Non-rigid point set registration of significantly uneven deformations is a challenging problem for many applications such as pose estimation, three-dimensional object reconstruction, human movement tracking. In this dissertation, we present a novel probabilistic non-rigid registration method to align point sets with significantly uneven deformations by enforcing constraints from corresponding key points and preserving local neighborhood structures. The registration method is treated as a density estimation problem. Incorporating correspondence among key points regulates the optimization process for large, uneven deformations. In addition, by leveraging neighborhood embedding using Stochastic Neighbor Embedding (SNE) as well as an alternative means based on Locally Linear Embedding (LLE), our method penalizes the incoherent transformation and hence preserves the local structure of point sets. Also, our method detects key points in the point sets based on geodesic distance. Correspondences are established using a new cluster-based, region-aware feature descriptor. This feature descriptor encodes the association of a cluster to the left-right (symmetry) or upper-lower regions of the point sets. We conducted comparison studies using public point sets and our Human point sets. Our experimental results demonstrate that our proposed method successfully reduced the registration error by at least 42.2% in contrast to the state-of-the-art method. Especially, our method …

In this research, three aspects of data quality with regard to artifical intelligence (AI) have been investigated: detection of misleading fake data, especially deepfakes, data scarcity, and data insufficiency, especially how much training data is required for an AI application. Different application domains where the selected aspects pose issues have been chosen. To address the issues of data privacy, security, and regulation, these solutions are targeted for edge devices. In Chapter 3, two solutions have been proposed that aim to preempt such misleading deepfake videos and images on social media. These solutions are deployable at edge devices. In Chapter 4, a deepfake resilient digital ID system has been described. Another data quality aspect, data scarcity, has been addressed in Chapter 5. One of such agricultural problems is estimating crop damage due to natural disasters. Data insufficiency is another aspect of data quality. The amount of data required to achieve acceptable accuracy in a machine learning (ML) model has been studied in Chapter 6. As the data scarcity problem is studied in the agriculture domain, a similar scenario—plant disease detection and damage estimation—has been chosen for this verification. This research aims to provide ML or deep learning (DL)-based methods to solve …

The vehicular community is moving towards a whole new paradigm with the advancement of new technology. Vehicular communication not only supports safety services but also provides non-safety services like navigation support, toll collection, web browsing, media streaming, etc. The existing communication frameworks like Dedicated Short Range Communication (DSRC) and Cellular V2X (C-V2X) might not meet the required capacity in the coming days. So, the vehicular community needs to adopt new technologies and upgrade the existing communication frameworks so that it can fulfill the desired expectations. Therefore, an increment in reliability and data rate is required. Multiple Input Multiple Output (MIMO), 5G New Radio, Low Density Parity Check (LDPC) Code, and Massive MIMO signal detection and equalization algorithms are the latest addition to the 5G wireless communication domain. These technologies have the potential to make the existing V2X communication framework more robust. As a result, more reliability and throughput can be achieved. This work demonstrates these technologies' compatibility and positive impact on existing V2X communication standard.

Computer vision technologies are rapidly improving and becoming more important in disaster response. The majority of disaster description techniques now focus either on identify objects or categorize disasters. In this study, we trained multiple deep neural networks on low-altitude imagery with highly imbalanced and noisy labels. We utilize labeled images from the LADI dataset to formulate a solution for general problem in disaster classification and object detection. Our research integrated and developed multiple deep learning models that does the object detection task as well as the disaster scene classification task. Our solution is competitive in the TRECVID Disaster Scene Description and Indexing (DSDI) task, demonstrating that it is comparable to other suggested approaches in retrieving disaster-related video clips.

With the advent of autonomous systems and deep learning systems, safety pertaining to these systems has become a major concern. The existing failure analysis techniques are not enough to thoroughly analyze the safety in these systems. Moreover, because these systems are created to operate in various conditions, they are susceptible to unknown safety issues. Hence, we need mechanisms which can take into account the complexity of operational design domains, identify safety issues other than failures, and expose unknown safety issues. Moreover, existing safety analysis approaches require a lot of effort and time for analysis and do not consider machine learning (ML) safety. To address these limitations, in this dissertation, we discuss an artificial-intelligence driven model-based methodology that aids in identifying unknown safety issues and analyzing ML safety. Our methodology consists of 4 major tasks: 1) automated model generation, 2) automated analysis of component state transition model specification, 3) undesired states analysis, and 4) causal factor analysis. In our methodology we identify unknown safety issues by finding undesired combinations of components' states and environmental entities' states as well as causes resulting in these undesired combinations. In our methodology, we refer to the behaviors that occur because of undesired combinations as off-nominal …

