Engineering Commons^™

Development Of A Fish Robot Equipped With Novel 3d-Printed Soft Bending Actuators, Steven Steele, Jorge Diaz Rodriguez, Sharun Sripathy, Turaj Ashuri, Saleh Gharaie, Yusun Chang, Amir Ali Amiri Moghadam

Symposium of Student Scholars

This paper reports on design and fabrication of a novel soft fish robot. Application of soft actuators for the fish tail will generates continuum bending motion which resembles the natural motion of the fish. However, most soft actuator mechanisms are complex and have low efficiency. Thus, to address this issue we have developed a 3D printed soft bending actuator which can be actuated with an electromotor. The basic design idea of the soft bending actuator is explained, and iteration of the design showed to create the desired motion for the soft tail. The soft actuator has been successfully integrated with …

Portable Diffuse Reflectance Spectroscopy For Non-Invasive And Quantitative Assessment Of The Parathyroid Glands Viability During Surgery, Mark Romine, Linh Luong, Alex Moazzen, Katie Cho, Paul Lee

Symposium of Student Scholars

Portable Diffuse Reflectance Spectroscopy for Non-invasive and Quantitative Assessment of the Parathyroid Glands Viability During Surgery

Mark Romine, Linh Luong, Alex Moazzen, Katie Cho and Paul Lee

The parathyroid glands (PTGs) are responsible for the regulation of calcium levels in the blood by secreting a parathyroid hormone. This parathyroid hormone then regulates the body’s absorption, storage, and secretion of calcium, which can directly affect the way muscles and nerves operate. PTGs are often at risk of damage, or accidental removal during thyroid surgeries, because it is challenging to identify PTGs and to determine their viability. Current methods of visual inspections …

A Study Of Iot-Optimized Low Power Asset Tracking With Cloud-Enabled Lorawan, Fatima Salman

Symposium of Student Scholars

The world of technology is expanding very quickly today, including technologies like cloud-based asset monitoring, but this makes it difficult to keep up with this technology's development and many other things. It is possible to monitor and manage your assets remotely with a cloud-based system thanks to its many features. The lifecycle of any commodity, including inventory, machinery, vehicles, and real estate, can be tracked using this kind of cloud-based system. Wide-area networks can be used to send data with the aid of low-power wide-area network (LPWAN) technologies like LoRa, SigFox, and NB-IoT. This project will examine traditional, cloud-based, LPWAN-based …

Wireless, Handheld Diffuse Reflectance Spectroscopy To Quantify Tissue Microvascular Hemodynamics, Linh Luong, Alex Moazzen, Mark Romine, Katie Cho, Paul Lee

Symposium of Student Scholars

Diffuse Reflectance Spectroscopy (DRS) is a non-invasive optical method to characterize tissue optical properties for disease diagnosis and health monitoring. Two optical fibers are often used in a DRS system: one to deliver light to the tissue and the other to gather diffuse reflectance spectra, which provide quantitative details about the structure and composition of the tissue. The conventional DRS system, however, is expensive, bulky, and composed of fragile optical fibers and multiple electrical connections. Here we propose to build a wireless, handheld, and fiber-less diffuse optical spectroscopy system. Unfortunately, the diffusion approximation utilized for data analysis of the conventional …

A Study Of Deep Neural Networks In The Application Of Distracted Driving Detection, Wesley M. Heikes

ATU Scholars Symposium

As the world continues to become more technologically advanced, distracted driving will continue to be a growing danger to the public. Convolutional neural networks can be used to monitor driving and differentiate distracted driving from safe driving. A popular distracted driving dataset created by State Farm called the Distracted Driver Dataset can be trained with the Auto-Keras model API. Auto-Keras is a system that taylors a machine learning model to fit a given dataset. While experienced neural network designers can create neural networks to produce incredibly accurate results, Auto-Keras gives those with less expertise a method of designing a network …

Kayak Automated Steering, Nick Gilbert, Emily Trotter, David Asimbaya, Jorge Garcia, Natalie Nyatsanza

ATU Scholars Symposium

The problem presented is to automate a fishing kayak to make traveling long distances more manageable while maintaining the flexibility, portability, and size of the kayak without making major modifications to the already working item. The proposed design is aimed to solve the steering issues that a fixed seat in a kayak may have and the consistent paddling by supporting them with an automated system. The device is not meant to interfere with the system's manual operation but to provide a sense of aid when needed.

Premium Single Phase Motor For Abb Group, Daniel Johnson, Sky Friar, Jesse Willer

ATU Scholars Symposium

ABB Group is in search of a next generation premium single phase motor. An integrated drive paired to a three phase motor will allow the company to take advantage of current three phase motor automation capacity while presenting the end user with a simple, ready to use motor operating off of single phase power. This project is the design of such an integrated drive.

