Computer Sciences Commons^™

Simultaneous Feedforward Online Command Rate Limiter Filters For Existingcontrollers, Gali̇p Serdar Tombul

Turkish Journal of Electrical Engineering and Computer Sciences

One of the biggest challenges in controller design for a mechatronics system is the actuator limitations. Either response time of the actuator or the input constraints creates limits for the controller performance and stability. In this study a novel feedforward online rate limiter scheme for arbitrary input signals is introduced by taking velocity, acceleration and jerk constraints into account, and it is investigated that how the control effort and system response is affected by the demand signal's rate of change limitations. A fin actuation system for a guided missile is given as an example where the demand signal comes from …

Design And Analysis Of A Truncated Elliptical-Shaped Chipless Rfid Tag, Ameer Taimour Khan, Yassin Abdullah, Sidra Farhat, Wasim Nawaz, Usman Rauf

Turkish Journal of Electrical Engineering and Computer Sciences

This article presents a novel polarization-insensitive chipless radio frequency identification tag having an encoding capacity of 11 bits. The proposed resonator design comprises discontinuous arc slots forming truncated elliptically shape offering 1:1 slot to bit correspondence with suppressed unwanted harmonic resonances. Electromagnetic performance analysis of the proposed tag design is done over an ungrounded Rogers RT duroid® 5880 laminate. The overall tag design covers a footprint of 15 × 15 × 0.508 mm3 offering convincingly appreciable bit density of 4.88 bits/cm2 . The realized tags are analyzed for real-world electromagnetic performance resulting in an agreement between measured and computed results. …

An Mih-Enhanced Fully Distributed Mobility Management (Mf-Dmm) Solutionfor Real And Non-Real Time Cvbr Traffic Classes In Mobile Internet, Sankaranarayanan Parasuraman, Gayathri Rajaraman, Tamijetchelvy Ramachandiran

Turkish Journal of Electrical Engineering and Computer Sciences

The integration of wireless access networks has progressed rapidly in recent years. Within the mobile communication environment, the operator provides multiple interface options for the mobile node (MN) to switch its connection to any access network during mobility to achieve the quality of service (QoS) for various traffic classes. The conventional centralised mobility management (CMM) scheme lacks reliability, dynamic anchoring and a single point of failure. This stimulates the distributed mobility management (DMM) scheme to handle mobility at the access network rather in a centralised manner. Therefore, in this paper, an IEEE 802.21 media independent handover (MIH) enhanced fully DMM …

Deep Learning For Turkish Makam Music Composition, İsmai̇l Hakki Parlak, Yalçin Çebi̇, Ci̇han Işikhan, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source …

Clustered Mobile Data Collection In Wsns: An Energy-Delay Trade-Of, İzzet Fati̇h Şentürk

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless sensor networks enable monitoring remote areas with limited human intervention. However, the network connectivity between sensor nodes and the base station (BS) may not be always possible due to the limited transmission range of the nodes. In such a case, one or more mobile data collectors (MDCs) can be employed to visit nodes for data collection. If multiple MDCs are available, it is desirable to minimize the energy cost of mobility while distributing the cost among the MDCs in a fair manner. Despite availability of various clustering algorithms, there is no single fits all clustering solution when different requirements …

Benchmarking Of Deep Learning Algorithms For Skin Cancer Detection Based On Ahybrid Framework Of Entropy And Vikor Techniques, Baidaa Al-Bander, Qahtan M. Yas, Hussain Mahdi, Rwayda Kh. S. Al-Hamd

Turkish Journal of Electrical Engineering and Computer Sciences

Skin cancer is one of the most common cancers worldwide caused by excessive development of skin cells. Considering the rapid growth of the use of deep learning algorithms for skin cancer detection, selecting the optimal algorithm has become crucial to determining the efficiency of computer-aided diagnosis (CAD) systems developed for the healthcare sector. However, a sufficient number of criteria and parameters must be considered when selecting an ideal deep learning algorithm. A generally accepted method for benchmarking deep learning models for skin cancer classification is unavailable in the current literature. This paper presents a multi-criteria decision-making framework for evaluating and …

