Digital Commons Network^™

Sol-Gel Synthesis And Microstructure Characterization Of Nio-Tio2 Semiconductor, Godlisten N. Shao

Tanzania Journal of Engineering and Technology (TJET)

NiO-TiO2 is a fascinating p-n semiconducting binary metal oxide with unique electronic and optical properties. The NiO-TiO2 has been synthesized using complicated preparation methods and expensive precursors that hamper their large-scale production. The present study reports the preparation of NiO-TiO2 semiconductor with improved physicochemical properties. NiO-TiO2 samples were prepared through one-pot sol-gel synthesis process followed by sintering of the as-synthesized materials at temperatures ranging from 600 to 1000 °C. The role of thermal treatment and NiO content on the microstructures was exquisitely studied. The microstructure of the NiO-TiO2 samples was examined by Raman spectroscopy, XRD, XRF, SEM-EDAX, high resolution TEM, …

Synthesis Algorithm Of Control System Based On Neural Network For The Gas And Oil Process, Jasur Sevinov, Abdishukurov Maqsudovich Shavkat, A.Z. Shodiyorov

Chemical Technology, Control and Management

Automatic control systems in the oil and gas processing are considered. After extraction of natural gas condensate, management of technological parameters plays an important role in its processing. Among the parameter of regulating algorithms, control based on neural network technology was considered. These control systems are created on the basis of algorithms of neural network technology. There are several structures of neural networks according to their structure. Differences in the process of information processing of these branching structures are considered.

Spectrophotometric Device For Early Malaria Detection, Adrian Wasylewski, Christopher Faber, Victor Lin

Honors Thesis

Malaria remains a significant global health challenge, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is crucial for effective treatment and disease management. Unfortunately, there are still significant challenges in existing testing procedures, which are often expensive, inaccurate, or impractical for mass testing. While spectrophotometry has been proposed as a detection tool for other tropical diseases such as dengue fever, no prototype has been made for any disease, including malaria. This report addresses this gap by presenting a spectrophotometric-based malaria detection tool that achieves high sensitivity at a low cost. The inclusion of a …

Channel Prediction For Underwater Acoustic Communication: A Review And Performance Evaluation Of Algorithms, Haotian Liu, Lu Ma, Zhaohui Wang, Gang Qiao

Michigan Tech Publications

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms …

Applications, Challenges, And Research Issues For Enabling A Uav Swarm, Jennifer Hahner

Senior Honors Theses

Unmanned aerial vehicle (UAV) swarms have the potential to be useful in numerous applications due to their versatility and ability to operate without human intervention. However, this promising technology still requires further investigation, research, and testing before UAV swarms can be implemented extensively. The level of human intervention needed to control the system determines the differing levels of autonomy for UAV swarms. For swarms to become more independent, efficient algorithms for task and path planning are essential. In addition, accurate communication is essential for swarms to be able to coordinate and accomplish tasks successfully. This paper seeks to provide a …

Sparse Ensemble Networks For Hypserspectral Image Classification, Rakesh Kumar Iyer, Okan Ersoy

Department of Electrical and Computer Engineering Technical Reports

We explore the efficacy of sparsity and ensemble model in the classification of hyperspectral images, a pivotal task in remote sensing applications. While Convolutional Neural Networks (CNNs) and Transformer models have shown promise in this domain, each exhibits distinct limitations; CNNs excel in capturing the spatial/local features but falter to capture spectral features, whereas Transformers captures the spectral features at the expense of spatial features. Furthermore, the computational cost associated with training several independent CNN and Transformer networks becomes expensive. To address these limitations, we propose a novel ensemble framework comprising pruned CNNs and Transformers, optimizing both spatial and spectral …

Advancing Brain Tumor Segmentation With Spectral–Spatial Graph Neural Networks, Sina Mohammadi, Mohamed Allali

Engineering Faculty Articles and Research

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and …

Techno-Economic Analysis Of Solar Pv Systems Under Various Environmental Conditions, Ramkiran B

Theses and Dissertations

World Energy Council has predicted that the global electricity demand will peak in 2030. The need for renewable energy has grown rapidly due to the rising energy demand in this ever-growing population. Green house gas emissions are predominantly due to the energy generation and consumption of fossil fuels. On the other hand, fossil fuels supply nearly 74% of India's energy demand, making India one of the world's largest coal consumers.

