Electrical and Computer Engineering Commons^™

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …

Retinomorphic Mid-Wave Infrared In-Sensor Processing Engine: Device-To-Architecture Co-Design, Hemalatha Nagaraju

Theses

Conventional frame-based CMOS image sensors acquire full-frame pixel data at discrete time intervals, resulting in substantial spatial redundancy and loss of temporal information between frames. The repeated conversion and transfer of redundant pixel data increases bandwidth and power consumption in machine vision systems. Retinomorphic sensing architectures address these limitations by enabling programmable, analog-domain processing directly at the sensor interface. A compact behavioral model of the PbSe device is developed in HSPICE based on calibrated TCAD simulation data to capture gate-controlled photocurrent modulation under varying illumination and gate bias conditions. Error analysis is performed to quantify the deviation between TCAD-generated photocurrent …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

Efficient Fpga Implementation Of A 1 Million-Point Fft, Jwaher Abdulqader Al Tamimi

Theses

The one-million-point Fast Fourier Transform is implemented using a radix-2 single-path delay feedback pipeline architecture. To minimize the computational overhead, twiddle factors were pre-computed and stored in memory. The design uses a fixed-point representation with two integer bits and seven fractional bits, achieving a measured signal-to-noise ratio of 37.98. Given the substantial memory requirements, a memory partitioning approach was used. It mapped the delay buffers in each stage lookup table memory, block random-access memory, or ultra random-access memory, based on word width and memory depth.

The implementation operates successfully at 100 megahertz on a mid-scale field-programmable gate array. Post-implementation reported …

Breath-Based Detection Of Liver Cancer Biomarkers Using An Swcnt-Fet Nano-Biosensor: Quantumatk, Mohamed Mohieb Rashdan

Theses

Early detection of liver cancer remains limited by the slow pace and invasiveness of current testing methods. This study proposes a single-walled carbon nanotube field-effect transistor (SWCNT-FET) designed to detect hexanal—a volatile organic compound (VOC) elevated in liver cancer—directly from exhaled breath. The device is modeled in QuantumATK using a semi-empirical Extended Hückel Hamiltonian within the non-equilibrium Green's function (NEGF) framework, emphasizing realistic contact physics by employing metallic SWCNT electrodes instead of conventional metal films. Zigzag channels with (11,0) and (12,0) chiralities are examined to analyze how geometry and contact matching influence charge transport. Simulations include current–voltage (I–V) characteristics and …

Analysis Of A Coaxial Transmission Line Filled With An Orthorhombic Dielectric-Magnetic Medium, Fathima Manikunnath Abdul Akbar

Theses

Coaxial transmission lines are fundamental means for Transverse Electromagnetic (TEM) wave propagation in RF, microwave and high-speed electronic systems. The study of transmission lines is often familiar when they are filled with isotropic materials; however modern engineered direction dependent materials reshape field distributions. In this thesis, we consider a coaxial transmission line of an inner radius and outer radius b filled with an orthorhombic dielectric-magnetic material, which is described by two anisotropy parameters αx and αy. The potential and field distributions are studied in relation to the ratio b/a as well as the anisotropy parameters αx and αy. Due to …

باستخدام مجموعة بيانات متعددة من الطائرات بدون طيار Yolo البحث والإنقاذ البحري القائم على في ظروف الطقس الصعبة, Aysha Ali Alshebli

Theses

Object detection models, powered by deep learning and computer vision, are revolutionizing marine search and rescue (SAR). By analyzing aerial imagery and live drone footage, these systems automatically identify critical targets like survivors, life rafts, and debris across vast and treacherous ocean areas. This capability enhances operational efficiency by reducing human workload and accelerating response times, even in challenging conditions such as poor light, high seas, or cluttered backgrounds. The result is continuous monitoring, faster decision-making, and a significantly improved probability of successful rescue.

Departing from prior methodologies, YOLO introduced a paradigm shift through its single-shot architecture, which concurrently predicts …

Microstrip Antenna Design Based On Ai And Machine Learning, Waleed Mohamed Sha Moulavi

Theses

Microstrip patch antennas (MPAs) rely on precise impedance matching for efficient power transfer between the antenna and feed line. This is often achieved using a number of different techniques, one of which is the quarter-wavelength transformer (QWT). While commercial electromagnetic (EM) solvers offer robust optimization capabilities, they often operate as "black boxes" without providing physical insights into parameter interdependencies. Furthermore, this thesis focuses on the specific scenario where the antenna input impedance is purely real. To address the lack of explicit design relationships for these specific conditions, this thesis develops and comparatively evaluates artificial intelligence (AI) models for QWT width …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

A Study Of The Impact Of Balancing, Geometric Transformation, Generative Networks, And Roi Techniques In Eye Diseases Classification, Sghaira Hareb Alnuaimi

Theses

Automatic detection of ocular diseases helps medical professionals efficiently identify eye disorders, reduce diagnostic errors, and accelerate diagnoses to prevent blindness. Deep learning has been successfully utilized in various fields, including medical image classification. However, in spite of these advancements, challenges remain in ocular disease classification.