The drive will accept 115/230 Vac, single phase supply power and send it through a full bridge rectifying, voltage doubling circuit. This circuit will rectify the alternating current to direct current and increase the voltage to …

Distracted Driving Detection System, Tristan Caja, Lizzi Riney, Chance Gregurek, Wesley M. Heikes

ATU Scholars Symposium

In 2020, distracted driving claimed 3,142 lives in the United States[1]. Using current technology, our project aims to prototype a device that uses computer vision to crack down on this dangerous behavior while working with insurance agencies that provide incentives for customers to adopt the device. At the heart of this project is a deep learning algorithm trained to classify images on a set of 10 classes: safe driving, texting - right, talking on the phone - right, texting - left, talking on the phone - left, operating the radio, drinking, reaching behind, hair and makeup, and talking to passenger. …

Functional Integrated Storage Housing, Thomas Dodds, Andrew L. Hilsdon, Alexander J. Holland, Vana Ducusin, Christopher Dew

ATU Scholars Symposium

The Corley lab director asked us to design and build a shelf for the robotics lab to help store parts and help organize the lab. The group decided on an electronic vertical rotating shelf design consisting of groups of bins on shelves. The unit is able to store and sort through thousands of pounds of parts and keep track of them using an computerized inventory system to streamline the operator’s workflow and improve efficiency.

Alzheimers Disease Stages Are Classified Based On Biomarkers Found In Cerebrospinal Fluid Using Machine Learning Classifiers, Vivek K. Tiwari

Electrical Engineering Theses

The inability of the current techniques to identify patients in the preclinical stage of Alzheimer's disease, which can persist up to ten years before clinical symptoms appear, makes early detection of the condition difficult. Several studies have shown the potential of the cerebrospinal fluid biomarkers amyloid beta 1-42, T-tau, and P-tau in the early stages of Alzheimer's disease. Based on the levels of these cerebrospinal fluid biomarkers, we employed machine learning models in this study to categorize various phases of Alzheimer's disease. The National Alzheimer's Coordination Centre database of 537 patients' electronic health records was examined, and the patients were …

Discriminating Wirelesshart Communication Devices Using Sub-Nyquist Stimulated Responses, Jeffrey D. Long, Michael A. Temple, Christopher M. Rondeau

Faculty Publications

Reliable detection of counterfeit electronic, electrical, and electromechanical devices within critical information and communications technology systems ensures that operational integrity and resiliency are maintained. Counterfeit detection extends the device’s service life that spans manufacture and pre-installation to removal and disposition activity. This is addressed here using Distinct Native Attribute (DNA) fingerprinting while considering the effects of sub-Nyquist sampling on DNA-based discrimination. The sub-Nyquist sampled signals were obtained using factor-of-205 decimation on Nyquist-compliant WirelessHART response signals. The DNA is extracted from actively stimulated responses of eight commercial WirelessHART adapters and metrics introduced to characterize classifier performance. Adverse effects of sub-Nyquist decimation …

Analysis And Model Of Sensor-Less Modified Direct Torque Control Surface Permanent Magnet Synchronous Machine For Electrical Submersible Pumping Applications, Mulu Woldeyohannes

USF Tampa Graduate Theses and Dissertations

This dissertation examines a novel sensor-less Direct Torque Control (DTC) strategy for Electrical Submersible Pump (ESP) systems using Surface Mounted Permanent Magnet Synchronous Motors (SPMSM) that are used for oil and gas production. As oil and gas are the two largest fuels in use today to generate energy, the technologies to improve efficiency, increase reliability and reduce carbon footprint are essential. SPMSM is one of the main motor topologies in use to improve the reliability and efficiency of ESP systems. Due to the absence of damper winding, SPMSM cannot be started using Direct On-Line (DOL) control. Instead, Variable Speed Drives …

Region-Specified Inverse Design Of Absorption And Scattering In Nanoparticles By Using Machine Learning, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Automatic Generation Of Virtual Work Guide For Complex Procedures: A Case, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Practical work guides for complex procedures are significant and highly affect the efficiency and accuracy of on-site users. This paper presents a technique to generate virtual work guides automatically for complex procedures. Firstly, the procedure information is extracted from the electronic manual in PDF format. And then, the extracted procedure steps are mapped to the virtual model parts in preparation for animation between adjacent steps. Next, smooth animations of the procedure are generated based on a 3D natural cubic spline curve to improve the spatial ability of the work guide. In addition, each step's annotation is automatically adjusted to improve …

Implementation Of Static Rfid Landmarks In Slam For Planogram Compliance, Brennan L. Drake

