Electrical and Computer Engineering Commons^™

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 …

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 …

Multiple Ssid Framework For Rss-Fingerprint Based Indoor Positioning Systems, Ahmed Kareem Abed

Dissertations

Location-Based Indoor positioning systems significance stems from the bloom of recent applications in various fields such as in tracking services for an elder or a patient within large living communities, mobile robot localization, and several other security applications. Currently, Global Positioning Systems (GPS) are the most widely used location-sensing technique. However, satellite-based GPS signals require line of sight (LOS) to work correctly, which is something cannot be achieved inside buildings. Fortunately, wireless LAN can be employed in indoor positioning systems (IPS), and since all large buildings such as malls, hospitals, airports, schools, and museums have hundreds of Wi-Fi access points, …

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 …

Epitaxial Growth Of Iii-Nitride Nanostructures And Their Optoelectronic Applications, Moab Rajan Philip

Dissertations

Light-emitting diodes (LEDs) using III-nitride nanowire heterostructures have been intensively studied as promising candidates for future phosphor-free solid-state lighting and full-color displays. Compared to conventional GaN-based planar LEDs, III-nitride nanowire LEDs exhibit numerous advantages including greatly reduced dislocation densities, polarization fields, and quantum-confined Stark effect due to the effective lateral stress relaxation, promising high efficiency full-color LEDs. Beside these advantages, however, several factors have been identified as the limiting factors for further enhancing the nanowire LED quantum efficiency and light output power. Some of the most probable causes have been identified as due to the lack of carrier confinement in …

Surface-Enhanced Raman Detection Of Glucose On Several Novel Substrates For Biosensing Applications, Laila Saad Alqarni

Dissertations

The small normal Raman cross-section of glucose is considered to be a major challenge for its detection by Surface Enhanced Raman Spectroscopy (SERS) for medical applications. These applications include blood glucose level monitoring of diabetic patients and evaluation of patients with other medical conditions, since glucose is a marker for many human diseases. This dissertation focuses on Surface-Enhanced Raman Scattering primarily for the detection of glucose. Some experiments also are carried out for the detection of the corresponding enzyme glucose oxidase that is used in electrochemical glucose sensors and in biofuel cells. This project explores the possibility of utilizing Surface …

Workload Allocation In Mobile Edge Computing Empowered Internet Of Things, Qiang Fan

Dissertations

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart "brain" to process and store the big data generated from …

Dual-Battery Empowered Green Cellular Networks, Xilong Liu

Dissertations

With awareness of the potential harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology (ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase sustainedly. It is essential to leverage green energy to power BSs to reduce their on-grid brown energy consumption. However, in order to furthest save on-grid brown energy and decrease the on-grid brown energy electricity expenses, most existing green energy related works only pursue to maximize the green energy utilization while compromising the …

Hardware/Software Co-Design Of A Reconfigurable Accelerator Platform For Fuzzy Logic-Based Spatial Image Processing Filters, Aous Hammad Kurdi

Dissertations

Rapid development in the architecture and fabrication technology of field programmable gate array (FPGA) and system on a chip (SoC) in the last two decades is dramatically increasing the reliability of those devices as a cost-effective alternative for traditional real-time image processing systems. Module reusability nature of FPGA and SoC development decreases design and implementation time noticeably. On-site reconfigurability allows for intensive design alterations to occur at a small cost. This study is presenting the design and implementation of a new hardware accelerator for an edge detector on Xilinx 7 series FPGA devices based on a fuzzy logic approach. The …

Network Equivalents Applied To Transient Analysis Of Large Distribution Systems, Ghassan Abdul-Hussein Bilal

Dissertations

Frequency domain (FD) methods for electromagnetic transient analysis of electric power systems offer outstanding simplicity and accuracy when dealing with distributed parameter elements and components that exhibit frequency-dependent behavior. However, the use of FD methods is usually restricted to the simulation of small and simple networks, since it is commonly believed that their application implies a very large computational burden when compared to time domain methods. This study demonstrates that FD methods can accommodate the detailed simulation of large distribution networks within acceptable computer times and in a very straightforward manner, thus offering an attractive complement or alternative to common …

Parameter Determination And Modeling Of Transformer Windings For Fast Transients Including Frequency Dependent Effects, Yazid Al-Kraimeen

Dissertations

Power transformers are static devices capable of transferring energy from one electrical circuit to another by means of electromagnetic induction at the same frequency. The most common application of transformers in an electric power system is to step up or down the voltage level of an electric grid. This is essential for the efficient transmission and distribution of electricity from generating plants to different types of consumers. Electrical energy used in residential, commercial and industrial settings is delivered to consumers by means of transformers, which normally operate 24 hours a day, 365 days a year. Therefore, design improvements that result …