People interact with each other through natural language to express feelings, thoughts, intentions, instructions etc. These interactions as a result form relationships. Besides names of relationships like siblings, spouse, friends etc., a number of dimensions (e.g. cooperative vs. competitive, temporary vs. enduring, equal vs. hierarchical etc.) can also be used to capture the underlying properties of interpersonal interactions and relationships. More fine-grained descriptors (e.g. angry, rude, nice, supportive etc.) can also be used to indicate the reasons or social-acts behind the dimension cooperative vs. competitive. The way people interact with others may also tell us about their personal traits, which in turn may be indicative of their probable success in their future. The works presented in the dissertation involve creating corpora with fine-grained descriptors of interactions and relationships. We also described experiments and their results that indicated that the processes of identifying the dimensions can be automated.

This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along …

In this dissertation, I first incorporate declustered redundant array of independent disks (RAID) technology in the existing system by maximizing the aggregated recovery I/O and accelerating post-failure remediation. Our analytical model affirms the accelerated data recovery stage significantly improves storage reliability. Then I present a proactive data protection framework that augments storage availability and reliability. It utilizes the failure prediction methods to efficiently rescue data on drives before failures occur, which significantly reduces the storage downtime and lowers the risk of nested failures. Finally, I investigate how an active storage system enables energy-efficient computing. I explore an emerging storage device named Ethernet drive to offload data-intensive workloads from the host to drives and process the data on drives. It not only minimizes data movement and power usage, but also enhances data availability and storage scalability. In summary, my dissertation research provides intelligence at the drive, storage node, and system levels to tackle the rising reliability challenge in modern HPC datacenters. The results indicate that this novel storage paradigm cost-effectively improves storage scalability, availability, and reliability.

This dissertation first conducts a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems for connected autonomous vehicles (CAVs). A LiDAR (Light Detection and Ranging sensor) point cloud-based 3D object detection method is deployed to enhance detection performance by expanding the effective sensing area, capturing critical information in multiple scenarios and improving detection accuracy. In addition, a point cloud feature based cooperative perception framework is proposed on edge computing system for CAVs. This dissertation also uses the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. In order to distinguish small sized objects such as pedestrian and cyclist in 3D data, an end-to-end multi-sensor fusion model is developed to implement 3D object detection from multi-sensor data. Experiments show that by solving multiple perception on camera and LiDAR jointly, the detection model can leverage the advantages from high resolution image and physical world LiDAR mapping data, which leads the KITTI benchmark on 3D object detection. At last, an application of cooperative perception is deployed on edge to heal the live map for autonomous vehicles. Through 3D reconstruction and multi-sensor fusion detection, experiments on real-world dataset demonstrate that a …

Epilepsy affects up to 1% of the world's population and approximately 2.5 million people in the United States. A considerable portion (30%) of epilepsy patients are refractory to antiepileptic drugs (AEDs), and surgery can not be an effective candidate if the focus of the seizure is on the eloquent cortex. To overcome the problems with existing solutions, a notable portion of biomedical research is focused on developing an implantable or wearable system for automated seizure detection and control. Seizure detection algorithms based on signal rejection algorithms (SRA), deep neural networks (DNN), and neighborhood component analysis (NCA) have been proposed in the IoMT framework. The algorithms proposed in this work have been validated with both scalp and intracranial electroencephalography (EEG, icEEG), and demonstrate high classification accuracy, sensitivity, and specificity. The occurrence of seizure can be controlled by direct drug injection into the epileptogenic zone, which enhances the efficacy of the AEDs. Piezoelectric and electromagnetic micropumps have been explored for the use of a drug delivery unit, as they provide accurate drug flow and reduce power consumption. The reduction in power consumption as a result of minimal circuitry employed by the drug delivery system is making it suitable for practical biomedical applications. …

In the first part of the dissertation, we provide an extensive overview of sub-6 GHz wireless access technology known as massive multiple-input multiple-output (MIMO) systems, highlighting its benefits, deployment challenges, and the key enabling technologies envisaged for 5G networks. We investigate the fundamental issues that degrade the performance of massive MIMO systems such as pilot contamination, precoding, user scheduling, and signal detection. In the second part, we optimize the performance of the massive MIMO system by proposing several algorithms, system designs, and hardware architectures. To mitigate the effect of pilot contamination, we propose a pilot reuse factor scheme based on the user environment and the number of active users. The results through simulations show that the proposed scheme ensures the system always operates at maximal spectral efficiency and achieves higher throughput. To address the user scheduling problem, we propose two user scheduling algorithms bases upon the measured channel gain. The simulation results show that our proposed user scheduling algorithms achieve better error performance, improve sum capacity and throughput, and guarantee fairness among the users. To address the uplink signal detection challenge in the massive MIMO systems, we propose four algorithms and their system designs. We show through simulations that the …

