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Articles 151 - 180 of 690

Full-Text Articles in Electrical and Electronics

Memory Module Design For High-Temperature Applications In Sic Cmos Technology, Affan Abbasi May 2021

Memory Module Design For High-Temperature Applications In Sic Cmos Technology, Affan Abbasi

Graduate Theses and Dissertations

The wide bandgap (WBG) characteristics of SiC play a significant and disruptive role in the power electronics industry. The same characteristics make this material a viable choice for high-temperature electronics systems. Leveraging the high-temperature capability of SiC is crucial to automotive, space exploration, aerospace, deep well drilling, and gas turbines. A significant issue with the high-temperature operation is the exponential increase in leakage current. The lower intrinsic carrier concentration of SiC (10-9 cm-3) compared to Si (1010 cm-3) leads to lower leakage over temperature. Several researchers have demonstrated analog and digital circuits designed in SiC. However, a memory module is …

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Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent May 2021

Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent

Graduate Theses and Dissertations

Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.

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App Development For Wearable Sensors, Connor Pittman, Christopher Chappie, Patrick A. Tetreault Apr 2021

App Development For Wearable Sensors, Connor Pittman, Christopher Chappie, Patrick A. Tetreault

Thinking Matters Symposium

The objective of this research project was to create a wearable device that monitors bodily functions for the user to view on their smartphone. Sensor data is processed using the Arduino Nano 33 BLE microcontroller. The sensors used in this project include: proximity, temperature, humidity, heart rate, pressure, and skin impedance. This project takes advantage of the Arduino's Bluetooth low energy (BLE) capabilities so that all the data can be transmitted to a smartphone. This presentation shows the challenges faced during the project and how they were overcome. Some of these challenges include: programming, how heart rate sensors work, and …

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Suppressing Bias Stress Degradation In High Performance Solution Processed Organic Transistors Operating In Air, Hamna F. Iqbal, Qianxiang Ai, Karl J. Thorley, Hu Chen, Iain Mcculloch, Chad Risko, John E. Anthony, Oana D. Jurchescu Apr 2021

Suppressing Bias Stress Degradation In High Performance Solution Processed Organic Transistors Operating In Air, Hamna F. Iqbal, Qianxiang Ai, Karl J. Thorley, Hu Chen, Iain Mcculloch, Chad Risko, John E. Anthony, Oana D. Jurchescu

Chemistry Faculty Publications

Solution processed organic field effect transistors can become ubiquitous in flexible optoelectronics. While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications. Here, we introduce a strategy to identify the most probable and severe degradation pathways in organic transistors and then implement a method to eliminate the main sources of instabilities. Real time monitoring of the energetic distribution and transformation of electronic trap states during device operation, in conjunction with simulations, revealed the nature of traps responsible for performance degradation. With this information, we …

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Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui Apr 2021

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

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Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa Mar 2021

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Electrical and Computer Engineering Faculty Publications

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …

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Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch Mar 2021

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

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Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou Jan 2021

Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …

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Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi Jan 2021

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Graduate Student Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

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Holistic Control For Cyber-Physical Systems, Yehan Ma Jan 2021

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

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Ultrafast Thermal Modification Of Strong Coupling In An Organic Microcavity, Bin Liu, Vinod M. Menon, Matthew Y. Sfeir Jan 2021

Ultrafast Thermal Modification Of Strong Coupling In An Organic Microcavity, Bin Liu, Vinod M. Menon, Matthew Y. Sfeir

Advanced Science Research Center

There is growing interest in using strongly coupled organic microcavities to tune molecular dynamics, including the electronic and vibrational properties of molecules. However, very little attention has been paid to the utility of cavity polaritons as sensors for out-of-equilibrium phenomena, including thermal excitations. Here, we demonstrate that non-resonant infrared excitation of an organic microcavity system induces a transient response in the visible spectral range near the cavity polariton resonances. We show how these optical responses can be understood in terms of ultrafast heating of electrons in the metal cavity mirror, which modifies the effective refractive index and subsequently the strong …

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Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari Jan 2021

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

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Network Function Virtualization Technology Adoption Strategies, Abdlrazaq Ayodeji Adeyi Shittu Jan 2021

