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Articles 211 - 240 of 699

Full-Text Articles in Electrical and Electronics

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel Jan 2020

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

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Saperi: Approaching Gender Gap Using Spatial Ability Training Week In High-School Context, Maria Giulia Ballatore, Gavin Duffy, Sheryl Sorby, Anita Tabacco Jan 2020

Saperi: Approaching Gender Gap Using Spatial Ability Training Week In High-School Context, Maria Giulia Ballatore, Gavin Duffy, Sheryl Sorby, Anita Tabacco

Articles

Maria Giulia Ballatore, Gavin Duffy, Sheryl Sorby, and Anita Tabacco. 2020. SAperI: approaching gender gap using Spatial Ability training week in high-school context. In Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’20), October 21–23, 2020, Salamanca, Spain. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3434780.3436577

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Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan Jan 2020

Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan

All Graduate Theses, Dissertations, and Other Capstone Projects

The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …

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Design, Construction, And Characterization Of A Combined Mini-Co₂/Voc Sensor And Gas Chromatograph For Field Research, Rishi Basdeo, Michael Hampton Jan 2020

Design, Construction, And Characterization Of A Combined Mini-Co₂/Voc Sensor And Gas Chromatograph For Field Research, Rishi Basdeo, Michael Hampton

Digital Repository: Showcase of Undergraduate Research Excellence

No abstract provided.

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Recent Developments In The Pyscf Program Package, Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S. Blunt, Nikolay A. Bogdanov, George H. Booth, Jia Chen, Zhi-Hao Cui, Janus J. Eriksen, Yang Gao, Sheng Gun, Jan Hermann, Matthew R. Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D. Mcclain, Mario Motta, Bastien Mussard, Hung Q. Pham, Artem Pulkin, Wirawan Purwanto, Paul J. Robinson, Enrico Ronca, Elvira R. Sayfutyarova, Maximillian Scheurer, Henry F. Schurkus, James E.T. Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K. Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J. Williamson, Sebastian Wouters, Jun Yang, Jason M. Yu, Tianyu Zhu, Timothy C. Berkelbach, Sandeep Sharma, Alexander Yu Sokolov, Garnet Kin-Lic Chan Jan 2020

Recent Developments In The Pyscf Program Package, Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S. Blunt, Nikolay A. Bogdanov, George H. Booth, Jia Chen, Zhi-Hao Cui, Janus J. Eriksen, Yang Gao, Sheng Gun, Jan Hermann, Matthew R. Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D. Mcclain, Mario Motta, Bastien Mussard, Hung Q. Pham, Artem Pulkin, Wirawan Purwanto, Paul J. Robinson, Enrico Ronca, Elvira R. Sayfutyarova, Maximillian Scheurer, Henry F. Schurkus, James E.T. Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K. Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J. Williamson, Sebastian Wouters, Jun Yang, Jason M. Yu, Tianyu Zhu, Timothy C. Berkelbach, Sandeep Sharma, Alexander Yu Sokolov, Garnet Kin-Lic Chan

University Administration Publications

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and …

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Developing A Uas-Deployable Methane Sensor Using Low-Cost Modular Open-Source Components, Gavin Demali Jan 2020

Developing A Uas-Deployable Methane Sensor Using Low-Cost Modular Open-Source Components, Gavin Demali

Williams Honors College, Honors Research Projects

This project aimed to develop a methane sensor for deployment on an unmanned aerial system (UAS), or drone, platform. This design is centered around low cost, commercially available modular hardware components and open source software libraries. Once successfully developed, this system was deployed at the Bath Nature Preserve in Bath Township, Summit County Ohio in order to detect any potential on site fugitive methane emissions in the vicinity of the oil and gas infrastructure present. The deliverables of this project (i.e. the data collected at BNP) will be given to the land managers there to better inform future management and …

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Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch Jan 2020

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

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Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel Jan 2020

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

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Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari Jan 2020

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

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Synthesis Of Graphene Using Plasma Etching And Atmospheric Pressure Annealing: Process And Sensor Development, Andrew Robert Graves Jan 2020

Synthesis Of Graphene Using Plasma Etching And Atmospheric Pressure Annealing: Process And Sensor Development, Andrew Robert Graves

Graduate Theses, Dissertations, and Problem Reports (ETD)

Having been theorized in 1947, it was not until 2004 that graphene was first isolated. In the years since its isolation, graphene has been the subject of intense, world-wide study due to its incredibly diverse array of useful properties. Even though many billions of dollars have been spent on its development, graphene has yet to break out of the laboratory and penetrate mainstream industrial applications markets. This is because graphene faces a ‘grand challenge.’ Simply put, there is currently no method of manufacturing high-quality graphene on the industrial scale. This grand challenge looms particularly large for electronic applications where the …

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Resource Efficient Design Of Quantum Circuits For Cryptanalysis And Scientific Computing Applications, Edgard Munoz-Coreas Jan 2020

