Electrical and Computer Engineering Commons^™

Intelligent And Secure Fog-Aided Internet Of Drones, Jingjing Yao

Dissertations

Internet of drones (IoD), which utilize drones as Internet of Things (IoT) devices, deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing. Computing tasks are usually offloaded to the cloud data center for intensive processing. However, many IoD applications require real-time processing and event response (e.g., disaster response and virtual reality applications). Hence, data processing by the remote cloud may not satisfy the strict latency requirement. Fog computing attaches fog nodes, which are equipped with computing, storage and networking resources, to IoD gateways to assume a substantial amount of …

Controlled Reduction Of Graphene Oxide And Graphene Oxide-Carbon Nanotube Hybrids And Their Applications, Samar Azizighannad

Dissertations

Graphene and graphene derivatives are widely used in diverse research and industrial applications. Graphene production on a large scale is carried out by exfoliating graphite oxide and producing graphene oxide (GO), which comprises of graphene sheets with different oxygen-containing functional groups such as hydroxyl, carboxyl, and carbonyl. GO is reduced to reduced graphene oxide (rGO), which has properties that are closer to graphene. Properties of GO and rGO depend upon the oxygen content, and the effect of reducing oxygen content on the aqueous behavior of rGOs is not well understood.

In an effort to understand how properties of rGO change …

Modeling And Analysis Of Intracellular Signaling Networks And Cellular Decisions, Mustafa Ozen

Dissertations

Developing molecular network analysis methods is important due to their applications on complex biological systems such as target discovery, development of drugs, discovering drug effects, and finding treatments for many complex diseases, e.g., cancer, autoimmune, and mental disorders. An example of analysis techniques is the fault diagnosis analysis, in which the purpose is to quantify how much vulnerable the entire network is to dysfunction of one or multiple molecules. Such analysis can be done after proper network models are implemented, trained, and tested against the experimental data. In this dissertation, a Boolean modeling framework is implemented and methods to train …

Coordination, Adaptation, And Complexity In Decision Fusion, Weiqiang Dong

Dissertations

A parallel decentralized binary decision fusion architecture employs a bank of local detectors (LDs) that access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H0 (“absent”) or H1 (“present”)). The k 1 LD uses a local decision rule to compress its local observations yk into a binary local decision uk; uk = 0 if the k 1 LD accepts H0 and uk = 1 if it accepts H1. The k 1 LD sends its decision uk over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the …

Drone-Assisted Emergency Communications, Di Wu

Dissertations

Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area …

Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni

Dissertations

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network's traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious …

Treated Hfo2 Based Rram Devices With Ru, Tan, Tin As Top Electrode For In-Memory Computing Hardware, Yuvraj Dineshkumar Patel

Theses

The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention …

Live Media Production: Multicast Optimization And Visibility For Clos Fabric In Media Data Centers, Ammar Latif

Dissertations

Media production data centers are undergoing a major architectural shift to introduce digitization concepts to media creation and media processing workflows. Content companies such as NBC Universal, CBS/Viacom and Disney are modernizing their workflows to take advantage of the flexibility of IP and virtualization.

In these new environments, multicast is utilized to provide point-to-multi-point communications. In order to build point-to-multi-point trees, Multicast has an established set of control protocols such as IGMP and PIM. The existing multicast protocols do not optimize multicast tree formation for maximizing network throughput which lead to decreased fabric utilization and decreased total number of admitted …

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Microglia Induced Neuroinflammation Through The Nlrp3 Inflammasome Following Blast Traumatic Brain Injury, Daniel Younger

Dissertations

The incidence of traumatic brain injury (TBI) among military personnel have been steadily increasing with modern conflicts. A recent RAND report estimated 320,000 service members, totaling 20% of deployed forces, suffer from TBI. However, of this population roughly 60% have not seen a medical professional specifically for TBI. Unlike the civilian population, the primary cause of TBI for active-duty military personnel is blast exposure. Blasts now account for over 70% of all US military casualties in operation Iraqi Freedom (OIF) and Operation enduring freedom (OEF) and are the major cause of TBI. Among many pathological mechanisms associated with blast TBI, …

Error Correction For Asynchronous Communication And Probabilistic Burst Deletion Channels, Chen Yi

Dissertations

Short-range wireless communication with low-power small-size sensors has been broadly applied in many areas such as in environmental observation, and biomedical and health care monitoring. However, such applications require a wireless sensor operating in "always-on" mode, which increases the power consumption of sensors significantly. Asynchronous communication is an emerging low-power approach for these applications because it provides a larger potential of significant power savings for recording sparse continuous-time signals, a smaller hardware footprint, and a lower circuit complexity compared to Nyquist-based synchronous signal processing.

