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Full-Text Articles in Engineering
Pervasive Spectrum Sharing For Improved Wireless Experience, Mostafizur Rahman
Pervasive Spectrum Sharing For Improved Wireless Experience, Mostafizur Rahman
Electronic Theses and Dissertations, 2020-
Spectrum sharing among cellular users has been a promising approach to attain better efficiency in the use of the limited spectral bands. The existing dynamic spectrum access techniques include sharing of the licensed spectrum bands by allowing other 'secondary' users to use the bands if the licensee 'primary' user is idle. This primary-secondary spectrum sharing is limited in terms of design space, and may not be sufficient to meet the ever-increasing demand of connectivity and high signal quality to improve the end-users' wireless experience. The next step to increase spectrum efficiency is to design markets where sharing takes place pervasively …
Investigations On The Use Of Hyperthermia For Breast Cancer Treatment, Sreekala Suseela
Investigations On The Use Of Hyperthermia For Breast Cancer Treatment, Sreekala Suseela
Electronic Theses and Dissertations, 2020-
Hyperthermia using electromagnetic energy has been proven to be an effective method in the treatment of cancer. Hyperthermia is a therapeutic procedure in which the temperature in the tumor tissue is raised above 42°C without causing any damage to the surrounding healthy tissue. This method has been shown to increase the effectiveness of radiotherapy and chemotherapy. Radio frequencies, microwave frequencies or focused ultrasound can be used to deliver energy to the tumor tissue to attain higher temperatures in the tumor region for hyperthermia application. In this dissertation the use of a near field focused (NFF) microstrip antenna array for the …
Distributed Multi-Agent Optimization And Control With Applications In Smart Grid, Towfiq Rahman
Distributed Multi-Agent Optimization And Control With Applications In Smart Grid, Towfiq Rahman
Electronic Theses and Dissertations, 2020-
With recent advancements in network technologies like 5G and Internet of Things (IoT), the size and complexity of networked interconnected agents have increased rapidly. Although centralized schemes have simpler algorithm design, in practicality, it creates high computational complexity and requires high bandwidth for centralized data pooling. In this dissertation, for distributed optimization of networked multi-agent architecture, the Alternating Direction Method of Multipliers (ADMM) is investigated. In particular, a new adaptive-gain ADMM algorithm is derived in closed form and under the standard convex property to greatly speed up the convergence of ADMM-based distributed optimization. Using the Lyapunov direct approach, the proposed …
Control Strategies For Multi-Controller Multi-Objective Systems, Raaed Al-Azzawi
Control Strategies For Multi-Controller Multi-Objective Systems, Raaed Al-Azzawi
Electronic Theses and Dissertations, 2020-
This dissertation's focus is control systems controlled by multiple controllers, each having its own objective function. The control of such systems is important in many practical applications such as economic systems, the smart grid, military systems, robotic systems, and others. To reap the benefits of feedback, we consider and discuss the advantages of implementing both the Nash and the Leader-Follower Stackelberg controls in a closed-loop form. However, closed-loop controls require continuous measurements of the system's state vector, which may be expensive or even impossible in many cases. As an alternative, we consider a sampled closed-loop implementation. Such an implementation requires …
Modeling Site Specific Urban Propagation Using A Variable Terrain Radiowave Parabolic Equation - Vertical Plane Launch (Vtrpe-Vpl) Hybrid Technique, Pierre Cadette
Electronic Theses and Dissertations, 2020-
The development of efficient algorithms for calculating propagation loss in site specific urban environments has been an active area of research for many years. This dissertation demonstrates that, for particular scenarios, a hybrid approach that combines the Variable Terrain Radiowave Parabolic Equation (VTRPE) and Vertical Plane Launch (VPL) models can be used to produce accurate results for a downrange region of interest. The hybrid approach consists of leveraging the 2-D parabolic equation method in the initial propagation region, where backscatter and out of plane energy can be neglected, then transitioning to the more computationally intensive 3-D ray launching method for …
Provably Trustworthy And Secure Hardware Design With Low Overhead, Qutaiba Alasad
Provably Trustworthy And Secure Hardware Design With Low Overhead, Qutaiba Alasad
Electronic Theses and Dissertations, 2020-
