Indiana University-Purdue University Indianapolis (IUPUI) / Sensor nodes forming a network and using wireless communications are highly useful in a variety of applications including battle field (military) surveillance, building security, medical and health services, environmental monitoring in harsh conditions, for scientific investigations on other planets, etc. But these wireless sensors are resource constricted: limited power supply, bandwidth for communication, processing speed, and memory space. One possible way of achieve maximum utilization of those constrained resource is applying signal processing and compressing the sensor readings. Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply data compression by trading computation for communication before transmitting data for reducing total power consumption by a sensor node. However the existing state of the art compression algorithms are not suitable for wireless sensor nodes due to their limited resource. Therefore there is a need to design signal processing (compression) algorithms considering the resource constraint of wireless sensors. In our work, we designed a lightweight codec system aiming surveillance as a target application. In designing the codec system, we have proposed new design ideas and also tweak the existing encoding algorithms to fit the target application. Also during data transmission among sensors and between sensors and base station, the data has to be secured. We have addressed some security issues by assessing the security of wavelet tree shuffling as the only security mechanism.

In this thesis, we develop efficient algorithms for the jointly optimal power and channel resource allocation in different wireless cooperative multiple access systems. In addition, in some cases insight into the structure of the optimal allocation enables the development of modified cooperation schemes with better performance, and efficient algorithms are developed for jointly optimal power and channel resource allocation for these modified schemes too. The goal of the jointly optimal allocation algorithms developed in this thesis is to maximize the achievable rate regions of the schemes under consideration. Two cooperative channel models are considered; namely, the cooperative multiple access channel, and the multiple access relay channel. For the cooperative multiple access channel, two relaying strategies are considered; namely decode-and-forward (DF), and amplify-and-forward (AF). For the cooperative multiple access channel with DF relaying, systems with full-duplex nodes and systems with half-duplex nodes are considered. In the case of full-duplex nodes, it is shown that the non-convex formulation of the power allocation problem can be simplified and re-cast in a convex form. In fact, closed-form expressions for the optimal power allocation for each point on the boundary of an achievable rate region are obtained. In the case of half-duplex nodes, we propose a modified version of an existing cooperation scheme that, with jointly optimal power and channel resource allocation, can achieve a large fraction of the achievable rate region of the full-duplex case. An efficient algorithm for the jointly optimal power and channel resource allocation is also developed for that scheme. For the cooperative multiple access channel with AF relaying, we consider optimal power and channel resource allocation for a system of two half-duplex source nodes that transmit orthogonal signals, and an efficient algorithm for the optimal power and channel resource allocation is developed. This efficient algorithm is based on a closed-form solution for the optimal power allocation for a given channel resource allocation and on showing that the channel resource allocation problem is quasi-convex. The analysis of the optimal power allocation for a given channel resource allocation shows that the existing scheme that we consider does not use the channel resource efficiently. Therefore, we propose a modified cooperation scheme that maintains the orthogonality property of the original scheme, but provides larger achievable rate regions than those provided by the original scheme. For the multiple access relay channel, the optimal allocation of the relay power and the channel resource between different source nodes is considered in order to maximize the achievable rate region. Four relaying strategies are used; namely, regenerative decode-and-forward, non-regenerative decode-and-forward, amplify-and-forward, and compress-and-forward. For each of these strategies, an efficient algorithm is developed for the jointly optimal power and channel resource allocation. These algorithms are based on closed-form solutions for the optimal power allocation for a given resource allocation and on proving and exploiting the quasi-convexity of the joint allocation problem. The algorithms developed for the multiple access relay channel can be used for homogeneous (using the same relaying strategy for all users) or heterogeneous (using different relaying strategies with different users) relaying and for any number of users. / Thesis / Doctor of Philosophy (PhD)