Honors College Theses

Autonomous robotic systems are becoming increasingly prevalent in everyday life and exhibit robust solutions in a wide range of applications. They face many obstacles with the foremost of which being SLAM, or Simultaneous Localization and Mapping, that encompasses both creation of the map of an unknown environment and localization of the robot in said environment. In this experiment, researchers propose the use of RFID tags in a semi-dynamic commercial environment to provide concrete landmarks for localization and mapping in pursuit of increased locational certainty. With this obtained, the ultimate goal of the research is to construct a robotics platform for …

Counterventions: A Reparative Reflection On Interventionist Hci, Rua Mae Williams, Louanne E. Boyd, Juan E. Gilbert

Engineering Faculty Articles and Research

Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a “counterventional approach” to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that …

From Point Estimates To Predictive Distributions In Machine Learning Models - A Statistical Importance Sampling Framework, Giuseppina Carannante

Theses and Dissertations

In this thesis, we leverage powerful statistical frameworks for optimal sequential estimation and tracking in non-linear and non-Gaussian dynamical models, which enjoy proven (asymptotic) optimality properties. Initially, we build upon our previous work, which employed first-order Taylor series approximation to propagate the first two predictive moments, to derive Bayesian encoder-decoder networks. This work introduced the notion of dense, pixel-level uncertainty map that is crucial in fields, such as autonomous vehicles and medical segmentation. We then extended the Bayesian framework to an ensembling scheme based on ensemble Kalman Filtering (EnKF). While EnKF represents the predictive distribution with an ensemble of draws, …

Stretchable Sensors For Soft Robotic Grippers In Edge-Intelligent Iot Applications, Prosenjit Kumar Ghosh, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated …

To Study Heart Rate Dynamics Of Preterm Infants Using Lstm Based Deep Learning Model, Soniya Poudel Chhetri

Electrical Engineering Theses

Babies born prematurely are more likely to experience serious medical problems called Bradycardia, where their heart rate drops below 100 beats per minute(bpm). Bradycardia can decrease cerebral blood velocities by 10-50% from their normal level [6], potentially harming the infant's developing brain and other vital organs. Healthcare professionals working in Neonatal Intensive Care Units (NICUs) face challenges evaluating the likelihood of these events occurring in preterm babies based on their physiological signals. Forecasting and tracking bradycardia events before time will help save thousands of preterm infants from losing their lives. While time series analysis of heart rate data is one …

Current Prospects And Challenges In Negative-Capacitance Field-Effect Transistors, Md. Sherajul Islam, Abdullah Al Mamun Mazumder, Changjian Zhou, Catherine Stampfl, Jeongwon Park, Cary Y. Yang

Electrical and Computer Engineering

For decades, the fundamental driving force behind energy-efficient and cost-effective electronic components has been the downward scaling of electronic devices. However, due to approaching the fundamental limits of silicon-based complementary metal-oxide-semiconductor (CMOS) devices, various emerging materials and device structures are considered alternative aspirants, such as negative-capacitance field-effect transistors (NCFETs), for their promising advantages in terms of scaling, speed, and power consumption. In this article, we present a brief overview of the progress made on NCFETs, including theoretical and experimental approaches, a current understanding of NCFET device physics, possible physical mechanisms for NC, and future functionalization prospects. In addition, in the …

3d Speaker Geometry Inference From Digital Video Using 2d Projective Geometry, Sebastian Alonso Janampa Rojas

Electrical and Computer Engineering ETDs

The thesis discusses the need for a 3D world model reconstruction from raw video frames using 2D projective geometry. We propose a computer-aided approach to reconstructing a 3D speaker geometry from classroom videos of students learning Python.

The proposed method uses a transformer model to detect line candidates. Once the users identify lines corresponding to three orthogonal directions, the method computes the three vanishing points and the camera matrix. The method identifies the student’s mouths based on face landmark detection. After the estimates of the projections of the students’ mouths on the table are verified by the users, the proposed …

Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings Of Distributions, Adam J. Thorpe

Electrical and Computer Engineering ETDs

Autonomous systems are increasingly being deployed in complex environments subject to real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible to compute a simple mathematical model of the system--for instance due to the presence of human elements, complex mechanics or system dynamics, or learning-enabled components. Data-driven control has recently gained significant attention in this area, where observations taken from the system evolution are used to compute an implicit representation of the system that is amenable to analysis and control. However, data-driven algorithms for control present new challenges, and require new insights to enable their use. The …

Evaluation Of The Dynamic Vision Sensor’S Photoreceptor Circuit For Infrared Event-Based Sensing, Zinah M. Alsaad

