Physical Sciences and Mathematics Commons^™

Does Green Energy Really Matter For Environment And Economic Sustainability? Validating The Long-Standing Existing Empirics On Pakistan Economy, Syed Kafait Hussain Naqvi

CBER Conference

The empirical outcomes of the study validate the widespread concern of the literature on the existence of the “growth hypothesis” which supports, that there is a systematic positive causation running from green energy to economic sustainability. The study findings suggest that regulations in the energy sector can encourage the applications of green energy resources, particularly in the real sector of the economy, leading to reduced emissions.

Food-Water-Energy Nexus In The Perspective Of Green Revolution, Green Energy, Legal And Institutional Framework: A Killian Based Adjusted Bootstrap Approach, Zia Ur Rahman

CBER Conference

Food and water energy is crucial for human well-being, sustainable development, and poverty reduction. The growing global demand driven by population growth, economic development, urbanization, changing diets, technological advancements, and climate change projections indicates a significant increase in the need for these resources. Understanding the intricate interdependencies between food, water, and energy is essential for effectively addressing these challenges and fostering a prosperous and sustainable future. Therefore, this study incorporated statistical data collected from the Pakistan Economic Survey and the World Governance Indicator from 1990 to 2022 to elucidate the complex connection between food, water, and energy.

Demand Analysis Of Energy Mix In District Kotli Azad Jammu And Kashmir, Pakistan, Syed Kafait Hussain Naqvi

CBER Conference

This study is an effort to empirically analyze the household’s demand for energy mix (electricity, liquefied petroleum gas (LPG), kerosene, and firewood) in the District Kotli, AJK. The study estimates the demand elasticities (price and expenditure) by employing the Linear Approximate Almost Ideal Demand System (LA-AIDS) to 384 households sampled across District Kotli, AJK in 2017. The empirical estimations are carried out by using the Seemingly Unrelated Regression (SUR), keeping intact the adding-up, homogeneity and symmetry restrictions.

Optimal And Robust Control Problems Of Microalgae Cultivation, Mariana Rodriguez-Jara, Luis A. Ricardez-Sandoval, Carlos E. Ramirez-Castelan, Hector Puebla

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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 …

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).

Session 12: Analysis Of State And Parameter Estimation Techniques Using Dynamic Perturbation Signals, Timothy M. Hansen

SDSU Data Science Symposium

The trend in electric power systems is the displacement of traditional synchronous generation (e.g., coal, natural gas) with renewable energy resources (e.g., wind, solar photovoltaic) and battery energy storage. These energy resources require power electronic converters (PECs) to interconnect to the grid and have different response characteristics and dynamic stability issues compared to conventional synchronous generators. As a result, there is a need for validated models to study and mitigate PEC-based stability issues, especially for converter dominated power systems (e.g., island power systems, remote microgrids).

This presentation will introduce methods related to dynamic state and parameter estimation via the design …

High Energy Blue Light Induces Oxidative Stress And Retinal Cell Apoptosis, Jessica Malinsky

Capstone Showcase

Blue light (BL) is a high energy, short wavelength spanning 400 to 500 nm. Found in technological and environmental forms, BL has been shown to induce photochemical damage of the retina by reactive oxygen species (ROS) production. Excess ROS leads to oxidative stress, which disrupts retinal mitochondrial structure and function. As mitochondria amply occupy photoreceptors, they also contribute to oxidative stress due to their selectively significant absorption of BL at 400 to 500 nm. ROS generation that induces oxidative stress subsequently promotes retinal mitochondrial apoptosis. BL filtering and preventative mechanisms have been suggested to improve or repair BL-induced retinal damage, …

Raman Scattering Measurements And Analyses Of Gan Thin Films Grown On Zno Substrates By Metalorganic Chemical Vapor Deposition, Zane Mcdaniel, Zhe Chuan Feng, Kevin Stokes

Symposium of Student Scholars

Metalorganic chemical vapor deposition (MOCVD) is a popularly used method of growing thin films of GaN on ZnO (GZ) substrates, which pair well due to their structural and characteristic similarities. In this research, optical characterization of the surface quality of GZ sample films is measured by analyzing Raman scattering (RS) using a Renishaw inVia spectrometer fitted with a 532nm laser. Samples were grown in an improved double injection block rotating disc reactor. Multiple samples' spectra show broad peaks that correspond with the E2 (high) and A1 (LO) branches of GaN, and nicely fitted curves are observed for the characteristic E2 …

Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi

Undergraduate Student Research Internships Conference

Data preprocessing is an essential step when building machine learning solutions. It significantly impacts the success of machine learning modules and the output of these algorithms. Typically, data preprocessing is made-up of data sanitization, feature engineering, normalization, and transformation. This paper outlines the data preprocessing methodology implemented for a data-driven predictive maintenance solution. The above-mentioned project entails acquiring historical electrical data from industrial assets and creating a health index indicating each asset's remaining useful life. This solution is built using machine learning algorithms and requires several data processing steps to increase the solution's accuracy and efficiency. In this project, the …

Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie

Student Research Symposium

Typical Artificial Neural Networks (ANNs) have static architectures. The number of nodes and their organization must be chosen and tuned for each task. Choosing these values, or hyperparameters, is a bit of a guessing game, and optimizing must be repeated for each task. If the model is larger than necessary, this leads to more training time and computational cost. The goal of this project is to evolve networks that grow according to the task at hand. By gradually increasing the size and complexity of the network to the extent that the task requires, we will build networks that are more …

Comparative Analysis Of Rgb-Based Eye-Tracking For Large-Scale Human-Machine Applications, Brett Thaman, Trung Cao

Posters-at-the-Capitol

Gaze tracking has become an established technology that enables using an individual’s gaze as an input signal to support a variety of applications in the context of Human-Computer Interaction. Gaze tracking primarily relies on sensing devices such as infrared (IR) cameras. Nevertheless, in the recent years, several attempts have been realized at detecting gaze by acquiring and processing images acquired from standard RGB cameras. Nowadays, there are only a few publicly available open-source libraries and they have not been tested extensively. In this paper, we present the result of a comparative analysis that studied a commercial eye-tracking device using IR …

Ciculant Matrix And Fft, Thomas S. Devries

Undergraduate Student Research Internships Conference

The goal was to produce all the eigen values for a BOHEMIAN matrices using coefficient set {0, 1, -1, i, -i} of a size 15 vector. There are 5^15 eigen values so it was attempted to be done in parrallel for parts of the algorithm that permitted.

App Development For Wearable Sensors, Connor Pittman, Christopher Chappie, Patrick A. Tetreault

Thinking Matters Symposium

The objective of this research project was to create a wearable device that monitors bodily functions for the user to view on their smartphone. Sensor data is processed using the Arduino Nano 33 BLE microcontroller. The sensors used in this project include: proximity, temperature, humidity, heart rate, pressure, and skin impedance. This project takes advantage of the Arduino's Bluetooth low energy (BLE) capabilities so that all the data can be transmitted to a smartphone. This presentation shows the challenges faced during the project and how they were overcome. Some of these challenges include: programming, how heart rate sensors work, and …

Dynamic Between Biomass, Ph And Acid Lactic By Microorganisms In Fermentation Of Fresh Milk, Emmanuel Rodriguez, Aurelio Castillo, Paul A. Valle, Yolocuauhtli Salazar

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Cybersecurity Strategy Against Cyber Attacks Towards Smart Grids With Pvs, Fangyu Li, Maria Valero, Liang Zhao, Yousef Mahmoud

KSU Proceedings on Cybersecurity Education, Research and Practice

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data …

Developing Arduino Coding Curriculum, Tyler Brown, Riley Bucheitte, Timothy Kidd

Summer Undergraduate Research Program (SURP) Symposium

No abstract provided.

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.

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.

The Challenge For Vision Of Fluctuating Real-World Illumination, David H. Foster

MODVIS Workshop

No abstract provided.

Autonomous Watercraft Simulation And Programming, Nicholas J. Savino

Student Scholar Showcase

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as …

Generating Spectra Using Pca-Based Spectral Mixture Models, Joseph S. Makarewicz, Heather D. Makarewicz

Scholar Week 2016 - present

PCA-based spectra mixture models have been created for several laboratory mixture data sets. This presentation provides examples of spectra that were generated using PCA-based spectra mixture models.

