account these variations that alter the machining mechanism from a proper cutting to ploughing and vice versa, and are the main cause of micro-burr formation. By applying the proposed model it is possible to estimate more accurately the resulting roughness owing to the dominance of the micro-burrs formation during the surface generation process in micro-milling of multi-phase materials. The model has been experimentally validated by machining two different samples of dual-phase steel, AISI 1040 and AISI 8620, under a range of chip-loads. The results have shown that the proposed model accurately predicts the roughness of the machined surfaces with average errors of 14.5% and 17.4% for the AISI 1040 and AISI 8620 samples, respectively. The developed model successfully elucidates the mechanism of micro-burr formation at the phase boundaries, and quantitatively describes its contributions to the resulting surface roughness after micro-endmilling. (Abstract shortened by UMI.)

Scalable and Cost Efficient Algorithms for Virtual CDN Migration

Virtual Content Delivery Network (vCDN) migration is necessary to optimize the use of resources and improve the performance of the overall SDN/NFV-based CDN function in terms of network operator cost reduction and high streaming quality. It requires intelligent and enticed joint SDN/NFV migration algorithms due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, two approaches for finding the optimal and near optimal path placement(s) and vCDN migration(s) are proposed (OPAC and HPAC). Moreover, several scenarios are considered to quantify the OPAC and HPAC behaviors and to compare their efficiency in terms of migration cost, migration time, vCDN replication number, and other cost factors. Then, they are implemented and evaluated under different network scales. Finally, the proposed algorithms are integrated in an SDN/NFV framework. Index Terms: vCDN; SDN/NFV Optimization; Migration Algorithms; Scalability Algorithms.Comment: 9 pages, 11 figures, 4 tableaux, conference Local Computer Networks (LCN), class

Hardware Implementation of Chipless RFID Reader and Tags for Moving Targets Identification and Tracking

Radio Frequency Identification (RFID) is a rapidly growing technology with significant security implications for humans and military ambushes. In this paper, the hardware implementation of a chipless RFID system using highly efficient Texas instrumentation components is introduced. We introduced the hardware implementation of both RFID reader and 5-bits chipless tags. The proposed reader system is equipped with an ultra-wideband (UWB) radio frequency (RF) power detector that allows the reader to read different types of tags with different code lengths over the frequency range from  to . The proposed spiral resonators based RFID tags are fabricated using microstrip technology on a cheap FR4 lossy substrate with a dielectric constant of , loss tangent , and thickness . The tags are designed using the computer simulation technology (CST) microwave studio software package. Fortunately, it is found that the experimental measurements of the scattering parameters of the fabricated tags are highly matched to the simulation results

Genetic and Phenotypic Aspects of Body Weights in Local Large Beladi Chicken Raised Under Two Different Dietary Constituents

