Signal Processing Commons^™

Dual-Interface Wifi Packet Sniffer System Using Esp32-Cam With Real-Time Pcap Generation For Iot Network Analysis, Boy Setiawan Boy, Maghfiroh Maulani, Zico Pratama Putra, Muhammad Senoyodha Brennaf

Makara Journal of Technology

This study focuses on designing and implementing a cost-effective and energy-efficient WiFi packet sniffer system using the ESP32. The ESP32-CAM module, which combines WiFi, Bluetooth, and microSD support, is used to capture IEEE 802.11 frames in real-time via promiscuous mode. Packets are stored in packet capture format, which is compatible with tools such as Wireshark and Scapy. Developed using the official ESP-IDF, it offers low-level control and high performance. Two user interfaces were implemented: a UART-based text menu and a web-based HTTPS menu hosted on the ESP32 itself. Functional and performance evaluations were conducted with a focus on capturing broadcast …

Wind Tunnel Instrumentation And Testing, Nicholas Marek, Abraham Mezera, Laura Jin, Hayden Smith, Curtis Cook

Student Scholar Symposium

The Raymond B. Jones College of Engineering was contacted by an automotive engineering firm seeking to use the college’s wind tunnel for gathering data on the aerodynamic performance of a proprietary prototype automotive door. Specifically, the client requested the quantification of the drag coefficient of the model at extreme wind speeds. The drag coefficient, a dimensionless number that quantifies the resistance of a specific geometric shape to airflow, will be a valuable datapoint for the client’s design iteration. Due to the sensitive nature of their work, the client has wished to remain anonymous. RBJCOE professors tasked a senior design team …

A Predictive Model For Multi- Week Respiratory Risk From Red Tide On Florida’S Gulf Coast., Elmer S. Ochaeta

Computer Science and Engineering Faculty Publications

Florida’s Gulf Coast red tide (Karenia brevis) can put toxins into the air, making people cough, irritating the throat, and worsening asthma or other breathing problems especially when winds blow from the ocean toward the beach. Right now, most public updates don’t really help with the question people actually ask when planning a weekend or vacation: “Will going to or close to the beach be risky in the next few weeks?”.

In this project, I build a weekly early warning system that estimates respiratory risk for specific beaches and predicts that risk 2 to 4 weeks ahead. The study covers …

Iso-Detr: A Novel Detection Transformer For Industrial Small Object Detection, Faisal Saeed, Anand Paul

School of Public Health Faculty Publications

Effectively detecting and assessing real-time structural and ecological parameters in contemporary manufacturing environments poses significant challenges, particularly in identifying minute objects within product images. The swift evolution of the industrial sector underscores the necessity for intelligent manufacturing environments to uphold stringent product quality standards. However, accelerating production processes at high speeds heightens the risk of defective product outcomes. This research addresses the challenges inherent in small object detection within industrial contexts, proposing an innovative detection transformer model tailored to modern manufacturing environments. The proposed model integrates a feature-enhanced multi-head self-attention block (FEMSA), merging cross-channel communication network and multiple multi-head self-attention …

Adaptive Deep Learning In Physical Layer Applications, Ali Owfi

All Dissertations

Traditionally, signal processing models in communication systems have been designed based on solid foundations in statistics and information theory, often assuming linearity and optimizing for simplified models. However, real-world communication systems exhibit numerous imperfections and non-linearities that traditional linear models struggle to capture accurately. Deep Learning (DL)-based approaches, unconstrained by rigid mathematical models, have shown promise in optimizing system performance by accommodating specific hardware configurations and dynamic channel conditions as an alternative to the traditional methods. Despite all the recent research efforts on DL-based methods for physical layer applications, DL models have still not been widely applied to physical layer …

Deterministic Methods To Improve The Field-Of-View For Direction Finding Using Sparse Digital Arrays, Nolan J. Egging

Master's Theses

Direction finding algorithms are used with digital phased arrays to determine the incoming angle of arrival (AoA) of an incident signal. These algorithms, and direction finding as a whole, have a wide range of civilian and military applications from radar, electronic reconnaissance, mobile communication, et cetera. However, for situations where the spacing between antenna elements needs to be large, gating lobes appear in the radiation pattern of analog arrays. This work demonstrates that for digital beamforming algorithms, the field of view (FoV) of a uniform linear digital array matches the grating lobe free range of a similarly spaced analog array. …

Beam Steering Control For A Small-Scale 5g Antenna Array, Omar Wagih Elkalesh

Graduate Theses and Dissertations

This thesis demonstrates the design, development, and validation of a full-scale adaptive beam steering control system of a small-scale 5G antenna array, designed and thoroughly evaluated with MATLAB Simulink. Overcoming the significant challenge of establishing strong wireless links in dynamic environments, the new system uses a new closed-loop control framework that can automatically detect the signal sources and automatically change the beam direction in real-time. The use of the adaptation is necessary to combat tracking errors that are caused by the mobility of the mobile users, environmental variations, as well as channel estimation errors, widespread in future wireless communication systems. …

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.

