Electrical and Computer Engineering Commons^™

Efficient Mathematical Modeling And Synthesis Of Realistic Musical Instrument Sounds, Andrew Chookaszian

Master's Theses

This thesis develops and evaluates a compact parametric additive synthesis model for isolated musical instrument tones. The method analyzes a single-note recording, estimates its fundamental frequency, extracts harmonic amplitude and frequency behavior, and stores the sound as a reduced set of interpretable parameters. These parameters include the note duration, pitch, per-harmonic amplitude envelopes, phase information, and amplitude- and frequency-modulation vibrato parameters. The stored model is then used to resynthesize the tone without directly using the original audio waveform.

The model was evaluated using synthetic signals, real instrument samples, objective error metrics, storage comparisons, pitch and duration modification tests, and listening …

Space Quacker Advanced Development (Squad): Lora Modulation On A Leo Cubesat Mission For Evaluation Of 916 Mhz Uplink, Samantha Brunton

Master's Theses

The LoRa Modulation format was developed by SEMTECH in 2018 and has revolutionized terrestrial Internet of Things (IoT) networks. LoRa (Long Range) has been successfully demonstrated as a long-range, low-data-rate communication modulation format and packet protocol for low-power terrestrial applications. LoRa offers low power consumption, low cost, robust signal sensitivity, and long communication range. Recently, interest has grown in using LoRa communication in Low Earth Orbit (LEO) satellite systems, especially for global IoT connectivity. Previous studies have investigated the theoretical feasibility and simulated the performance of LoRa satellite communication. Companies such as Lacuna Space have demonstrated the practical potential of …

Analysis And Design Of A 2-Bit Millimeter-Wave Reconfigurable Intelligent Surface And Investigation Of Fss-Assisted Angular Stability, Max Spalek

Master's Theses

Millimeter-Wave (mm-wave) technology utilizes high frequency spectrum and can deliver multi-gigabit speeds, ultra-low latency, and massive bandwidth capacity. However, this technology is rather new and many technical challenges remain. On the other hand, Reconfigurable Intelligent Surfaces (RIS) have emerged as promising structures for controllable electromagnetic wavefront manipulation in wireless communication systems. This thesis presents the design of a 2-bit RIS unit-cell element operating throughout the 30–34 GHz mm-wave band, where the structure was progressively refined from an initial resonating geometry toward a grounded reflective RIS configuration. The proposed structure utilizes PIN-diode switching states to produce four discrete reflected phase states …

Evaluating The Cost-Benefit Tradeoffs Of Simt Control Mechanisms In Resource-Constrained Gpus, Nikolas Tambornini

Master's Theses

This thesis conducts a hardware-level analysis of the cost–benefit tradeoffs associated with increasingly complex SIMT control mechanisms in a resource-constrained GPU core. Four design implementations are evaluated: a Base Tiny GPU without warp scheduling capability; a Warp Scheduler using round-robin warp selection; a Branch Divergence implementation incorporating a dedicated divergence stack and post-dominator reconvergence mechanism; and a Dynamic Warp Allocation implementation that replaces the static warp structure with a runtime warp manager, regrouping threads by current program counter to recover SIMD lane utilization during active divergence. Each design is evaluated using two complementary measurements: functional simulation implemented using the CocoTB …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Transcranial Ac Modulation Of Cerebellar Nuclear Activity In Awake Animals, Nuran Kavakli

Dissertations

Entrainment of cerebellar nuclear (CN) cells via cerebellar transcranial alternating current stimulation (ctACS) has been reported in animals under ketamine/xylazine anesthesia. Our main objective was to demonstrate modulation of CN activity in unanesthetized, freely moving animals using ctACS. Multi-channel carbon-fiber electrodes were implanted into the interpositus nucleus for recording multi-unit (MU) activity, and thin-film electrodes were implanted subcutaneously over the posterior cerebellum for stimulation. A frequency-domain-based metric was developed to quantify modulation from MU signals. The results demonstrated modulation in a wide range of frequencies (4 Hz-300 Hz) as in anesthetized animals. In contrast, the amplitude of the peak in …

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 …

Developing A Framework For Mooc Dropout: The Role Of Utilitarian And Hedonic Values In Student Retention, Bipllab Roy, Mave D'Souza, Madhura Laghane

Northeast Journal of Complex Systems (NEJCS)

Massive Open Online Courses (MOOCs) have expanded access to higher education but continue to face persistently high dropout rates, raising concerns about their long‑term effectiveness and sustainability. This study develops and empirically tests a structural framework that links utilitarian values (perceived usefulness, certificate value, time flexibility), hedonic values (enjoyment, variety and novelty, personal interest alignment), and individual characteristics (goal orientation, self‑efficacy, motivation type) to MOOC student retention. Data were collected through a structured questionnaire administered to 200 MOOC learners from Christ University, Lavasa Campus, and analyzed using Structural Equation Modelling (SEM) in AMOS. The results show that goal orientation, self‑efficacy …

Modulating Electronic Structure With Linearly Fused Pyrazine Units For High-Voltage And Stable Zinc-Organic Batteries Cathode, Min-Jian Zhao, Li-Bin Zhang, Jin-Tao Wang, Kun Ding, Hai-Mei Liu, Yong-Gang Wang

