Digital Commons Network^™

Design And Implementation Of A Cross-Platform, Modular Electronic-Nose System, Kaushika Rudraraju

USF Tampa Graduate Theses and Dissertations

Electronic noses (e-noses) are a subject of great research. They are useful in multiple platforms and serve a great purpose in both studies and safety. This thesis presents a portable, modular e-nose platform that spans the full pipeline from sensing to visualization, with emphasis on scalability, repeatability, and straightforward integration. The hardware is organized as swappable sensor modules each hosting multiple commercial sensors and local signal-conditioning networked to a master microcontroller via structured communication protocols. High-resolution acquisition, time alignment, and efficient streaming to a host enable reliable logging and analysis. A controlled exposure chamber and a repeatable sampling protocol (baseline-exposure-purge …

The Method Of Power Networks Mode Optimization In Basis Of Genetic Algorithm, Tulkin Shernazarovich Gayibov, Gulnaz Makhmutovna Turmanova

Technical science and innovation

One of the main tasks solved in planning short-term and managing operational modes of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters. For modern complex EPS, this task is often characterized by the multi-extremality of the objective function, the appearance of discontinuous functions, the presence of initial information of a probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is associated with a number of difficulties in simplifying them and bringing them to a convenient form for calculations. …

Measures To Improve The Structure And Operating Principle Of Perovsite Light-Emitting Devices, Latofat Utkir Qizi Shuhratova, Rustam Rashidovich Kabulov, Farrux Anvar Ugli Akbarov, Anvar Akbarovich Alimov

Technical science and innovation

Since the discovery of the unique properties of perovskite materials, these materials have been used as active layers in various optoelectronic devices. In particular, perovskite-based light-emitting diodes (LEDs) have attracted the attention of many researchers as a highly efficient and versatile technology for lighting and display devices. Perovskite-based LEDs allow for tunable light emission and can be manufactured at low cost and with flexible fabrication technology. These advantages make them suitable for use in devices ranging from modern displays to wearable electronics. Despite their potential, solving problems such as material stability and industrial scale is one of the current challenges …

Methodology For Creating An Intelligent Mechatron Module Based On A Synergistic Approach, Nurbek Rustamovich Matyokubov, Temurbek Omonboyevich Rakhimov

Technical science and innovation

This article is devoted to the methodology of creating an intelligent mechatron module based on a synergistic approach. Creating a multi-coordinate intelligent mechatron module and expanding the functional capabilities of the controlled object based on them, allows to optimize the dynamic characteristics. Also, the methodology for creating an intelligent mechatronic module based on a synergistic approach allows for the integration of electrical, magnetic, and mechanical components into a single circuit, the integration of technical parameter modification, and the integration of processing functions into a single information-measuring module. In addition, the article proposes a Eurorhythm scheme for expressing how …

Climate Change Projections Through Optimized Machine Learning And Deep Learning Models: A Comprehensive Analysis, Hala H. Youssef, Salma S. Mohammed, Hanan M. Amer, Abeer T. Khalil

Mansoura Engineering Journal

One major worldwide issue that has far-reaching effects on environmental stability and the avoidance of natural disasters is climate change. Developing efficient mitigating strategies depends on precise climate change forecasts. To predict important climate change indicators, including temperature fluctuations, greenhouse gas (CO2, N2O, CH4, and SF6) emissions, population dynamics, sea level changes, Arctic Sea ice extent, and Antarctica mass, this study evaluated the predictive capabilities of several Machine Learning (ML) and Deep Learning (DL) techniques. The set of machine learning algorithms includes Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANN), Random Forest (RF), …

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Resilience Oriented Distribution System Service Restoration Considering Overhead Power Lines Affected By Hurricanes, Kehkashan Fatima, Hussain Shareef, Flavio Costa

Michigan Tech Publications

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based …

Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …

Adoption Of Agrivoltaics In Developing Countries: A Review On Challenges, Opportunities And Future Prospects, Abdi J. Athumani, Pater Makolo

Tanzania Journal of Engineering and Technology (TJET)

This paper provides a review of agrivoltaics technology and how it has been applicable in developing countries, including African countries. Agrivoltaics, the integration of agricultural production with photovoltaic energy generation, offers promising solutions to water- energy-food nexus in developing countries. This technology offers the dual benefit of increasing agricultural productivity while generating clean energy, which is very important for regions facing frequent electricity shortages and declining agricultural yields due to climate change. However, despite its potential, the adoption of agrivoltaics in developing nations remains limited in comparison to developed countries due to financial, technical and policy constraints. This paper explores …

