Electrical and Computer Engineering Commons^™

Heavy Metals Pollution In Roadside Ecosystems, Lilian Matafu

Tanzania Journal of Engineering and Technology (TJET)

Heavy metals refer to metallic elements that are characterized by having a relatively high density and they are toxic or poisonous even at low concentration. They are environmental pollutant owing to toxicity and longevity in atmosphere and ability to accumulate in living things via bioaccumulation. They tend to enter in different system such as food chain. Analyses of water, soil sediment and the surrounding growing plants (Cynodon dactylon and Cyperus species) for selected heavy metals (Cd, Pb, Zn and Cu) were conducted on a roadside pond located at Boko MSB, Dar es Salaam. Pond water, soil sediments and plant samples …

Assessment Of Wind Speed Characteristics And Available Wind Power Potential For Electricity Generation In Tanzania, Mwingereza John Kumwenda

Tanzania Journal of Engineering and Technology (TJET)

The energy demand and its associated crises are attracting significant attention due to population increase and economic growth especially in developing countries. Fossil fuel-based energy source stand as a prominent anthropogenic resource but they are accompanied with increased carbon emissions and heightened environmental concerns. Renewable energy sources such as wind energy can offers a lot of potential for sustainable growth in the energy sector of developing nations like Tanzania. Thus, this work investigated wind speed characteristics and available wind power potential in six selected regions in Tanzania with different topographical features for future electricity generation. The data of the wind …

Performance Evaluation Of Free Space Optical Communication In Dar Es Salaam: Impact Of Scintillation And Modulation Schemes, Mustafa H. Mohsini

Tanzania Journal of Engineering and Technology (TJET)

Free space optical communication (FSO) holds significant relevance in the modern communication system as it offers high and unlimited data rates, enhanced security, rapid deployment, and low cost for installation. However, the performance of FSO transmission is greatly affected by harsh atmospheric conditions such as wind, temperature, and humidity, which induce scintillation. With the rapid growth of internet users and Dar es Salaam being a business city in Tanzania, higher and unlimited bandwidth for communication is highly demanded. This study primarily aims to evaluate the performance of FSO transmission in Dar es Salaam, Tanzania, by investigating the impact of atmospheric …

Efficient Operation Of Direct Coupled Solar Home Pv System: A Case Of Solar Home Pv System Installed In Dodoma, Tanzania, Sarah P. Ayeng'o

Tanzania Journal of Engineering and Technology (TJET)

Direct coupled Photovoltaic (PV) system is a common topology among most of the off-grid communities in the world. This topology has less components hence resulting in low investment costs. However, it suffers much losses when battery operating voltage is far from maximum power point voltage of PV modules. This scenario can be attributed by different factors such as; variation in solar radiation, load profile and temperature. Efficient operation of these systems is required in order to reduce losses. Usually in direct coupled system, PV modules are connected in parallel with batteries through a charge controller hence making PV output to …

Improved Minimum Variance Channel Estimation Techniques For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

Orthogonal frequency division multiplexing (OFDM) systems face challenges in channel estimation due to noise, variability, and the doubly dispersive nature of wireless channels, which degrade performance. To address these challenges, a multichannel minimum variance double dispersive channel estimator is proposed. The method employs a hybrid approach that combines subspace and minimum variance techniques, optimizing the filter bank output power under a signal-to-noise ratio (SNR) constraint. This design preserves the desired signal while effectively suppressing disturbances, achieving robust performance with reduced computational complexity compared to existing methods. Simulation results demonstrate that the proposed estimator outperforms subspace and asymptotic methods in terms …

Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo

Tanzania Journal of Engineering and Technology (TJET)

The integration of smart grid and Internet of Things (IoT) technologies plays a crucial role in enhancing the quality of services provided by traditional electrical grids. This combination has enabled the introduction of new services, such as demand response, automatic meter reading, and IoT-enabled Distribution Automation (IoT-DA), which incorporates sensors, actuators, intelligent electrical devices (IEDs), and information and communication technologies to monitor and control the grid. However, this integration also introduces network security risks, including Denial of Service (DoS) attacks, false data injection, and masquerading attacks, such as system node impersonation that can transmit incorrect readings, trigger false alarms, and …

Effects Of Technical Debt On Software Interoperability, Leonard Peter Binamungu

Tanzania Journal of Engineering and Technology (TJET)

Technical debt (TD) refers to sub-optimal development decisions that make the software costly to maintain and evolve. Examples of TD include structural complexity, violation of coding styles, and code complexity. Existing research has investigated the nature, causes and indicators of TD, as well as tools and strategies for managing TD. However, although TD could hinder the ability of a software system to be interoperable with others, existing literature has limited evidence on how TD affects systems interoperability. This limits the ability of software engineering teams to manage TD in ways that do not hinder systems interoperability. To fill this void, …

Towards A New Era Of Surgery Using Intelligent Robot, Ahmed Mahfouz, Hassan A. El Gamal, T. Awad, M.B. Badawi

Journal of Engineering Research

This research aims to overcome the difficulties of surgical tumor removal, especially in brain tumors that needs precision and time saving of the operations. The traditional surgical approach often is slow and targeting accurate manual cutting of the tumor, which is time-consuming and increase the risk to the patient. The error in cutting the deep layers of the tumor can occur, which increase the cost and dangerous for the patient.