The objective of this work is to address these challenges using data processing, data augmentation in combination with Region of Interest (ROI) techniques. Medical datasets often suffer from scarcity, imbalance, and low-quality images, leading to inaccurate classification. To mitigate these issues, we utilize the ODIR dataset, which contains 7,000 labelled training images for both …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Theses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.

The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

Performance Analysis Of Underground-To-Aboveground Communication In Agricultural Iot Networks, Irfana Ilyas Manzil

Theses

This thesis investigates the potential of LoRa, a low-power, wide-area networking technology, for establishing reliable communication between underground sensors and aboveground infrastructure. We comprehensively analyze LoRa's performance in both single-hop and multi-hop configurations, considering the impact of diverse environmental factors such as soil composition, moisture content, underground transmission distance, and path loss on signal propagation. We delve into the crucial role of the spreading factor (SF) within the LoRa communication system, analyzing its influence on network performance. Furthermore, we develop a comprehensive mathematical model for bit error rate (BER) under various channel conditions, including additive white Gaussian noise (AWGN) and …

Marking Estimation In Petri Nets Using Dynamic Mode Decomposition, Aditya Kale

Theses

Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Head Impact Measurement Using Piezoelectric Sensors, Huda Abdulla Alnuaimi

Theses

The importance of safety measures cannot be overstated, especially when it comes to protecting the human head. Head injuries can have severe, life-altering consequences, as the head is crucial for controlling the entire body. Unlike machines that store data, the human brain's capacity to retain thoughts and memories can be significantly affected by even a single injury. This thesis introduces a method for predicting the specific area of the head that might be injured during an impact. The prediction is based on the intensity and duration of the impact. The innovation of this thesis lies in the use of piezoelectric …

Disturbance Observer-Based Control For Pmsg-Based Wind Turbiner Considering Unbalanced Grid Conditions, Moayad Alanani Maher

Theses

A typical Permanent Magnet Synchronous Generator (PMSG)-based wind turbine system consists of an electrical generator, Machine-Side Converter (MSC), Grid-Side Converter (GSC), dc-link capacitor, and a passive filter such as L filter or LCL filter to connect the GSC to the host grid. When the wind turbine operates under unbalanced grid voltages, a negative sequence component is introduced to the system. This negative sequence voltage can pose challenges for classical controllers to ensure an efficient control of the PMSG-based wind turbine system. On one hand, when the control objective is to ensure injecting sinusoidal and balanced three-phase currents to the grid, …

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 …

Economical And Environmental Evaluation Of Non-Residential Demand Response In The European Transition To Zero-Carbon Energy, Markus Fleshutz

Theses

To meet climate objectives, major energy consumers with local multi-energy systems (L- MESs) must transition to renewable energy sources soon. The variability of renewable energies requires increased operational flexibility for integration. In this context, demand response (DR), a strategy in which electricity consumers adjust their load profiles in response to incentives, has become crucial, offering cost-effective flexibility. It supports L-MESs in integrating renewable energy, reducing decarbonization costs, and enhancing resilience. However, quantifying the economic DR potentials for L-MESs under carbon emission constraints is complex, especially when considering investment options in distributed energy resources. This complexity hinders the rapid adoption of …

Gen-Acceleration: Pioneering Work For Hardware Accelerator Generation Using Large Language Models, Durga Lakshmi Venkata Deepak Vungarala

Theses

Optimizing computational power is critical in the age of data-intensive applications and Artificial Intelligence (AI)/Machine Learning (ML). While facing challenging bottlenecks, conventional Von-Neumann architecture with implementing such huge tasks looks seemingly impossible. Hardware Accelerators are critical in efficiently deploying these technologies and have been vastly explored in edge devices. This study explores a state-of-the-art hardware accelerator; Gemmini is studied; we leveraged the open-sourced tool. Furthermore, we developed a Hardware Accelerator in the study we compared with the Non-Von-Neumann architecture. Gemmini is renowned for efficient matrix multiplication, but configuring it for specific tasks requires manual effort and expertise. We propose implementing …

Real-Time Object Tracking With Yolov5 And Recurrent Network On A Hardware Platform, Mohammed Abdulhakeem Alameri

Theses

The advancement of computer vision, particularly in the domain of object tracking, involves the integration of conventional feature-based approaches with contemporary deep learning methodologies. The initial phase of object detection plays a fundamental role in generating prospective objects for further tracking, and its level of success has a direct influence on the overall effectiveness of the tracking process. Many of the methods used to generate candidate objects start with object detection, and then object tracking algorithms are developed to link the object instances together to create the trajectories.
The primary difficulties in object tracking pertain to the establishment of reliable …

A Magneto-Thermally Controllable Microstrip-Patch Antenna For Low Rcs Applications, Sallam Mohammad Al Hendi

