Electrical and Computer Engineering Commons^™

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Distributed Energy Trading Models Utilizing Reinforcement Learning And Game-Theoretic Based Approaches In Smart Grids, Nicholas Kemp

Electrical and Computer Engineering ETDs

Community-driven energy initiatives have become crucial for effective energy management, particularly in trading and management. The rise of Distributed Energy Resources in smart grids demands a redesign of traditional Demand Response Management (DRM) models to account for prosumers' dynamic behavior in energy markets. Decentralization, including peer-to-peer (P2P) energy trading, is vital for resilience and sustainability. This thesis introduces two coalitional DRM models: one based on hedonic community formation games, and the other combining matching theory with coalition games. These models empower prosumers to autonomously select energy trading communities using partially available data. To optimize energy consumption, two additional models are …

Data Lakes: A Survey Of Concepts And Architectures, Sarah Azzabi, Zakiya Alfughi, Abdelkader Ouda

Electrical and Computer Engineering Publications

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further …

Deep Integration Of Technological Innovation And Industrial Innovation In Modern Industrial System: Inspiration From Global New Generation Lithography Systems, Jiang Yu, Feng Chen, Yue Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

Utilizing technological innovation to lead the construction of a modern industrial system is a strategic choice for seizing the opportunities of the new round of technological revolution and industrial transformation. It is also a necessary step for winning the strategic initiative towards high-level self-reliance and self-improvement. Technological innovation is the intrinsic driving force behind industrial innovation, and industrial innovation is the value embodiment of technological innovation. The deep integration of technological innovation and industrial innovation is the key to constructing and improving a modern industrial system. Taking the global extreme ultra-violet (EUV) lithography system as an example, based on the …

Insights On Strategy And Approach For China To Construct A Modern Integrated Circuits Industrial System, Ximing Yin, Beibei Zhang, Tailun Chen, Jiang Yu, Jin Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

The integrated circuit (IC) industry is highly complex and systematic, and its key core technology breakthroughs are highly dependent on the support of systematic capabilities. The West especially the United States has accelerated the promotion of the “small-yard, high-fence” strategy, the “New Washington Consensus”, the “de-risking”, and other systematic policies to curb China’s rise. China’s IC industry chain is facing extreme risks such as rupture or blockage. Meanwhile, facing the new mission and requirements of Chinese modernization and new-quality productivity, China needs to accelerate the modernization of the IC industry with new development paradigms, new strategies, and new approaches. Based …

Optoelectronic And Morphological Surface Resistance Evaluation Of Laser-Induced Graphene Counter Electrodes For Potential Applications In Cesium Lead Halide Perovskite Solar Cells, Ziad Khalifa, Sameh Osama Abdellatif, Rami Ghannam

Chemical Engineering

This study investigates the physicochemical, optical, and electrical characterization of laser-induced graphene (LIG) samples for integration as counter electrodes in cesium lead halide perovskite solar cells. The impact of laser processing parameters on electrode performance is explored, including laser power, laser speed, and beam defocus. Density functional theory (DFT) computational modeling is employed for atomistic investigation and work function estimation, demonstrating the density of states (DOS), quantum capacitance of the graphene sheets, and energy work function. NiO is utilized as a hole transport layer, and the energy work function of LIG is tuned accordingly. SEM measurements estimate thin film porosity, …

Development And Evaluation Of An Expedited System For Creation Of Single Walled Carbon Nanotube Platforms, Ivon Acosta Ramirez, Omer Sadak, Wali Sohail, Xi Huang, Yongfeng Lu, Nicole M. Iverson

Department of Electrical and Computer Engineering: Faculty Publications

Single-walled carbon nanotubes (SWNT) have a strong and stable near-infrared (nIR) fluorescence that can be used to selectively detect target analytes, even at the single molecule level, through changes in either their fluorescence intensity or emission peak wavelength. SWNTs have been employed as NIR optical sensors for detecting a variety of analytes. However, high costs, long fabrication times, and poor distributions limit the current methods for immobilizing SWNT sensors on solid substrates. Recently, our group reported a protocol for SWNT immobilization with high fluorescence yield, longevity, fluorescence distribution, and sensor response, unfortunately this process takes 5 days to complete. Herein …

Exploration Of Semiconductor Gain Medium, Resonator, Pump, And Frequency Stabilization For Laser Guide Star Applications, Mingyang Zhang

Optical Science and Engineering ETDs

Laser Guide Star (LGS) systems are essential for adaptive optics in ground-based astronomical observation. This dissertation demonstrates the feasibility of semiconductor-based LGS systems using the membrane external-cavity surface-emitting laser (MECSEL) platform, employing multiple quantum wells. Various laser cavity configurations were analyzed through simulations and experiments. The in-well pumping method was explored to reduce the quantum defect and address thermal limitations. Multi-pass pumping schemes were designed with Zemax modeling and demonstrated experimentally. To simplify multi-pass pumping, the hybrid-MECSEL (H-MECSEL) design was introduced. COMSOL modeling studied thermal management and thermal lensing effect.

The H-MECSEL achieved approximately 30 W of output power at …

Embedding Direction Of Arrival And Antenna Beamforming Algorithms On Automated Software Defined Radio Platforms, Adrian J. Lewis

Electrical and Computer Engineering ETDs

Antenna arrays are an essential technology in the modern era in both communications and radar applications. The algorithms used for antenna arrays are beginning to be embedded in smart device applications to enable greater communication abilities. This thesis explores the idea of embedding direction of arrival and beamforming algorithms using a software defined radio platform. Specifically, the Multiple Signal Classification (MuSiC) algorithm and Minimum Variance Distortionless Response (MVDR) algorithm are combined. The combination of similar algorithms has the potential to be used in many internet of things applications to enable more dynamic communication ability.

Bio-Inspired Robotic Framework For Spatiotemporal Analyses In Environmental Monitoring Applications, Maliha Kabir

Electrical Engineering Theses

Frequent fluctuations in the environment's temperature and climate significantly impact our crop fields. Floods and droughts occur in different places in different seasons, which is very unusual and affects the crops' health and productivity. During these calamities, they are also not able to monitor their crop fields. However, if it is possible by providing farmers with information about their fields' conditions like temperature, pressure, and soil moisture, we can help them assess environmental conditions for specific areas. This data can also aid farmers in determining the appropriate amounts of fertilizers and pesticides to apply to their crops and which crops …

Adaptive Screen Capture Video Analysis, Ugesh Egala

Electrical and Computer Engineering ETDs

This thesis presents a system for analyzing student activities during class sessions to gain insights into the learning process. A dataset consisting of 14 screen recordings, with 2 videos labeled across two stages, was used for training, validation, and testing. The methodology employs adaptive sampling, initially at 10 frames per minute to identify active regions, followed by detailed analysis at 1 frame per second using OCR to detect typing activities through character changes. The results demonstrate over a 50% reduction in computational load while maintaining high accuracy in detecting student engagement. A total of 26 hours of screen capture videos …

Experimentally Verified Effective Doping Model For Lactate And Troponin Ofet Biosensors Using Machine Learning Algorithm, Sameh O. Abdellatif, Hana Masalam, Salma Ahmed

Electrical Engineering

No abstract provided.

Experimental Validation Of An Analytical Transient Model For Saturated Boosting Gain In Dc–Dc Converters With Variable Duty Cycle, Goliana Samir, Simon Ezzat, Wagdy Anis, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.

Low-Power Dc-Dc Converters For Smart And Environmentally-Friendly Electric Vehicles: Design, Simulation, And Fabrication On A Glass Substrate, Michelle Makar, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.