Due to the globalization of IC design in the semiconductor industry and outsourcing of chip manufacturing, 3PIPs become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware Trojans. To thwart such attacks, ICs can be protected using logic encryption techniques. However, strong resilient techniques incur significant overheads. SCAs further complicate matters by introducing potential attacks post-fabrication. One of the most severe SCAs is PA attacks, in which an attacker can observe the power variations of the device and analyze them to extract the secret key. PA attacks can be mitigated via adding large extra hardware; however, the overheads of …
Statistical And Stochastic Learning Algorithms For Distributed And Intelligent Systems, Jiang Bian
Statistical And Stochastic Learning Algorithms For Distributed And Intelligent Systems, Jiang Bian
Electronic Theses and Dissertations, 2020-
In the big data era, statistical and stochastic learning for distributed and intelligent systems focuses on enhancing and improving the robustness of learning models that have become pervasive and are being deployed for decision-making in real-life applications including general classification, prediction, and sparse sensing. The growing prospect of statistical learning approaches such as Linear Discriminant Analysis and distributed Learning being used (e.g., community sensing) has raised concerns around the robustness of algorithm design. Recent work on anomalies detection has shown that such Learning models can also succumb to the so-called 'edge-cases' where the real-life operational situation presents data that are …
Deep Hashing For Image Similarity Search, Ali Al Kobaisi
Deep Hashing For Image Similarity Search, Ali Al Kobaisi
Electronic Theses and Dissertations, 2020-
Hashing for similarity search is one of the most widely used methods to solve the approximate nearest neighbor search problem. In this method, one first maps data items from a real valued high-dimensional space to a suitable low dimensional binary code space and then performs the approximate nearest neighbor search in this code space instead. This is beneficial because the search in the code space can be solved more efficiently in terms of runtime complexity and storage consumption. Obviously, for this method to succeed, it is necessary that similar data items be mapped to binary code words that have small …
Extracting Data-Level Parallelism In High-Level Synthesis For Reconfigurable Architectures, Juan Andres Escobedo Contreras
Extracting Data-Level Parallelism In High-Level Synthesis For Reconfigurable Architectures, Juan Andres Escobedo Contreras
Electronic Theses and Dissertations, 2020-
High-Level Synthesis (HLS) tools are a set of algorithms that allow programmers to obtain implementable Hardware Description Language (HDL) code from specifications written high-level, sequential languages such as C, C++, or Java. HLS has allowed programmers to code in their preferred language while still obtaining all the benefits hardware acceleration has to offer without them needing to be intimately familiar with the hardware platform of the accelerator. In this work we summarize and expand upon several of our approaches to improve the automatic memory banking capabilities of HLS tools targeting reconfigurable architectures, namely Field-Programmable Gate Arrays or FPGA's. We explored …
Data-Driven Nonlinear Control Designs For Constrained Systems, Roland Harvey
Data-Driven Nonlinear Control Designs For Constrained Systems, Roland Harvey
Electronic Theses and Dissertations, 2020-
Systems with nonlinear dynamics are theoretically constrained to the realm of nonlinear analysis and design, while explicit constraints are expressed as equalities or inequalities of state, input, and output vectors of differential equations. Few control designs exist for systems with such explicit constraints, and no generalized solution has been provided. This dissertation presents general techniques to design stabilizing controls for a specific class of nonlinear systems with constraints on input and output, and verifies that such designs are straightforward to implement in selected applications. Additionally, a closed-form technique for an open-loop problem with unsolvable dynamic equations is developed. Typical optimal …
Improving Usability Of Genetic Algorithms Through Self Adaptation On Static And Dynamic Environments, Reamonn Norat
Improving Usability Of Genetic Algorithms Through Self Adaptation On Static And Dynamic Environments, Reamonn Norat
Electronic Theses and Dissertations, 2020-
We propose a self-adaptive genetic algorithm, called SAGA, for the purposes of improving the usability of genetic algorithms on both static and dynamic problems. Self-adaption can improve usability by automating some of the parameter tuning for the algorithm, a difficult and time-consuming process on canonical genetic algorithms. Reducing or simplifying the need for parameter tuning will help towards making genetic algorithms a more attractive tool for those who are not experts in the field of evolutionary algorithms, allowing more people to take advantage of the problem solving capabilities of a genetic algorithm on real-world problems. We test SAGA and analyze …