According to World Resource Institute, India ranks fourth in the world in terms of carbon emissions. Investments in renewable energy sources and energy-saving technologies should be part of a solution …

1st Person Ev With Electronic Differential, Tessa Biondo

Honors Theses

The First Person EV with Electronic Differential for Vehicle Dynamics Analysis project makes a significant leap forward in the exploration of electric vehicle (EV) stability and performance enhancement. Lead by a team of skilled electrical engineering students under the guidance of WMU’s associate Professor Dr. Sandun Kuruppu, this project aims to improve EV stability by optimizing power and torque distribution between wheels using an electronic differential system. The project encompasses the design and assembly of a 1/7^th scale custom electric vehicle, capable of remote control via a first-person view (FPV) camera and modified USB wheel and pedal assembly. The …

Controlling The Broadband Enhanced Light Chirality With L-Shaped Dielectric Metamaterials, Ufuk Kilic, Matthew Hilfiker, Shawn Wimer, Alexander Ruder, Eva Schubert, Mathias Schubert, Christos Argyropoulos

Department of Electrical and Computer Engineering: Faculty Publications

The inherently weak chiroptical responses of natural materials limit their usage for controlling and enhancing chiral light-matter interactions. Recently, several nanostructures with subwavelength scale dimensions were demonstrated, mainly due to the advent of nanofabrication technologies, as a potential alternative to efficiently enhance chirality. However, the intrinsic lossy nature of metals and the inherent narrowband response of dielectric planar thin films or metasurface structures pose severe limitations toward the practical realization of broadband and tailorable chiral systems. Here, we tackle these problems by designing all-dielectric silicon-based L-shaped optical metamaterials based on tilted nanopillars that exhibit broadband and enhanced chiroptical response in …

W/V-Band Propagation Modeling, Nolan Rebernick

Electrical and Computer Engineering ETDs

Enhancing the accuracy of atmospheric loss modeling holds the potential to significantly refine estimations of received power, thereby enhancing the overall quality of satellite communication links. This study aims to pioneer and validate an innovative modeling framework to reliably predict atmospheric attenuation along satellite-to-ground propagation paths, particularly focusing on portions of the W-band (81-86 GHz) and V-band (71-76 GHz). By leveraging meteorological data, this approach will encompass various weather scenarios, including clear skies, cloud cover, and diverse forms of precipitation. Utilizing the generated time domain data, the research aims to construct complementary cumulative distribution functions, enabling the analysis of satellite …

Quantum Search Algorithms For Constraint Satisfaction And Optimization Problems Using Grover's Search And Quantum Walk Algorithms With Advanced Oracle Design, Abdirahman Sheikh Hassan Alasow

Dissertations and Theses

The field of quantum computing has emerged as a powerful tool for solving and optimizing combinatorial optimization problems. To solve many real-world problems with many variables and possible solutions for constraint satisfaction and optimization problems, the required number of qubits of scalable hardware for quantum computing is the bottleneck in the current generation of quantum computers. In this dissertation, we will demonstrate advanced, scalable building blocks for the quantum search algorithms that have been implemented in Grover's search algorithm and the quantum walk algorithm. The scalable building blocks are used to reduce the required number of qubits in the design. …

Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti

Electrical and Computer Engineering Faculty Publications

The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of …

Meso-Scale Seabed Quantification With Geoacoustic Inversion, Tim Sonnemann, Jan Dettmer, Charles W. Holland, Stan E. Dosso

Electrical and Computer Engineering Faculty Publications and Presentations

Abstract Knowledge of sub-seabed geoacoustic properties, for example depth dependent sound speed and porosity, is of importance for a variety of applications. Here, we present a semi-automated geoacoustic inversion method for autonomous underwater vehicle data that objectively adapts model inference to seabed structure. Through parallelized trans-dimensional Bayesian inference, we infer seabed properties along a 12 km survey track on the scale of about 10 cm and 50 m in the vertical and horizontal, respectively. The inferred seabed properties include sound speed, attenuation, density, and porosity as a function of depth from acoustic reflection coefficient data. Parameter uncertainties are quantified, and …

Nowcasting Solar Euv Irradiance With Photospheric Magnetic Fields And The Mg Ii Index, Kara L. Kniezewski, Samuel J. Schonfeld, Carl J. Henney