Optimal Location And Sizing Of Facts Devices To Improve Voltage Profiles On A 132 Kv Mwanza-Musoma-Nyamongo Transmission Line, Peter Makolo

Tanzania Journal of Engineering and Technology (TJET)

Recently, utility companies have desired to supply quality, stable and reliable power to customers, and ensure they meet the demand. Flexible AC Transmission Systems (FACTS) dynamic compensator devices such as Static Synchronous Compensator (STATCOM), Static VAr Compensator (SVC) and Unified Power Flow Controller (UPFC) are an impeccable choice, however, cost is one of the limiting factors following these technologies. In addition, using FACTS devices in the system requires a detailed steady state, dynamic and optimisation analysis to effectively meet the purpose and ensure reduced cost. This paper proposes using an optimised FACTS device to improve voltage profile, power transfer, system …

Investigating The Harmonic Content Of A Pwm Inverter With Varying Modulation Index, Peter Makolo

Tanzania Journal of Engineering and Technology (TJET)

Photovoltaic energy is a clean and endless vital renewable energy. In order to generate electricity from solar energy an inverter is required to transform the direct current into alternating current. Also, with the emerging of new ultra-high voltage direct current transmission technology, the rectification and inverters play a great role. Most three-phase two-level inverters draw harmonics that cause heat dissipation, waveform distortions hence affect the electrical loads. Knowing that Total Harmonic Distortion content of voltage source inverter is important and must be within the allowable range. Several schemes are suggested to mitigate the distortion in order to produce as much …

Trustworthy Navigation With Variational Policy In Deep Reinforcement Learning, Karla Bockrath, Liam Ernst, Rohaan Nadeem, Bryan Joseph Pedraza, Dimah Dera

Electrical and Computer Engineering Faculty Publications

Introduction: Developing a reliable and trustworthy navigation policy in deep reinforcement learning (DRL) for mobile robots is extremely challenging, particularly in real-world, highly dynamic environments. Particularly, exploring and navigating unknown environments without prior knowledge, while avoiding obstacles and collisions, is very cumbersome for mobile robots.

Methods: This study introduces a novel trustworthy navigation framework that utilizes variational policy learning to quantify uncertainty in the estimation of the robot’s action, localization, and map representation. Trust-Nav employs the Bayesian variational approximation of the posterior distribution over the policy-based neural network’s parameters. Policy-based and value-based learning are combined to guide the robot’s actions …

Towards Autonomous Energy Management: Machine Learning For Effective Auditing And Optimization, Sameh O. Abdellatif, Sherif Ashraf, Mira Mohsen

Electrical Engineering

This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC …

Impulse, Fall 2025, Jill Fier, Sierra Brown, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty News
3 | SDSU Vet Hua to Head Civil Engineering
6 | Ad Lunam — To the Moon
10 | Building A Safety Culture
11 | Department News
12 | Surface Mount Technology Kickoff
14 | Construction Management Student, Volleyball Player Building Success Piece by Piece
16 | Student Awards, Honors
18 | Student Competition Results
20 | NASA Win Opens Doors for New Product Development Order
22 | SDSU Engineering Student Tabbed as Noblereach Fellow
26 | Knabach Award Recipient from Apprentice Lineman to Company President
28 | Metzger Create Faculty Endowment for SDSU Engineering
30 …

Inductorless Cascaded Low-Power Dc-Dc Converter: Optimizing Performance Metrics Through Machine Learning Techniques, Ahmed Khaled, Sameh O. Abdellatif

Electrical Engineering

This study presents a groundbreaking methodology for optimizing the operational efficiency of a three-stage boost DC-DC cascaded converter through the application of a Random Forest(RF) machine learning algorithm. A novel figure of merit is meticulously formulated to quantitatively evaluate the converter’s performance, focusing on critical metrics such as power conversion efficiency, output DC ripple levels, and response time. The Random Forest model is trained on a comprehensive dataset encompassing a wide range of resistive and capacitive design parameters, with the figure of merit serving as the output indicator. Rigorous simulations and analyses demonstrate that the integration of LM741 operational amplifiers …

Research On The Construction Of Domain Knowledge Graph For Assisted Decision Making And Its Scenario-Oriented Application, Hao Xu, Linlin Ge, Yan Zhang, Sanhong Deng

Journal of Scientific Information Research

[Purpose/significance] This research constructed a domain knowledge graph and its scenario-oriented application framework for decision support at four levels: the data foundation layer, the key technology layer, the domain knowledge graph construction layer, and the scenario-oriented application layer. This framework aims to provide systematic support for knowledge discovery.

