Digital Commons Network^™

Integration Of Real-Time Semantic Building Map Updating With Adaptive Monte Carlo Localization (Amcl) For Robust Indoor Mobile Robot Localization, Matthew Peavy, Pileun Kim, Hafiz Oyediran, Kyungki Kim

Durham School of Architectural Engineering and Construction: Faculty Publications

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by nonstructural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed …

Flood Risk Assessment For Residences At The Neighborhood Scale By Owner/Occupant Type And First-Floor Height, Ayat Al Assi, Rubayet Bin Mostafiz, Carol J. Friedland, Md Adilur Rahim, Robert V. Rohli

Faculty Publications

Evaluating flood risk is an essential component of understanding and increasing community resilience. A robust approach for quantifying flood risk in terms of average annual loss (AAL) in dollars across multiple homes is needed to provide valuable information for stakeholder decision-making. This research develops a computational framework to evaluate AAL at the neighborhood level by owner/occupant type (i.e., homeowner, landlord, and tenant) for increasing first-floor height (FFH). The AAL values were calculated here by numerically integrating loss-exceedance probability distributions to represent economic annual flood risk to the building, contents, and use. A simple case study for a census block in …

Estimation And Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices In Benoa Bay Of Bali Province, Indonesia, A. A. Md. Ananda Putra Suardana, Nanin Anggraini, Muhammad Rizki Nandika, Kholifatul Aziz, Abd. Rahman As-Syakur, Azura Ulfa, Agung Dwi Wijaya, Wiji Prasetio, Gathot Winarso, Ratih Dewanti

Forest and Society

Carbon dioxide (CO₂) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO₂ three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate …

How Good Is A Tactical-Grade Gnss + Ins (Mems And Fog) In A 20-M Bathymetric Survey?, Johnson O. Oguntuase, Anand Hiroji, Peter Komolafe

Faculty Publications

This paper examines how tactical-grade Inertial Navigation Systems (INS), aided by Global Navigation Satellite System (GNSS) modules, vary from a survey-grade system in the bath-ymetric mapping in depths less than 20 m. The motivation stems from the advancements in sensor developments, measurement processing algorithms, and the proliferation of autonomous and uncrewed surface vehicles often seeking to use tactical-grade systems for high-quality bathymetric products. While the performance of survey-grade GNSS + INS is well-known to the hydrographic and marine science community, the performance and limitations of the tactical-grade micro-electro-mechanical system (MEMS) and tactical-grade fiber-optic-gyro (FOG) INS aided with GNSS require some …

Rheology Of 3d Printable Ceramic Suspensions: Effects Of Non-Adsorbing Polymer On Discontinuous Shear Thickening, Ria D. Corder, Yuan-Jung Chen, Pattiya Pibulchinda, Jeffrey P. Youngblood, Arezoo M. Ardekani, Kendra Erk

School of Materials Engineering Faculty Publications

Concentrated suspensions of particles at volume fractions (ϕ) ≥ 0.5 often exhibit complex rheological behavior, transitioning from shear thinning to shear thickening as the shear stress or shear rate is increased. These suspensions can be extruded to form 3D structures, with non-adsorbing polymers often added as rheology modifiers to improve printability. Understanding how non-adsorbing polymers affect the suspension rheology, particularly the onset of shear thickening, is critical to the design of particle inks that will extrude uniformly. In this work, we examine the rheology of concentrated aqueous suspensions of colloidal alumina particles and the effects of adding non-adsorbing polyvinylpyrrolidone (PVP). …

Network Slicing Via Transfer Learning Aided Distributed Deep Reinforcement Learning, Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle

School of Computing: Faculty Publications

Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the …

Changes Of Air Pollutants In Urban Cities During The Covid-19 Lockdown-Sri Lanka, Buddhi Pushpawela, Sherly Shelton, Gayathri Liyanage, Sanduni Jayasekara, Dimuthu Rajapaksha, Akila Jayasundara, Lesty Das Jayasuriya

Department of Earth and Atmospheric Sciences: Faculty Publications

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and particulate matter (PM₁₀, PM_2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime …

Energy Optimized Ldpc Codes For Next-Generation Mimo Ofdm Systems, Indu Bala, Komal Arora, Maad M. Mijwil

