Engineering Commons^™

Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li

Doctoral Dissertations

In the past decades, the computing capability has shown an exponential growth trend, which is observed as Moore’s law. However, this growth speed is slowing down in recent years mostly because the down-scaled size of transistors is approaching their physical limit. On the other hand, recent advances in software, especially in big data analysis and artificial intelligence, call for a break-through in computing hardware. The memristor, or the resistive switching device, is believed to be a potential building block of the future generation of integrated circuits. The underlying mechanism of this device is different from that of complementary metal-oxide-semiconductor (CMOS) …

Multi-Sensor Localization And Tracking In Disaster Management And Indoor Wayfinding For Visually Impaired Users, Zhuorui Yang

Doctoral Dissertations

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual …

Transiency-Driven Resource Management For Cloud Computing Platforms, Prateek Sharma

Doctoral Dissertations

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient …

Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry

Doctoral Dissertations

Clinical studies have shown that features of a person's eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders …

Integration Of Robotic Perception, Action, And Memory, Li Yang Ku

Doctoral Dissertations

In the book "On Intelligence", Hawkins states that intelligence should be measured by the capacity to memorize and predict patterns. I further suggest that the ability to predict action consequences based on perception and memory is essential for robots to demonstrate intelligent behaviors in unstructured environments. However, traditional approaches generally represent action and perception separately---as computer vision modules that recognize objects and as planners that execute actions based on labels and poses. I propose here a more integrated approach where action and perception are combined in a memory model, in which a sequence of actions can be planned based on …

Hybrid Black-Box Solar Analytics And Their Privacy Implications, Dong Chen

Doctoral Dissertations

The aggregate solar capacity in the U.S. is rising rapidly due to continuing decreases in the cost of solar modules. For example, the installed cost per Watt (W) for residential photovoltaics (PVs) decreased by 6X from 2009 to 2018 (from $8/W to $1.2/W), resulting in the installed aggregate solar capacity increasing 128X from 2009 to 2018 (from 435 megawatts to 55.9 gigawatts). This increasing solar capacity is imposing operational challenges on utilities in balancing electricity's real-time supply and demand, as solar generation is more stochastic and less predictable than aggregate demand. To address this problem, both academia and utilities have …

An Architecture Evaluation And Implementation Of A Soft Gpgpu For Fpgas, Kevin Andryc

Doctoral Dissertations

Embedded and mobile systems must be able to execute a variety of different types of code, often with minimal available hardware. Many embedded systems now come with a simple processor and an FPGA, but not more energy-hungry components, such as a GPGPU. In this dissertation we present FlexGrip, a soft architecture which allows for the execution of GPGPU code on an FPGA without the need to recompile the design. The architecture is optimized for FPGA implementation to effectively support the conditional and thread-based execution characteristics of GPGPU execution without FPGA design recompilation. This architecture supports direct CUDA compilation to a …

Skybridge-3d-Cmos: A Fine-Grained Vertical 3d-Cmos Technology Paving New Direction For 3d Ic, Jiajun Shi

Doctoral Dissertations

2D CMOS integrated circuit (IC) technology scaling faces severe challenges that result from device scaling limitations, interconnect bottleneck that dominates power and performance, etc. 3D ICs with die-die and layer-layer stacking using Through Silicon Vias (TSVs) and Monolithic Inter-layer Vias (MIVs) have been explored in recent years to generate circuits with considerable interconnect saving for continuing technology scaling. However, these 3D IC technologies still rely on conventional 2D CMOS’s device, circuit and interconnect mindset showing only incremental benefits while adding new challenges reliability issues, robustness of power delivery network design and short-channel effects as technology node scaling. Skybridge-3D-CMOS (S3DC) is …

Power Laws In Complex Graphs: Parsimonious Generative Models, Similarity Testing Algorithms, And The Origins, Shan Lu

Doctoral Dissertations

This dissertation mainly discussed topics related to power law graphs, including graph similarity testing algorithms and power law generative models. For graph similarity testing, we proposed a method based on the mathematical theory of diffusion over manifolds using random walks over graphs. We show that our method not only distinguishes between graphs with different degree distributions, but also graphs with the same degree distributions. We compare the undirected power law graphs generated by Barabasi-Albert model and directed power law graphs generated by Krapivsky's model to the random graphs generated by Erdos-Renyi model. We also compare power law graphs generated by …

Analog Signal Processing Solutions And Design Of Memristor-Cmos Analog Co-Processor For Acceleration Of High-Performance Computing Applications, Nihar Athreyas

Doctoral Dissertations

Emerging applications in the field of machine vision, deep learning and scientific simulation require high computational speed and are run on platforms that are size, weight and power constrained. With the transistor scaling coming to an end, existing digital hardware architectures will not be able to meet these ever-increasing demands. Analog computation with its rich set of primitives and inherent parallel architecture can be faster, more efficient and compact for some of these applications. The major contribution of this work is to show that analog processing can be a viable solution to this problem. This is demonstrated in the three …

Adaptive Dynamic Programming With Eligibility Traces And Complexity Reduction Of High-Dimensional Systems, Seaar Jawad Kadhim Al-Dabooni

Doctoral Dissertations

"This dissertation investigates the application of a variety of computational intelligence techniques, particularly clustering and adaptive dynamic programming (ADP) designs especially heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Moreover, a one-step temporal-difference (TD(0)) and n-step TD (TD(λ)) with their gradients are utilized as learning algorithms to train and online-adapt the families of ADP. The dissertation is organized into seven papers. The first paper demonstrates the robustness of model order reduction (MOR) for simulating complex dynamical systems. Agglomerative hierarchical clustering based on performance evaluation is introduced for MOR. This method computes the reduced order denominator of the transfer …

Deep Learning And Localized Features Fusion For Medical Image Classification, Haidar A. Almubarak

Doctoral Dissertations

"Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible.

This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical …

Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery

Doctoral Dissertations

"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for …