This work introduces three new coverage criteria based on combinatorial-based event and element sequences that occur in the mobile environment. The novel combinatorial-based criteria are used to reduce, prioritize, and generate test suites for mobile applications. The combinatorial-based criteria include unique coverage of events and elements with different respects to ordering. For instance, consider the coverage of a pair of events, e1 and e2. The least strict criterion, Combinatorial Coverage (CCov), counts the combination of these two events in a test case without respect to the order in which the events occur. That is, the combination (e1, e2) is the same as (e2, e1). The second criterion, Sequence-Based Combinatorial Coverage (SCov), considers the order of occurrence within a test case. Sequences (e1, ..., e2) and (e2,..., e1) are different sequences. The third and strictest criterion is Consecutive-Sequence Combinatorial Coverage (CSCov), which counts adjacent sequences of consecutive pairs. The sequence (e1, e2) is only counted if e1 immediately occurs before e2. The first contribution uses the novel combinatorial-based criteria for the purpose of test suite reduction. Empirical studies reveal that the criteria, when used with event sequences and sequences of size t=2, reduce the test suites by 22.8%-61.3% while the reduced …

Is there a significant difference in the design of deep networks for the tasks of classifying object-centric images and scenery images? How to design networks that extract the most representative features for scene recognition? To answer these questions, we design studies to examine the scales and richness of image features for scenery image recognition. Three methods are proposed that integrate the scale factor to the deep networks and reveal the fundamental network design strategies. In our first attempt to integrate scale factors into the deep network, we proposed a method that aggregates both the context and multi-scale object information of scene images by constructing a multi-scale pyramid. In our design, integration of object-centric multi-scale networks achieved a performance boost of 9.8%; integration of object- and scene-centric models obtained an accuracy improvement of 5.9% compared with single scene-centric models. We also exploit bringing the attention scheme to the deep network and proposed a Scale Attentive Network (SANet). The SANet streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the inter-dependency among scales, and further assists feature re-calibration as well as the aggregation process. The proposed network achieved a Top-1 accuracy increase by 1.83% on …

Recently, disaster scene description and indexing challenges attract the attention of researchers. In this dissertation, we solve a disaster-related multi-labeling task using a newly developed Low Altitude Disaster Imagery dataset. In the first task, we realize video content by selecting a set of summary key frames to represent the video sequence. Through inter-frame differences, the key frames are generated. The key frame extraction of disaster-related video clips is a powerful tool that can efficiently convert video data into image-level data, reduce the requirements for the extraction environment and improve the applicable environment. In the second, we propose a novel application of using deep learning methods on low altitude disaster video feature recognition. Supervised learning-based deep-learning approaches are effective in disaster-related features recognition via foreground object detection and background classification. Performed dataset validation, our model generalized well and improved performance by optimizing the YOLOv3 model and combining it with Resnet50. The comprehensive models showed more efficient and effective than those in prior published works. In the third task, we optimize the whole scene labeling classification by pruning the lightweight model MobileNetV3, which shows superior generalizability and can disaster features recognition from a disaster-related dataset be accomplished efficiently to assist disaster recovery.

Compressed sensing has been proposed as a computationally efficient method to estimate the finite-dimensional signals. The idea is to develop an undersampling operator that can sample the large but finite-dimensional sparse signals with a rate much below the required Nyquist rate. In other words, considering the sparsity level of the signal, the compressed sensing samples the signal with a rate proportional to the amount of information hidden in the signal. In this dissertation, first, we employ compressed sensing for physical layer signal processing of directional millimeter-wave communication. Second, we go through the theoretical aspect of compressed sensing by running a comprehensive theoretical analysis of compressed sensing to address two main unsolved problems, (1) continuous-extension compressed sensing in locally convex space and (2) computing the optimum subspace and its dimension using the idea of equivalent topologies using Köthe sequence. In the first part of this thesis, we employ compressed sensing to address various problems in directional millimeter-wave communication. In particular, we are focusing on stochastic characteristics of the underlying channel to characterize, detect, estimate, and track angular parameters of doubly directional millimeter-wave communication. For this purpose, we employ compressed sensing in combination with other stochastic methods such as Correlation Matrix Distance …