Network Function Virtualization Technology Adoption Strategies, Abdlrazaq Ayodeji Adeyi Shittu

Walden Dissertations and Doctoral Studies

Network function virtualization (NFV) is a novel system adopted by service providers and organizations, which has become a critical organizational success factor. Chief information officers (CIOs) aim to adopt NFV to consolidate and optimize network processes unavailable in conventional methods. Grounded in the diffusion of innovation theory (DOI), the purpose of this multiple case research study was to explore strategies chief information officers utilized to adopt NFV technology. Participants include two CIOs, one chief security information officer (CSIO), one chief technical officer (CTO), and two senior information technology (IT) executives. Data were collected through semi-structured telephone interviews and eight organizational …

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Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta Jan 2021

Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta

Electrical and Computer Engineering Faculty Publications

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

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Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis Jan 2021

Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis

Williams Honors College, Honors Research Projects

Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …

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Obstructive Wiring Patterns To Circular Planarity In Electrical Networks, Hannah Lebo Jan 2021

Obstructive Wiring Patterns To Circular Planarity In Electrical Networks, Hannah Lebo

Williams Honors College, Honors Research Projects

In order for an electrical network to be printed on a flat surface without changing the network’s input or output, it is important to consider if any wires will cross and if this problem can be avoided. If a circular network can be printed so that no wires cross, the network is said to be circular planar. In this paper, we identify a number of wiring patterns that make circular planarity impossible. We find exactly 3 wiring patterns using circular pairs with sets of two nodes, and we find exactly 78 wiring patterns using circular pairs with sets of three …

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Electroosmotic Flow Of Viscoelastic Fluid Through A Constriction Microchannel, Jianyu Ji, Shizhi Qian, Zhaohui Liu Jan 2021

Electroosmotic Flow Of Viscoelastic Fluid Through A Constriction Microchannel, Jianyu Ji, Shizhi Qian, Zhaohui Liu

Mechanical & Aerospace Engineering Faculty Publications

Electroosmotic flow (EOF) has been widely used in various biochemical microfluidic applications, many of which use viscoelastic non-Newtonian fluid. This study numerically investigates the EOF of viscoelastic fluid through a 10:1 constriction microfluidic channel connecting two reservoirs on either side. The flow is modelled by the Oldroyd-B (OB) model coupled with the Poisson–Boltzmann model. EOF of polyacrylamide (PAA) solution is studied as a function of the PAA concentration and the applied electric field. In contrast to steady EOF of Newtonian fluid, the EOF of PAA solution becomes unstable when the applied electric field (PAA concentration) exceeds a critical value for …

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Coordination, Adaptation, And Complexity In Decision Fusion, Weiqiang Dong Dec 2020

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 …

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Drone-Assisted Emergency Communications, Di Wu Dec 2020

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 …

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Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni Dec 2020

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 …

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Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari Dec 2020

Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2 × 2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the spatially-modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work we propose a conditional generative adversarial …

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Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru Dec 2020

Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and …

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Under Frequency Load Shedding Scheme For Islanded Distribution System Based On Mixed Integer Linear Programming, Sohail Sarwar Nov 2020

Under Frequency Load Shedding Scheme For Islanded Distribution System Based On Mixed Integer Linear Programming, Sohail Sarwar

Student Works (2020-2029)

In recent years, significant climate changes have pivoted the distribution system towards utilization of renewable energy, particularly through distributed generators (DGs). Although DGs offer many benefits, its integration affects the stability of the system, which could lead to blackout, when the grid is disconnected. The system frequency will drop drastically if DG’s generation capacity is less than the total load demand in the network. In order to sustain the system stability, Under Frequency Load Shedding (UFLS) is a commonly used technique to minimize the difference in load demand and power generation. However, existing load shedding techniques are lack in accurate …

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Fusion-Features And Visual-Dictionary Image Recognition Methods For Apple Classification In Smart Manufacturing, Ahsiah Ismail Nov 2020

Fusion-Features And Visual-Dictionary Image Recognition Methods For Apple Classification In Smart Manufacturing, Ahsiah Ismail

Student Works (2020-2029)