To overcome these challenges, a proposed solution involves designing a robot that can quickly and accurately remove tumors by following their contours as it comes from the medical imaging data, …

An Ultra-Low-Power 0.8 V, 60 Nw Temperature Sensor For Battery-Less Wireless Sensor Networks, Fnu Naveed, Jeff Dix

Electrical Engineering and Computer Science Faculty Publications and Presentations

This work presents a nano-watt digital output temperature sensor featuring a supply-insensitive, self-biased current source. Second-order temperature dependencies of the MOS diode are canceled to produce a stable reference and a linear temperature-sensitive voltage. The sensor integrates a sensing unit, voltage-controlled differential ring oscillators, and a low-power frequency-to-digital converter, utilizing a resistor-less design to minimize power and area. The delay element in the ring oscillator reduces stage count, improving noise performance and compactness. Fabricated in 65 nm CMOS, the sensor occupies 0.02 mm2 and consumes 60 nW at 25 °C and 0.8 V. Measurements show an inaccuracy of +1.5/−1.6 °C …

State-Of-Charge Estimation Using Deep Learning For Electric Vehicles, Samer Yahya Ribhe Tahboub

LSU Master's Theses

This thesis investigates the application of deep learning models for State of Charge (SOC) estimation in Battery Management Systems (BMS) for electric vehicles (EVs), focusing on optimizing EV range, lifespan, and performance while addressing challenges like range anxiety. The study explores three deep learning architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—each designed to capture complex temporal dependencies in battery data. The LSTM model is trained on EV battery data, including voltage, current, temperature, and SOC, providing a strong baseline for SOC estimation. The BiLSTM model enhances accuracy by processing data in both forward and backward …

A Survey On Food Ingredient Substitutions, Hyunwook Kim, Revathy Venkataramanan, Amit P. Sheth

Publications

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to …

Optimizing An Integrated Hybrid Energy System With Hydrogen-Based Storage To Develop An Off-Grid Green Community For Sustainable Development In Bangladesh, Asif Jaman, Rafid Al Mahmud, Barun K. Das, Mohammad Shahed H.K. Tushar

Research outputs 2022 to 2026

An integrated renewable system that utilizes solid waste-based biogas is important steps towards the sustainable energy solutions to rural off-grid communities in Bangladesh. In this study, a hybrid energy system consisting of photovoltaic modules, wind turbines, biogas generators, fuel cells, and electrolyzer-hydrogen tank-based energy storage is optimized using non-dominated sorting genetic algorithm (NSGA-II). The hybrid system is optimized based on the cost of energy and human health damage as objective functions, and a fuzzy decision-making technique is employed to determine the optimal solution to the multi-objective approach. Additionally, several economic, ecological, and social indicators are also investigated while meeting a …

Infinitesimal Elements In Cartesian, Cylindrical And Spherical Coordinate Systems, Ashanthi Maxworth Phd

EM Fields With Prof. Maxworth

In this chapter we introduce you to the infinitesimal surface and volument elements in Cartesian, Cylindrical and Spherical coordinate systems. Electromagnetic fields is based on multi-variate vector calculus. The following three documents show, how the infinitesimal surface area elements are defined in Catesian, Cylindrical and Spherical coordinate systems.

Maxwell's Second Equation: The Gauss's Law For Magnetostatic Fields, Ashanthi Maxworth Phd

EM Fields With Prof. Maxworth

Gauss's Law for magnetostatic fields says that the total magnetic flux through a closed surface is equal to zero. In otherwords, there are no magnetic monopoles. This law has been applied in multiple mathematical derivations. Watch the short video where Dr. Maxworth explains this law. In the worksheet you can observe the magnetic flux generated due to a current through a straight coaxial cable. Note that the magnetic flux always exisit as close loops making the total net flux through a closed surface zero and the direction of the magnetic field can be derived by the right hand rule, i.e …

