Signal Processing Commons^™

Design Of A Portable Fast Scan Cyclic Voltammetry Device Utilizing Pulse Width Modulation For Waveform Generation, Nora Szymkowski

Masters Theses

Fast Scan Cyclic Voltammetry (FSCV) is a widely used electrochemical technique for real-time measurement of the brain’s chemical messengers, including the molecule dopamine, with high temporal resolution. Currently the financial burden of performing FSCV is quite high, ranging from $8,000 to $20,000+ making the barrier to entry nearly insurmountable for laboratories and classrooms at small institutions. The purpose of this project was to develop a Do-It-Yourself (DIY), portable, and cost-effective FSCV system for use in laboratory and classroom settings. The project aimed to create a compact and cost-effective system that could be used by researchers and educators to study dopamine …

Novel Sensors, Algorithms And Metrics For Human-Robot Interaction., Henry Lee Reynolds

Electronic Theses and Dissertations

The increased presence and deployment of robotics in sectors such as the medical field results in the demand for robots to, directly and indirectly, interface with people and their environment, making human-robot interaction (HRI) a vital thrust of robotics research. Assistive robots, for example, aid humans in accomplishing tasks or by providing support in the workforce. As the demand for nurses and the aging population increases, the assistive robots deployed will be deeply rooted in environments that require constant interaction with humans. This work contributed to improving aspects of HRI through 1) Expanding accessibility of the methods used for interfacing …

Computer Vision Algorithms For Assessment Of Surgical Suturing Skill Using Hand And Needle Motion, Jianxin Gao

All Dissertations

Surgical suturing skill assessment is a crucial part of surgical education. Vascular surgery educators have developed a simulation-based examination called Fundamentals of Vascular Surgery, which includes a clock-face model for assessing open surgical suturing skills. The clock-face model, however, requires the valuable time of expert surgeons to determine examinees' skills. Moreover, expert surgeons have different judgments for appropriate sutures, which leads to inconsistent grading. These limitations motivate us to use sensors to measure examinees' needle motions and hand motions during the clock-face suturing exercises, and then use the measurements for objective suturing skill assessment.

To assess suturing skills based on …

Modeling, Optimization, And Characterization For Choke Horn Antennas, Ibrahim Nasser I Alquaydheb

Graduate Theses and Dissertations

This dissertation presents a comprehensive study focusing on the modeling, optimization, and characterization of choke horn antennas (CHAs). An analytical model designed to capture the parameters of CHA and derive the total radiated fields from the choke and waveguide elements is primarily focused on in this work. Compared to the use of simulation software, such as ANSYS HFSS (High Frequency Structure Simulator) or CST Studio, which employs numerical methods to simulate and calculate antenna performance, numerous advantages are offered by the analytical model. Analytical models provide deeper insight into electromagnetic interactions and the principles governing antenna behavior, leading to a …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

Segmentation And Classification Of Left Ventricular Abnormalities In Cardiac Mri Using Initial Point Prediction Based Deformable Model, Md. Asadur Rahman, Md. Al Noman, A. B. M. Aowlad Hossain

Future Computing and Informatics Journal

The shape of the left ventricle (LV) of a cardiac magnetic resonance image (CMRI) helps physicians to diagnose different cardiac abnormalities. The similarity of pixel intensity and shape of LV with neighbor tissues, the imprecision of boundaries, and the presence of noise are the challenges to accurate segmentation of LV. This paper contributes to the successful implementation of an automatic edge contouring method to segment LV area from CMRI and detect whether the ventricle belongs to abnormalities. This method proposes the regression-based artificial neural network to predict the possible initial position of the deformable edge-based active contour model for precise …

Multi-Classification Model For Brain Tumor Early Prediction Based On Deep Learning Techniques, Abdelrahman T. Elgohr, Mohamed S. Elhadidy, Mahmoud Elazab Dr, Raneem Ahmed Hegazii, Moataz M. El Sherbiny

Journal of Engineering Research

Brain tumor early prediction is a critical task in medical imaging, as early detection and classification of tumors can significantly improve patient outcomes and treatment planning. In this study, we propose multi-classification models based on deep learning techniques for early prediction of brain tumors using magnetic resonance imaging (MRI) scans. Specifically, we investigate the effectiveness of Convolutional Neural Networks (CNN) in the You Only Look Once (YOLO) approach for an accurate classification of brain tumors into multiple classes based on their morphological characteristics. The proposed model is designed to extract spatial features from MRI images, capturing local patterns and structures …

Using A Neural Network To Remove Noise From Images, Anvar Asatilloyevich Ravshanov

Chemical Technology, Control and Management

This article proposes modern approaches to the problem of noise reduction in images using neural networks and also analyses the possibilities of noise reduction using neural networks. The convolutional neural network model and the Mediana, Sobel filter were considered for image denoising. The quality improvement of the trained neural network and the comparison with classical noise reduction methods have been carried out.

Shape Memory Alloy Capsule Micropump For Drug Delivery Applications, Youssef Mohamed Kotb

Theses and Dissertations

Implantable drug delivery devices have many benefits over traditional drug administration techniques and have attracted a lot of attention in recent years. By delivering the medication directly to the tissue, they enable the use of larger localized concentrations, enhancing the efficacy of the treatment. Passive-release drug delivery systems, one of the various ways to provide medication, are great inventions. However, they cannot dispense the medication on demand since they are nonprogrammable. Therefore, active actuators are more advantageous in delivery applications. Smart material actuators, however, have greatly increased in popularity for manufacturing wearable and implantable micropumps due to their high energy …