New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin

Turkish Journal of Electrical Engineering and Computer Sciences

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things …

A Transfer Learning-Based Deep Learning Approach For Automated Covid-19diagnosis With Audio Data, Devri̇m Akgün, Abdullah Talha Kabakuş, Zehra Karapinar Şentürk, Arafat Şentürk, Enver Küçükkülahli

Turkish Journal of Electrical Engineering and Computer Sciences

The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical …

A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that …

Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites, and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving their performance. In this study, prediction performances of neural networks are evaluated and their performances are improved using genetic algorithms. Performances obtained in this study are compared with those of other studies. After that, superiority …

Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining …

Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood

Turkish Journal of Electrical Engineering and Computer Sciences

The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means …

A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a new approach that combines stability and optimization in the design of proportional? integral (PI) controller of automatic voltage regulators (AVR) of synchronous generators with variable system parameters. Thanks to this approach, a PI controller, providing the desired performance and the stability of the AVR system, has been designed. The approach follows a method investigating the PI gain values to achieve the desired goals. In the first step of the study, a new stability boundary locus is calculated for the case in which AVR system?s parameters have changed. The stability boundary locus (SBL) method is a graphic-based …

A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan

Turkish Journal of Electrical Engineering and Computer Sciences

High impact scientific applications processed in distributed data centers often involve big data. To avoid the intolerable delays due to huge data movements across data centers during processing, the concept of moving tasks to data was introduced in the last decade. Even after the realization of this concept termed as data locality, the expected quality of service was not achieved. Later, data colocality was introduced where data groupings were identified and then data chunks were placed wisely. However, the aspect of the expected data traffic during run time is generally not considered while placing data. To identify the expected data …

Performance Improvement Of The Shunt Active Power Filter Using A Novel Adaptivefiltering Approach, Abderrezzaq Zoghbi, Daoud Berkani

Turkish Journal of Electrical Engineering and Computer Sciences

This paper introduces an efficient control approach to enhance harmonics mitigation performance of the shunt active power filter (SAPF). This approach is based on adaptive filters favored by their built-in automatic parameters adaptation capability. The proposed filter, which uses a variable leaky least mean square (VLLMS) adaptation, is applied with a modified instantaneous power PQ theory. This enhances its dynamic performance over the use of conventional time-invariant filters, and overcomes its limitations in the presence of nonsinusoidal voltage conditions. The studied SAPF model is simulated in MATLAB/SIMULINK with combinations of nonlinear and unbalanced loads. Simulation results indicate a significant improvement …

A Two-Stage Hair Region Localization Method For Guided Laser Hair Removal, Murat Avşar, İmam Şami̇l Yeti̇k

Turkish Journal of Electrical Engineering and Computer Sciences

Removal of hair using laser is a widely used method, where our goal is to permanently remove hair by using laser to cause heat in order to thermally damage the hair follicle. However, currently available laser hair removal systems affect the outer skin layers besides hair follicles. This is a disadvantage of classical methods with major health risks. We propose a method to overcome these health risks by guiding the laser beam only to automatically localized hair regions. This study aims to develop an automated feature-based hair region localization method as an integral part of the proposed hair removal system …

Log Analysis And Visualization Of Hpc Application Performance Data, Ryan David Lewis

Graduate Research Theses & Dissertations

High-performance computing (HPC) resources at facilities such as Argonne National Laboratory's Leadership Computing Facility (ALCF) enable a wide array of scientific experiments and research applications. In day-to-day operation, these platforms collect copious amounts of system, performance, and debugging logs, capturing data about how jobs, individual tasks, and the system as a whole, operate and perform. This thesis builds on previous efforts to examine how these logs can be used to better understand user and application behavior and system resource usage, in addition to demonstrating machine-learning-based (ML-based) techniques for characterizing applications and predicting job behavior using log data. Five datasets collected …

Statistical And Machine Learning Approaches To Depressive Disorders Among Adults In The United States: From Factor Discovery To Prediction Evaluation, Minhwa Lee

Senior Independent Study Theses

According to the National Institutes of Mental Health (NIMH), depressive disorders (or major depression) are considered one of the most common and serious health risks in the United States. Our study focuses on extracting non-medical factors of depressive disorders diagnosis, such as overall health states, health risk behaviors, demography, and healthcare access, using the Behavioral Risk Factor Surveillance System (BRFSS) data set collected by the Centers for Disease Control and Prevention (CDC) in 2018.