In this dissertation, the classical Nyquist-based synchronous signal sampling is replaced by asynchronous sampling strategies, i.e., …

Subspace Portfolios: Design And Performance Comparison, Anqi Xiong

Dissertations

Data processing and engineering techniques enable people to observe and better understand the natural and human-made systems and processes that generate huge amounts of various data types. Data engineers collect data created in almost all fields and formats, such as images, audio, and text streams, biological and financial signals, sensing and many others. They develop and implement state-of-the art machine learning (ML) and artificial intelligence (AI) algorithms using big data to infer valuable information with social and economic value. Furthermore, ML/AI methodologies lead to automate many decision making processes with real-time applications serving people and businesses. As an example, mathematical …

Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu

Dissertations

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating …

Communications With Spectrum Sharing In 5g Networks Via Drone-Mounted Base Stations, Liang Zhang

Dissertations

The fifth generation wireless network is designed to accommodate enormous traffic demands for the next decade and to satisfy varying quality of service for different users. Drone-mounted base stations (DBSs) characterized by high mobility and low cost intrinsic attributes can be deployed to enhance the network capacity. In-band full-duplex (IBFD) is a promising technology for future wireless communications that can potentially enhance the spectrum efficiency and the throughput capacity. Therefore, the following issues have been identified and investigated in this dissertation in order to achieve high spectrum efficiency and high user quality of service.

First, the problem of deploying DBSs …

Scanless Optical Coherence Tomography For High-Speed 3d Biomedical Microscopy, Yahui Wang

Dissertations

Optical coherence tomography (OCT) is a high-resolution cross-sectional imaging modality that has found applications in a wide range of biomedical fields, such as ophthalmology diagnosis, interventional cardiology, surgical guidance, and oncology. OCT can be used to image dynamic scenes, in quantitative blood flow sensing and visualization, dynamic optical coherence elastography, and large-scale neural recording. However, the spatiotemporal resolution of OCT for dynamic imaging is limited by the approach it takes to scan the three-dimensional (3-D) space. In a typical OCT system, the incident light is focused to a point at the sample. The OCT system uses mechanical scanners (galvanometers or …

Identification Of Neurobiological Mechanisms Associated With Attention Deficits In Adults Post Traumatic Brain Injury, Ziyan Wu

Dissertations

Traumatic Brain Injury (TBI) is one of the major public health concerns with approximately 70 million new cases occurring worldwide per year. It is often caused by a forceful bump, blow, or jolt to the head, resulting in brain tissue damage and normal brain functions disruption. All grades of TBI, ranging from mild to severe, can cause wide-ranging and long-term effects on affected individuals, resulting in physical impairments, and neurocognitive consequences that permanently affect their abilities to perform daily activities. Attention deficits are the most common persisting neurocognitive consequences following TBI, which significantly contribute to poor academic and social functioning, …

Coding Against Stragglers In Distributed Computation Scenarios, Malihe Aliasgari

Dissertations

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially. The huge amount of data needs to be transferred and stored with extremely high reliability. The concept of "coded computing", or a distributed computing paradigm that utilizes coding theory to smartly inject and leverage data/computation redundancy into distributed computing systems, mitigates the fundamental performance bottlenecks for running large-scale data analytics.

In this dissertation, a distributed computing framework, first for input files distributedly stored on the uplink of a cloud radio access network architecture, is studied. It focuses on that decoding …

Deep Learning For Quantitative Motion Tracking Based On Optical Coherence Tomography, Peter Abdelmalak

Theses

Optical coherence tomography (OCT) is a cross-sectional imaging modality based on low coherence light interferometry. OCT has been widely used in diagnostic ophthalmology and has found applications in other biomedical fields such as cancer detection and surgical guidance.

In the Laboratory of Biophotonics Imaging and Sensing at New Jersey Institute of Technology, we developed a unique needle OCT imager based on a single fiber probe for breast cancer imaging. The needle OCT imager with sub-millimeter diameter can be inserted into tissue for minimally invasive in situ breast imaging. OCT imaging provides spatial resolution similar to histology and has the potential …

An Information Theoretic Approach To Assess Perceived Audio Quality Using Eeg With Reduced Number Of Electrodes, Sansit Das

Theses

Electroencephalograph(EEG) is a process mainly used in medical and research fields to study the electrical activities in a brain. In this technique, 128 or 256 electrodes are attached to the scalp and the electrical activities of the human brain is recorded with the help of a software. In the global scenario, the EEG responses are studied and analysed to acknowledge any disorders in the brain, such as epilepsy or head injury.

Recent studies performed by researchers, have focused on analysing these electrical activities to access perceived audio quality from users by using information theoretic approaches, such as mutual information. Experiments …

Spectral 3d Reconstruction Based On Macroscopic Oct Imaging, Xingyu Zhou

Theses

Various optical technologies have been utilized to improve art conservation by art conservators, such as laser triangulation, stereophotogrammetry, structured light, laser scanner and time of flight sensors. These methods have been deployed to capture the 3D or surface topography information of sculptures and architectures. Optical coherence tomography (OCT) has introduced new imaging methods to study the surface features and subsurface structures of delicate cultural heritage objects. However, despite its higher spatial resolution, the field of view (FOV) of OCT severely limits the size of the scanning area and does not allow macroscopic examination. To solve this issue, we develop and …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Study And Modelling Of Lithium Ion Cell With Accurate Soc Measurement Algorithm Using Kalman Filter For Electric Vehicles, Kasthuriramanan Mahendravadi Sivaguru

Theses

Lithium Ion cells are preferred over lead acid cells for electric vehicles due to their energy density, higher discharge current and size. The cost of lithium ion cells is scaling down compared to ten years earlier, but as their performance characteristics increase, the need for safety and accurate modelling also increases.

