Computer Engineering Commons^™

A Novel Hybrid Decision-Based Filter And Universal Edge-Based Logical Smoothingadd-On To Remove Impulsive Noise, Rajanbir Singh Ghumaan, Prateek Jeet Singh Sohi, Nikhil Sharma, Bharat Garg

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a novel hybrid filter along with a universal extension to remove salt and pepper noise even at a very high noise density. The proposed filter initially specifies a threshold and then denoises the image using a combination of linear, nonlinear, and probabilistic techniques. Furthermore, to improve the quality, a universal add-on is presented which uses edge detection and smoothening techniques to brush out fine details from the restored image. To evaluate the efficacy, the proposed and existing filtering techniques are implemented in MATLAB and simulated with benchmark images. The simulation results show that the proposed filter is …

Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz

Turkish Journal of Electrical Engineering and Computer Sciences

Autonomous transport vehicles (ATVs) are one of the most substantial components of smart factories of Industry 4.0. They are primarily considered to transfer the goods or perform some certain navigation tasks in the factory with self driving. The recent developments on computer vision studies allow the vehicles to visually perceive the environment and the objects in the environment. There are numerous applications especially for smart traffic networks in outdoor environments but there is lack of application and databases for autonomous transport vehicles in indoor industrial environments. There exist some essential safety and direction signs in smart factories and these signs …

An Enhanced Bandwidth Disturbance Observer Based Control- S-Filter Approach, Mehmet Önder Efe, Coşku Kasnakoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

A continuous time enhanced bandwidth disturbance observer based control (DOBC) scheme is proposed in this paper. The classical Q -filter is implemented in feedback form and a signum function is inserted into the loop. The loop with this modification becomes capable of detecting small magnitude matched disturbances and we present an in depth discussion of the stability and performance issues comparatively. The proposed approach is called S-filter approach and the results outperform the classical approach under certain conditions. The contribution of the current paper is to advance the subject area to nonlinear filters for DOBC loops with guaranteed stability and …

A Linear Programming Approach To Multiple Instance Learning, Emel Şeyma Küçükaşci, Mustafa Gökçe Baydoğan, Zeki̇ Caner Taşkin

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of …

Towards An Ontology-Based Approach To The "New Normality" After Covid-19:The Spanish Case During Pandemic First Wave, Evelio Gonzalez

Turkish Journal of Electrical Engineering and Computer Sciences

The impact of the pandemic caused by COVID-19 has been immense in all fields of human activity. In most of the affected countries, the authorities have decreed a series of legal measures to try to stop the growth of the disease and the number of people affected by it. These legal measures involved, in most cases, restrictions on the free movement of people and on work and trade activities, new hygiene procedures, and social distancing. In the particular case of Spain, the rapid evolution of the pandemic led to the declaration of a so-called state of alarm and a period …

Detecting And Correcting Automatic Speech Recognition Errors With A New Model, Recep Si̇nan Arslan, Necaatti̇n Barişçi, Nursal Arici, Sabri̇ Koçer

Turkish Journal of Electrical Engineering and Computer Sciences

The purpose of automatic speech recognition (ASR) systems is to recognize speech signals obtained from people and convert them into text so that they can be processed by a computer. Although many ASR applications are versatile and widely used in the real world, they still generate relatively inaccurate results. They tend to generate spelling errors in recognized words, especially in noisy environments, in situations where the vocabulary size is increased, and at times when the input speech is of poor quality. The permanent presence of errors in ASR systems has led to the need to find alternative methods for automatic …

Exploring The Attention Process Differentiation Of Attention Deficit Hyperactivity Disorder (Adhd) Symptomatic Adults Using Artificial Intelligence Onelectroencephalography (Eeg) Signals, Gökhan Güney, Esra Kisacik, Canan Kalaycioğlu, Görkem Saygili

