Sar Analog-To-Digital Converters For Point-Of-Care Biosensors: A Comparison,
2025
Effat University
Sar Analog-To-Digital Converters For Point-Of-Care Biosensors: A Comparison, Eithar Alammari, Joud Alamro, Sara Alashwali, Ghadah S. Alyami, Aziza I. Hussein
Effat Undergraduate Research Journal
The SAR ADC, recognized for its low power consumption, moderate resolution, and satisfactory processing speed, stands as an ideal choice for ultra-low power biomedical applications. Recent efforts have been concentrated on enhancing the power efficiency of the SAR ADC sub-components through intricate refinements and innovative techniques. These efforts aim to minimize energy consumption without compromising the ADC's performance. Therefore, this paper aims to thoroughly examine various implementation approaches for the main components of the SAR ADC, highlighting their individual strengths, weaknesses, and limitations within wireless biomedical applications.
Electrical Energy Consumption Forecasting Based On Conventional And Artificial Intelligence Methods: A Comparison,
2025
Effat University
Electrical Energy Consumption Forecasting Based On Conventional And Artificial Intelligence Methods: A Comparison, Haya H. Binsalim, Jana Kamal, Salma Badaam, Danah Milyani, Ghadah S. Alyami, Aziza I. Hussein
Effat Undergraduate Research Journal
Artificial Intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review and compare the classical techniques and the emerging new AI-based techniques used for forecasting electrical energy consumption in buildings. The findings revealed that the Artificial Neural Network (ANN) model achieved the lowest Mean Absolute Percentage Error (MAPE) of 0.928%. AI-based techniques have many advantages over classical techniques, such as their ability to handle a large amount of data and provide accurate and fast results.
Continuous Versus Discrete-Time Sigma-Delta Analog To Digital Converters For Biomedical Applications: A Comparison,
2025
Effat University
Continuous Versus Discrete-Time Sigma-Delta Analog To Digital Converters For Biomedical Applications: A Comparison, Haya H. Binsalim, Batool Alaidroos, Basma Shigdar, Salma Badaam, Aziza I. Hussein
Effat Undergraduate Research Journal
Continuous-time (CT) and discrete-time (DT) sigma-delta (ΔΣ) converters are two commonly used techniques for analog-to-digital conversion. While both methods operate based on the principles of oversampling and noise shaping, they differ in their implementation and performance characteristics. CT ΔΣ converters use analog circuits to sample and process signals continuously, while DT ΔΣ utilizes digital circuits to sample and process signals at discrete intervals. This paper presents a comprehensive comparison between CT and DT ΔΣ converters, highlighting their advantages and limitations. The comparison is made in terms of design complexity, power consumption, signal-to-noise ratio (SNR), and other essential parameters in medical …
Wideband Spectrum Sensing For Cognitive Radio Using Under-Sampled Successive Approximation Adc,
2025
Association of Arab Universities
Wideband Spectrum Sensing For Cognitive Radio Using Under-Sampled Successive Approximation Adc, Aziza I. Hussein
Effat Undergraduate Research Journal
The radio spectrum, an inherently limited resource, has been increasingly utilized owing to the recent exponential growth of wireless services. This has led to a new approach, termed cognitive radio, predicated upon exploitation of spectrum holes for omnipresent spectrum utilization. This is made possible via cognitive radio networks’ employment of spectrum sensing. Wideband spectrum sensing has been the focal challenge point in cognitive radio technology, since existing techniques are reliant on analog to digital converters (ADC) with sampling at the Nyquist rate. Unfortunately, in order to perform digitization of wideband RF signals at the Nyquist rate, a very high sampling …
Fault Detection In Analog Vlsi Circuits Based On Artificial Intelligence,
2025
Association of Arab Universities
Fault Detection In Analog Vlsi Circuits Based On Artificial Intelligence
Effat Undergraduate Research Journal
In thepastdecade,artificialintelligence(AI)hasbeenonthe rise asatooltobeutilizedacrossallthefieldsavailableintheindustry. Specificallyinthemicroelectronicsfield,exploringthecurrentoptionsof VLSI faultdetectionandtheirtypesiscrucialforadvancementandis importantinidentifyingwhetherthereisroomforimprovementorthe optimumisalreadybeingdone.Therefore,thepurposeofthisresearch is tocompareandcontrastbetweenVLSIfaultdetectionindigitaland analog circuitsusingAI.Techniquestoimproveefficiencysuchastrou- bleshootinginsmallsegmentsratherthanthewholesystemandsome resolutions todrawbacksthataren’tdetectibletohumansliketimingare discussed andresolvedinthispaper.Moreover,somenon-idealitiesand risks likemarginalstabilitywereconsidered.Finally,presentelements that areusedintoday’sfaultdetectioncircuitslikeneuralcontrollers and theANNswerediscussedaswell.Currently,theANNsarethemost utilized toolforfaultdiagnosesanddetection;however,forthecontinu- ation ofthistechnology’sgrowth,developersneedtofindmoreefficient methodstomovepastit.
