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2024

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Articles 8851 - 8880 of 9226

Full-Text Articles in Engineering

Power Generation And Water Conservation Potentials For Several Photovoltaic Configurations Installed Over A Fresh Water Surface, Rana A. Kewan, Mohamed S. Salem, Mohamed R. Elmarghany, Maher Bekheit, Gamal I. Sultan Jan 2024

Power Generation And Water Conservation Potentials For Several Photovoltaic Configurations Installed Over A Fresh Water Surface, Rana A. Kewan, Mohamed S. Salem, Mohamed R. Elmarghany, Maher Bekheit, Gamal I. Sultan

Mansoura Engineering Journal

One of the challenges that face photovoltaic systems is depleting performance if their average temperature increases due to solar radiation. Floating photovoltaic (FPV) and submerged photovoltaic (SPV) systems offer a potential solution to such problem. Another major benefit is the decrease of water evaporation by creating an artificial shadow over water. In this paper, an experimental study is presented that includes three different systems: an inclined PV by 30⸰ over water (IPV), an SPV, and an FPV systems and compares their performance with that of a conventional PV system under the same operating conditions. The FPV and SPV systems exhibited …

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Smart Agriculture Based On Iot Using Drones: A Survey, Shymaa G. Eladl, Hanaa Y. Zaineldin, Amira Y. Haikal, Mahmoud M. Saafan Jan 2024

Smart Agriculture Based On Iot Using Drones: A Survey, Shymaa G. Eladl, Hanaa Y. Zaineldin, Amira Y. Haikal, Mahmoud M. Saafan

Mansoura Engineering Journal

Researchers have studied the potential of unmanned aerial vehicles (UAVs) for real-time visual data acquisition and processing using potent deep learning (DL) algorithms over the past few decades. In this regard, drone use in smart cities has become one of the most common uses for them in recent years. UAVs and the Internet of Things (IoTs) are two popular technologies being used in smart agriculture that are ushering in a new era of agriculture by replacing traditional farming methods. The combination of drones and IoT has the potential to drastically change our lives through real-time data collection and analysis that …

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Establishing A Quantitative Assessment Tool For Biophilia In Healthcare Facilities, Sara Moustafa Fouda, Mohanad Ali Fouda Jan 2024

Establishing A Quantitative Assessment Tool For Biophilia In Healthcare Facilities, Sara Moustafa Fouda, Mohanad Ali Fouda

Mansoura Engineering Journal

This study aims at developing a novel assessment tool that can quantitatively evaluate the level of biophilia integration in healthcare facilities. The developed assessment tool helps to address the difficulty of biophilia quantification in the built environment due to the broad nature of the biophilia concept. The methodological approach for the tool depends on employing biophilic design patterns as a basis for biophilia, where these patterns are correlated to a group of biophilia-related features of the WELL Building Standard. This correlation is attained through a list of Integrated Design Guidelines (IDGs) that is proposed to act as an intermediate ground …

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Maximum/Minimum Output Current Extraction Based Open-Switch Fault Diagnosis Of Voltage Source Inverter, Ahmed S. Gardouh, Eid Gouda, Abdelhady Ghanem Jan 2024

Maximum/Minimum Output Current Extraction Based Open-Switch Fault Diagnosis Of Voltage Source Inverter, Ahmed S. Gardouh, Eid Gouda, Abdelhady Ghanem

Mansoura Engineering Journal

Three-phase induction motor drive systems face significant susceptibility to critical failures arising from open switch faults within the power electronic converter. Implementing monitoring and early fault detection mechanisms for such occurrences not only enhances system reliability but also ensures the safe and uninterrupted operation of the motor drive system. This study introduces an innovative real-time open switch fault detection technique for voltage source inverters fed induction motor drives, leveraging motor stator current analysis. The method aims to track maximum and minimum values of current signals over one cycle. Unlike many current signal-based approaches that require modifications to be integrated with …

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An Experimental Study Of The Effect Of Spindle Speed On The Spike Energy Of The Light & Heavy Spindles, Wael A. Hashima, Ibrahim A. Elhawary Jan 2024

An Experimental Study Of The Effect Of Spindle Speed On The Spike Energy Of The Light & Heavy Spindles, Wael A. Hashima, Ibrahim A. Elhawary