Resource Efficient Design Of Quantum Circuits For Cryptanalysis And Scientific Computing Applications, Edgard Munoz-Coreas

Theses and Dissertations--Electrical and Computer Engineering

Quantum computers offer the potential to extend our abilities to tackle computational problems in fields such as number theory, encryption, search and scientific computation. Up to a superpolynomial speedup has been reported for quantum algorithms in these areas. Motivated by the promise of faster computations, the development of quantum machines has caught the attention of both academics and industry researchers. Quantum machines are now at sizes where implementations of quantum algorithms or their components are now becoming possible. In order to implement quantum algorithms on quantum machines, resource efficient circuits and functional blocks must be designed. In this work, we …

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Dynamic Allocation/Reallocation Of Dark Cores In Many-Core Systems For Improved System Performance, Xingxing Huang, Xiaohang Wang, Yingtao Jiang, Amit Kumar Singh, Mei Yang Jan 2020

Dynamic Allocation/Reallocation Of Dark Cores In Many-Core Systems For Improved System Performance, Xingxing Huang, Xiaohang Wang, Yingtao Jiang, Amit Kumar Singh, Mei Yang

Electrical & Computer Engineering Faculty Research

A significant number of processing cores in any many-core systems nowadays and likely in the future have to be switched off or forced to be idle to become dark cores, in light of ever increasing power density and chip temperature. Although these dark cores cannot make direct contributions to the chip's throughput, they can still be allocated to applications currently running in the system for the sole purpose of heat dissipation enabled by the temperature gradient between the active and dark cores. However, allocating dark cores to applications tends to add extra waiting time to applications yet to be launched, …

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Photovoltage Enhancement For Stable Perovskite Solar Cells With A Temperature-Controlled Grain Growth Technique, Luis Eduardo Valerio Jan 2020

Photovoltage Enhancement For Stable Perovskite Solar Cells With A Temperature-Controlled Grain Growth Technique, Luis Eduardo Valerio

Open Access Theses & Dissertations

By performing strong characterizations methods, one can begin to fully understand the chemistry and composition behind a great performing perovskite solar cell. Understanding how the interaction between layers inside a solar cell is driven by the temperature and overall environment is a key element to improve the fabrication process and overall efficiency of such cells. This Thesis will present a study of the hybrid organic-inorganic, mixed-cation, mixed-halide, temperature and thickness-controlled perovskite solar cell. A constant power conversion efficiency (PCE) ranging between 15-17% and an open circuit voltage V¬oc above 1.05 V for a wide-band gap perovskite cell is presented.

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The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1 Dec 2019

The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1

Tanzania Journal of Engineering and Technology (TJET)

Worldwide, every day human beings generate millions of tons of Faecal Sludge (FS), which is rich in water, nutrients, energy, and organic compounds. Yet FS is not being managed in a way that permits us to derive value from its reuse, while at the same time, millions of farmers struggle with depleted soils and lack of water. In most of the developing countries, energy for cooking is mainly derived from cutting of trees, either as wood or charcoal. Resource recovery and reuse from FS can create livelihoods, enhance food security, support green economies, reduce waste and contribute to cost recovery …

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Blind Algorithm Development For Peak To Average Power Ratio Reduction In Ofdm Systems Under Frequency Selective Channels, Kwame S. Ibwe Dec 2019

Blind Algorithm Development For Peak To Average Power Ratio Reduction In Ofdm Systems Under Frequency Selective Channels, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

One major drawback of orthogonal frequency division multiplexing (OFDM) system is peak to average power ratio (PAPR). This effect causes high power amplifier (HPA) to introduce intermodulation and out of band radiation as the signal goes through, thus degrades the performance of OFDM systems. This paper proposes blind algorithms which takes advantage of signal transformation technique and signal distortion technique. Simulation results show that at complementary cumulative distribution function (CCDF) level of 10-3, the proposed algorithm achieved 3.2 dB PAPR improvement compared to discrete Fourier transform with interleaved frequency division multiple access (DFT-IFDMA) based algorithm. The bit error rate (BER) …

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Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park Dec 2019

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …

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Gamma-Ray Radiation Effects In Graphene-Based Transistors With H-Bn Nanometer Film Substrates, E. J. Cazalas, Michael R. Hogsed, S. R. Vangala, Michael R. Snure, John W. Mcclory Nov 2019

Gamma-Ray Radiation Effects In Graphene-Based Transistors With H-Bn Nanometer Film Substrates, E. J. Cazalas, Michael R. Hogsed, S. R. Vangala, Michael R. Snure, John W. Mcclory

Faculty Publications

Radiation effects on graphene field effect transistors (GFETs) with hexagonal boron nitride (h-BN) thin film substrates are investigated using 60Co gamma-ray radiation. This study examines the radiation response using many samples with varying h-BN film thicknesses (1.6 and 20 nm thickness) and graphene channel lengths (5 and 10 μm). These samples were exposed to a total ionizing dose of approximately 1 Mrad(Si). I-V measurements were taken at fixed time intervals between irradiations and postirradiation. Dirac point voltage and current are extracted from the I-V measurements, as well as mobility, Dirac voltage hysteresis, and the total number of GFETs that remain …