From Cnns To Transformers In Multimodal Human Action Recognition: A Survey, Muhammad Bilal Shaikh, Douglas Chai, Syed Muhammad Shamsul Islam, Naveed Akhtar

Research outputs 2022 to 2026

Due to its widespread applications, human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance as compared to relying on a single data modality. During the adoption of deep learning for visual modelling in the past decade, action recognition approaches have mainly relied on Convolutional Neural Networks (CNNs). However, the recent rise of Transformers in visual modelling is now also causing a paradigm shift for the action recognition task. This survey captures this transition while focusing on Multimodal Human Action Recognition …

On The Surface Topography Of Local Overheating And Plasma Formation Due To Initial Condition Perturbations Sourcing The Electrothermal Instability On High-Current-Density Conductors, Maren Whiting Hatch

Electrical and Computer Engineering ETDs

The electrothermal instability (ETI) is a Joule heating-driven instability that instigates runaway heating on conductors driven to high current density, altering the 3D evolution of the expansion and phase state. Most metals include complex distributions of imperfections (voids, resistive inclusions) which seed ETI. To simplify comparison with modeling and theory, experiments examined growth of ETI from various alloys of stainless steel as well as relatively void/inclusion free, 99.999% pure, diamond-turned, 1 mm-diameter aluminum rods. Aluminum surfaces included a variety of deliberately machined and well-characterized perturbations, including 10-micron-scale quasi-hemispherical voids, or “engineered” defects (ED), and sinusoidal patterns of varying wavelength and …

Fundamental Limits In Measuring The Anisotropic Rotational Diffusion Of Single Molecules, Weiyan Zhou, Tingting Wu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Many biophysical techniques, such as single-molecule fluorescence correlation spectroscopy, Förster resonance energy transfer, and fluorescence anisotropy, measure the translation and rotation of biomolecules to quantify molecular processes at the nanoscale. These methods often simplify data analysis by assuming isotropic rotational diffusion, e.g., that molecules wobble within a circular cone. This simplification ignores the anisotropy present in many biological contexts that may cause molecules to exhibit different degrees of diffusion in different directions. Here, we loosen this assumption and establish a theoretical framework for describing and measuring anisotropic rotational diffusion using fluorescence imaging. We show that anisotropic wobble is directly quantified …

Pulsed Secondary Electron Yield Measurements Of Metallic And Dielectric Materials Of Interest For High Power Vacuum Electron Devices, Bilal Ahmed Malik

Electrical and Computer Engineering ETDs

Secondary electron emission (SEE) from conductors is thought to play an important role in the initiation of Multipactor breakdown in vacuum electron devices. The SEE of dielectric materials plays a crucial role in numerous scientific and technological applications, including particle accelerators, spacecraft charging, and plasma diagnostics. Additionally, the SEE of an insulator may depend on its electrical stress history. In both cases, SEE strongly depends on any conditioning of the material surface. We present measurements on both conducting and insulating materials of interest in these applications. Measurements are being made in an existing test stand, recently modified to enable measurements …

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 …

"Blockchain-Enhanced Electronic Health Records: A Secure And Immutable Approach To Ehr Management", Mostafa Abdelwahed Eltabakh, Mohamed E. Nasr, Emad Abd-Elrahman, Roayat Ismail Abdelfatah

Journal of Engineering Research

Traditional Electronic Health Record (EHR) systems face security risks, data misuse, and audit trail concerns. This paper proposes leveraging blockchain technology to improve EHRs, emphasizing Blockchain's potential to enhance data security, accessibility, and privacy through its encrypted and interconnected data block system, thereby addressing the core challenges of conventional EHR practices. This paper introduces a blockchain-based framework designed to consolidate and secure patient data within a singular record owned by the patient. Developed on the Ethereum network utilizing Ganache, this system employs programming languages and tools such as Solidity and web3.js. It leverages the blockchain platform for storing patient data …

Heat And Mass Transfer Characteristics During Vacuum Drying Of Wood, Mohamed Salah Elmetwaly, Lotfy Hassan Rabie Saker, Mohamed Sameh Salem