The absence of a generic cell model is associated to the different makes of cells and different chemistries of Lithium ion cells behave differently under the testing conditions required for every unique application. The focus of this thesis will be on how to provide intelligence to the battery management …

Magnetic Field Effects On Lithium Ion Batteries, Kevin Mahon

Theses

The Nobel Prize in Chemistry 2019 was just recently awarded to John B. Goodenough, M. Stanley Whittingham, and Akira Yoshino for the development of lithium-ion batteries. Lithium-ion batteries have seen use in many different industries and applications such as in portable devices, power grids, and electric vehicles. As lithium-ion batteries become more commonplace they will need to be modeled more extensively. The magnetic field effect on lithium-ion batteries has not been studied significantly since they were first discovered.

Modeling these batteries is still difficult because of the many complexities of the operation of a battery. Lithium-ion batteries are commonly modeled …

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Data Multiplexing In Vector Acoustic Communication: System Design And Experiments, Erjian Zhang

Dissertations

Acoustic particle velocity channels, which are vector components of the acoustic field, have been recently explored to achieve communication purposes underwater. Vector sensors and transducers that can utilize acoustic particle velocity channels have compact multichannel designs, which are perfect for compact size underwater platforms. In this dissertation, an underwater acoustic multiple-input multiple-output (MIMO) communication system featuring orthogonal frequency division multiplexing (OFDM) and frequency shift keying (FSK) modulation is discussed. By transmitting multiple independent data streams simultaneously over several channels within the same bandwidth, the proposed MIMO system increases the transmission rate. A variety of components of the system such as …

Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong

Dissertations

Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on …

Probabilistic Spiking Neural Networks : Supervised, Unsupervised And Adversarial Trainings, Alireza Bagheri

Dissertations

Spiking Neural Networks (SNNs), or third-generation neural networks, are networks of computation units, called neurons, in which each neuron with internal analogue dynamics receives as input and produces as output spiking, that is, binary sparse, signals. In contrast, second-generation neural networks, termed as Artificial Neural Networks (ANNs), rely on simple static non-linear neurons that are known to be energy-intensive, hindering their implementations on energy-limited processors such as mobile devices. The sparse event-based characteristics of SNNs for information transmission and encoding have made them more feasible for highly energy-efficient neuromorphic computing architectures. The most existing training algorithms for SNNs are based …

Supercapacitors With Gate Electrodes, Tazima Selim Chowdhury

Dissertations

A new approach to improve the capacitance of supercapacitors (SC) is proposed in this study. A typical SC is composed of an anode and a cathode; a separator in between them assures an unintentional discharge of the capacitor. The study focuses on a family of structured separators, either electronically active or passive which are called gates. An active structured separator layer has been fabricated and analyzed. The structured separator has characteristics of electrical diode and is fabricated out of functionalized carbon nanotubes (CNT). Improvement of the overall capacitance of SC, equipped with either active or passive structured separators demonstrated a …

Dual Modality Optical Coherence Tomography : Technology Development And Biomedical Applications, Farzana Rahmat Zaki

Dissertations

Optical coherence tomography (OCT) is a cross-sectional imaging modality that is widely used in clinical ophthalmology and interventional cardiology. It is highly promising for in situ characterization of tumor tissues. OCT has high spatial resolution and high imaging speed to assist clinical decision making in real-time.

OCT can be used in both structural imaging and mechanical characterization. Malignant tumor tissue alters morphology. Additionally, structural OCT imaging has limited tissue differentiation capability because of the complex and noisy nature of the OCT signal. Moreover, the contrast of structural OCT signal derived from tissue’s light scattering properties has little chemical specificity. Hence, …

High-Performance Learning Systems Using Low-Precision Nanoscale Devices, Nandakumar Sasidharan Rajalekshmi

Dissertations

Brain-inspired computation promises a paradigm shift in information processing, both in terms of its parallel processing architecture and the ability to learn to tackle problems deemed unsolvable by traditional algorithmic approaches. The computational capability of the human brain is believed to stem from an interconnected network of 100 billion compute nodes (neurons) that interact with each other through approximately 10¹⁵ adjustable memory junctions (synapses). The conductance of synapses is modifiable allowing the network to learn and perform various cognitive functions. Artificial neural networks inspired by this architecture have demonstrated even super-human performance in many complex tasks.

Computational systems based …