Student Publications

A new method to nowcast spectral irradiance in extreme ultraviolet (EUV) and far ultraviolet (FUV) bands is presented here, utilizing only solar photospheric magnetograms and the Mg II index (i.e., the core-to-wing ratio). The EUV and FUV modeling outlined here is a direct extension of the SIFT (Solar Indices Forecasting Tool) model, based on Henney et al. (2015, https://doi.org/10.1002/2014sw001118). SIFT estimates solar activity indices using the earth-side solar photospheric magnetic field sums from global magnetic maps generated by the ADAPT (Air Force Data Assimilative Photospheric Flux Transport) model. Utilizing strong and weak magnetic field sums from ADAPT maps, Henney …

Designing Embedded Systems With Large Language Models, Keeshan Rama, Paul Truong

Electrical and Computer Engineering Senior Theses

AI has become increasingly involved in many development, research, and education facets. Tools have been made available to lend help in almost every field imaginable. From students asking for homework help on ChatGPT (GPT) to full-stack AI developers with Devin, the future of generative AI tools is now. Large language models (LLMs) have been developed for a breadth of applications and this is just the beginning. The main functionality this research is focused on is ChatGPT’s ability to code, specifically on FPGA boards in Verilog. As generative AI such as ChatGPT becomes more powerful and sophisticated, people should learn how …

Hardware Software Co-Design Of Zero-Knowledge Succinct Non-Interactive Argument Of Knowledge, Dev Shah, Allen Chellasamy

Electrical and Computer Engineering Senior Theses

Zero-Knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) is an important security verification protocol in cryptography. However, zk-SNARK is a computationally expensive protocol in software, meaning that it takes a lot of time. In this paper, we focus on how we can increase the efficiency of the zk-SNARK protocol. The zk-SNARK protocol comprises of many algorithms put together. Our project focuses on optimizing one specific algorithm within the zk-SNARK protocol, the Number Theoretic Transform (NTT). We developed a hardware implementation of the NTT on a Field Programmable Gate Array (FPGA) board. To ensure the proper execution of the hardware, an external …

Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder

Dissertations

Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …

Modeling, Characterization, And Machine Learning Algorithm For Rectangular Choke Horn Antennas, Ibrahim N. Alquaydheb, Saleh A. Alfawaz, Amirreza Ghadimi Avval, Sara Ghayouraneh, Samir M. El-Ghazaly

Electrical Engineering and Computer Science Faculty Publications and Presentations

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical …

Towards A Wearable Device For Measuring Impedance Plethysmography Of The Radial Artery, Pritom Chowdhury

Dartmouth College Master’s Theses

Recent advancements in bioimpedance technology have demonstrated significant promise in the application of cardiac health monitoring. This research explores the design and development of a forearm-based wearable bioimpedance device for non-invasive measurement of heart rate and respiratory rate at an accuracy level comparable to medical-grade monitors. It utilizes a tetrapolar electrode configuration to analyze bioimpedance changes in the radial artery due to blood flow.

An ongoing aspect of this work involves the preliminary development of an embedded framework intended to integrate signal generation, acquisition, and processing within the device to achieve compact and efficient system design, anticipated to contribute to …

Exploring Lstm-Based Attention Mechanisms With Pso And Grid Search Under Different Normalization Techniques For Energy Demands Time Series Forecasting, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao, Bambang Widi Pratolo, Aji Prasetya Wibawa, Agung Bella Putra Utama, Abdoul Fatakhou Ba, Abdullahi Uwaisu Muhammad

Knowledge Engineering and Data Science

Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data. This research aims to forecast the energy demand utilising sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hyperparameters for LSTM models, while the attention mechanism selects the important input for the LSTM. …

Convolutional Neural Network In Motion Detection For Physiotherapy Exercise Movement, Dika Fikri Laistulloh, Anik Nur Handayani, Rosa Andrie Asmara, Phillip Taw

Knowledge Engineering and Data Science

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high …

Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback

Doctoral Dissertations and Master's Theses

College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …

Low Cost Magnetometer Calibration And Distributed Simultaneous Multipoint Ionospheric Measurements From A Sounding Rocket Platform, Joshua W. Milford

Doctoral Dissertations and Master's Theses

Low-cost and low-size-weight-and-power (SWaP) magnetometers can provide greater accessibility for distributed simultaneous measurements in the ionosphere, either onboard sounding rockets or on CubeSats. The Space and Atmospheric Instrumentation Laboratory (SAIL) at Embry-Riddle Aeronautical University has launched a multitude of sounding rockets in recent history: one night-time mid-latitude rocket from Wallops Flight Facility in August 2022 and three mid-latitude rockets from White Sands Missile Range during the October 2023 annular solar eclipse. All rockets had a comprehensive suite of instruments for electrodynamics and neutral dynamics measurements. Among this suite was one science-grade three-axis fluxgate magnetometer (Billingsley TFM65VQS / TFM100G2) and up …