Smart manufacturing enables an efficient manufacturing process to optimize production. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this research, two image recognition feature extraction methods namely Curvelet Wavelet-Gray Level Co-occurrence Matrix (CWGLCM) and Fuzzy-Spatial Pyramid Matching (F-SPM) are proposed to provide reliable inputs for vision-based apple classification in smart manufacturing. Feature extraction is one of the major steps that could influent the efficiency of the manufacturing process. The CW-GLCM method is a feature extraction of fusion-features with Decision Tree classifier, while the F-SPM method uses a visual-dictionary based method to …

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New Metal Based Saturable Absorber For Ultrashort Pulse Generation, Muhammad Taufiq Ahmad Nov 2020

New Metal Based Saturable Absorber For Ultrashort Pulse Generation, Muhammad Taufiq Ahmad

Student Works (2020-2029)

Cost-efficient, robust and flexible laser systems are vital for a wide-range of applications ranging from high-precision material processing to biomedicine. This thesis aims to explore pure silver and gold based saturable absorbers (SAs) as Q-switcher and mode-locker in various fibre laser cavities. The generations of Q-switched pulses in 1-,1.55-, and 2-micron regions have been successfully demonstrated using a pure gold, which was deposited using electron beam deposition onto a PVA thin-film as SA. For instance, a stable Q-switched thulium-doped fibre laser (TDFL) operating at 1949 nm was successfully demonstrated with the gold SA. The proposed laser generates Q switched pulses …

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Cybersecurity Strategy Against Cyber Attacks Towards Smart Grids With Pvs, Fangyu Li, Maria Valero, Liang Zhao, Yousef Mahmoud Oct 2020

Cybersecurity Strategy Against Cyber Attacks Towards Smart Grids With Pvs, Fangyu Li, Maria Valero, Liang Zhao, Yousef Mahmoud

KSU Proceedings on Cybersecurity Education, Research and Practice

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data …

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Hu Aquaponics Monitoring And Control System : European Annual Edunet Conference 2020, Rachel L. Fogle, Glenn P. Williams, Josh R. Krug Oct 2020

Hu Aquaponics Monitoring And Control System : European Annual Edunet Conference 2020, Rachel L. Fogle, Glenn P. Williams, Josh R. Krug

Harrisburg University Presidential Research Grants

The functional purpose of the HU Aquaponics Monitoring and Control System Project is to develop an environmental and plant monitoring and control system for the HU Aquaponics Lab, located in the Student Union. The project involves the design and implementation of technology that will regularly take measurements from the environment (e.g., air temperature, water temperature, pH, dissolved oxygen, etc). PLCnext Technology will systematically collect, store, and web-publish the measurement data for HU researchers and the public to use for scientific research.

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Schottky And Poole-Frenkel Conduction Mechanisms In Zron/Sic System, Lusus Vanessa Sep 2020

Schottky And Poole-Frenkel Conduction Mechanisms In Zron/Sic System, Lusus Vanessa

Student Works (2020-2029)

In this work, the conduction mechanisms of Schottky Emission (SE) and Poole–Frenkel (PF) Emission for the ZrON/SiC system with a temperature range between 400 oC to 900 oC were analyzed. Understanding of the conduction charge mechanisms will help to estimate leakage characteristics of the ZrON/SiC system. Based on the J-E plot measured at different temperatures for 15 minutes, the obtained r2 was ranged between 0.90–0.98, which confirmed the data obtained complied with the standard SE model. Furthermore, the data plotted from the Effective Barrier Height Extracted from the SE plot showed the highest values of

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Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan Aug 2020

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 …

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Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi Aug 2020

Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Single-molecule localization microscopy (SMLM) techniques have become advanced bioanalytical tools by quantifying the positions and orientations of molecules in space and time at the nanoscale. With the noisy and heterogeneous nature of SMLM datasets in mind, we discuss leveraging particle-gradient flow 1) for quantifying the accuracy of localization algorithms with and without ground truth and 2) as a basis for novel, model-driven localization algorithms with empirically robust performance. Using experimental data, we demonstrate that overlapping images of molecules, a typical consequence of densely packed biological structures, cause biases in position estimates and reconstruction artifacts. To minimize such biases, we develop …

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