Energy-Efficient Signal Conversion And In-Memory Computing Using Emerging Spin-Based Devices, Soheil Salehi Mobarakeh
Energy-Efficient Signal Conversion And In-Memory Computing Using Emerging Spin-Based Devices, Soheil Salehi Mobarakeh
Electronic Theses and Dissertations, 2020-
New approaches are sought to maximize the signal sensing and reconstruction performance of Internet-of-Things (IoT) devices while reducing their dynamic and leakage energy consumption. Recently, Compressive Sensing (CS) has been proposed as a technique aimed at reducing the number of samples taken per frame to decrease energy, storage, and data transmission overheads. CS can be used to sample spectrally-sparse wide-band signals close to the information rate rather than the Nyquist rate, which can alleviate the high cost of hardware performing sampling in low-duty IoT applications. In my dissertation, I am focusing mainly on the adaptive signal acquisition and conversion circuits …
Enabling Recovery Of Secure Non-Volatile Memories, Mao Ye
Enabling Recovery Of Secure Non-Volatile Memories, Mao Ye
Electronic Theses and Dissertations, 2020-
Emerging non-volatile memories (NVMs), such as phase change memory (PCM), spin-transfer torque RAM (STT-RAM) and resistive RAM (ReRAM), have dual memory-storage characteristics and, therefore, are strong candidates to replace or augment current DRAM and secondary storage devices. The newly released Intel 3D XPoint persistent memory and Optane SSD series have shown promising features. However, when these new devices are exposed to events such as power loss, many issues arise when data recovery is expected. In this dissertation, I devised multiple schemes to enable secure data recovery for emerging NVM technologies when memory encryption is used. With the data-remanence feature of …
Selective Subtraction: An Extension Of Background Subtraction, Adeel Bhutta
Selective Subtraction: An Extension Of Background Subtraction, Adeel Bhutta
Electronic Theses and Dissertations, 2020-
Background subtraction or scene modeling techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically "learned" as part of the background using a large training set of video data. Traditional techniques exhibit a number of limitations including inability to model partial background or subtract partial foreground, inflexibility of the model being used, need for large training data and computational inefficiency. In this thesis, …
Detecting Small Moving Targets In Infrared Imagery, Adam Cuellar
Detecting Small Moving Targets In Infrared Imagery, Adam Cuellar
Electronic Theses and Dissertations, 2020-
Deep convolutional neural networks have achieved remarkable results for detecting large and medium sized objects in images. However, the ability to detect smallobjects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distantfrom the sensor. We first examine the ability of several state-of-the-art object detection networks (YOLOv3 and Mask R-CNN) to find small moving targets in infraredimagery using a publicly released dataset by the US Army Night Vision and Electronic Sensors Directorate. We then introduce a novel Moving Target Indicator Network (MTINet) and …
Scalable Communication Frameworks For Multi-Agency Data Sharing, Shafaq Chaudhry
Scalable Communication Frameworks For Multi-Agency Data Sharing, Shafaq Chaudhry
Electronic Theses and Dissertations, 2020-
With the rise in frequency and magnitude of natural disasters, there is a need to break down monolithic organizational barriers and engage with community volunteers. This calls for ease of systems interoperability to facilitate communication, data-sharing and scalability of real-time response, essential for crisis communications. We propose two scalable frameworks that enable multi-agency interoperability and real-time data-sharing. The first framework harnesses the power of social media, artificial intelligence, and community volunteers to form an extended rescue-and-response network that alleviates call center burden and augments the finite capacity of dispatch units. Through an "online 9-1-1" service, affected people can request help …
Mfpa: Mixed-Signal Field Programmable Array For Energy-Aware Compressive Signal Processing, Adrian Tatulian
Mfpa: Mixed-Signal Field Programmable Array For Energy-Aware Compressive Signal Processing, Adrian Tatulian
Electronic Theses and Dissertations, 2020-
Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the …
Multi-Element Multi-Datastream Visible Light Communication Networks, Sifat Ibne Mushfique
Multi-Element Multi-Datastream Visible Light Communication Networks, Sifat Ibne Mushfique
Electronic Theses and Dissertations, 2020-
Because of the exponentially increasing demand of wireless data, the Radio Frequency (RF) spectrum crunch is rising rapidly. The amount of available RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (400 THz to 780 THz) and it has 10,000 times bigger bandwidth than radio waves (3 kHz to 300 GHz). Due to this tremendous potential, VLC has captured a lot of interest recently as there is already an extensive deployment …