Mansoura Engineering Journal

In the present work, the experimental studies were carried out the investigation of the effect of some factors on the spike energy of the light and heavy spindles used frequently; in cotton spinning and in twisting continuous filament, speed with and without the spool and also with different values of full yarn package. Depending on the rotating mass, it was found that by the increase of the heavy spindle speed from 4000 r.p.m to 12000 r.p.m, the spike energy of the spindle with full package increase from 0.5 gSE to 2.3 gSE. At the working speed (10000 r.p.m) of the …

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Design And Construction Of A Novel Reciprocating Pin-On-Plate Wear Testing Machine, Yasser S. Mohamed, Ahmed Abdelbary Jan 2024

Design And Construction Of A Novel Reciprocating Pin-On-Plate Wear Testing Machine, Yasser S. Mohamed, Ahmed Abdelbary

Mansoura Engineering Journal

This manuscript introduces a new pin-on-plate tribometer established for a study of the tribological behavior of materials at a constant sliding speed. The originality of this test rig lies in its capability to perform six independent tests at the same time. Wear tests can be conducted with high reliability and low error thanks to the high rigidity of the machine. The reciprocating motion of the counterface was designed to be in an arc path. The tribometer has the facility of changing the applied load and the length of the wear path. The test machine can used to perform both dry …

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Distribution System Losses Allocation Based On Circuit Theory With Distributed Generation, Ahmed S. Abdelkader, Ibrahim I. Mansy, Abdelfattah A. Eladl Jan 2024

Distribution System Losses Allocation Based On Circuit Theory With Distributed Generation, Ahmed S. Abdelkader, Ibrahim I. Mansy, Abdelfattah A. Eladl

Mansoura Engineering Journal

Electricity energy is the most convenient and efficient form of energy transmission and conversion. Nonetheless, losses take place while electrical power is traveling from power stations through transmission and distribution networks to the loads. Despite the high efficiency of electrical transmission and distribution networks, the huge amounts of power moving through these networks make the financial loss due to the lost energy very high and its management becomes a critical consideration for both utility providers and regulatory bodies. The efficient management of losses encompasses two major functions: the first is running the system in a way that minimizes the losses; …

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A Decision-Making Approach To Reduce The Risk Of Measurement Uncertainty For Product Size, Mostafa Awadalla Sedeek, Fatma Abdallah Elerian, Ossama Badie Abouelatta, Mona. A. Aboueleaz Jan 2024

A Decision-Making Approach To Reduce The Risk Of Measurement Uncertainty For Product Size, Mostafa Awadalla Sedeek, Fatma Abdallah Elerian, Ossama Badie Abouelatta, Mona. A. Aboueleaz

Mansoura Engineering Journal

The measurement uncertainty has a significant impact on quality control processes. This is particularly true of standards that are assessed to determine if a product is compliant. This ambiguity introduces two potential risks of decision-making: accepting a non-conventional product (the consumer's risk) or rejecting a conventional product (the producer's risk). This investigation explores the potential for false decisions caused by measurement errors in product assessments. By studying data near the control limits, a greater understanding of the manufacturing process is intended to facilitate informed decision-making, and guide the implementation of effective quality control methods. The investigation highlights the importance of …

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Minimizing The Production Cost Of The Cam-Follower Profile Using Shape Optimization Techniques, Khaled Ahmed, Magdy Samuel, Rania Mostafa Jan 2024

Minimizing The Production Cost Of The Cam-Follower Profile Using Shape Optimization Techniques, Khaled Ahmed, Magdy Samuel, Rania Mostafa

Mansoura Engineering Journal

The study of shape optimization techniques in structural products helps in reducing mechanical stresses and vibrations in related mechanical elements, such as cam follower mechanisms. This, in turn, has a positive impact on cost savings in the manufacturing process and enhances production quality and reliability. The point of view in this research depends on the idea of minimizing the total production cost while ensuring good functional performance of the cam profile during the shape-manufacturing optimization problem-solving process. The investigation of the impact of changing the motion type of a cam, such as Uniform Velocity Motion (UVM) and Simple Harmonic Motion …

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Using Highly Twisted Roving As A Rainwater Purification Medium, Mohamed Hakam, Hassan Ahmed, Amr Abdelkader, Wael A. Hashima Jan 2024