Background: Before making any treatment decisions for patients with non-small cell lung cancer (NSCLC), it is crucial to determine whether the cancer has spread to the mediastinal lymph nodes (LNs). The preferred method for mediastinal staging is Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA). However, EBUS-TBNA has been reported to generate inconclusive results as much as 40% of the time. Since this jeopardizes good patient care, there is near-universal consensus on the need to develop and study a novel method for LN staging. Recent research has shown that AI and deep learning are used to accurately interpret images with comparisons to clinicians in radiology, pathology, and cardiology. Additionally, EBUS-Elastography is a novel modality which could be used as an adjunct to EBUS-TBNA for LN staging. This technology uses impedance ultrasonography to measure tissue stiffness. Methods: There are three parts to this thesis. The first part involved the training, validating, and testing NeuralSeg, a deep neural network, to predict LN malignancy based on B-mode EBUS-TBNA images. The second part of this thesis involves EBUS-Elastography, defining the blue colour threshold and the optimal SAR cut-off value based on the blue threshold that most accurately distinguished benign and malignant LN. Finally, this thesis's third part involves validating part II's findings. Results: Part I resulted in an overall accuracy of 80.63% (76.93% to 83.97%), a sensitivity of 43.23% (35.30% to 51.41%), a specificity of 96.91% (94.54% to 98.45%), a positive predictive value of 85.90% (76.81% to 91.80%), a negative predictive value of 79.68% (77.34% to 81.83%), and an AUC of 0.701 (0.646 to 0.755). Part II Level 60 was chosen as the blue threshold with an AUC of 0.89 (95% CI: 0.77-1.00), and the optimal SAR cut off was found to be 0.4959 with a sensitivity of 92.30% (95% CI: 62.10% to 99.60%) and a specificity of 76.50% (95% CI: 49.80% to 92.20%). Using the blue threshold and SAR cut-off, the results of part III resulted in an overall accuracy of 70.59% (95% (CI) 63.50% to 77.01%), the sensitivity of 43.04% (CI: 31.94% to 54.67%), and a specificity of 90.74% (CI: 83.63% to 95.47%). Conclusion: It was observed that both AI and AI-powered EBUS-Elastography achieved high specificities on larger sample sizes, indicative that these methods may be helpful in identifying LN malignancy. However, due to the novelty of these technologies, more extensive multi-centre studies must be conducted before these processes can be standardized. / Thesis / Master of Health Sciences (MSc) / Non-Small Cell Lung Cancer (NSCLC) treatment decisions are made using vital information by performing biopsies to collect tissue from the lymph nodes near the lungs. The current method is called Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA), which involves a scope with a fine needle attached to it. This scope is led down the airway and guided by ultrasound to obtain the tissue needed to determine whether the lymph nodes have cancerous tissue. If the lymph nodes contain cancerous tissue, the patient may need chemotherapy; however, lung surgery may be the best treatment option if they do not. Many factors impact how successfully these tissue samples can be obtained, such as the skill and experience of the surgeon. These factors often lead to inconclusive results, making it difficult to make correct treatment decisions. Novel technologies such as Artificial Intelligence and Elastography are being used to diagnose lung cancer by interpreting images and providing information on tissue stiffness. We trained an Artificial Intelligence program to predict malignancy based on EBUS-TBNA images. Additionally, we trained the AI program to analyze Elastography images to aid us in understanding the relationship between the colour pattern of the elastography images and cancerous tissue. This thesis assesses how these novel technologies contribute to lung cancer diagnosis.

No description available.

