Digital Commons Network^™

Autoplug: An Automated Metadata Service For Smart Outlets, Lurdh Pradeep Reddy Ambati

Masters Theses

Low-cost network-connected smart outlets are now available for monitoring, controlling, and scheduling the energy usage of electrical devices. As a result, such smart outlets are being integrated into automated home management systems, which remotely control them by analyzing and interpreting their data. However, to effectively interpret data and control devices, the system must know the type of device that is plugged into each smart outlet. Existing systems require users to manually input and maintain the outlet metadata that associates a device type with a smart outlet. Such manual operation is time-consuming and error-prone: users must initially inventory all outlet-to-device mappings, …

Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification, Xinyu Song

McKelvey School of Engineering Theses & Dissertations

Hearing loss is a critical public health concern, affecting hundreds millions of people worldwide and dramatically impacting quality of life for affected individuals. While treatment techniques have evolved in recent years, methods for assessing hearing ability have remained relatively unchanged for decades. The standard clinical procedure is the modified Hughson-Westlake procedure, an adaptive pure-tone detection task that is typically performed manually by audiologists, costing millions of collective hours annually among healthcare professionals. In addition to the high burden of labor, the technique provides limited detail about an individual’s hearing ability, estimating only detection thresholds at a handful of pre-defined pure-tone …

Compiler And Runtime Optimization Techniques For Implementation Scalable Parallel Applications, Zahra Khatami

LSU Doctoral Dissertations

The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided by a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and desired application scalability. These compiler techniques should consider both the static information gathered at compile time and dynamic analysis captured at runtime about the system to generate a safe parallel application. On the other hand, runtime information is often speculative. Solely relying on it doesn't guarantee …

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …

Lithography Hotspot Detection, Jea Woo Park

Dissertations and Theses

The lithography process for chip manufacturing has been playing a critical role in keeping Moor's law alive. Even though the wavelength used for the process is bigger than actual device feature size, which makes it difficult to transfer layout patterns from the mask to wafer, lithographers have developed a various technique such as Resolution Enhancement Techniques (RETs), Multi-patterning, and Optical Proximity Correction (OPC) to overcome the sub-wavelength lithography gap.

However, as feature size in chip design scales down further to a point where manufacturing constraints must be applied to early design phase before generating physical design layout. Design for Manufacturing …

A Novel Application Of Machine Learning Methods To Model Microcontroller Upset Due To Intentional Electromagnetic Interference, Rusmir Bilalic

Electrical and Computer Engineering ETDs

A novel application of support vector machines (SVMs), artificial neural networks (ANNs), and Gaussian processes (GPs) for machine learning (GPML) to model microcontroller unit (MCU) upset due to intentional electromagnetic interference (IEMI) is presented. In this approach, an MCU performs a counting operation (0-7) while electromagnetic interference in the form of a radio frequency (RF) pulse is direct-injected into the MCU clock line. Injection times with respect to the clock signal are the clock low, clock rising edge, clock high, and the clock falling edge periods in the clock window during which the MCU is performing initialization and executing the …

Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee

Electrical & Computer Engineering Theses & Dissertations

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …

A Data-Driven Approach To Cubesat Health Monitoring, Serbinder Singh

Master's Theses

Spacecraft health monitoring is essential to ensure that a spacecraft is operating properly and has no anomalies that could jeopardize its mission. Many of the current methods of monitoring system health are difficult to use as the complexity of spacecraft increase, and are in many cases impractical on CubeSat satellites which have strict size and resource limitations. To overcome these problems, new data-driven techniques such as Inductive Monitoring System (IMS), use data mining and machine learning on archived system telemetry to create models that characterize nominal system behavior. The models that IMS creates are in the form of clusters that …

Uncovering Exceptional Predictions Using Exploratory Analysis Of Second Stage Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Nowadays, algorithmic systems for making decisions are widely used to facilitate decisions in a variety of fields such as medicine, banking, applying for universities or network security. However, many machine learning algorithms are well-known for their complex mathematical internal workings which turn them into black boxes and makes their decision-making process usually difficult to understand even for experts. In this thesis, we try to develop a methodology to explain why a certain exceptional machine learned decision was made incorrectly by using the interpretability of the decision tree classifier. Our approach can provide insights about potential flaws in feature definition or …

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.

Models For Pedestrian Trajectory Prediction And Navigation In Dynamic Environments, Jeremy N. Kerfs

Master's Theses

Robots are no longer constrained to cages in factories and are increasingly taking on roles alongside humans. Before robots can accomplish their tasks in these dynamic environments, they must be able to navigate while avoiding collisions with pedestrians or other robots. Humans are able to move through crowds by anticipating the movements of other pedestrians and how their actions will influence others; developing a method for predicting pedestrian trajectories is a critical component of a robust robot navigation system. A current state-of-the-art approach for predicting pedestrian trajectories is Social-LSTM, which is a recurrent neural network that incorporates information about neighboring …

Data Driven Discovery Of Materials Properties., Fadoua Khmaissia

Electronic Theses and Dissertations

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning …

Strategies For Reducing Preventable Hospital Readmissions On Medicare Patients, Andres Patricio Garcia-Arce

USF Tampa Graduate Theses and Dissertations

The high expenditure of healthcare in the United States (U.S.) does not translate into better quality of care. Indeed, the U.S. healthcare system is recognized by its lack of efficiency and waste (which represents about 20% of the country’s healthcare expenses). Lack of coordination is one of the most referenced causes of waste in the U.S. healthcare system, and preventable hospital readmissions have been acknowledged to be evidence of poor coordination of care. In fiscal year 2013, the Centers for Medicare and Medicaid Services (CMS) established financial penalties for inpatient care reimbursements in hospitals with excessive readmissions. All the same, …

Forecasting Harmful Algal Blooms For Western Lake Erie Using Data Driven Machine Learning Techniques, Nicholas L. Reinoso

ETD Archive

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, …

Parkinson's Symptoms Quantification Using Wearable Sensors, Md Nafiul Alam

Theses and Dissertations

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, …

Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon

Electronic Theses and Dissertations

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …

Bruise Detection In Apples Using 3d Infrared Imaging And Machine Learning Technologies, Zilong Hu

Dissertations, Master's Theses and Master's Reports

Bruise detection plays an important role in fruit grading. A bruise detection system capable of finding and removing damaged products on the production lines will distinctly improve the quality of fruits for sale, and consequently improve the fruit economy. This dissertation presents a novel automatic detection system based on surface information obtained from 3D near-infrared imaging technique for bruised apple identification. The proposed 3D bruise detection system is expected to provide better performance in bruise detection than the existing 2D systems.

We first propose a mesh denoising filter to reduce noise effect while preserving the geometric features of the meshes. …