Journal of Engineering Research

The properties affecting the characteristics of heat and mass transfer during vacuum drying of wood are studied in this paper. The experimental work is carried out in 0.0365 m³ test rig vacuum dryer. The drying chamber dimensions are 0.5 m long and 0.305 m diameter carbon steel cylinder. This drying chamber is internally coated with epoxy paint, also this chamber is detachable closing caps at both ends meaning welded at one end and bolted at the other end to facilitate loading and unloading of the specimen. Two stainless steel heat exchanger plates with dimensions 0.3 m length, 0.15 m …

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 …

Investigation Of Various Mimo Antenna For 5g Mobile Phone Applications, Rajesh Kumar D

Theses and Dissertations

The rapid progress of wireless communication systems has been driven by the persistent demand for higher data rates, improved connectivity, and seamless user experiences. The introduction of fifth-generation (5G) wireless technology introduces fresh challenges and possibilities within the domain of antenna design, particularly concerning Multiple-Input Multiple-Output (MIMO) systems operating in the sub-6 GHz frequency range. This thesis conducts a comprehensive exploration and conceptualization of MIMO antennas expressly crafted for smooth integration into mobile phones, with a particular emphasis on addressing the distinctive requisites of sub-6 GHz 5G communication.

The proposed antenna designs effectively harnesses the advantages inherent in multiple-input multiple-output …

Deep Learning For Multiple Unmanned Aerial Vehicle Coordination In Air Corridors, Liangkun Yu

Electrical and Computer Engineering ETDs

In the future, city skies will be filled with Unmanned Aerial Vehicles (UAVs) for rapid urban transport, including parcel deliveries and air taxis. NASA's Urban Air Mobility (UAM) envisions UAVs navigating air corridors. These virtual pathways ensure safety and compliance with regulations. However, current research on UAM practical applications is limited. This dissertation focuses on designing air corridors, developing UAV control systems, and ensuring the robustness of control algorithms against disturbances in real-world environments.

Our design features an air corridor system with horizontal lanes and on-off ramps, conceptualized as cylindrical spaces and tori, respectively. To enable each UAV to locally …

Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker

Theses and Dissertations

This paper proposes a new security module based on non-volatile memory. The module uses a memristor-based true random number generator to generate random numbers which can be used for cryptography. The module is implemented in software using a modified RISC-V instruction set architecture. The paper evaluates the performance of the module using the RISC-V simulator Gem5. The results show that the module can generate random numbers at a rate of 63 microseconds per number, which is faster than the standard C library’s random number generator. The module can also be used to scramble strings of characters and generate hashes of …

Reconfigurable Metasurface For 5g & Beyond Wireless Communication, Monisha S

Theses and Dissertations

The potential new applications and increasing demands of future 5th generation (5G) and beyond wireless communication indicate a promising future for mobile communications. However, the transmission medium has traditionally been seen as an unpredictable factor between the sender and receiver. As we enter the digital era of wireless communications, signal quality is deteriorating due to environmental interferences.

With the continuous advancement of technology, there is an increasing need for advanced solutions that can handle complex applications such as haptic communications, the Internet of Things for smart cities, automation, and manufacturing. One technology that has received much attention is the reconfigurable …

React: Recognize Every Action Everywhere All At Once, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Page Daniel Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

In the realm of computer vision, Group Activity Recognition (GAR) plays a vital role, finding applications in sports video analysis, surveillance, and social scene understanding. This paper introduces Recognize Every Action Everywhere All At Once (REACT), a novel architecture designed to model complex contextual relationships within videos. REACT leverages advanced transformer-based models for encoding intricate contextual relationships, enhancing understanding of group dynamics. Integrated Vision-Language Encoding facilitates efficient capture of spatiotemporal interactions and multi-modal information, enabling comprehensive scene understanding. The model’s precise action localization refines joint understanding of text and video data, enabling precise bounding box retrieval and …