Iraqi Journal for Computer Science and Mathematics

With the increasing prevalence of internet services in our daily lives, there is a growing demand for spectrum, resulting in a shortage of available frequencies. This is primarily due to the recent surge in subscribers seeking faster and more dependable data rates. Researchers globally have suggested various solutions to combat the spectrum shortage through technologies, modulation schemes, beamforming, intelligent reflective surfaces, and channel coding schemes. With respect to channel coding, multiple codes have been proposed to overcome challenging and uncertain channel conditions including Turbo, Fountain, and LDPC (Low Density Parity Check) Codes. In addition to this, several transformation techniques such …

Mechanical Engineering News, Georgia Southern University

Mechanical Engineering: News & Publications (2013-2023)

• Student Wins Award for Research on Jet Fuel at Combustion Confererence

• Career Fair on Wednesday, February 1st

• STEM Night on Tuesday, January 31st

  
  

Normalization And Generalization In Deep Learning, Griffin Hurt

Theses

In this thesis, we discuss the importance of data normalization in deep learning and its relationship with generalization. Normalization is a staple of deep learning architectures and has been shown to improve the stability and generalizability of deep learning models, yet the reason why these normalization techniques work is still unknown and is an active area of research. Inspired by this uncertainty, we explore how different normalization techniques perform when employed in different deep learning architectures, while also exploring generalization and metrics associated with generalization in congruence with our investigation into normalization. The goal behind our experiments was to investigate …

Not A Waste: Wastewater Surveillance To Enhance Public Health, Anna Gitter, Jeremiah Oghuan, Anuja Rajendra Godbole, Carlos A. Chavarria, Carlos Monserrat, Tao Hu, Yun Wang, Anthony W. Maresso, Blake M. Hanson, Kristina D. Mena, Fuqing Wu

Pharmacy Faculty Articles and Research

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve …

Exploring Barriers To The Adoption Of Internet Of Things-Based Precision Agriculture Practices, Gaganpreet Singh Hundal, Chad Laux, Dennis R. Buckmaster, Mathias J. Sutton, Michael Langemeier

Computer Information Technology Faculty Publications

The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review …

Relations Of Current And Past Cancer With Severe Outcomes Among 104,590 Hospitalized Covid-19 Patients: The Covid Ehr Cohort At The University Of Wisconsin, Margaret B Nolan, Li-Shiun Chen, Et Al.

2020-Current year OA Pubs

BACKGROUND: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics.

METHODS: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined.

RESULTS: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current …

New Types Of Topologies And Neutrosophic Topologies, Florentin Smarandache

Neutrosophic Systems with Applications

In this paper, we recall the six new types of topologies that we introduced in the last years (2019-2022), such as: Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, NeutroTopology, AntiTopology, SuperHyperTopology, and Neutrosophic SuperHyperTopology.

Full Issue: Winter 2015

DePaul Magazine

In this issue of DePaul Magazine, learn about DePaul's Multi-Faith Veterans Support Project, meet experts on cybersecurity, and find out about two programs that introduce students to the cultural richness of Chicago and job-hunting skills in the legal field.

Faculty Senate Meeting Agenda January 8, 2023, University Of Rhode Island Faculty Senate

Faculty Senate Meeting Agendas

No abstract provided.

Metaverse: Open Possibilities, Mohammed Aljanabi, Sahar Yousif Mohammed

Iraqi Journal for Computer Science and Mathematics

To create a completely immersive, highly spatiotemporal, and self-sustaining virtual shared place for humans to play, work, and interact, the Metaverse is an emerging paradigm of the next-generation Internet. Metaverse is transitioning from science fiction to a near-future reality thanks to recent developments in new technologies like extended reality, artificial intelligence, and blockchain.In this study, we investigate the Metaverse, a shared, fully immersive virtual environment that could serve as a model for the Internet’s future generation. Safety, education, games, entertainment, social effects, the economy, ethics, and future issues are just few of the many areas covered. The Metaverse’s innovative tools …

Numerical Approach For Solving Fuzzy Integro-Differentialequations, Fatima K. Dawood, Rokan Khaji

Iraqi Journal for Computer Science and Mathematics

In this paper, we consider a new class of fuzzy functions called Fuzzy Integro- Differential Equations.Some numerical methods, such as Euler, have been used to determine the solutions of these equations. We extendthese numerical techniques to find the optimal solutions by using control parameters, the extended difference Eulertechnique is used for this. Based on the parametric form of the fuzzy number, the Integro- Differential Equation isdivided into two systems of the second kind. Illustrative examples are given to demonstrate the high precision andgood performance of the new class. Graphical representations reveal the symmetry between lower and upper-cutrepresent of fuzzy solutions …