Students with upper body motor impairments, such as cerebral palsy, multiple sclerosis, ALS, etc., face challenges when learning to program in block-based programming environments, because these environments are highly dependent on the physical manipulation of a mouse or keyboard to drag and drop elements on the screen. In my dissertation, I make the block-based programming environment Blockly, accessible to students with upper body motor impairment by adding speech as an alternative form of input. This voice-enabled version of Blockly will reduce the need for the use of a mouse or keyboard, making it more accessible to students with upper body motor impairments. The voice-enabled Blockly system consists of the original Blockly application, a speech recognition API, predefined voice commands, and a custom function. Three user studies have been conducted, a preliminary study, a usability study, and an A/B test. These studies revealed a lot of information, such as the need for simpler, shorter, and more intuitive commands, the need to change the target audience, the shortcomings of speech recognition systems, etc. The feedback received from each study influenced design decisions at different phases. The findings also gave me insight into the direction I would like to go in the future. …

In this work, we leverage the recent developments in computer science to address several of the challenges in current literature-based discovery (LBD) solutions. First, LBD solutions cannot use semantics or are too computational complex. To solve the problems we propose a generative model OverlapLDA based on topic modeling, which has been shown both effective and efficient in extracting semantics from a corpus. We also introduce an inference method of OverlapLDA. We conduct extensive experiments to show the effectiveness and efficiency of OverlapLDA in LBD. Second, we expand LBD to a more complex and realistic setting. The settings are that there can be more than one concept connecting the input concepts, and the connectivity pattern between concepts can also be more complex than a chain. Current LBD solutions can hardly complete the LBD task in the new setting. We simplify the hypotheses as concept sets and propose LBDSetNet based on graph neural networks to solve this problem. We also introduce different training schemes based on self-supervised learning to train LBDSetNet without relying on comprehensive labeled hypotheses that are extremely costly to get. Our comprehensive experiments show that LBDSetNet outperforms strong baselines on simple hypotheses and addresses complex hypotheses.

COVID-19 is a highly contagious and virulent disease caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). COVID-19 disease induces lung changes observed in lung computerized tomography (CT) and the percentage of those diseased areas on the CT correlates with the severity of the disease. Therefore, segmentation of CT images to delineate the diseased or lesioned areas is a logical first step to quantify disease severity, which will help physicians predict disease prognosis and guide early treatments to deliver more positive patient outcomes. It is crucial to develop an automated analysis of CT images to save their time and efforts. This dissertation proposes CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose COVID-19 in CT images. It also proposes CoviNet Enhanced, a hybrid approach with 3D-CNN and support vector machines. It also proposes CoviSegNet and CoviSegNet Enhanced, which are enhanced U-Net models to segment ground-glass opacities and consolidations observed in computerized tomography (CT) images of COVID-19 patients. We trained and tested the proposed approaches using several public datasets of CT images. The experimental results show the proposed methods are highly effective for COVID-19 detection and segmentation and exhibit better accuracy, precision, sensitivity, specificity, F-1 score, Matthew's correlation coefficient (MCC), dice …

NAND flash-based solid state drives (SSDs) have been widely adopted in data centers and high performance computing (HPC) systems due to their better performance compared with hard disk drives. However, little is known about the reliability characteristics of SSDs in production systems. Existing works that study the statistical distributions of SSD failures in the field lack insights into distinct characteristics of SSDs. In this dissertation, I explore the SSD-specific SMART (Self-Monitoring, Analysis, and Reporting Technology) attributes and conduct in-depth analysis of SSD reliability in a production environment with a focus on the unique error types and health dynamics. QLC SSD delivers better performance in a cost-effective way. I study QLC SSDs in terms of their architecture and performance. In addition, I apply thermal stress tests to QLC SSDs and quantify their performance degradation processes. Various types of big data and machine learning workloads have been executed on SSDs under varying temperatures. The SSD throughput and application performance are analyzed and characterized.

In recent years, there have been incidents which decreased people's trust in some organizations and authorities responsible for ratings and accreditation. For a few prominent examples, there was a security breach at Equifax (2017), misconduct was found in the Standard & Poor's Ratings Services (2015), and the Accrediting Council for Independent Colleges and Schools (2022) validated some of the low-performing schools as delivering higher standards than they actually were. A natural solution to these types of issues is to decentralize the relevant trust management processes using blockchain technologies. The research problems which are tackled in this thesis consider the issue of trust in reputation for assessment and review credibility at different angles, in the context of blockchain applications. We first explored the following questions. How can we trust courses in one college to provide students with the type and level of knowledge which is needed in a specific workplace? Micro-accreditation on a blockchain was our solution, including using a peer-review system to determine the rigor of a course (through a consensus). Rigor is the level of difficulty in regard to a student's expected level of knowledge. Currently, we make assumptions about the quality and rigor of what is learned, but …

Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.