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Analog Implementation Of The Hodgkin-Huxley Model Neuron, Zachary D. Mobille, George H. Rutherford, Jordan Brandt-Trainer, Rosangela Follmann, Epaminondas Rosa Oct 2019

Analog Implementation Of The Hodgkin-Huxley Model Neuron, Zachary D. Mobille, George H. Rutherford, Jordan Brandt-Trainer, Rosangela Follmann, Epaminondas Rosa

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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Polarization Division Multiplexing For Optical Data Communications, Darko Ivanovich Aug 2019

Polarization Division Multiplexing For Optical Data Communications, Darko Ivanovich

McKelvey School of Engineering Graduate Student Theses & Dissertations

Multiple parallel channels are ubiquitous in optical communications, with spatial division multiplexing (separate physical paths) and wavelength division multiplexing (separate optical wavelengths) being the most common forms. In this research work, we investigate the viability of polarization division multiplexing, the separation of distinct parallel optical communication channels through the polarization properties of light. We investigate polarization division multiplexing based optical communication systems in five distinct parts. In the first part of the work, we define a simulation model of two or more linearly polarized optical signals (at different polarization angles) that are transmitted through a common medium (e.g., air), filtered …

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Energy Transformation And Conservation Investigation, Mike Jackson, Holly Haney Jul 2019

Energy Transformation And Conservation Investigation, Mike Jackson, Holly Haney

High School Lesson Plans

Students will use a thermoelectric generator module to analyze the relationship between thermal and electrical energies. Using data collection sensors and analysis software, students will investigate the relationship between the temperature gradient across a thermoelectric generator module and the resulting electrical potential. Students will then use their data and analysis to solve problems relating to waste thermal energy in electrical systems and communicate their work to their peers and teacher.

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Blind Separation For Intermittent Sources Via Sparse Dictionary Learning, Annan Dong May 2019

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 …

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Probabilistic Spiking Neural Networks : Supervised, Unsupervised And Adversarial Trainings, Alireza Bagheri May 2019

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 …

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Supercapacitors With Gate Electrodes, Tazima Selim Chowdhury May 2019

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 …

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A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari Mar 2019

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

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Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari Jan 2019

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

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Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai Jan 2019

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

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Autonomous Spectrum Enforcement : A Blockchain Approach, Maqsood Ahamed Abdul Careem Jan 2019

Autonomous Spectrum Enforcement : A Blockchain Approach, Maqsood Ahamed Abdul Careem

Legacy Theses & Dissertations (2009 - 2024)

A core limitation in existing wireless technologies is the scarcity of spectrum, to support the exponential increase in Internet-connected and multimedia-capable mobile devices and the increasing demand for bandwidth-intensive services. As a solution, Dynamic Spectrum Access policies are being ratified to promote spectrum sharing for various spectrum bands and to improve the spectrum utilization. This poses an equally challenging problem of enforcing these spectrum policies. The distributed and dynamic nature of policy violations necessitates the use of autonomous agents to implement efficient and agile enforcement systems. The design of such a fully autonomous enforcement system is complicated due to the …

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Exploring Gated Nanoelectronic Devices Fabricated From 1d And 2d Materials, Prathamesh A. Dhakras Jan 2019

Exploring Gated Nanoelectronic Devices Fabricated From 1d And 2d Materials, Prathamesh A. Dhakras

Legacy Theses & Dissertations (2009 - 2024)

One and two dimensional materials are being extensively researched toward potential application as ultra-thin body channel materials. The difficulty of implementing physical doping methods in these materials has necessitated various alternative doping schemes, the most promising of which is the electrostatic gating technique due to its reconfigurability. This dissertation explores the different fundamental devices that can be fabricated and characterized by taking advantage of the electrostatic gating of individual single-walled carbon nanotubes (SWNTs), dense SWNT networks and exfoliated 2D tungsten diselenide (WSe2) flakes.

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Straintronic Nanomagnetic Devices For Non-Boolean Computing, Md Ahsanul Abeed Jan 2019

Straintronic Nanomagnetic Devices For Non-Boolean Computing, Md Ahsanul Abeed

Theses and Dissertations

Nanomagnetic devices have been projected as an alternative to transistor-based switching devices due to their non-volatility and potentially superior energy-efficiency. The energy efficiency is enhanced by the use of straintronics which involves the application of a voltage to a piezoelectric layer to generate a strain which is ultimately transferred to an elastically coupled magnetostrictive nanomaget, causing magnetization rotation. The low energy dissipation and non-volatility characteristics make straintronic nanomagnets very attractive for both Boolean and non-Boolean computing applications. There was relatively little research on straintronic switching in devices built with real nanomagnets that invariably have defects and imperfections, or their adaptation …

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Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari Jan 2019

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

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