Remove Noisefrom Movies By Using Hybrid Smooth Algorithm, Faten Azeez Mustafa, Hanan Hamid Ali, Aseel Waleed Ali

Iraqi Journal for Computer Science and Mathematics

Hybrid algorithms are increasingly being employed to reduce noise from videos and images. In this study, new hybrid method was used to eliminate noise from movies by first converting themovies to frames, then the framesto images which the suggested algorithm has been applied.This study aims to create the most noise-free, closest-possible copies of the original movie images in order to produce the purest movie possible. Two traditional images noise removal algorithmswere integrated into the suggested algorithm. That is to say, Median filter and the conservative smoothing filter (max &min) algorithms were implemented sequentially by utilizing Matlab softwareversion (2016R).The outcomes of …

Extensive Review Of State-Of-The-Art Classification Techniques For Cuneiform Symbolimaging: Open Issues And Challenges, Belal Al Khateeb, Farah Maath, Maha Mahmood

Iraqi Journal for Computer Science and Mathematics

The cuneiform script reveals some previously unknown aspects of our past. However, reading ancientclay tablets demands a substantial investment of time and persistent practice over a long period of time. As the fourth millennium came to a close,earlier recording methods gave way to the development of writing–the visualrepresentation of spoken language. The first language to be transcribed in written form in Mesopotamiawas Sumerian. Predominantly,the earliest tablets originate from the Uruksitein southern Mesopotamia, possibly marking its birthplace. Digitizationcuneiform documents is imperativeto boost research focused on the ancient Middle East.A few initiatives embarked upon this endeavoraround the year 2000. Nonetheless, the digitizationprocess …

Facial Recognition Technology-Based Attendancemanagement System Application In Smart Classroom, Ahmad S. Lateef, Mohammed Y. Kamil

Iraqi Journal for Computer Science and Mathematics

Traditional attendance recording methodscan be replacedwith artificialintelligence (AI)methods. Thispaper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction.The Python programming language is used to build the system, which employs object detection and feature extraction algorithms.This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detectionalgorithms to detectthe faces in the video. The face locations are then sentto the feature extraction algorithms to extract and compare features with the features stored in the database. Our paperconcludes that automating the attendance registration process using facial …

Phishing Attacks Detection By Using Artificial Neural Networks, Majeed Jasim Nabet, Loay E. George

Iraqi Journal for Computer Science and Mathematics

Today's world is heading towards complete digital transformation, and with all its advantages, this transformation involves many risks, the most important of which is phishing. This paper proposes a system that classifies the email as phishing or legitimate. Initially, the samples were brought from different data sets, and then the system extracts the features from all parts of the email. The proposed system uses one of the machine learning algorithms (K-means algorithm) to select the valuable features; the proposed system uses four methods to calculate the distance in the K-means algorithm. After features selection, The paper uses ANN as a …

Improved Artificial Neural Networks Based Whale Optimization Algorithm, Mostafa Abdulghafoor Mohammed, Royida A.Ibrahem Alhayali, Mostafa Khaled Abd Alrahman Aladamey, Mohammed Rashid Subhi, Amiza Amir, Zahraa A. Abdalkareem

Iraqi Journal for Computer Science and Mathematics

Owing to the increasing interest in artificial neural networks(ANNs)acrossvarious fields of study, many studies have focused onenhancing theirperformance through theutilisationof differentlearning algorithms. This study examinesthe use of the Whale Optimization Algorithm (WOA) as a training algorithm toimprove the classification accuracy ofANNsGetting artificial neural networks (ANNs) to categorize things accurately requires good model design -you need to pick the right structure, training algorithm, and activation functions. For this project, the researchers tested using the Whale OptimizationAlgorithm (WOA) method to train the ANN models instead of the commonly used backpropagation approach. They gathered 10 real-world datasets from the UCI machine learning repository …

Moving Beyond Dosage And Adherence: A Protocol For Capturing Dimensions Of Active Child Engagement As A Measure Of Fidelity For Social-Emotional Learning Interventions, Brianna L. Devlin, Tanya M. Paes, Elyssa A. Geer, Lindsey M. Bryant, Tracy M. Zehner, Irem Korucu, Kathleen Morse, Robert Duncan, David J. Purpura, Sara A. Schmitt

Purdue Center for Early Learning Faculty and Staff Publications

Social-emotional competencies are important for school-readiness and can be supported through social-emotional learning (SEL) interventions in the preschool years. However, past research has demonstrated mixed efficacy of early SEL interventions across varied samples, highlighting a need to unpack the black box of which early interventions work, under what conditions, and for whom. In the present article we discuss the critical implementation component of active child engagement in an intervention as a potential point of disconnect between the intervention as designed and as implemented. Children who are physically present but unengaged during an intervention may lead to decreased average impacts of …