[Method/process] Based on the construction of a domain knowledge graph and its scenario-oriented application framework for decision support, this research focuses on the improvement of models and performance evaluation for fine-grained entity and relationship extraction at the discourse level within texts. The optimal model is selected to construct a domain knowledge graph. …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Safety-Critical Formation Control Of Non-Holonomic Multi-Robot Systems In Communication-Limited Environments, Logan Beard

College of Engineering Summer Undergraduate Research Program

This project explores advanced methodologies for distributed planning, formation control, and coordination in heterogenous multi-agent robotic systems consisting of small mobile robots and nano unmanned aerial vehicles (UAVs). Leveraging decentralized control theories and optimization techniques, the research aims to develop robust algorithms for real-time collaborative task execution, efficient formation maintenance, and adaptive coordination strategies. Python and ROS will be used extensively to simulate, validate, and experimentally deploy distributed robotics algorithms. The student researcher significantly contributes to the theoretical and practical advancement of multi-agent system technologies.

Eggbeater Antenna Design 915 Mhz, Anshul Deshmukh

College of Engineering Summer Undergraduate Research Program

The Sal-E cube sat mission that is planned for launch in 2026 includes a 902-928 MHz receiving module called the Space Quacker Advanced Development (SQUAD) module. The SQUAD module uses the LoRa modulation format for unlicensed uplink to the satellite using the 902-928 MHz Industrial, Scientific, and Medical (ISM) band. The goal of the SQUAD module is to demonstrate the link robustness of the LoRa communication standard to a low earth orbit (LEO) satellite using this ISM band. To demonstrate this communication link, a 902-928 MHz uplink ground station needs to be established at Cal Poly. The goal of this …

Building A Smart Transportation Network To Prevent Multi-Vehicle Collisions During Sudden Slowdowns, Leo Huang, Patrick Zhao

College of Engineering Summer Undergraduate Research Program

This proposed SURP project aims to design and evaluate a smart transportation network capable of preventing multiple-vehicle collisions due to sudden slowdowns in traffic. This project will simulate abrupt braking scenarios and implement adaptive vehicle-to-vehicle (V2V) communication protocols. By enhancing real-time awareness and responsiveness among vehicles, the system will reduce pileup risks and improve road safety.

Building Pathways To Computer Science Careers For Latinx Students Through Multilingual Collaborative Block-Based Programming, Cis Garcia, Noemi Corona Calvario

College of Engineering Summer Undergraduate Research Program

The underrepresentation of Latinx students in computer science highlights the need for innovative and inclusive educational approaches. This project addresses challenges such as limited access to educational resources and the demand for multilingual learning tools by developing a co-located, collaborative, game-based programming environment. Designed for use on phones, tablets, and laptops, this tool supports English, Spanish, and Mixtec, facilitating broader engagement. By promoting peer collaboration and interactive learning, our approach challenges traditional notions of solitary programming and reinforces the idea that expertise is shared, fostering an inclusive and equitable learning environment.

Spectral Modeling Of Electromagnetic Radiation From Power Converters, Regan A. Varner

Theses and Dissertations

Switching power converters are increasingly utilized in contemporary electronics. They are becoming more compact and operate at higher frequencies, which contributes to radiated electromagnetic interference (EMI). This EMI is important to quantify for electromagnetic compatibility of the converters with other nearby systems. In this thesis, analytical models have been developed for both ideal and non-ideal switching waveforms, employing piecewise functions to describe time-domain waveforms, with their corresponding frequency spectra derived and compared to empirical spectra. The results demonstrate that the comparisons with measured frequency data exhibit agreement with the analytical models up to approximately 25 MHz. Additionally, our analysis indicates …

Towards Robust Bmocz Schemes For Non-Coherent Wireless Communication, Anthony Joseph Perre

