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Planar Ultra-Wideband Modular Antenna (Puma) Arrays For High-Volume Manufacturing On Organic Laminates And Bga Interfaces, James R. LaCroix 2022 University of Massachusetts Amherst

Planar Ultra-Wideband Modular Antenna (Puma) Arrays For High-Volume Manufacturing On Organic Laminates And Bga Interfaces, James R. Lacroix

Masters Theses

This work proposes wideband and broadband Planar Ultra-wideband Modular Antenna (PUMA) arrays designed to improve cost and reliability for high production volume commercial and military applications. The designs feature simplified PCB stack-ups with high dielectric constant (Dk) dimensionally stable materials to improve the manufacturing cost and yield. Additionally, the packages use ball grid array (BGA) interconnects, commonly used in digital electronics, for simple solder reflow integration with radio frequency (RF) electronics. While high Dk materials present practical manufacturing benefits, theoretical background will show how and why PUMA arrays lose frequency bandwidth and scan volume with high Dk materials. Further, a …


A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin 2022 The University of Texas at El Paso

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …


Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison 2022 Chapman University

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …


Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor 2022 University of Denver

Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor

Electrical and Computer Engineering: Faculty Scholarship

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …


Investigation Of Green Strawberry Detection Using R-Cnn With Various Architectures, Daniel W. Rivers 2022 California Polytechnic State University, San Luis Obispo

Investigation Of Green Strawberry Detection Using R-Cnn With Various Architectures, Daniel W. Rivers

Master's Theses

Traditional image processing solutions have been applied in the past to detect and count strawberries. These methods typically involve feature extraction followed by object detection using one or more features. Some object detection problems can be ambiguous as to what features are relevant and the solutions to many problems are only fully realized when the modern approach has been applied and tested, such as deep learning.

In this work, we investigate the use of R-CNN for green strawberry detection. The object detection involves finding regions of interest (ROIs) in field images using the selective segmentation algorithm and inputting these regions …


Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead 2022 Chapman University

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …


A Neural Network Based Proportional Hazard Model For Iot Signal Fusion And Failure Prediction, Yuxin Wen, Xingxin Guo, Junbo Son, Jianguo Wu 2022 Chapman University

A Neural Network Based Proportional Hazard Model For Iot Signal Fusion And Failure Prediction, Yuxin Wen, Xingxin Guo, Junbo Son, Jianguo Wu

Engineering Faculty Articles and Research

Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this paper, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and …


Eye-Gaze-Controlled Hmds And Mfd For Military Aircraft, LRD Murthy, Abhishek Mukhopadhyay, Somnath Arjun, Varshith Yelleti, Peter Thomas, Dilli Babu Mohan, Pradipta Biswas 2022 Indian Institute of Science

Eye-Gaze-Controlled Hmds And Mfd For Military Aircraft, Lrd Murthy, Abhishek Mukhopadhyay, Somnath Arjun, Varshith Yelleti, Peter Thomas, Dilli Babu Mohan, Pradipta Biswas

Journal of Aviation Technology and Engineering

Eye-gaze-controlled interfaces allow the direct manipulation of a graphical user interface by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots’ hands are occupied with flying the aircraft. This paper reports studies on analyzing the accuracy of eye-gaze-controlled interfaces inside aircraft undertaking representative flying missions. We report that using eye-gaze-controlled interfaces, pilots can undertake representative pointing and selection tasks at less than two seconds on average in a transport aircraft. Further, we analyzed the accuracy of eye-gaze-tracking glasses under various G load factors and analyzed the failure modes. We …


A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran 2022 University of Petroleum and Energy Studies

A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran

Electrical and Computer Engineering Faculty Publications

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …


Sustainable Computing - Without The Hot Air, Noman Bashir, David irwin, Prashant Shenoy, Abel Souza 2022 University of Massachusetts Amherst

Sustainable Computing - Without The Hot Air, Noman Bashir, David Irwin, Prashant Shenoy, Abel Souza

Publications

The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. Yet, after decades of research, further improving energy-efficiency is becoming increasingly challenging, as it is already highly optimized. As a result, at some point, increases in computing demand are likely to outpace increases in its energy-efficiency, potentially by a wide margin. Such exponential growth, if left unchecked, will position computing as a substantial contributor to global carbon emissions. While prominent technology companies have recognized the problem and sought to …


Investigating The Capacitive Properties Of All-Inorganic Lead Halides Perovskite Solar Cells Using Energy Band Diagrams, Zahraa Ismail, eman Farouk Sawires, Fathy Zaki Amer, Sameh Osama Abdellatif Dr 2022 Helwan University

Investigating The Capacitive Properties Of All-Inorganic Lead Halides Perovskite Solar Cells Using Energy Band Diagrams, Zahraa Ismail, Eman Farouk Sawires, Fathy Zaki Amer, Sameh Osama Abdellatif Dr

Electrical Engineering

Capacitance response of perovskite solar cells (PSCs) can be oppressed to deduce underlying physical mechanisms, both in the materials at external interfaces and in bulk materials. Accordingly, this paper investigates the Capacitance-Voltage (C-V) characteristic curves of cesium lead halides (CsPbX3: X = I, Br, or Cl) used as an active layer in PSCs. The SCAPS-1D simulator was used to harness the actual device (CsPbX3: X = I Br, or Cl) with material parameters from previous experimental work. The energy-band diagrams, J-V curves, and C-V curves of the three PSC structures were constructed and compared to carry out and investigate their …