An Automated Lion-Butterfly Optimization (Lbo) Based Stacking Ensemble Learning Classification (Selc) Model For Lung Cancer Detection, N Ashokkumar, S Swapna Rani, V Suganya, S Selvi, S Prema

Iraqi Journal for Computer Science and Mathematics

Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for cliniciansto utilize in order to determine the stages of cancer. Computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technologythat is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of …

Development Of An Intelligent Detection Method Of Dc Series Arcfault In Photovoltaic System Using Multilayer Perceptron Andbi-Directional Long Short-Term Memory, Alaa Hamza Omran, Dalila Mat Said, Siti Maherah Hussin, Sadiq H. Abdulhussein, Nasarudin Ahmad, Haidar Samet

Iraqi Journal for Computer Science and Mathematics

A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings.The production of extremely high temperatures may lead to the ignition of nearby combustible materials. Theapplications of arc fault diagnosis based machine learning are a global interest due to the immense challengeto create an accurate and efficient detection method. In this paper, a detection and classification method using amultilayer perceptron incorporated with Bi-Directional Long short-term Memory (MLP-BiLSTM) is proposed. Inorder to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observationsfor simulation purposes using Power System …

Metacognitive Thinking And Relationship With Some Variables Among Students Of Class Teacher At Fesa/ Unrwa, Adarbeh Khawla

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

This study aimed to examine the level of metacognitive thinking between class teacher students at FESA, and its relationship to students’ cumulative rate, level of study, engaging in practical practice at schools and marital status. A metacognitive thinking questionnaire was applied on a sample of (104) students from (412) class teacher students. The results revealed that; the level of metacognition thinking is high, no significant differences in the first, second and total score refer to marital status, whereas significant difference was found in the third domain in favor of single. No significant difference was found in the first, second and …

Concentrations And Estimation Of The Annual Effective Doses Of Natural Radioactivity Uptake Through Ingestion Of Virgin And Extra Virgin Olive Oil, Eman Ahmed Albdady, Mahmoud El Ghazaly, Saad Abd El Aaal, Mostafa Abdelrahman, Nasef Mansour, Mona Elsayed Frarrag

International Journal of Thin Film Science and Technology

This study assesses the natural radioactive concentrations in virgin olive oil in Egypt. The presence and concentrations of naturally occurring radioactive materials (NORM) in 26 olive oil samples collected from different locations in Saini were conducted using two techniques: gamma spectroscopy using a high purity germanium detector (HPGe) and inductively coupled plasma-mass spectroscopy (ICP-MS). Because of their very low concentrations, the radioactivity of NORM was below the detection limits of the HPGe detector for the time of measurement, On the other hand ICP-MS results showed the concentrations of natural radioactive elements such as uranium and thorium in some examined samples. …

Microstructures And Optical Properties Of Nanocrystalline Pr- Doped Batio3 Thin Film, Y. Iriani, A. Suparmi, A. Marzuki, D. K. Sandi

International Journal of Thin Film Science and Technology

This study aimed to synthesize Praseodymium (Pr) doped Barium Titanate (BT) or Ba1-xPrxTiO3 thin film and investigate the influence of Pr doping concentration on their microstructure and optical properties. The thin films with different Ba/Pr ratios (x=0, 0.03, 0.05, and 0.15) were fabricated on the quartz substrates using the sol-gel method with a spin coater. The microstructure and optical properties were observed by X-Ray Diffractometer (XRD) and UV-Vis Spectrophotometer, respectively. The microstructure results revealed that the lattice parameter, unit cell volume, and crystallite size of the Pr doped thin films decreased by the addition of Pr dopant. Meanwhile, the lattice …

Lead-Free Perovskite Materials For Solar Cell: An Update Of Recent Trends, S. M. Ho, I. Hassan

International Journal of Thin Film Science and Technology

Solar energy has emerged as renewable energy to support human and social development. The development of low-cost, high-efficiency perovskite solar cell has attracted intensive attention because of unique properties such as band gap tenability, stability and high absorption co-efficient value. Research indicates that the power conversion efficiency of lead perovskite solar cell has increased from 3.8% to 25.2% recently. However, the main challenges in this type of solar cell was lead element. Lead is very toxic and very hard to discharge from the body. Therefore, researchers work hard to develop non or low toxic metal ions to substitute lead in …