Wireless Power Transfer Using Cavity With Height Discontinuities, Linsheng Zhang

Electrical Engineering Theses and Dissertations

A novel device for efficient wireless power transfer (WPT) system is presented. The proposed WPT system utilizes modified microstrip antennas with height discontinuities (MAHDs) to achieve power transfer. Similar to the conventional WPT system using inductive devices, the power transmission relies on the magnetic field coupling in the near field region. However, the researcher can treat the isolated transmitter or receiver as a modified microstrip antenna, which resembles a capacitive device. Like a capacitive antenna, we can utilize the cavity model, which significantly simplifies field analysis, design and impedance matching process, reduces the overall space requirement, and potentially increases the …

Transformer-Based Deep Learning Model For Sign Language Recognition, Ganzorig Batnasan

Theses

Sign language recognition research aims to develop systems and tools that can interpret and translate sign language into text or spoken language. During the past two decades, the challenges faced in this domain are multifaceted. The first and foremost challenge is the complexity of sign language, which includes intricate hand gestures, facial expressions, and body movements. Recognizing and interpreting these components accurately is challenging. The second challenge is variability among different regions and communities, leading to variations in signs and gestures. This variability poses a challenge for developing universal recognition systems.
Limited data is another challenge which makes it difficult …

Random Forest Algorithm To Measure The Air Pollution Standard Index, Ariyono Setiawan, Untung Lestari Wibowo, Ahmad Mubarok, Khoirunnisa Larasati

Knowledge Engineering and Data Science

This study uses the Random Forest algorithm to measure and predict the Air Pollution Standard Index (APSI) at Blimbing Banyuwangi Airport. Air pollution data, including concentrations of O3, CO, NO2, SO2, PM2.5, and PM10, were collected from air monitoring stations at the airport from April 15-30, 2024. APSI measurement followed established formulas by relevant authorities. Data analysis utilized statistical approaches and computational algorithms. The findings reveal that air quality at the airport is generally "Moderate," with occasional "Good" days. The Random Forest algorithm effectively predicts APSI based on existing pollution data. These results provide insights for improving air pollution management …

A Novel Approach To Defect Detection In Arabica Coffee Beans Using Deep Learning: Investigating Data Augmentation And Model Optimization, Yusriel Ardian, Novta Danyel Irawan, Sutoko Sutoko, I Nyoman Gede Arya Astawa

Knowledge Engineering and Data Science

Arabica coffee beans have valuable market worth because of their taste and quality, and there are defects like wholly and partially black beans that can lower the standards of a product, especially in the premium coffee sector. However, the manual processes used to detect the defects take an inordinate amount of time and are inefficient. This study aims to bridge the knowledge gap on the automated detection and recognition of the defects present in the Arabica coffee beans by creating and optimizing a CNN model based on a modified VGG16 architecture. The model applies data augmentation, rotation, cropping, and Bayesian …

Voxart: Handcrafting Vocal Audio Effects, Samuel Stephen, Anthony Ryan

Electrical and Computer Engineering Senior Theses

Musicians, specifically vocalists, need new ways to integrate artistic expression into their craft. VoxArt is a system utilizing hand movements to control digital audio effects. Giving artistic expression into the hands of the vocalist as a conductor is to an orchestra emphasizing vocals with hand movements will have new meaning for vocalists. The development of the project aimed to use simple, easy to use off the shelf hardware such as a sensor on a glove to measure hand angle positions and in effect, alter audio effect parameters using a closed loop control system. Research and development has led to the …

An Introduction To Nanomaterials For Nanopackaging, James E. Morris

Electrical and Computer Engineering Faculty Publications and Presentations

The multiple purposes of an electronics “package” include the provision of mechanical support to the silicon chip, for example, and protection from the environment, the delivery of power in and the facilitation of heat out, and the reliable input and output of information signals, whether electrical or optical. In the age of heterogeneous integration, this includes the internal conversion of signal modes between multiple technologies within the package, while maintaining the traditional requirement of reliable information transmission between packages, e.g., on a traditional circuit board. This article presents some selected examples of nanopackaging, i.e., the application of nanotechnologies, (nanoparticles, carbon …