Technologies For Wearable Seizure Detection: A Systematic Review, Rhema Losli

University Honors Theses

Knowing when a seizure occurred is helpful because this information can be used to evaluate the effectiveness of seizure interventions and possibly alert caregivers to emergency situations. The current practice for recording seizures outside of a hospital and without sensors is through keeping a self-reported seizure diary. This practice may be unreliable if the diary is not updated or the person having the seizure does not realize it is happening. Wearable seizure detectors aim to solve this problem by reliably recording when a seizure happened and either sending out an alert or storing the data for later analysis. In this …

Digital Audio Synthesizer, Philip A. Schremp, Alexander J. Elliott

Electrical Engineering

With more and more of the modern music production and live performance workflow becoming digitized synthesizers are the perfect middle ground between the traditional instrument layout and the progressive electronic sound. Despite the fact that synthesizers have made their place in the modern music pipeline they have one significant drawback. Synthesizers are expensive. Whether they are analog or digital, many of the synthesizers produced today are not affordable for the average musician or synth hobbyist. We will be designing a cost effective digital audio synthesizer that will still have enough versatility and a high enough quality to be useful in …

Harmonic Flow Modeling And Analysis Of A Green Hybrid Ac/Dc Seaport Power System, Krishan Kaushal Ram, Taufik, Helen Yu, Siddharth Vyas

Master's Theses

This thesis aims to design and develop a model for a proposed Green Seaport power system and perform harmonic analysis. The model was developed using MATLAB Simulink and tests were performed by dividing the system into several Battery Energy Storage System (BESS) operating modes such as simultaneous charging and discharging, simultaneous charging, and simultaneous discharging. For each mode, BESS state of charge and other factors such as solar irradiance for the PV system, load levels and power factor were varied to observe the impact on system’s voltage Total Harmonic Distortion (THD) level and current Total Demand Distortion (TDD). 62 separate …

Remote Profiling Of Atmospheric Turbulence: Enhanced Resolution With Stacked Rayleigh Beacons In Tardis, Benjamin C. Wilson

Theses and Dissertations

A stacked beacon turbulence profiling methodology has been introduced and applied to have increased profiling on the Turbulence and Aerosol Research and Investigation System (TARDIS). The model was derived and demonstrated the applicability through discussion on data processing and inversion processes. The methodology was applied for two different nighttime experiments, one in Summer and one in Fall. C 2 n profiles were derived into the 1300 m altitude ranges for both nights and the summer experiment was compared to a co-located DELTA Sky measurements and LEEDR generated Climatological profiles. The comparison implied promise in the methodology with additional work needed …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Design Of High Efficiency Doherty Power Amplifier, Kobi G. Kelly, Severin Pindell

Electrical Engineering

The project includes design and fabrication of a high efficiency power amplifier for a student design competition held at International Microwave Symposium (IMS) 2023. Efficient power amplifiers are critical for base station communication requiring efficient use of available power. The final design optimizes power added efficiency (PAE) and linearity. The amplifier will operate at 2.45 GHz. Competitive PAE above 50%, and C/I above 30 dB is achieved by leveraging a Doherty class amplifier using accurate discrete CGH4006P transistor models to simulate an efficient and linear design. Unique design features include optimal transistor bias point selection and power split ratios between …

2-Channel Eeg Neurofeedback System, Tim Erwin, Donna Nikjou, Sebastian Turkewitz

Electrical Engineering

This work describes the design of an EEG-based neurofeedback system which provides users with real-time feedback on their level of focus or relaxation. By analyzing the spectral content of the brain activity measured via scalp electrodes, focus and relaxation levels can be quantified. Based on these measurements, live feedback in the form of moving bar graphs is provided to users, allowing them to gain awareness of their mental-state and more efficiently learn how to consciously relax or focus. This project covers the design of the system, including amplification and filtering stages, digitization, and signal processing. The system interfaces with a …

Enhancing Direction Finding Accuracy In Perturbed Digital Arrays Via Rf Ranging-Based Self Calibration, Ariel Freiman

Master's Theses

Direction finding with radio-frequency (RF) waves have numerous applications in radio navigation, wireless localization, emergency aid, and air traffic control, among others. Direction-finding using digital arrays outperforms traditional analog techniques but requires precise knowledge of the location of the array elements to obtain accurate results. Array perturbations can lead to algorithm failures and false detection, compromising direction-finding capabilities.

This research proposes implementing a Matched Filter - Least Square (MF-LS) algorithm for Two-Way Ranging (TWR) to enhance direction-finding accuracy in arrays with perturbed element locations. The MF-LS algorithm leverages the properties of matched filters to accurately determine element positions by measuring …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Aerospace Vehicle Comprising Module For Method Of Terrain, Terrain Activity And Material Classification, Matthew E. Nussbaum, Marissa S. Herron

AFIT Patents

A method of classifying terrain, terrain activity and materials through panchromatic imagery a module programmed to provide such classification and aerospace vehicles comprising such module is provided. Panchromatic images of known materials terrains and terrain activities are taken and processed to form a multiband texture cube, that due it amount of data, is stored as a computer data base. New panchromatic images of unclassified materials, terrains and/or terrain activities are processed and compared via computer with such database that allows for inexpensive, quick and efficient classification of such new images.

Scalability And Connectivity Challenges For The Future Of Digital Radio Communication, Christopher Bayliss

Honors Program: Senior Projects (Public)

This thesis explores future challenges in digital radio communication, particularly focusing on scalability and connectivity issues that could impede technological advancements. With the increasing production of digital technology and reliance on radio frequencies, there is a need for innovative solutions to overcome limitations in spectrum availability, interference management, and bandwidth constraints. This work evaluates current modulation techniques, spectrum allocation strategies, and advanced communication technologies such as cognitive radio systems and reconfigurable intelligent surfaces. Through a comprehensive literature review and analysis, this thesis identifies promising developments, including dynamic spectrum sharing and symbiotic radio systems, that could significantly enhance spectrum efficiency and …