Using Highly Twisted Roving As A Rainwater Purification Medium, Mohamed Hakam, Hassan Ahmed, Amr Abdelkader, Wael A. Hashima

Mansoura Engineering Journal

This work aims to study engineering aspects of roving wound cartridge water filtration media according to material of filtration media and their packing densities, number of filtration cycles, and concentrations of suspended solids to investigate the effect of these factors on filtration efficiency (suspended solids removal), and water turbidity. Modified construction of filter cartridges depends on roving as filtration media were produced for water filtration. Filter cartridges were produced with three materials 100 % CO, 50%CO/50%PES blend, and 100% PES. Filtration processed through one and two filtration stages or cycles and filtration using two levels of suspended solids concentrations 3.6 …

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Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth Jan 2024

Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth

Publications

Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …

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Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth Jan 2024

Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth

Publications

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …

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Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth Jan 2024

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

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Underlying Substrate Effect On Electrochemical Activity For Hydrogen Evolution Reaction With Low-Platinum-Loaded Catalysts, Baleeswaraiah Muchharla, Peter V. Sushko, Kishor K. Sadasivuni, Wei Cao, Akash Tomar, Hani Elsayed-Ali, Adetayo Adedeji, Abdennaceur Karoui, Joshua M. Spurgeon, Bijandra Kumar Jan 2024

Underlying Substrate Effect On Electrochemical Activity For Hydrogen Evolution Reaction With Low-Platinum-Loaded Catalysts, Baleeswaraiah Muchharla, Peter V. Sushko, Kishor K. Sadasivuni, Wei Cao, Akash Tomar, Hani Elsayed-Ali, Adetayo Adedeji, Abdennaceur Karoui, Joshua M. Spurgeon, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Platinum is known as the best catalyst for the hydrogen evolution reaction (HER) but the scarcity and high cost of Pt limit its widespread applicability. Herein, the role of the underlying substrate on the HER activity of dispersed Pt atoms is uncovered. A direct current magnetron sputtering technique is utilized to deposit transition metal (TM) thin films of W, Ti, and Ta as underlying substrates for extremely low loading of Pt (

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Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo Jan 2024

Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo

Electrical & Computer Engineering Faculty Publications

We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the …

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Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li Jan 2024

Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …

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Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna Jan 2024

Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …

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Decompositions Of Nonlinear Input-Output Systems To Zero The Output, W. Steven Gray, Kurusch Ebrahimi-Fard, Alexander Schmeding Jan 2024

Decompositions Of Nonlinear Input-Output Systems To Zero The Output, W. Steven Gray, Kurusch Ebrahimi-Fard, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Consider an input–output system where the output is the tracking error given some desired reference signal. It is natural to consider under what conditions the problem has an exact solution, that is, the tracking error is exactly the zero function. If the system has a well defined relative degree and the zero function is in the range of the input–output map, then it is well known that the system is locally left invertible, and thus, the problem has a unique exact solution. A system will fail to have relative degree when more than one exact solution exists. The general goal …

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Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin Jan 2024

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

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Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen Jan 2024

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

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Quest For An Optimal Spin-Polarized Electron Source For The Electron-Ion Collider, J. Biswas, E. Wang, O. Rahman, J. Sharitka, K. Kisslinger, Adam Masters, S. Marsillac, T. Lee Jan 2024

Quest For An Optimal Spin-Polarized Electron Source For The Electron-Ion Collider, J. Biswas, E. Wang, O. Rahman, J. Sharitka, K. Kisslinger, Adam Masters, S. Marsillac, T. Lee

Electrical & Computer Engineering Faculty Publications

Superlattice GaAs photocathodes play a crucial role as the primary source of polarized electrons in various accelerator facilities, including the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson National Laboratory and the Electron-Ion Collider (EIC) at Brookhaven National Laboratory. To increase the quantum efficiency (QE) of GaAs/GaAsP superlattice photocathodes, a Distributed Bragg Reflector (DBR) is grown underneath using metal-organic chemical vapor deposition (MOCVD). There are several challenges associated with DBR photocathodes: the resonance peak may not align with the emission threshold of around 780 nm, non-uniform doping density in the top 5 nm may significantly impact QE and spin polarization, …

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Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin Jan 2024

Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …

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Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim Jan 2024

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

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Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen Jan 2024