Low Cost Vehicular Autonomy Using Radar And Gps, Nathan Jessurun, Ryan Gordon, Danielle Fredette

The Research and Scholarship Symposium (2013-2019)

This presentation describes a subset of the systems devised for this year's autonomous golf cart senior design project. Our goal is to explore the possibilities of low cost autonomy using only radar and GPS for environmental sensing and navigation. Although autonomous and semi-autonomous ground vehicles are a relatively new reality, prototypes have been a subject of engineering research for decades, often utilizing an array of sensors and sensor fusion techniques. State of the art autonomous ground vehicle prototypes typically use a combination of LIDAR and other distance sensors (such as radar or sonar) as well as cameras and GPS, sometimes …

Creating A Computational Tool To Simulate Vibration Control For Piezoelectric Devices, Ahmet Ozkan Ozer, Emma J. Moore

Posters-at-the-Capitol

Piezoelectric materials have the unique ability to convert electrical energy to mechanical vibrations and vice versa. This project takes a stab to develop a reliable computational tool to simulate the vibration control of a novel “partial differential equation” model for a piezoelectric device, which is designed by integrating electric conducting piezoelectric layers constraining a viscoelastic layer to provide an active and lightweight intelligent structure. Controlling unwanted vibrations on piezoelectric devices (or harvesting energy from ambient vibrations) through piezoelectric layers has been the major focus in cutting-edge engineering applications such as ultrasonic welders and inchworms. The corresponding mathematical models for piezoelectric …

Genetic Algorithm Design Of Photonic Crystals For Energy-Efficient Ultrafast Laser Transmitters, Troy A. Hutchins-Delgado

Shared Knowledge Conference

Photonic crystals allow light to be controlled and manipulated such that novel photonic devices can be created. We are interested in using photonic crystals to increase the energy efficiency of our semiconductor whistle-geometry ring lasers. A photonic crystal will enable us to reduce the ring size, while maintaining confinement, thereby reducing its operating power. Photonic crystals can also exhibit slow light that will increase the interaction with the material. This will increase the gain, and therefore, lower the threshold for lasing to occur. Designing a photonic crystal for a particular application can be a challenge due to its number of …

End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai

Shared Knowledge Conference

Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an …

Study Of Physical Layer Security And Teaching Methods In Wireless Communications, Zhijian Xie, Christopher Horne

KSU Proceedings on Cybersecurity Education, Research and Practice

In most wireless channels, the signals propagate in all directions. For the communication between Alice and Bob, an Eavesdropper can receive the signals from both Alice and Bob as far as the Eavesdropper is in the range determined by the transmitting power. Through phased array antenna with beam tracking circuits or cooperative iteration, the signals are confined near the straight line connecting the positions of Alice and Bob, so it will largely reduce the valid placement of an Eavesdropper. Sometimes, this reduction can be prohibitive for Eavesdropper to wiretap the channel since the reduced space can be readily protected. Two …

Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison

The Summer Undergraduate Research Fellowship (SURF) Symposium

Measurement of soil moisture, especially the root zone soil moisture, is important in agriculture, meteorology, and hydrology. Root zone soil moisture is concerned with the first meter down the soil. Active and passive remote sensing methods used today utilizing L-band(1-2GHz) are physically limited to a sensing depth of about 5 cm or less. To remotely sense the soil moisture in the deeper parts of the soil, the frequency should be lowered. Lower frequencies cannot be used in active spaceborne instruments because of their need for larger antennas, radio frequency interference (RFI), and frequency spectrum allocations. Ground-based passive remote sensing using …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Investigation Of The Skin Effect In Alternating Currents, Matthew Liang

The International Student Science Fair 2018

My research is on the investigation of the skin effect in alternating currents. The skin effect is when an alternating current tends to flow on the surface of the conductor, such that the current density is highest near the surface, and decreases with greater depths in the conductor. This is due to the alternating current inducing changing magnetic fields, which in turn induces currents that oppose the original flow of current, resisting the current flowing through the centre the most. This reduces the effective cross-sectional area of the conductor and increases the resistance, causing increased power losses. This effect becomes …