Maxwell's Third Equation: The Faraday's Law, Ashanthi Maxworth Phd

EM Fields With Prof. Maxworth

Faraday's Law states that the voltage induced in a closed loop is equal to the rate of change of magnetic flux through that loop. As we know, when there is no fluctuating magnetic flux, there is no voltage difference across a closed loop. But when there is a changing magnetic field, a voltage is induced within a closed loop known as the electromotive force or the EMF. There are two types of electromotive forces: the motional EMF and the transformer EMF. In motional EMF the magnetic field is kept steady while the loop is moved such that it cuts the …

Exploring The Potential Of Large Language Models For Assisting With Mental Health Diagnostic Assessments: The Depression And Anxiety Case, Kaushik Roy, Harshul Surana, Darssan Eswaramoorthi, Yuxin Zi, Vedant Palit, Ritvik Garimella, Amit Sheth

Publications

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various …

Very Large Scale Robotics Path Planning With Centroidal Voronoi Tessellation, Xu (James) Gao

Theses, Dissertations and Capstones

Swarm robotics, also referred to as very large-scale robotics (VLSR), has emerged as a transformative approach for addressing complex tasks that are infeasible for single-robot systems. Applications range from environmental monitoring and disaster response to large-scale agricultural and industrial operations. However, as the number of robots in a swarm increases, so do the challenges associated with motion control, energy efficiency, and scalability. These challenges necessitate innovative solutions that balance microscopic robot behaviors with macroscopic system-level objectives.

In this thesis, we address these challenges by building upon existing research [40], which introduced novel methods for optimizing swarm robotics systems using macroscopic …

Lagrange Multipliers As A Metric For Early-Stage Shipboard Power System Design, Magdalen A. Barnes

Theses and Dissertations--Electrical and Computer Engineering

Integrated power systems are essential in the operation of electric ships and must function under a diverse range of conditions. In addition to routine operation, ships should be prepared to respond to challenging events that require significant amounts of power. Demanding scenarios, coupled with complex and interconnected power systems, present challenging issues. The power and energy ratings of equipment are crucial factors in the operational success of the ship; however, to meet economic constraints, balance between performance and cost must be upheld. Details regarding equipment models and specifications are typically unavailable during early-stage design. Power system modeling and simulation can …

Explaining Deep Learning-Based Anomaly Detection In Energy Consumption Data By Focusing On Contextually Relevant Data, Mohammad Noorchenarboo, Katarina Grolinger

Electrical and Computer Engineering Publications

Detecting anomalies in energy consumption data is crucial for identifying energy waste, equipment malfunction, and overall, for ensuring efficient energy management. Machine learning, and specifically deep learning approaches, have been greatly successful in anomaly detection; however, they are black-box approaches that do not provide transparency or explanations. SHAP and its variants have been proposed to explain these models, but they suffer from high computational complexity (SHAP) or instability and inconsistency (e.g., Kernel SHAP). To address these challenges, this paper proposes an explainability approach for anomalies in energy consumption data that focuses on context-relevant information. The proposed approach leverages existing explainability …

Kolmogorov–Arnold Recurrent Network For Short Term Load Forecasting Across Diverse Consumers, Muhammad Umair Danish, Katarina Grolinger

Electrical and Computer Engineering Publications

Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as Long Short- Term Memory Networks (LSTMs) have shown considerable success in this domain. However, these models often struggle to accurately capture complex and sudden variations in energy consumption, and their applicability is typically limited to specific consumer types, such as offices or schools. To address these challenges, this paper proposes the Kolmogorov–Arnold Recurrent Network (KARN), a novel load forecasting approach that …

Leveraging Hypernetworks And Learnable Kernels For Consumer Energy Forecasting Across Diverse Consumer Types, Muhammad Umair Danish, Katarina Grolinger

Electrical and Computer Engineering Publications

Consumer energy forecasting is essential for managing energy consumption and planning, directly influencing operational efficiency, cost reduction, personalized energy management, and sustainability efforts. In recent years, deep learning techniques, especially LSTMs and transformers, have been greatly successful in the field of energy consumption forecasting. Nevertheless, these techniques have difficulties in capturing complex and sudden variations, and, moreover, they are commonly examined only on a specific type of consumer (e.g., only offices, only schools). Consequently, this paper proposes HyperEnergy, a consumer energy forecasting strategy that leverages hypernetworks for improved modeling of complex patterns applicable across a diversity of consumers. Hypernetwork is …

Recycled Filtered Contaminants From Liquid-Fed Pyrolysis As Novel Building Composite Material, Alessia Romani, Daniel Kulas, Joseph Curro, David R. Shonnard, Joshua Pearce