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

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Thermal Diffusivity And Acoustic Properties Of Nb Thin Films Studied By Time-Domain Thermoreflectance, Md. Obidul Islam, Hani Elsayed-Ali Jan 2024

Thermal Diffusivity And Acoustic Properties Of Nb Thin Films Studied By Time-Domain Thermoreflectance, Md. Obidul Islam, Hani Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

The thermal diffusion and acoustic properties of Nb impacts the thermal management of devices incorporating Nb thin films such as superconducting radiofrequency (SRF) cavities and superconducting high-speed electronic devices. The diffusion and acoustic properties of 200-800 nm thick Nb films deposited on Cu substrates were investigated using time-domain thermoreflectance (TDTR). The films were examined by X-ray diffraction, scanning electron microscopy, and atomic force microscopy. The grain size and thermal diffusivity increase with film thickness. The thermal diffusivity increased from 0.100± 0.002 cm2s-1 to 0.237± 0.002 cm2s-1 with the increase in film thickness from 200 …

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Real-Time Spectroscopic Ellipsometry For Flux Calibrations In Multi-Source Co-Evaporation Of Thin Films: Application To Rate Variations In Cuinse₂ Deposition, Dhurba R. Sapkota, Balaji Ramanujam, Puja Pradhan, Mohammed A. Razooqi Alaani, Ambalanath Shan, Michael J. Heben, Sylvain Marsillac, Nikolas J. Podraza, Robert W. Collins Jan 2024

Real-Time Spectroscopic Ellipsometry For Flux Calibrations In Multi-Source Co-Evaporation Of Thin Films: Application To Rate Variations In Cuinse₂ Deposition, Dhurba R. Sapkota, Balaji Ramanujam, Puja Pradhan, Mohammed A. Razooqi Alaani, Ambalanath Shan, Michael J. Heben, Sylvain Marsillac, Nikolas J. Podraza, Robert W. Collins

Electrical & Computer Engineering Faculty Publications

Flux calibrations in multi-source thermal co-evaporation of thin films have been developed based on real-time spectroscopic ellipsometry (RTSE) measurements. This methodology has been applied to fabricate CuInSe2 (CIS) thin film photovoltaic (PV) absorbers, as an illustrative example, and their properties as functions of deposition rate have been studied. In this example, multiple Cu layers are deposited step-wise onto the same Si wafer substrate at different Cu evaporation source temperatures (TCu). Multiple In2Se3 layers are deposited similarly at different In source temperatures (TIn). Using RTSE, the Cu and In2Se3 deposition rates are determined as …

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Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen Jan 2024

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

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A Fresh Revisit Of The Issues And Improvements In Impulse Invariance Filter Design For Infinite Impulse Response Filters, Chiman Kwan, Hal Ferguson Jan 2024

A Fresh Revisit Of The Issues And Improvements In Impulse Invariance Filter Design For Infinite Impulse Response Filters, Chiman Kwan, Hal Ferguson

Electrical & Computer Engineering Faculty Publications

The objective of this paper is to first present some issues with impulse invariance filter (IIF) design during the design of digital infinite impulse response (IIR) filters. Engineers are often confused about some inconsistent observations. For instance, if the impulse response of a digital filter is designed using the impulse invariance procedure, then the analog and digital filters’ frequency and step responses are very different. Two simple remedies are presented in this paper. One is a post-processing approach that scales the frequency and step responses of the digital filter by the sampling interval T. Another one is a pre-processing approach …

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Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang Jan 2024

Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang

Electrical & Computer Engineering Faculty Publications

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach …

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Parametrization Of Fluid Models For Electrical Breakdown Of Nitrogen At Atmospheric Pressure, Shirshak K. Dhali Jan 2024

Parametrization Of Fluid Models For Electrical Breakdown Of Nitrogen At Atmospheric Pressure, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

In the transient phase of an atmospheric pressure discharge, the avalanche turns into a streamer discharge with time. Hydrodynamic fluid models are frequently used to describe the formation and propagation of streamers, where charge particle transport is dominated by the creation of space charge. The required electron transport data and rate coefficients for the fluid model are parameterized using the local mean energy approximation (LMEA) and the local field approximation (LFA). In atmospheric pressure applications, the excited species produced in the electrical discharge determine the subsequent conversion chemistry. We performed the fluid model simulation of streamers in nitrogen gas at …

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