Managing Uncertainty In Sensor Data: An Evidence Theory Based Multisensor Data Fusion Approach, Gabriel Idowu Awogbami

Dissertations

Sensors play a critical role in the development of intelligent systems. Intelligent agents are equipped with an array of sensors to acquire information about themselves and the environment to make a reasonable decision. Information extracted from the sensor data is often characterized by uncertainty. Modeling and reasoning under such uncertainty poses a great challenge. Multisensor data fusion is a viable approach to address this problem. The Dempster Shafer (DS) theory of belief functions, also known as the evidence theory, is a well-known data fusion formalism due to its close relationship with other mathematical theories of uncertainty and its elegant way …

A Formal Systems Engineering Methodology For Cyber-Physical Systems: The Verifiable Design Process, Nadew S. Kibret

Dissertations

Cyber-physical systems (CPS) are systems that exhibit tight integration between their physical and computational components. They are hybrid systems containing continuous states and discrete states emanating from the physical and computational components, respectively. Systems engineering of these systems is challenging due to the tight integration of their computational, physical and communication technologies. As their level of acceptance increases in mission critical applications such as health care, smart grid, autonomous vehicles and smart cities, the need to ensure their safe operation is paramount importance as well. Therefore, design methodologies followed in their development are required to result in system behavior that …

Wireless Coverage Using Unmanned Aerial Vehicles, Hazim Shakhatreh

Dissertations

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civilian application domains including real-time monitoring, search and rescue, and wireless coverage. UAVs can be used to provide wireless coverage during emergency cases where each UAV serves as an aerial wireless base station when the cellular network goes down. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. During such situations, the UAVs need to return periodically to a charging station for recharging, due to their limited battery capacity. Given the recharging requirements, …

Impairments In Ground Moving Target Indicator (Gmti) Radar, Phuoc Doan Huu Vu

Dissertations

Radars on multiple distributed airborne or ground based moving platforms are of increasing interest, since they can be deployed in close proximity to the event under investigation and thus offer remarkable sensing opportunities. Ground moving target indicator (GMTI) detects and localizes moving targets in the presence of ground clutter and other interference sources. Space-time adaptive processing (STAP) implemented with antenna arrays has been a classical approach to clutter cancellation in airborne radar. One of the challenges with STAP is that the minimum detectable velocity (MDV) of targets is a function of the baseline of the antenna array: the larger the …

Optimizing Resource Allocation In Eh-Enabled Internet Of Things, Ali Shahini

Dissertations

Internet of Things (IoT) aims to bridge everyday physical objects via the Internet. Traditional energy-constrained wireless devices are powered by fixed energy sources like batteries, but they may require frequent battery replacements or recharging. Wireless Energy Harvesting (EH), as a promising solution, can potentially eliminate the need of recharging or replacing the batteries. Unlike other types of green energy sources, wireless EH does not depend on nature and is thus a reliable source of energy for charging devices. Meanwhile, the rapid growth of IoT devices and wireless applications is likely to demand for more operating frequency bands. Although the frequency …

Characterization Of Self-Heating Effects And Assessment Of Its Impact On Reliability In Finfet Technology, Peter Christopher Paliwoda

Dissertations

The systematically growing power (heat) dissipation in CMOS transistors with each successive technology node is reaching levels which could impact its reliable operation. The emergence of technologies such as bulk/SOI FinFETs has dramatically confined the heat in the device channel due to its vertical geometry and it is expected to further exacerbate with gate-all-around transistors.

This work studies heat generation in the channel of semiconductor devices and measures its dissipation by means of wafer level characterization and predictive thermal simulation. The experimental work is based on several existing device thermometry techniques to which additional layout improvements are made in state …

Load Balancing And Scalable Clos-Network Packet Switches, Oladele Theophilus Sule

Dissertations

In this dissertation three load-balancing Clos-network packet switches that attain 100% throughput and forward cells in sequence are introduced. The configuration schemes and the in-sequence forwarding mechanisms devised for these switches are also introduced. Also proposed is the use of matrix analysis as a tool for throughput analysis.

In Chapter 2, a configuration scheme for a load-balancing Clos-network packet switch that has split central modules and buffers in between the split modules is introduced. This switch is called split-central-buffered Load-Balancing Clos-network (LBC) switch and it is cell based. The switch has four stages, namely input, central-input, central-output, and output stages. …