Other Electrical and Computer Engineering Commons^™

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Deep Learning And Polar Transformation To Achieve A Novel Adaptive Automatic Modulation Classification Framework, Pejman Ghasemzadeh

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal's most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture …

Towards Hybrid Quantum-Classical Ciphersuite Primitives, H. Shelton Jacinto

Boise State University Theses and Dissertations

With the dawn of quantum computing in scale, current secure classical primitives are at risk. Protocols with immediate risk of breach are those built on the advanced encryption standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms. To secure classical data against a quantum adversary, a secure communications ciphersuite must be developed. The ciphersuite developed in this work contains components that do not necessarily rely on quantum key distribution (QKD), due to recent insecurities found when a QKD-based protocol is faced with a quantum eavesdropper.

A set of quantum-classical ciphersuite primitives were developed using less common mathematical methods where a quantum adversary will …

Low-Cost Test And Characterization Platform For Memristors, Lyle Jones

Boise State University Theses and Dissertations

The electrical Testing and Characterization of the devices built under research conditions on silicon wafers, diced wafers, or package parts have hampered research since the beginning of integrated circuits. The challenges of performing electrical characterization on devices are to acquire useful and accurate data, the ease of use of the test platform, the portability of the test equipment, the ability to automate quickly, to allow modifications to the platform, the ability to change the configuration of the Device Under Test (DUT) or the Memristor Based Design (MBD), and to do this within budget. The devices that this research is focused …

Ensemble Malware Classification System Using Deep Neural Networks, Barath Narayanan Narayanan, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft's Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using …

Heuristic-Based Threat Analysis Of Register-Transfer-Level Hardware Designs, Wesley Layton Ellington

Electrical Engineering Theses and Dissertations

The development of globalized semiconductor manufacturing processes and supply chains has lead to an increased interest in hardware security as new types of hardware based attacks, called hardware Trojans, are being observed in industrial and military electronics. To combat this, a technique was developed to help analyze hardware designs at the register-transfer-level (RTL) and locate points of interest within a design that might be vulnerable to attack. This method aims to eventually enable the creation of an end-to-end design hardening solution that analyzes existing designs and suggests countermeasures for potential Trojan attacks. The method presented in this work uses a …

Supporting Self-Regulation Of Children With Adhd Using Wearables: Tensions And Design Challenges, Franceli L. Cibrian, Kimberley D. Lakes, Arya Tavakoulnia, Kayla Guzman, Sabrina Schuck, Gillian R. Hayes

Engineering Faculty Articles and Research

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch …

Comparison Of Visual Simultaneous Localization And Mapping Methods For Fixed-Wing Aircraft Using Slambench2, Patrick R. Latcham

Theses and Dissertations

Visual Simultaneous Localization and Mapping (VSLAM) algorithms have evolved rapidly in the last few years, however there has been little research evaluating current algorithm's effectiveness and limitations when applied to tracking the position of a fixed-wing aerial vehicle. This research looks to evaluate current monocular VSLAM algorithms' performance on aerial vehicle datasets using the SLAMBench2 benchmarking suite. The algorithms tested are MonoSLAM, PTAM, OKVIS, LSDSLAM, ORB-SLAM2, and SVO, all of which are built into the SLAMBench2 software. The algorithms' performance is evaluated using simulated datasets generated in the AftrBurner Engine. The datasets were designed to test the quality of each …

Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi

Engineering Faculty Articles and Research

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …

Effects Of Long-Term Evolution Waveform On Synthetic Aperture Radar Image Quality Metrics, Blake M. Colson

Theses and Dissertations

As a greater demand by the private sector for bandwidth drives spectrum allocations away from defense, new methods for coexistence in the spectrum are being explored. One of the prominent areas in defense for this coexistence is passive radar. This mode of radar system allows for data collection by referencing signals already established in the environment of interest. Some of the most prolific signals currently available are those used for mobile communication networks. In particular, Long- Term Evolution (LTE) is a common waveform that could be leveraged for discrete collection of image intelligence. Seeking to build a base of knowledge, …

Electricity Price Forecasting Using A Convolutional Neural Network, Elliott Winicki

Master's Theses

Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional …

Dual-Axis Solar Tracker, Bryan Kennedy

All Undergraduate Projects

Renewable energies, and fuels that are not fossil fuel-based, are one of the prolific topics of debate in modern society. With climate change now becoming a primary focus for scientists and innovators of today, one of the areas for the largest amount of potential and growth is that of the capturing and utilization of Solar Energy. This method involves using a mechanical system to track the progression of the sun as it traverses the sky throughout the day. A dual-axis solar tracker such as the one designed and built for this project, can follow the sun both azimuthally and in …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Application Of Quantum Cryptography To Cybersecurity And Critical Infrastructures In Space Communications, Rita Meraz, Linda Vahala

OUR Journal: ODU Undergraduate Research Journal

As society becomes more dependent on technology and the internet, critical infrastructure, which provides the fundamental services that millions of people depend on, becomes more vulnerable to cyber threats. This paper presents the importance of cybersecurity in critical infrastructure addressing the communications sector which is prevailed by space systems. It gives an overview of laser communications via satellite, and it argues the utility that quantum cryptography can provide to secure the data transmitted between communication satellites and ground stations from cyber attacks. Common encryption algorithms are briefly introduced as well as a review on quantum computing. Quantum cryptography is still …

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 …

Accurate Range-Based Indoor Localization Using Pso-Kalman Filter Fusion, Paul Bupe Jr

College of Graduate Studies: Theses & Dissertations

Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion …

Analysis Of Antenna Designs For The Maximum Power Transmission, Lauryn P. Smith

College of Graduate Studies: Theses & Dissertations

Since Nikola Tesla discovered wireless power transmission, it has become a very interesting topic of study in the antennas and wireless propagation community. Various aspects and applications for wireless power transmission are studied today, a few of which are investigated in this work. First, various antenna geometries are analyzed for radiative near-field wireless power transfer in terms of electrical field strength. It is determined that the meander antenna is ideal for maximum power transfer in its radiative near-field region, contrary to its far-field behavior. Next, in the application of radio frequency identification, a directive, UHF RFID tag antenna is designed …

Reliable Navigation For Suas In Complex Indoor Environments, Andrew J. Fabian

Theses and Dissertations

Indoor environments are a particular challenge for Unmanned Aerial Vehicles (UAVs). Effective navigation through these GPS-denied environments require alternative localization systems, as well as methods of sensing and avoiding obstacles while remaining on-task. Additionally, the relatively small clearances and human presence characteristic of indoor spaces necessitates a higher level of precision and adaptability than is common in traditional UAV flight planning and execution. This research blends the optimization of individual technologies, such as state estimation and environmental sensing, with system integration and high-level operational planning.

The combination of AprilTag visual markers, multi-camera Visual Odometry, and IMU data can be used …

Deep Learning Towards Intelligent Vehicle Fault Diagnosis, Mohammed Al-Zeyadi, Javier Andreu-Perez, Hani Hagras, Chris Royce, Darren Smith, Piotr Rzonsowski, Ali Malik

Conference papers

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, …