Journal of Electrochemistry

High-voltage n-type organic cathode materials are critical for constructing zinc-organic batteries (ZOBs) with high energy density and long cycle life. However, the intrinsically unfavorable electronic structures and relatively high LUMO energy levels of most n-type materials often lead to sluggish kinetics, high solubility, and suboptimal discharge voltages (< 0.8 V). Here, we design a small molecule, quinoxalino[2’,3’:5,6]pyrazino[2,3-f][1,10]phenanthroline (DPQP), as a ZOB cathode by introducing locally electron-deficient motifs into the conjugated backbone of aromatic compounds. The linearly fused pyrazine units extending the pyrazine–benzene framework effectively optimize the electronic structure, thereby significantly enhancing the discharge voltage. Meanwhile, the expanded π-conjugated plane suppresses dissolution and accelerates charge-transfer kinetics. Benefiting from these features, the DPQP electrode exhibits an exceptional increase in average operating voltage from 0.61 V to 1.07 V (vs. Zn²⁺/Zn) at 0.1 A·g^–1, with an overpotential of only 140 mV. Notably, no discernible voltage decay occurs as the current density increases, indicating rapid and highly reversible redox kinetics. Furthermore, the DPQP cathode delivers outstanding cycling stability, maintaining over 2000 h of continuous operation at 0.1 A·g^{–1 …}

(Co,Ni,Mn,Cu,Zn)O High-Entropy Oxide Nanotubes As Efficient Bifunctional Electrocatalyst For Oxygen Evolution And Hydrazine Oxidation Reactions, Pan-Yan Chen, Wan-Wan Wu, Heng Bian, Wei-Wei Li, Xin-Sheng Zhao, Lu Wei

Journal of Electrochemistry

High-entropy oxides (HEOs) present significant scientific challenges in both design and synthesis due to their multielement and high-entropy nature, which involves complex combinations of multiple metal cations and oxygen anions, typically arranged in equimolar ratios to achieve structural stability. Herein, one-dimensional (Co,Ni,Mn,Cu,Zn)O high-entropy oxide nanotubes (HEO-NTs) are fabricated by means of a gradient electrospinning strategy with a tailored polyvinyl alcohol (PVA) molecular weight distribution and controlled pyrolysis. Benefiting from the HEO features and the synergistic effect of multicomponent sites, the as-synthesized (Co,Ni,Mn,Cu,Zn)O HEO-NTs exhibit exceptional bifunctional electrocatalytic activity for the oxygen evolution and hydrazine oxidation reactions (OER/HzOR). This study offers …

Exploring Maximal Length Cellular Automata To Generate Primitive Polynomials In Gf(2), Sumit Adak, Subhrajit Deb, Anurag Ghosh, Angshuman Roy, Souvik Roy

Northeast Journal of Complex Systems (NEJCS)

We present a simple method that uses cellular automata (CAs) to find primitive polynomials over GF(2). We used maximal length CAs as tools to generate primitive polynomials. It is usually very difficult to find maximal length CAs or primitive polynomials since they require exponential time, and there is no linear time method. However, in our work, given an n-size specific sequence of CA with reasonable probability, our technique computes a cycle of length at most 2^n-1 (maximal length) in O(n) time. The characteristic polynomials of synthesized maximal length CAs are claimed to be primitive since it was previously established that …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Les Grands Chantiers De La Transition Énergétique Au Maroc: Le Cas De Dakhla-Oued Eddahab Entre Réalisations, Facteurs De Réussite Et Perspectives, Limam Boussif, Jalila Ait Soudane

Journal of Maya Heritage

Résumé: Cet article examine la dynamique de la transition énergétique au Maroc à travers une analyse descriptive et analytique du cas de Dakhla-Oued Eddahab. Dans un contexte marqué par l’accélération des politiques de décarbonation à l’échelle mondiale, le Maroc s’impose comme un acteur stratégique en matière de développement des énergies renouvelables et de l’hydrogène vert. L’étude met en lumière les principaux chantiers structurants engagés dans la région de Dakhla, notamment le développement des énergies éolienne et solaire, les projets de production d’hydrogène vert, le dessalement de l’eau de mer, ainsi que la réalisation du port Dakhla Atlantique. L’analyse montre que …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

Characteristics And Implementation Paths Of Goal For Building Energy Powerhouse, Liye Xiao, Jiaofeng Pan, Xiaojiong Wang, Deqiang Sun, Mingliang Qi, Jianlei Mo, Huimin Li, Ting Wang, Jie Yang, Jie Lin, Yuchao Wang, Haiting Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

China is a major energy-consuming country, and ensuring effective energy supply is one of the core tasks for promoting national development and national rejuvenation. To guarantee national energy supply and energy security, and to vigorously develop and utilize clean and low-carbon energy, the Outline of the 15th Five-Year Plan for National Economic and Social Development of the People’s Republic of China clearly states: “We will thoroughly implement the new energy security strategy, accelerate the construction of a clean, low-carbon, safe, and efficient new energy system, and build a strong energy nation. We will promote the safe, reliable, and orderly replacement …

Visualization And Marker-Less Tracking Of User-Defined Pre-Processed Mri Articulator Data Using Deep Learning, Michael De George

Student Theses

This thesis presents a comprehensive framework for the automated tracking and visualization of articulatory movements based on magnetic resonance imaging (MRI) data. A well-known data analysis tool for markerless pose estimation, known as DeepLabCut, is investigated for this purpose. The performance of this tool is enhanced through the design and implementation of a pre-processor. DeepLabCut is a markerless pose estimation toolbox based on deep learning, which overcomes the issue of making manual annotations frame-by-frame. Limitations from manually marking the MRI images are addressed by implementing transfer learning with convolutional neural networks to achieve accurate, user-defined articulator tracking without markers. Current …