Background: At the time of endobronchial ultrasound (EBUS) staging, ultrasonographic features can be used to predict mediastinal lymph node (LN) malignancy. Predictive tools have been developed, however they have not gained widespread use due to lack of research demonstrating validity and reliability. We sought to develop a novel predictive tool, the Canada Score, capable of predicting malignancy and potentially guide LN biopsy decision making. Methods: We prospectively analyzed the ultrasonographic features of LNs from patients with NSCLC. Ultrasonographic features were identified by a single experienced endoscopist, this data was used to develop the Canada Score. Pathological specimens were used as the gold standard for determination of malignancy. Videos were then circulated to endoscopists across Canada, who were also asked to identify ultrasonographic features for each LN. Hosmer- Lemeshow test, logistic regression, receiver operator characteristic (ROC) curve, and Gwet’s AC1 analyses were used to test the performance, discriminatory capacity, and inter-rater reliability of the Canada Score. Results: A total of 300 LNs from 140 patients were analyzed by 12 endoscopists across 7 Canadian centres. Backwards elimination was used to create a multivariate model. Hosmer-Lemeshow test and ROC curves indicated the model was well-calibrated (chi2=11.86, p=0.1567) with good discriminatory power (c- statistic= 0.72 ±0.042, 95%CI: 0.64-0.80). Beta-coefficients were used to create a simplified score out of four. Evaluation of the tool showed that LNs scoring 3 or 4 had odds ratios of 15.17 (p<0.0001) and 50.56 (p=0.001), respectively for predicting malignancy. A score of 4/4 was associated with 99.59% specificity and a positive likelihood ratio of 22.78. Inter-rater reliability for a score ≥ 3 was 0.81 ± 0.02 (95%CI: 0.77-0.85). Conclusions: The Canada Score shows excellent performance in identifying malignant LN at the time of EBUS. A cut-off of ≥ 3 has the potential to inform decision-making regarding biopsy or repeat/mediastinoscopy if the initial results are inconclusive. / Thesis / Master of Science (MSc) / During lymph node staging for lung and esophageal cancer, specific features of lymph nodes can be seen. Using diagnostic tools these features can be used to predict whether a lymph node is cancerous or benign. However, many of these diagnostic tools are inaccurate or unreliable. To address this, this thesis aimed to develop a novel diagnostic tool based on lymph node features seen during staging procedures and determine its clinical usefulness and application to the wider lung and esophageal cancer population. This thesis also aimed to use improved methods to develop this diagnostic tool such that patient and clinician experiences would be significantly improved. The results of this thesis may contribute to a reduction in the number of repeat procedures required for patients undergoing staging prior to their treatment for lung and esophageal cancers.

Fatigue crack growth in engineered structures reduces the structures load carrying capacity and will eventually lead to failure. Cycles required to grow a crack from an initial length to the critical length is called the fatigue fracture life. In this thesis, five different methods for analyzing the fatigue fracture life of a center cracked plate were compared to experimental data previously collected by C.M. Hudson in a 1969 NASA report studying the R-ratio effects on crack growth in 7075-T6 aluminum alloy. The Paris, Walker, and Forman fatigue crack growth models were fit the experimental data. The Walker equation best fit the data since it incorporated R-ratio effects and had a similar Root Mean Square Error (RMSE) compared to the other models. There was insufficient data in the unstable region of crack growth to adequately fit the Forman equation. Analytical models were used as a baseline for all fatigue fracture life comparisons. Life estimates from AFGROW and finite elements with mid-side nodes moved to their quarter point location compared very with the analytical model with errors less than 3%. The Virtual Crack Closure Technique (VCCT) was selected as a method for crack propagation along a predefined path. Stress intensity factors (SIFs) for shorter crack lengths were found to be low, resulting in an overestimated life of about 8%. The eXtended Finite Element Method with Phantom Nodes (XFEM-PN) was used, allowing crack propagation along a solution dependent path, independent of the mesh. Low SIFs throughout growth resulted in life estimates 20% too large. All finite element analyses were performed in Abaqus 6-13.3. An integrated polynomial method was developed for calculating life based on Abaqus' results, leading to coarser meshes with answers closer to the analytical estimate. None of the five methods for estimating life compared well with the experimental data, with analytical errors on life ranging from 10-20%. These errors were attributed to the limited number of crack growth experiments run at each R-ratio, and the large variability typically seen in growth rates. Monte Carlo simulations were run to estimate the distribution on life. It was shown that material constants in the Walker model must be selected based on their interrelation with a multivariate normal probability density function. Both analytical and XFEM-PN simulations had similar coefficients of variation on life of approximately 3% with similar normal distributions. It was concluded that Abaqus' XFEM-PN is a reasonable means of estimating fatigue fracture life and its variation, and this method could be extended to other geometries and three-dimensional analyses. / Master of Science