Electrical Energy Consumption Forecasting Analysis Based On Conventional And Artificial Intelligence Methods: A Comparison,
2025
Association of Arab Universities
Electrical Energy Consumption Forecasting Analysis Based On Conventional And Artificial Intelligence Methods: A Comparison, Aziza I. Hussein
Effat Undergraduate Research Journal
Artificial intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review the classical techniques and the emerging new techniques based on AI of the building electrical energy consumption forecasting. The advantages of AI-based techniques over the classical are that AI methods can handle a large amount of data yet gives accurate results, the results can be found very quickly, in addition to AI having the ability to solve complex nonlinear patterns of raw data. This paper will discuss several studies using different models of forecasting based on …
Evaluation Of The Performance Of The Traveling Wave Differential Element In Protective Relays,
2025
Louisiana Tech University
Evaluation Of The Performance Of The Traveling Wave Differential Element In Protective Relays, Niranjan Kc
Master's Theses
This thesis evaluates the dependability, security, and limitations of the traveling wave differential protection function (TW87) in modern time-domain-based protective relays, using a combination of simulations and hardware testing in a laboratory environment. Fault transients are first generated using the electromagnetic transients program model of a real, 230 kV, 65.7 km long overhead transmission line, which are then played back on real time-domain-based protective relays. Various fault scenarios are chosen to evaluate the impacts of factors such as fault inception angle, distance to fault from line terminals, fault type, fault impedance, and external faults on the relay functions’ performance. Results …
The Design Of Coplanar Waveguide Traveling-Wave Kinetic-Impedance Parametric Amplifiers,
2025
Louisiana Tech University
The Design Of Coplanar Waveguide Traveling-Wave Kinetic-Impedance Parametric Amplifiers, Jordan Scott Savoie
Master's Theses
Astronomical observations and many physics experiments rely on cryogenic amplifiers for readout. Current sensitivity is limited by the noise figure of high-electronmobility transistor (HEMT) amplifiers, which have proven di!cult to decrease further in recent years. Traveling-wave kinetic-impedance parametric amplifiers (TKIPAs) are an emerging class of amplifiers which have the potential to substantially improve the sensitivity of microwave low-noise amplifiers (LNAs) while also accepting relatively high input powers and amplifying over a wide bandwidth. In this thesis, I present the design, modeling, and testing procedures for coplanar waveguide (CPW) TKIPAs developed by our group at the National Radio Astronomy Observatory. Using …
Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data,
2025
University of Denver
Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani
Electronic Theses and Dissertations
Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …
Assessing Cascaded Op-Amp And Pre-Current Amplifier Configurations In Transimpedance Amplifier Interfacing Circuits For Nano-Scale Current-Based Sensor Applications,
2025
The British University in Egypt
Assessing Cascaded Op-Amp And Pre-Current Amplifier Configurations In Transimpedance Amplifier Interfacing Circuits For Nano-Scale Current-Based Sensor Applications, Mohamed Mamdouh, Sameh O. Abdellatif
Electrical Engineering
This study provides a comprehensive analysis of two proposed configurations for current-to-voltage converters tailored for nano-scale current-based sensor applications, focusing on key performance metrics such as linearity, dynamic range, DC noise immunity, and power losses. The evaluation employed frequency-dependent analyses, including transient response, step response, input impedance, and frequency-dependent noise spectra, to compare a cascaded double output configuration with a pre-current amplifier design. The results indicate a trade-off between dynamic range and linearity, with the pre-current amplifier demonstrating a higher dynamic range but lower linearity compared to the cascaded configuration. Additionally, the current amplifier configuration exhibited advantages in power consumption …
Investigating Resilience Of Cyberattack Detection Using Lyapunov-Based Economic Model Predictive Control To Data Poisoning,
2025
Wayne State University Department of Chemical Engineering and Materials Science
Investigating Resilience Of Cyberattack Detection Using Lyapunov-Based Economic Model Predictive Control To Data Poisoning, Helen Durand, Akkarakaran Francis Leonard
Chemical Engineering and Materials Science Faculty Research Publications
Cyberattacks may be performed on process control systems due to their integration of networking and computing with physical systems. Prior work in our group has developed detection strategies for nonlinear systems under sensor, actuator, and combined sensor and actuator attacks which can ensure, under characterizable conditions, that attacks can be detected before they cause safety issues. However, this work did not take into account the potential that an attacker could attempt to provide data to a process that causes an attack to remain undetected but that also is consistent with different process dynamics than those which the process has. This …
Response Of Dynamic Processes With Control Implemented On A Noisy Quantum Computer,
2025
Wayne State University Department of Chemical Engineering and Materials Science
Response Of Dynamic Processes With Control Implemented On A Noisy Quantum Computer, Shilpa Narashimhan, Dominic Messina, Henrique Oyama, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
A major challenge to determining the applicability (and potential outperformance over classical computers) of a quantum computer (QC) within chemical manufacturing processes is quantum noise. Computations by a QC are error-prone due to the influence of quantum noise inherent to the hardware. Errors in control inputs may destabilize a chemical process and lead to unsafe conditions for manufacturing personnel and the environment. The response of a process with control implemented on a QC to errors due to noise must be investigated thoroughly. In this work, the impacts of control input errors due to quantum noise on a process are modeled …
Heuristic Strategies For Process Stabilization Using Proportional Control Implemented By A Noisy Quantum Simulator,
2025
Wayne State University Department of Chemical Engineering and Materials Science
Heuristic Strategies For Process Stabilization Using Proportional Control Implemented By A Noisy Quantum Simulator, Keshav Kasturi Rangan, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
Processing and storage demands of industrial processes are causing fields such as optimization, scheduling, and control to assess the effectiveness of quantum devices in their applications. A key objective of control systems is to ensure process safety. This paper focuses on the potential of quantum devices to compute control inputs that maintain system safety despite sources of nondeterminism inherent to currently available quantum devices (quantum noise). In our previous work, we employed a quantum simulator to assess whether a quantum implementation of a proportional (P) control law could stabilize a single-input/single-output system under quantum noise approximated from a real quantum …
Tools To Design Algorithms For Implementing Control Over Quantum Computers,
2025
Wayne State University Department of Chemical Engineering and Materials Science
Tools To Design Algorithms For Implementing Control Over Quantum Computers, Shilpa Narashimhan, Jihan Abou Halloun, Kip Nieman, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.