Theses and Dissertations

Non-coherent communication has emerged as a promising approach to address several challenges at the physical layer. A particular scheme called binary modulation on conjugate-reciprocal zeros (BMOCZ) uses the zeros (i.e, roots) of a polynomial to convey information bits. In this thesis, we strive to improve the reliability of BMOCZ in various scenarios. In particular, we utilize machine learning (ML) methods to determine the parameters for BMOCZ and improve upon the decoding performance. Moreover, we introduce a smooshed BMOCZ variant to combat typical impairments encountered within wireless communications, including timing offsets (TOs) for noncoherent orthogonal frequency division multiplexing (OFDM). Through numerical …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Pendant Micro-Droplet Evaporation Fabricates Fiber-Optic Mof Gas Sensor In Seconds, Abhishek Prakash Hungund, Bohong Zhang, Narasimman Subramaniyam, Thomas Spudich, Ryan O'Malley, Farhan Mumtaz, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The development of photonic-based gas sensors using metal–organic frameworks (MOFs) and other microporous solids is often a multistep, complex process, typically involving MOF synthesis, purification, and attachment of microcrystals to an optical fiber end face. This study introduces a one-step method that integrates MOF synthesis and sensor head fabrication directly onto the fiber end face, forming an extrinsic Fabry–Perot interferometer (EFPI) with a thin film of MOF microcrystals. The resulting film, only 3–10-μm-thick, enhances sensor response by enabling rapid gas detection within seconds. Utilizing a pendant micro-droplet evaporation technique, this method forms a microporous MOF layer in situ, allowing unreacted …

Improvement Of Lithium-Metal Electrode All-Solid-State Batteries Performance By Shot Peening And Magnetron Sputtering, Atsuro Okumura, Manabu Kodama

15th International Conference on Shot Peening

To enable fast charging in lithium-metal anode all-solid-state batteries, suppressing lithium dendrite formation at the solid electrolyte (SE) interface is critical. Increasing fracture toughness via shot peening (SP) and improving interfacial contact with Au sputtering can inhibit dendrite growth. However, conventional sputtering may reduce toughness due to localized thermal damage. This study investigated magnetron sputtering as a low-damage, plasma-based Au deposition method. SEs with and without SP were fabricated and coated via normal and magnetron sputtering. Critical current density (CCD) and fracture toughness were evaluated. Without SP, CCD improvement was limited regardless of sputtering method due to poor bonding. With …

Robust Fault Detection And Classification In Power Systems Via Physics-Informed And Data-Driven Learning, Biswash Basnet, Varsha Sen

Graduate Student Scholarship

Electrical faults in power transmission systems can severely affect grid stability, equipment safety, and operational reliability. Traditional protection schemes, particularly distance relays, depend on apparent impedance computation that changes with error, creating a risk of misclassification. The results from relay overreach, underreach, or complete maloperation due to CT/PT saturation lead to developing problems in high impedance situations. These limitations highlight the need for adaptive, data-driven alternatives. This paper proposes an intelligent fault detection and classification model based on supervised machine learning techniques that overcome these challenges. The system’s robustness was validated under different training sizes and Gaussian noise levels, demonstrating …

An Event-Based Time-Incremented Snn Architecture Supporting Energy-Efficient Device Classification, David L. Weathers, Michael A. Temple, Brett J. Borghetti

Faculty Publications

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF …

Enhancement Of The Received Signal Strength In Smart Grid Communication Systems, Doaa Talaat Elsherbiny, Mona Mohamed Shokair Prof., Mohamed Shalaby, Salah Elden Khamis, Sameh A. Napoleon

Journal of Engineering Research

Smart grids are networks that contain intelligence from the generation stage to the distribution stage. These grids can actually have excellent power efficiency and automated control thanks to intelligence. Within smart grids, the communication mechanism is a crucial area of study. There should be a dependable communication system in the smart grid. A comprehensive mathematical model for a communication system within smart grids is derived in this work. Additionally, the simulation findings validate the mathematical model that was derived. Additionally, experiments are being conducted to apply polar convolutional parallel code PCPC to enhance the performance of the suggested communication system. …

Interference Management For Device-To-Device Communications In Heterogeneous Cellular Networks Using Deep Reinforcement Learningdevice-To-Device Communication; Mmwave Communication; Spectrum Resource Allocation; Deep Reinforcement Learning; Hcns, Suzan Mohamed Shukry

Journal of Engineering Research

Integrating Device-to-Device (D2D) communication into Heterogeneous Cellular Networks (HCNs) augmented with Millimeter Wave (mmWave) technology presents a compelling approach to fulfill the escalating demands for ultra-high data throughput in next-generation wireless systems. Although these advancements significantly improve data transmission efficiency and network scalability, the coexistence of D2D and cellular users within a shared spectral environment triggers considerable interference, complicating network coordination. To mitigate this, the interference scenario is modeled as a unified optimization task involving mode selection and resource allocation, aiming to enhance the aggregate system throughput while adhering to strict SINR constraints for both communication tiers. To tackle this …