Electrical and Computer Engineering ETDs

For space surveillance applications, neuromorphic imaging is being studied as it may perform sensing and tracking tasks with less power and downstream datalink demand. The read-out of the event-based camera is made to only be sensitive to changes in the signals it receives from the photodetector, which results in a datastream of events indicating where and when changes in illumination occur. This is in contrast to the conventional framing camera, which produces images by essentially counting the electrons produced by light incident on each pixel’s photodetector. These cameras are commercially available with siliconbased detectors for applications involving visible wavelengths. However, …

Chance Constrained Stochastic Optimal Control Of Discrete Time Linear Stochastic Systems With Applications In Multi-Satellite Operations, Shawn Priore

Electrical and Computer Engineering ETDs

Stochastic disturbances arise in a variety of engineering applications. For tractability, Gaussian disturbances are often assumed. However, this may not always be valid, such as when a disturbance exhibits heavy-tailed or skewed phenomena. As autonomous systems become more ubiquitous, non-Gaussian disturbances will become more common due to the compounding effects of sensing, actuation, and external forces. Despite this, little has been done to develop formal methods that are both computationally efficient and allow for analytical assurances with non-Gaussian disturbances. Addressing convex polytopic set acquisition and non-convex collision avoidance chance constraints with quantile and moment-based reformulations, this dissertation proposes novel stochastic …

Long-Term Human Participation Detection Using A Dynamic Scene Analysis Model, Wenjing Shi

Electrical and Computer Engineering ETDs

The dissertation develops new methods for assessing student participation in long (>1 hour) classroom videos. First, the dissertation introduces the use of multiple image representations based on raw RGB images and AM-FM components to detect specific student groups. Second, a dynamic scene analysis model is developed for tracking under occlusion and variable camera angles. Third, a motion vector projection system identifies instances of students talking.

The proposed methods are validated using digital videos from the Advancing Out-of-school Learning in Mathematics and Engineering (AOLME) project. The proposed methods are shown to provide better group detection, and better talking detection at …

Reclaiming Fault Resilience And Energy Efficiency With Enhanced Performance In Low Power Architectures, Noel Daniel Gundi

Student Research Symposium

Shrinking technology node and the massive increase in data workloads has witnessed a swift migration of the system towards the Low-Power Computing (LPC) paradigm. Additionally, to accelerate the redundant yet mammoth AI instructions, novel ASIC design architectures have been explored. Google’s Tensor Processing Unit (TPU) is one such architectural innovation deployed in the commercial space to speedup the processing of AI workloads. In an effort to achieve a superior energy efficiency, Near-Threshold Computing (NTC) has been marginalized to be an efficient LPC paradigm. Due to an underscaling of voltage, NTC offers quadratic savings is power consumption in comparison to operating …

Active Energy Management Of Hybrid Lithium-Ion Batteries For Electric Vehicle Applications, Marium Rasheed

Student Research Symposium

Energy storage systems that incorporate hybrid Lithium-ion (Li-ion) battery packs leveraging the energy density and power density characteristics of different Li-ion chemistries have shown promise in reducing weight and volume while improving overall lifetime. A capacitively-coupled architecture, composite hybrid energy storage system (CHESS), has been proposed and demonstrated as a viable design for hybrid energy storage systems that combine energy-dense and power-dense battery packs. However, this architecture requires effective energy balancing among the battery packs to ensure optimal utilization.This work proposes an energy transfer unit (ETU) that consists of modular low-power dc-dc converters configured in a series-input, parallel-output arrangement. This …

Mitigating Inaudible Ultrasound Attacks On Voice Assistants With Acoustic Metamaterials, Joshua S. Lloyd, Cole G. Ludwikowski, Cyrus Malik, Chen Shen

Henry M. Rowan College of Engineering Departmental Research

Voice assistants play an important role in facilitating human–machine interactions and have been widely used in audio consumer electronic products. However, it has been shown that they are susceptible to inaudible attacks in which the malicious signals are in the ultrasound regime and cannot be heard by human ears. In this study, we show that a judiciously designed acoustic metamaterial filter can mitigate such attacks by modulating the received signals by the microphones. The metamaterial filter is composed of rigid plates with individual holes which exhibit local resonance phenomena that suppress incoming waves at specific frequencies. The effectiveness of the …

Electric Vehicle Charger Reliability Study, Conner Deppe

Student Research Symposium

The reliability of electric vehicle chargers is critical for the widespread adoption of EVs. To consumers, an indication of charging reliability is the unscheduled downtime of EV charging equipment. Unscheduled downtime brings the need for online EV health monitoring and remaining useful lifetime estimation technology. Though there are many different topologies and configurations for EV chargers, the core processing components remain the same. These components, known as silicon carbide MOSFETs, are important devices that provide better efficiency and power density than their silicon counterparts. In recent years, it is found that device on-resistance is a common indicator used in health …