Turkish Journal of Electrical Engineering and Computer Sciences

Attention deficit and hyperactivity disorder (ADHD) onset in childhood and its symptoms can last up till adulthood. Recently, electroencephalography (EEG) has emerged as a tool to investigate the neurophysiological connection of ADHD and the brain. In this study, we investigated the differentiation of attention process of healthy subjects with or without ADHD symptoms under visual continuous performance test (VCPT). In our experiments, artificial neural network (ANN) algorithm achieved 98.4% classification accuracy with 0.98 sensitivity when P2 event related potential (ERP) was used. Additionally, our experimental results showed that fronto-central channels were the most contributing. Overall, we conclude that the attention …

A 1-Kw Wireless Power Transfer System For Electric Vehicle Charging Withhexagonal Flat Spiral Coil, Emrullah Aydin, Mehmet Ti̇mur Aydemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless power transfer (WPT) technology is getting more attention in these days as a clean, safe, and easy alternative to charging batteries in several power levels. Different coil types and system structures have been proposed in the literature. Hexagonal coils, which have a common usage for low power applications, have not been well studied for high and mid power applications such as in electric vehicle (EV) battery charging. In order to fill this knowledge gap, the self and mutual inductance equations of a hexagonal coil are obtained, and these equations have been used to design a 1 kW WPT system …

A New Classification Method For Encrypted Internet Traffic Using Machine Learning, Mesut Uğurlu, İbrahi̇m Alper Doğru, Recep Si̇nan Arslan

Turkish Journal of Electrical Engineering and Computer Sciences

The rate of internet usage in the world is over 62% and this rate is increasing day by day. With this increase, it becomes important to ensure the confidentiality of the information in the traffic flowing over the internet. Encryption algorithms and protocols are used for this purpose. This situation, which is beneficial for normal users, is also used by attackers to hide. Cyber attackers or hackers gain the ability to bypass security precautions such as IDS/IPS and antivirus systems with using encrypted traffic. Since payload analysis cannot be performed without deciphering the encrypted traffic, existing commercial security solutions fall …

Real-Time Motion Tracking Enhancement Via Data-Fusion Based Particle Filter, Tuğrul Taşci, Numan Çelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Motion tracking is a well-defined yet application-specific problem of computer vision field, mostly entailing real-time constraints. Methods addressing such problems are expected also to ensure achievements such as high accuracy and robustness. A probabilistic estimation-based approach is proposed in this paper, in order to enhance the real-time motion tracking process of an RGB-Depth device, in terms of accuracy. A novel method is presented for tracking handpalm of a moving human subject to this end, under a sequence of assumptions such as indoor environment, single object, smooth movement and stable illumination. Tracking accuracy is improved within a particle filter framework by …

Multiagent Q-Learning Based Uav Trajectory Planning For Effective Situationalawareness, Erdal Akin, Kubi̇lay Demi̇r, Hali̇l Yetgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In the event of a natural disaster, arrival time of the search and rescue (SAR) teams to the affected areas is of vital importance to save the life of the victims. In particular, when an earthquake occurs in a geographically large area, reconnaissance of the debris within a short-time is critical for conducting successful SAR missions. An effective and quick situational awareness in postdisaster scenarios can be provided via the help of unmanned aerial vehicles (UAVs). However, off-the-shelf UAVs suffer from the limited communication range as well as the limited airborne duration due to battery constraints. If telecommunication infrastructure is …

Sleep Staging With Deep Structured Neural Net Using Gabor Layer And Dataaugmentation, Ali Erfani Sholeyan, Fereidoun Nowshiravan Rahatabad, Kamal Setaredan

Turkish Journal of Electrical Engineering and Computer Sciences

Slow wave sleep (SWS) and rapid eye movement (REM) are two of the most important sleep stages that are considered in many studies. Detection of these two sleep stages will help researchers in many applications to detect sleeprelated diseases and disorders and also in many fields of neuroscience studies such as cognitive impairment and memory consolidation. Since manual sleep staging is time-consuming, subjective, and expensive; designing an efficient automatic sleep scoring system will overcome some of these difficulties. Many studies have proposed automatic sleep staging systems with different methods. In recent years, deep learning methods show their potential in different …