Improving High Frequency Absorptive Filters For Quantum Computing,
2025
University of Massachusetts Boston
Improving High Frequency Absorptive Filters For Quantum Computing, Christopher B. Sutton
Graduate Masters Theses
Coaxial transmission lines in superconducting quantum circuits are subject to infrared radiation (IR) leakage as signals travel from room temperature (≈ 300 K) to low temperature (≈ 10 mK). Current experimental configurations typically include an epoxy based filter on all cables connecting classical devices (pulse generators, VNA, etc.) to the quantum circuit. The epoxy is characterized by its dielectric properties per unit length and acts as an absorber, attenuating IR noise. Historically, such IR filters have been fabricated by the experimentalists who require them, more recently they have become commercially available. This work investigates the performance of filters which were …
Design And Evaluation Of An Electromagnetic Band Gap Structure For Self-Interference Reduction In Mmwave Full-Duplex Systems,
2025
Portland State University
Design And Evaluation Of An Electromagnetic Band Gap Structure For Self-Interference Reduction In Mmwave Full-Duplex Systems, Adewale Kehinde Oladeinde
Dissertations and Theses
Full-duplex (FD) wireless is a new technology that allows a device to transmit and receive at the same time and on the same frequency band. It has the potential to double the capacity and spectral efficiency of a wireless link compared to the family of conventional half-duplex wireless systems. The key challenge in implementing FD wireless communication is self-interference (SI): a node's transmitting signal generates significant interference to its receiver. Several previous studies have demonstrated the potential to reduce SI and develop FD radios; however, these studies are mostly limited to sub-6 GHz systems. Previous and on-going research explored various …
Performance Analysis Of Multivariable Control Structures Applied To A Neutral Point Clamped Converter In Pv Systems,
2025
Universidade Federal de Campina Grande
Performance Analysis Of Multivariable Control Structures Applied To A Neutral Point Clamped Converter In Pv Systems, Renato Santana Ribeiro Junior, Eubis Pereira Machado, Damásio Fernandes Júnior, Tárcio André Dos Santos Barros, Flavio Bezerra Costa
Michigan Tech Publications
This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ((Formula presented.)) and stationary ((Formula presented.)) reference frames, offering a broad perspective on system behavior under various operating …
Exploring Longitudinal Stability Of Spatiotemporal Sequential Patterns By Means Of Autoencoder Schemes For Eeg Based Personal Identification,
2025
Louisiana State University and Agricultural and Mechanical College
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 …
Performance Analysis Of Pem Fuel Cells Via Puma Optimizer With The Aid Of Practical Verifications,
2025
The British University in Egypt
Performance Analysis Of Pem Fuel Cells Via Puma Optimizer With The Aid Of Practical Verifications, Hossamm Ashraf, Sameh O. Abdellatif, Mahmoud M. Elkholy \, Attia A. El-Fergany
Electrical Engineering
This manuscript presents a novel application of the recently developed Puma Optimizer (PO) for identifying the unknown parameters in Mann’s model, which is widely used for characterizing the behavior of Polymer Electrolyte Membrane Fuel Cells (PEMFCs). The proposed PO-based methodology is rigorously evaluated using three test cases. One test case involves experimental I–V measurements under various operating conditions from a commercial PEMFC stack, the Horizon H-100 (100 W), which was assembled and tested in the laboratory. The other two cases are established benchmark PEMFC systems, the Ballard Mark V 5 kW and BCS 500 W units. Comprehensive statistical analyses over …
Multi-Layered Framework For Llm Hallucination Mitigation In High-Stakes Applications: A Tutorial,
2025
Navan Inc.
Multi-Layered Framework For Llm Hallucination Mitigation In High-Stakes Applications: A Tutorial, Sachin Hiriyanna, Wenbing Zhao
Electrical and Computer Engineering Faculty Publications
Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth …