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon

Student Theses

For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable …

Analog To Digital Converters Topologies For Radar System Application: A Comparison, Kulsoom Mateen, Wafa Alharbi, Aziza I. Hussein

Effat Undergraduate Research Journal

Analog-to-digital converters (ADCs) that convert analog signals into digital ones play a significant role in radar systems. The accuracy and resolution of radar readings are significantly influenced by the quality and performance of ADCs. This paper discusses and compares the application of five different types of ADCs in radar systems. It also elaborates on each ADC's working principle, advantages, and limitations. The parameters compared are resolution and dynamic range, signal-to-noise ratio (SNR), latency and sampling rate, power consumption, size, and cost. After thorough research, we concluded that each ADC differs depending on the designer’s desired application. For example, flash ADCs …

Exploring Longitudinal Stability Of Spatiotemporal Sequential Patterns By Means Of Autoencoder Schemes For Eeg Based Personal Identification, Muhammed E. Oztemel

LSU Doctoral Dissertations

Robust personal identification remains a critical and challenging task in the digital era. Electroencephalography (EEG) offers a unique biometric modality that captures individual brain dynamics through complex neural signals. This dissertation proposes autoencoder (AE) based feature extraction and subject identification through these features. EEG recordings are first transformed into topographic maps to represent spatial brain activity. Consecutive topomaps are then concatenated to capture temporal transitions across frames. Convolutional autoencoders (CAEs) are used to learn spatial and temporal patterns, while domain-adaptive AEs are designed to model evoked potential based responses. Additionally, self-attention mechanism is incorporated to enhance feature representation. To analyze …

Method For Target Detection In A High Noise Environment Through Frequency Analysis Using An Event-Based Vision Sensor, Will Johnston, Shannon Young, David Howe, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter

Faculty Publications

Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

A Deep Learning Approach For Semantic Segmentation And Its Application On Ctc., Samir Farag Harb

Electronic Theses and Dissertations

This dissertation explores the modeling and analysis of medical images, focusing on the intricate task of colon segmentation and subsequent 3D reconstruction, which are critical steps in Computed Tomography Colonography (CTC) systems. The primary objective of this research is to develop precise segmentation approaches to enhance the accuracy of colon identification and reconstruction from abdominal CT scans. Three distinct segmentation approaches are proposed and evaluated: a Markov Random Field (MRF)-based approach, a convolutional neural network (CNN)-based deep learning (DL) approach, and a sequential episodic training with dual contrastive learning Approach (G-SET-DCL) that has a flavor of few-shot learning (FSL). To …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

3d Solid Models, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

3D solid models for model vehicles, target panels, objects, and the desert terrain model in STL file format.

Ground Truth Images, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Laboratory and scenario ground truth images for the Model Desert Terrain Monochromatic DoT dataset.

Asl File Reader (Matlab), Bradley M. Ratliff

Source Code

This zip file contains the necessary files for reading binary ASL data and reading and parsing ASL header file metadata in MATLAB.

Polarimetric Data: Scenario 01, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric data Scenario 01 collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

Data Annotations, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Pixel-wise object masks for each polarimetric scene in ASL file format for the Model Desert Terrain Monochromatic DoT data.

Dataset Description, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric dataset containing data collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. A model desert terrain model was constructed and imaged for eight different scenarios consisting of different model panel and vehicle targets. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

A Yolo-Based Deep Learning Approach For Vibration-Based Rotating Shaft Imbalance Detection, Abdelhameed Zayed Dr., Ali Siam Dr.

Journal of Engineering Research

One of the prevailing causes of vibrations in machines is rotor imbalance. Rotor balancing can be used to fix the majority of rotating machinery issues. When it comes to high-speed running equipment, even a slight imbalance can lead to serious issues and decrease the operational efficiency of rotating machinery. This work proposed a deep learning approach for the detection of binary and multiclass imbalance in rotating shafts. A YOLOv11 model-based approach is developed to detect imbalance and identify unbalanced rotor positions. To precisely identify unbalanced positions, this method trains the YOLOv11 model using numerous sets of measured response data and …

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Leveraging Machine-Learning Algorithms In Two Car Crash Detection Systems On A Custom Dataset, Addison Jacob Sandvik

Master's Theses

Traffic accidents pose a significant threat to public safety, causing millions of deaths and injuries worldwide each year. While efforts to reduce accidents have seen limited progress in recent years, improving emergency response times through automated detection systems is a promising avenue for saving lives. This thesis describes the development of machine learning-based traffic accident detection systems, exploring both video classification and image detection models. The models are trained on a new dataset deemed the Cal Poly Traffic Accident Dataset, an extension of the existing Car Accident Detection and Prediction (CADP) dataset with a precise collision annotations. Two systems were …

Coherent Synchronization For Distributed Digital Phased Arrays, Zachary C. Numa

Master's Theses

Distributed digital phased arrays are rising technologies that help enable applications such as search and rescue operations, wireless communication, radar navigation, and military operations, among many others. Due to their improved angular resolution, digital phased arrays offer superior direction-finding capabilities compared to traditional analog phased array systems. However, this improvement comes at the cost of increased complexity—specifically, the need for precise synchronization of phase, time, and frequency across physically separated nodes. Without synchronization, the distributed phased array's gain and direction-of-arrival (DoA) estimations deteriorate significantly.

There are multiple aspects to implementing and synchronizing a non-stationary distributed digital phased array. This research …

Single-Sideband Pulse Width Modulation For Parametric Acoustic Arrays, Douglas Liu

Master's Theses

Parametric acoustic arrays are directional loudspeakers that operate using ultrasonic carriers to project sound within a narrow beam. Input audio is first modulated onto an ultrasonic carrier and transmitted through air, where it self-demodulates into audible frequencies in the far field.

This thesis introduces a method for preprocessing audio into scaled quadrature signals using a passive analog polyphase filter. These signals are modulated using microcontroller-generated waveforms to create quadrature ultrasonic pulse-width modulation (PWM) signals. The modulated outputs are combined through a wired-OR summer, producing single-sideband ultrasonic content at the desired frequency of 40 kHz. This signal is amplified through high-efficiency …

Single-Sided Hearing Assistance Application, Harryson Nguyen, Jp Haratani

Electrical Engineering

The product is a hearing assistance smartphone application which aims to improve the quality of life of people with single-sided hearing loss/deafness by providing them with a cheaper and more reliable alternative to medically-prescribed hearing aids. Due to development setbacks, the product instead uses open-ear earbuds. Since the earbuds are not directly inserted into the user’s ear canals, they will not obstruct the hearing of the user’s non-deaf ear, and the user will still be able to hear the sounds that come from their non-deaf side. Some users may find that Bluetooth earbuds can be quite expensive, but are ultimately …

Piezoelectric Actuator Driver System, Daniel Hoefer

Electrical Engineering

This work presents the design, fabrication, and testing of a high-voltage piezoelectric actuator driver system purposed for integration into defense and aerospace precision motion control applications, particularly those in laser optics. The core challenge addressed was the mismatch between the standardized 28V DC power bus used in military systems and the significantly higher voltage requirements of piezoelectric actuators. Existing commercial drivers were evaluated and found unsuitable for field deployment due to inadequate ruggedness, excessive complexity, or power limitations. Consequently, a modular system architecture was designed featuring four independent driver channels, each using a high-voltage, high-current linear amplifier topology to scale …

Synthetic Aperture Radar Processing For Increased Resolution Of Englacial Strata, Ava Stockman

Electrical Engineering

Glaciological research relies on a variety of remote-sensing methods to study the internal structure of glaciers and other bodies of ice. Synthetic Aperture Radar (SAR) serves as one method for imaging large cross sections of englacial strata, reducing the need for labor-intensive, costly, and hazardous ice coring expeditions and operations. Characterizing englacial structures using SAR images allows scientists to learn about the ice dynamics of past and present which provides the basis for discoveries about climate history and glacial dynamics. This project explores the benefits of employing various SAR processing methods to raw phase history data acquired through the British …

Field Deployable Mobile Manipulator For Autonomous Apple Harvesting, Gabriel K. Basus, Antonio Bowen, Andrew Daouda

Electrical Engineering

Increasing labor costs and agricultural demands have created a need for automated apple harvesting. However, automated apple harvesting via robotic manipulation must overcome certain challenges to be an effective and efficient method. First, the system must compete with or perform better than manual human labor. Second, it must safely and accurately navigate an apple orchard autonomously. Third, the robotic manipulator must be able to securely grasp and pick apples without causing damage. To address these challenges, this project utilizes a Husky UGV equipped with a 2D LiDAR sensor, an RGB-D camera, an IMU, an OpenManipulator-X robot arm, and a soft …