Study The C-V Behavior Of Cesium-Lead Halides Perovskite Solar Cells Under Various Simulation Parameters, Zahraa Ismail, Eman Farouk Sawires, Fathy Zaki Amer, Sameh O. Abdellatif Dr 2022 Helwan University

Study The C-V Behavior Of Cesium-Lead Halides Perovskite Solar Cells Under Various Simulation Parameters, Zahraa Ismail, Eman Farouk Sawires, Fathy Zaki Amer, Sameh O. Abdellatif Dr

Electrical Engineering

Capacitance response of perovskite solar cells (PSCs) can be oppressed to deduce underlying physical mechanisms, both in the materials at external interfaces and in bulk materials. Accordingly, this paper investigates the Capacitance-Voltage (C-V) characteristic curves of cesium lead halides (CsPbX3: X = I, Br, or Cl) used as an active layer in PSCs. The SCAPS-1D simulator harnessed the actual device (CsPbX3: X = I Br, or Cl) with material parameters from previous experimental work. Three main simulation parameters were investigated: the thickness of the active layer, the doping, and the defects impacts.


Enhanced Study Of Complex Systems By Unveiling Hidden Symmetries With Dynamical Visibility, Nhat Vu Minh Nguyen 2022 Eastern Washington University

Enhanced Study Of Complex Systems By Unveiling Hidden Symmetries With Dynamical Visibility, Nhat Vu Minh Nguyen

2022 Symposium

One of the great challenges in complex and chaotic dynamics is to reveal its deterministic structures. These temporal dynamical structures are sometimes a consequence of hidden symmetries. Detecting and understanding them can allow the study of complex systems even without knowing the full underlying mathematical description of the system. Here we introduce a new technique, called Dynamical Visibility, that quantifies temporal correlations of the dynamics based upon some symmetry conditions. This visibility measures the departure of the dynamics from internal symmetries. We apply this technique to well-known chaotic systems, such as the logistic map and the circle map, as well …


Ai-Driven Automated Medical Imaging Analysis, Jingya Liu 2022 CUNY City College

Ai-Driven Automated Medical Imaging Analysis, Jingya Liu

Dissertations and Theses

Medical imaging has been applied widely in many clinical diagnoses to detect and differentiate abnormalities by revealing the internal structure of the human body at normal anatomical and physiological levels. Manual analyzing medical images demands attention and is time-consuming, requiring well-trained expertise. The speed, fatigue, and experience may limit the diagnostic performance, leading to delays and even false diagnoses that significantly impact patient treatment. Therefore, accurate systematic systems based on medical image analysis are crucial for timely clinical diagnosis.

This dissertation focuses on advancing automatic computer-aided diagnosis systems to detect cancer, assisting radiologists with early intervention to improve survival rates. …


Learning Approach For Fast Approximate Matrix Factorizations, Haiyan Yu 2022 University of Denver

Learning Approach For Fast Approximate Matrix Factorizations, Haiyan Yu

Electronic Theses and Dissertations

Efficiently computing an (approximate) orthonormal basis and low-rank approximation for the input data X plays a crucial role in data analysis. One of the most efficient algorithms for such tasks is the randomized algorithm, which proceeds by computing a projection XA with a random projection matrix A of much smaller size, and then computing the orthonormal basis as well as low-rank factorizations of the tall matrix XA. While a random matrix A is the de facto choice, in this work, we improve upon its performance by utilizing a learning approach to find an adaptive projection matrix A from a set …


Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei 2022 Michigan Technological University

Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei

Dissertations, Master's Theses and Master's Reports

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.

Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …


Use Of Battery Systems For Var Support In Con Edison’S Distribution Network/Substation, Elihu Nyemah 2022 CUNY City College

Use Of Battery Systems For Var Support In Con Edison’S Distribution Network/Substation, Elihu Nyemah

Dissertations and Theses

Battery Energy Storage System (BESS) can facilitate the integration of Distributed Energy Resources (DER) and help create a more reliable grid by providing multiple services including reactive power (VAR) support. This research will investigate the use of smart inverters to provide VAR support, assess the impact it has on the lifetime of a BESS and determine how the adverse effects (if any) can be mitigated/eliminated. To achieved this, a 7.5MW/30MWh grid connected BESS located at Con Edison substations have been modeled in MATLAB/Simulink. Preliminary assessment of the system showed that DC current to/from the battery is oscillating (non-zero) during reactive …


Multimodal Adversarial Learning, Uche Osahor 2022 West Virginia University

Multimodal Adversarial Learning, Uche Osahor

Graduate Theses, Dissertations, and Problem Reports

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models …


Smart City Management Using Machine Learning Techniques, Mostafa Zaman 2022 Virginia Commonwealth University

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …


"Demeter" Soil Monitoring System, Ryan Matthews, Rachel Rummer, Temilolu Fayomi, Alex Fuller 2022 The University of Akron

"Demeter" Soil Monitoring System, Ryan Matthews, Rachel Rummer, Temilolu Fayomi, Alex Fuller

Williams Honors College, Honors Research Projects

The purpose of this project is to develop a soil monitoring system that can remotely sense and relay soil conditions back to a user. The deMETER soil probe, Demeter is the Greek goddess of the harvest, is designed to aid hobbyist gardeners, small-scale farms, and nurseries to monitor their dynamic soil conditions and maximize their harvest. The probe is a self-powered system that can monitor the moisture and essential nutrients of the soil profile to determine which areas should receive water and fertilizer. This would significantly cut water and fertilizer waste. The solution will include an embedded system with sensors …


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