Electrical and Computer Engineering Publications

Liquid-fed pyrolysis allows the conversion of contaminated postconsumer plastic waste into valuable resources, removing contaminants through wax dissolution and filtration. One of the main challenges is currently represented by the management of its main byproduct, the contaminant-rich retentate from the filtration process. New circular economy strategies are needed to use this waste plastic-based composite as secondary raw materials. Despite the increasing trend in using plastic and plastic-waste composites for the building sector, there are currently limited applications of industrial recycling waste as engineering construction materials, e.g., from pyrolysis. This study evaluates the suitability of contaminant-rich retentate from liquid-fed pyrolysis of …

Parametric Design Of Easy-Connect Pipe Fitting Components Using Open-Source Cad And Fabrication Using 3d Printing, Abolfazl Taherzadeh Fini, Cameron K. Brooks, Alessia Romani, Anthony G. Straatman, Joshua M. Pearce

Electrical and Computer Engineering Publications

The amount of non-revenue water, mostly due to leakage, is around 126 billion cubic meters annually worldwide. A more efficient wastewater management strategy would use a parametric design for on-demand, customized pipe fittings, following the principles of distributed manufacturing. To fulfill this need, this study introduces an open-source parametric design of a 3D-printable easy-connect pipe fitting that offers compatibility with different dimensions and materials of pipes available on the market. Custom pipe fittings were 3D printed using a RepRap-class fused filament 3D printer, with polylactic acid (PLA), polyethylene terephthalate glycol (PETG), acrylonitrile styrene acrylate (ASA), and thermoplastic elastomer (TPE) as …

3d-Printable Pva-Based Inks Filled With Leather Particle Scraps For Uv-Assisted Direct Ink Writing: Characterization And Printability, Luca Guida, Alessia Romani, Davide Negri, Marco Cavallaro, Marinella Levi

Electrical and Computer Engineering Publications

Despite its significant environmental impacts, leather remains a popular material due to its durability, aesthetics, and mechanical properties. Recycling leather scraps is gaining increasing attention to reduce waste, pollutants, and emissions from pristine raw materials in the tanning industry. Material Extrusion additive manufacturing represents a promising way to recycle leather byproducts as secondary raw materials for new applications. This paper investigates the characterization and printability of photo- and thermal-curable PVA-based inks for UV-assisted Direct Ink Writing filled with leather filler scraps from the tanning industry, i.e., leather shavings. As a cold extrusion process, Direct Ink Writing reduces energy consumption and …

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

The Rhythm Of Renewables: Minute-By-Minute Insights From Kentucky, David S. Beyerle, Noah M. Stewart, Shaun E. Lavin, Chad C. Alkire, Heather Nikolic, Samuel Kelty, Ezekiel A. Boggs, Declan Boyle, Lawrence E. Holloway, Aron Patrick

Electrical and Computer Engineering Faculty Publications

Kentucky’s renewable energy landscape beats with a distinct rhythm shaped by ever-changing variations in sunlight, wind, rainfall, and the ever-modernizing grid that connects them. This paper analyzes minute-by-minute performance data from seven renewable and storage assets owned and operated by the PPL Corporation in Kentucky, including hydroelectric, solar, wind, and lithium-ion battery systems. Using a full year of synchronized, high-resolution data from multiple sites in the Commonwealth, the study examines daily and seasonal capacity factor trends, explores correlations among generation types, and evaluates their alignment with utility load profiles. Our analysis found 279 hours—about 3.2% of the year—with zero renewable …

Smart Homes, Grids, And Electric Vehicles Large-Scale Integration Studies Employing Machine Learning And Optimization Techniques, Rosemary E. Alden

Theses and Dissertations--Electrical and Computer Engineering

Residential digital twins are fundamental to the smart grid transition, and thus, must be both accurate and representative of existing homes and scalable for large distribution systems. Within this dissertation, new machine learning (ML) and physics-based methodologies are applied to the major individual residential loads, energy storage devices, and resources in the US to develop computationally efficient digital twins and new optimal control strategies for the virtual power plant (VPP) concept. Big data from experimental field demonstrations with dedicated metering, thousands of residential smart meter profiles, and large national human behavior surveys are employed to develop ultra-fast scalable residential load …

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Guided-Mode Resonant Nanopatterns For Raman Generation And Photonic Devices, Renjie Chen

Electrical Engineering Dissertations - Archive

This dissertation explores advanced strategies for enhancing Raman amplification in silicon photonic devices, focusing on guided-mode resonance engineering and resonant mode manipulation. Silicon, despite its indirect bandgap, exhibits a strong Raman scattering coefficient, enabling it to function as a viable gain medium for integrated photonic systems. However, the realization of efficient, compact, and low-threshold silicon Raman amplifiers and lasers necessitates innovative design approaches that overcome inherent material and structural limitations.

The first chapter provides a fundamental overview of optics, including physical principles, spectral characteristics, guided-mode resonance, simulation methods, and nanopattern fabrication methods.

The second chapter delves into silicon-based Raman amplification …