The foundation stock was primarily established by purchasing 50 cockerels and 160 pullets of the indigenous fowl from the Blue Nile area (Sinar state). This work was conducted to compare the genetic and phenotypic parameters estimates for body weights of large Beladi chicken under two different feeding regimes and proposing outlines of a strategy for improving body weights at different ages. Each cockerel was randomly assigned to mate with three pullets in a rotational pattern. Eggs for incubation purpose were collected from the individual breeding pens three times a day and recorded on daily basis. Eggs were weighed, graded and pedigreed and then incubated to obtain 13 consecutive hatches at weekly intervals. The total number of birds used in the experiment was 1718. According to the experimental protocol, which based on feeding regime, chicks were divided into two groups (A) and (B). Birds in group (A) were fed on broiler-formulated rations, whereas birds in group (B) were fed on layer-formulated rations. For group (A) individual body weights were taken at biweekly interval up to 12 weeks. However, for group (B) individual body weights were taken at monthly interval up to 3 month. The overall average body weights for birds at hatch, 4, 8 and 12 weeks in group (A), were 26.78±2.91, 147.16±30.87, 393.51±64.12 and 800.93±155.97g, respectively, whereas, the corresponding weights in group (B), were 29.31±2.66, 170.80±49.27, 353.43±102.70 and 512.75±116.11. Mean body weights of birds in group (B) were significantly (P < 0.05) higher than those in group (A) at hatch and 4 weeks of age, however, the reverse was true for body weights at 8 and 12 weeks of age. Growth pattern revealed an increasing trend with advanced ages. On the other hand, the monthly weight gain showed marked decline at 8 weeks of age for birds in group (B). Comparing growth of birds in A and B groups revealed that, increasing dietary protein and metabolizable energy (ME), resulted in increase body weights at 8 and 12 weeks of age by 11.3 and 56.2 percents, respectively. In group (A), the mean body weights of males were significantly (P < 0.05) higher than those of females at 4, 6, 8 and 12 weeks of age. This may express the presence of heterogeneity between sexes at these ages. Sire and dam effects on body weights were found to be significant at various ages. Hatch had significant effect (P < 0.01) on body weight at all ages. Heritability estimates for body weights at different ages from sire, dam and sire plus dam components of variance were obtained for both groups. The estimates in group (A) and (B) ranged from low (0.02) to high (0.97) and low (0.06) to moderate (0.25), respectively. In group (A), heritability estimates from dam component were higher than those from sire component, whereas the reverse was true for the estimates in group (B). Heritability estimates for body weights in group (A) were slightly higher than those in group (B). This may reveal the tendency for increasing heritability magnitudes with improved levels of dietary protein and metabolizable energy (ME). For both groups (A) and (B), the genetic and phenotypic correlation estimates ranged from low (0.16) to high (0.97) and followed similar trends. However, the magnitudes were relatively higher in group (A) than those in group (B). The environmental correlation estimates were also positive, ranged from low (0.08) to high (0.99), and followed similar trend. Based on sire component of variance, the highest heritability estimate in group (A) was at 4 weeks of age, whereas the corresponding estimate in group (B) was at 8th week. Generally, it may be concluded that mass or individual selection for body weight is better to be conducted at 4th week, when birds are fed on diet with high protein and metabolizable energy (Broiler ration) and at 8th week, when birds are fed on diet with low protein and metabolizable energy (Layer ration)

Validity of Strain Elastography in Renal Allograft Infection

Background: Strain elastography is not routinely used by many clinicians to determine allograft dysfunction. Validity of strain elastography and renal histopathologic characteristics especially infected allograft have not been sufficiently evaluated in renal transplant recipients. Objective: To study the correlation between strain elastography and renal allograft infection in Kasr Al Ainy school of medicine -Cairo University. Design/Methods: In a single-center, prospective study involving 109 renal-allograft recipients, the strain elastography was evaluated in 109 renal transplant recipients to be correlated with renal allograft infection that was proved in (64 patients) by the laboratory and histopathological finding and (45 patients) without allograft infection. Results: There was no statistically significant difference between renal allograft infection and strain elastography (P value 0.447). Causes of allograft infection were CMV in (30.3%), UTI in (18.3%),) and BK polyomavirus in (10.1%). Histopathological findings in renal allograft biopsy were active ABMR in (6.4%), acute interstitial nephritis with neutrophils with bacterial infection in (18.3%), Acute TCMR in (6.4%), BK polyomavirus nephropathy with SV40 positive in (10.1%), chronic ABMR in (17.4%), chronic active ABMR in (7.3%), CMV nephropathy in (13.8%), mixed rejection in (3.7%) and tubular injury with viral infection in (16.5%).Conclusion: Strain elastography may not be useful&nbsp; in renal allograft infection evaluatio

A Predictive Model for Student Performance in Classrooms using Student Interactions with an eTextbook

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into their students’ performance and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues: the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification was Random Forest Classifier with an accuracy equal to 91.7%, while in the regression it was Random Forest Regression with an R2 equal to 0.977

Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.Comment: 6 pages,5 figure