2, 3, 7, 8-Tetrachlorodibenzo-p-dioxin is well known for its immunotoxic effects on the thymus, as well as on B and T lymphocyte functions. Previous studies suggested that TCDD exerted immunotoxic effects only on cells differentiating in response to antigenic challenge. To this date, no work has been done to characterize the long-term effects of TCDD on the activated cells. Additionally, no studies have been done to determine whether TCDD has any effect on resting T cells. In the current study, therefore, we investigated the effects of TCDD on activated and resting cells within the same animal. T cells in the popliteal lymph node cells were activated by rear footpad immunizations with anti-CD3 antibodies. Distally-located axillary lymph nodes were chosen as a source of naive and resting T cells. Our results demonstrate that TCDD acted at the time of cell differentiation to suppress the immune responses of activated T cells, but failed to suppress, and at times, enhanced the immune responses of resting T cells. The TCDD-induced immunomodulations were temporary; responsiveness of both activated and resting T cells from TCDD-treated animals returned to normal by two weeks post-treatment, suggesting that TCDD does not affect memory cells. Futhermore, we provide direct evidence that the TCDD-induced immunosuppression in activated cells is due to increased apoptosis of CD3+ T cells. TCDD also induced significant changes in cell surface markers expressed by naive and activated T cells. Together our data suggested that TCDD suppresses the proliferative responsiveness of only the activated, but not naive, T cells and that this is accomplished by induction of increased apoptosis of activated T cells. These studies shed new light on the mechanism through which TCDD induces increased susceptibility to infections and cancer in the vertebrate host. / Master of Science

This thesis describes the development and evaluation of a manual black-box testing method inspired by Zweben's test adequacy criteria, which apply white-box analogues of all-DU-pairs and all-nodes to a flow graph generated from the black-box specification. The approach described herein generates tests from a matrix representation of a class interface based on the flow graph concept. In this process, separate matrices for all-DU-pairs and all-nodes guide the generation of the required tests. The primary goal of the research is not to optimize the number of tests generated but to describe the process in a user-friendly manner so that practitioners can utilize it directly, quickly, and efficiently for real-world testing purposes. The approach has been evaluated to assess its effectiveness at detecting bugs. Both strategies - all-DU-pairs and all-nodes - were compared against three other testing methods: the commercial white-box testing tool Jtest, Orthogonal Array Testing Strategy (OATS), and test cases generated at random. The five approaches were applied across a sample of eleven java classes selected from java.util.*. Experimental results indicate that the two versions resulting from this research performed on par with or better than their respective equivalent approaches. The all-DU-pairs method performed better than all other approaches except for the random approach, with which it compared equally. Experimental evaluation results thus indicate that an automated approach based on the manual method is worth exploring. / Master of Science

No / This chapter describes an approach to modeling C2 and other types of networks as a series of nodes (people, groups, resources, locations, concepts, etc.). The nodes are linked by one or more weighted arcs describing the type and the strength of the dependency that one node has on another node. This model allows analysts to identify the most important nodes in a network in terms of their direct and indirect dependencies and to rank them accordingly. The same model also supports consequence analysis in which the direct, indirect, cascading, and cumulative effects of changes to node capabilities can be propagated across the networks. The chapter describes the basic modeling technique and two types of dependency propagation that it supports. These are illustrated with two examples involving the modeling and reasoning across insurgent networks and an Integrated Air Defense System. These show how aspects of the networks can be analyzed and targeted. Details are also provided on the mechanisms to link the analysis to a planning system through which plans can be developed to bring about desired effect(s) in the networks.

The in vitro cell culture models of human pluripotent stem cells (hPSC)-derived cardiomyocytes (CMs) have gained a predominant value in the field of drug discovery and is considered an attractive tool for cardiovascular disease modellings. However, despite several reports of different protocols for the hPSC-differentiation into CMs, the development of an efficient, controlled and reproducible 3D differentiation remains challenging. The main aim of this research study was to understand the changes in the gene expression as an impact of spatial orientation ofhPSC-derived CMs in 2D(two-dimensional) and 3D(three-dimensional) culture conditions and to identify the topologically important Hub and Hub-Bottleneck proteins using centrality measures to gain new knowledge for standardizing the pre-clinical models for the regeneration of CMs. The above-mentioned aim was achieved through an extensive bioinformatic analysis on the list of differentially expressed genes (DEGs) identified from RNA-sequencing (RNA-Seq). Functional annotation analysis of the DEGs from both 2D and 3D was performed using Cytoscape plug-in ClueGO. Followed by the topological analysis of the protein-protein interaction network (PPIN) using two centrality parameters; Degree and Betweeness in Cytoscape plug-in CenTiScaPe. The results obtained revealed that compared to 2D, DEGs in 3D are primarily associated with cell signalling suggesting the interaction between cells as an impact of the 3D microenvironment and topological analysis revealed 32 and 39 proteins as Hub and Hub-Bottleneck proteins, respectively in 3D indicating the possibility of utilizing those identified genes and their corresponding proteins as cardiac disease biomarkers in future by further research.