We set the two objectives of our study about depressive disorders diagnosis in the United States as follows. First, we aim to utilize machine learning algorithms …

A Methodology For Detecting Credit Card Fraud, Kayode Ayorinde

All Graduate Theses, Dissertations, and Other Capstone Projects

Fraud detection has appertained to many industries such as banking, retails, financial services, healthcare, etc. As we know, fraud detection is a set of campaigns undertaken to avert the acquisition of illegal means to obtain money or property under false pretense. With an unlimited and growing number of ways fraudsters commit fraud crimes, detecting online fraud was so tricky to achieve. This research work aims to examine feasible ways to identify credit card fraudulent activities that negatively impact financial institutes. In the United States, an average of U.S consumers lost a median of $429 from credit card fraud in 2017, …

Optimal Construction Of A Layer-Ordered Heap And Its Applications, Jake Pennington

Graduate Student Theses, Dissertations, & Professional Papers

The layer-ordered heap (LOH) is a simple data structure used in algorithms that perform optimal top-$k$ on $X+Y$, algorithms with the best known runtime for top-$k$ on $X_1+X_2+\cdots+X_m$, and the fastest method in practice for computing the most abundant isotopologue peaks in a chemical compound. In the analysis of these algorithms, the rank, $\alpha$, has been treated as a constant and $n$, the size of the array, has been treated as the sole parameter. Here, we explore the algorithmic complexity of LOH construction with $\alpha$ as a parameter, introduce a few algorithms for constructing LOHs, analyze their complexity in both …

Ensemble Protein Inference Evaluation, Kyle Lee Lucke

Graduate Student Theses, Dissertations, & Professional Papers

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …

Information Technology Service Continuity Practices In Disadvantaged Business Enterprises, Allen Edward Raub

Walden Dissertations and Doctoral Studies

Disadvantaged business enterprises (DBEs) not using cloud solutions to ensure information technology (IT) service continuity may not withstand the impacts of IT disruption caused by human-made and natural disasters. The loss of critical IT resources leads to business closure and a resource loss for the community, employees, and families. Grounded in the technology acceptance model, the purpose of this qualitative multiple case study was to explore strategies IT leaders in DBEs use to implement cloud solutions to minimize IT disruption. Participants included 16 IT leaders in DBEs in the U.S. state of Maryland. Data were generated through semi-structured interviews and …

Security Awareness Strategies Used In The Prevention Of Cybercrimes By Cybercriminals, Pascal Pouani Tientcheu

Walden Dissertations and Doctoral Studies

Cybercrime is a growing phenomenon that impacts many lives worldwide. Businesses, organizations, and governments continue to search for ways to protect their data and intellectual property from cybercrimes. Grounded in the routine activity theory, the purpose of this general qualitative study was to explore strategies information security officers used to prevent cybercrimes. The participants included seven information security officers listed on social media who manage information security within organizations located in the northeast geographic region of the United States. Data were collected using semistructured interviews, the National Institute of Standards and Technology documentations and analyzed using thematic analysis. Four key …

Mitigating It Security Risk In United States Healthcare: A Qualitative Examination Of Best Practices, Joann Hemann

Walden Dissertations and Doctoral Studies

AbstractCyberattacks are ranked as third in the top 10 highest global threats in terms of likelihood, ranked after extreme weather events and natural disasters. Traditional technology risk management plans for preventative, detective, and recovery measures have failed to mitigate cybersecurity risks created by new technologies. The social problem addressed was the impact of cybercrime to the healthcare industry. The purpose of this qualitative classical Delphi study was to determine how a panel of 25 healthcare cybersecurity experts, based in the United States, viewed the desirability, feasibility, and importance of information technology (IT) cybersecurity risk mitigation techniques. The conceptual framework selected …