Theses

This research focuses on the development of a novel technique for reducing the Radar Cross Section (RCS) of a microstrip antenna operating in the terahertz spectral regime. This can be accomplished by loading the antenna with Indium Antimonide (InSb); a thermally-magnetically controllable semi-conductor. The low-RCS feature of the antenna implies that it becomes hardly detectable by detecting radars; a desired feature in stealthy applications. For an optimal operation beside low RCS, the other antenna parameters (e.g., radiated power, gain, standing-wave ratio, and reflection coefficient) must be within tolerable ranges. Thus, a study on the effects of the temperature and the …

Energy-Aware Resource Control For Dual Connectivity Devices Running Multipath Tcp, Ramiza Shams

Theses

The introduction of Multipath Transmission Control Protocol (MPTCP) allows uninterrupted data transmission through different wireless interfaces simultaneously. It surpasses the performance and reliability of conventional Transmission Control Protocol (TCP). Alternatively, Software Defined Networking (SDN) has revolutionized traditional network management and control by introducing significant changes. It enables the networks to be programmed through a centralized controller that oversees the entire network. Despite that, energy consumption is a remarkable issue when using dual-connectivity wireless devices, most of which are battery-powered. This thesis work primarily investigates the energy value differences of devices under different congestion control algorithms by using different interface configurations …

Characterization Of Low Power Hfo2 Based Switching Devices For In-Memory Computing, Aseel Zeinati

Theses

Oxide based Resistive Random Access Memory (RRAM) devices are investigated as one of the promising non-volatile memories to be used for in-memory computing that will replace the classical von Neumann architecture and reduce the power consumption. These applications required multilevel cell (MLC) characteristics that can be achieved in RRAM devices. One of the methods to achieve this analog switching behavior is by performing an optimized electrical pulse. The RRAM device structure is basically an insulator between two metals as metal-insulator-metal (MIM) structure. Where one of the primary challenges is to assign an RRAM stack with both low power consumption and …

Characterization Of Piezoelectric Coefficient Using Combined Optical And Electrical Methods, Limna Sainudeen Attoor

Theses

The characterization of piezoelectric coefficients, specifically, the extraction of D33 charge constant versus applied voltage profiles plays an important role in MEMS (Micro Electro Mechanical Systems) design and actuation. MEMS structures widely use piezoelectric thin films due to their advantages of possessing high piezoelectric constants and lightweight properties. Hence there is a need for characterizing piezoelectric materials so that one can predict their response during different operating conditions. In this work, a combined electrical and light-based methodology to characterize the voltage dependency profile of d33 is developed. A set of equations are extracted based on the electrical capacitance and light …

A Parametric Numerical Analysis Investigation To Optimize The Performance Of The Ranque-Hilsch Vortex Tube, Muneer Ahmad Sungur

Theses

A vortex tube is a device that separates compressed air into two streams: one with a higher temperature (hot stream) and the other with a lower temperature (cold stream). It is a popular cooling option because it is small, safe, and affordable. The main objective of this thesis is to examine the energy separation performance of RHVT by varying the internal tapering angles of convergent angles (2°, 1.75°, 1.5°, 1.25°, 1°, 0.75°, and 0.5°), straight angles (0°), and divergent angles (0.5°, 1°, 2°, 4°, and 6°), While the cold mass fraction is constant (0.317). Length-to-diameter ratio (Lt/Dt), inlet pressure, and …

A Novel Approach For Detection Fault In The Aircraft Exterior Body Using Image Processing, Noura Nayef Almansoori

Theses

The primary objective of this thesis is to develop innovative techniques for the inspection and maintenance of aircraft structures. We aim to streamline the entire process by utilizing images to detect potential defects in the aircraft body and comparing them to properly functioning images of the aircraft. This enables us to determine whether a specific section of the aircraft is faulty or not. We achieve this by employing image processing to train a model capable of identifying faulty images. The image processing methodology we use involves the use of images of both defective and operational parts of the aircraft's exterior. …

Influencing Factors Of Ionic-Wind In A Spherical Structure Propulsion System For Silent Airplane, Tha’Er Khaled Ibrahem

Theses

Since the first airplane invented 100 years ago, most or all airplanes propulsion systems are based on moving parts powered by fossil-fuel which is impacting our environment by producing unwanted gases such as carbon dioxide, methane, greenhouse gases and more, furthermore, it is well known that there is a degradation in Oil & Gas resources on earth. Therefore, scientists are interested in finding an alternative way to fly an object. Recently, ionic wind-induced by Direct-Current (DC) Corona discharge have been introduced to enhance the future generation of airplanes without the need for moving objects. Corona discharge phenomena occurs when high …

Modeling Of Quad-Station Module Cluster Tools Using Petri Nets, Aung Nay

Theses

The semiconductor industry is highly competitive, and with the recent chip shortage, the throughput of wafers has become more important than ever. One of the tools that the industry has deployed is to use of quad-station modules instead of the traditional single-station modules that allow for higher throughput and better wafer consistency by processing multiple wafers at the same time and distributing work. The industry trend is to use multiple transfer chamber robots to stack the quad-station modules in a series, particularly for etch products. In this work, the quad-station cluster tool wafer movement is modeled by using Petri net …