Security Strategies Information Technology Security Mangers Use In Deploying Blockchain Applications, Prince Nana Yaw Gyedu Nkrumah

Walden Dissertations and Doctoral Studies

Blockchain is seen as a potential game-changer in many industries and a transformational technology in the 21st century. However, security concerns have made blockchain technology adoption relatively slow. Massive security breaches in cryptocurrency, an example of blockchain technology, have caused organizations to lose $11.3 billion in illegal transactions, exacerbating these security concerns for information technology (IT) security managers who are worried about the safety of blockchain. Grounded in the routine activity theory, the purpose of this multiple case study was to explore strategies used by IT security managers to deploy blockchain applications securely. The participants were 4 IT security managers …

Enhancing Employee Engagement To Improve Financial Performance, Christine Lamacchia

Walden Dissertations and Doctoral Studies

Business leaders are negatively affected when many employees are not engaged in their jobs. Business leaders who struggle to achieve employee engagement suffer decreased profitability, sales, employee retention, and customer satisfaction. Grounded in employee engagement theory, the purpose of this qualitative single case study was to explore strategies IT business leaders use to engage employees. The participants were five IT business leaders who successfully developed strategies to engage employees. Data were collected from semistructured interviews, company documents, and artifacts. Thematic analysis was used to analyze the data. Two themes emerged: creating a company culture conducive to employee engagement and using …

Implementation Strategies For Modeling And Simulation In Military Organizations, Cody Lynn Taylor

Walden Dissertations and Doctoral Studies

Some IT project managers working for U.S. military organizations are struggling to implement modern modeling and simulation (M&S) technology. Implementation strategies are needed to help IT practitioners deliver meaningful simulations and models that ultimately help senior leaders make logical and science-based decisions. Grounded in the extended technology acceptance model, the purpose of this qualitative multiple-case study was to explore strategies some IT project managers supporting U.S. military organizations use to implement modern M&S technology. The participants included 10 civil servants who successfully implemented modeling and simulation technology for military organizations located in the United States eastern region. Data was collected …

Mobile Network Infrastructure Security In Developing Countries – A Kenya Case Study, James M. Omanwa

Walden Dissertations and Doctoral Studies

The usage of mobile network infrastructure to access internet resources for organizations is getting higher year by year in sub-Saharan Africa. However, there was an increase in malicious attacks on mobile networks and devices accessing mobile network infrastructure, targeting organizations’ private information. Grounded in Bandura’s social cognitive theory, the purpose of this multiple case study was to explore strategies security managers used to secure mobile network infrastructures from cyberattacks. Participants comprised four security managers in Kenya in two major cities who successfully implemented strategies to mitigate cyberattacks on the mobile network infrastructures. Data were gathered from video conference, face-to-face, semi-structured …

Knowledge Workers’ Daily Experiences With Technostress And Presenteeism: A Single Case Study, Teresa Himes Mcgovern

Walden Dissertations and Doctoral Studies

Despite the prominence of Information and Communication Technologies (ICT)-enabled technostress in organizations, there is a gap in the literature on how knowledge workers cope with technostress, and managers know little about the interface of ICT-induced presenteeism on employee productivity. The overarching research question was developed to address the purpose of this qualitative single case study with embedded units, which was to understand the perceptions of knowledge workers on the interface of technostress and ICT-induced presenteeism on their work productivity. This study was framed by the concept of presenteeism developed by Lohaus and Habermann within their comprehensive presenteeism model, a decision-integrated …

The Relationship Between Technology Adoption Determinants And The Intention To Use Software-Defined Networking, Wendell Russ

Walden Dissertations and Doctoral Studies

AbstractThe advent of distributed cloud computing and the exponential growth and demands of the internet of things and big data have strained traditional network technologies' capabilities and have given rise to software-defined networking's (SDN's) revolutionary approach. Some information technology (IT) cloud services leaders who do not intend to adopt SDN technology may be unable to meet increasing performance and flexibility demands and may risk financial loss compared to those who adopt SDN technology. Grounded in the unified theory of acceptance and use of technology (UTAUT), the purpose of this quantitative correlational study was to examine the relationship between IT cloud …