Design Development And Performance Analysis Of Distributed Least Square Twinsupport Vector Machine For Binary Classification, Bakshi Rohit Prasad, Sonali Agarwal

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning (ML) on Big Data has gone beyond the capacity of traditional machines and technologies. ML for large scale datasets is the current focus of researchers. Most of the ML algorithms primarily suffer from memory constraints, complex computation, and scalability issues.The least square twin support vector machine (LSTSVM) technique is an extended version of support vector machine (SVM). It is much faster as compared to SVM and is widely used for classification tasks. However, when applied to large scale datasets having millions or billions of samples and/or large number of classes, it causes computational and storage bottlenecks. This paper …

Robust And Efficient Ebg-Backed Wearable Antenna For Ism Applications, Ayesha Saeed, Asma Ejaz, Humayun Shahid, Yasar Amin, Hannu Tenhunen

Turkish Journal of Electrical Engineering and Computer Sciences

A structurally compact, semiflexible wearable antenna composed of a distinctively miniaturized electromagnetic band gap (EBG) structure is presented in this work. Designed for body-centric applications in the 5.8 GHz band, the design draws heavily from a novel planar geometry realized on Rogers RT/duroid 5880 laminate with a compact physical footprint spanning lateral dimensions of $0.6$$\lambda$$_0$$\times$$0.06$$\lambda$$_0$. Incorporating a 2$\times$2 EBG structure at the rear of the proposed design ensures sufficient isolation between the body and the antenna, doing away with the performance degradation associated with high permittivity of the tissue layer. The peculiar antenna geometry allows for reduced backward radiation and …

A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur

Turkish Journal of Electrical Engineering and Computer Sciences

The need for high-volume data is one of the challenging requirements of the deep learning methods, and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words, small. These domains may vary from medical diagnosis to satellite imaging. The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning. In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks …

A Hybrid Convolutional Neural Network Approach For Feature Selection Anddisease Classification, Prajna Paramita Debata, Puspanjali Mohapatra

Turkish Journal of Electrical Engineering and Computer Sciences

: Many researchers have analyzed the high dimensional gene expression data for disease classification using several conventional and machine learning-based approaches, but still there exists some issues which make this task nontrivial. Due to the growing complexities of the unstructured data, the researchers focus on the deep learning approach, which is the latest form of machine learning algorithm. In the presented work, a kernel-based Fisher score (KFS) approach is implemented to extract the notable genes, and an improvised chaotic Jaya (CJaya) algorithm optimized convolutional neural network (CJaya-CNN) model is applied to classify high dimensional gene expression or microarray data. This …

Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli

Turkish Journal of Electrical Engineering and Computer Sciences

The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for …

Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara

Turkish Journal of Electrical Engineering and Computer Sciences

Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient?s life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequencydomain measures using wavelet transform, and nonlinear …

Attention Augmented Residual Network For Tomato Disease Detection Andclassification, Getinet Yilma Abawatew, Seid Belay, Kumie Gedamu, Maregu Assefa, Melese Ayalew, Ariyo Oluwasanmi, Zhiguang Qin

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning techniques help agronomists efficiently identify, analyze, and monitor tomato health. CNN (convolutional neural network) locality constraint and existing small train sample adversely influenced disease recognition performance. To alleviate these challenges, we proposed a discriminative feature learning attention augmented residual (AAR) network. The AAR network contains a stacked pre-activated residual block that learns deep coarse level features with locality context, whereas the attention block captures salient feature sets while maintaining the global relationship in data points, attention features augment the learning of the residual block. We used conditional variational generative adversarial network (CVGAN) image reconstruction network and augmentation techniques …

Pause For A Cybersecurity Cause: Assessing The Influence Of A Waiting Period On User Habituation In Mitigation Of Phishing Attacks, Amy Antonucci

CCAC Theses and Dissertations

Social engineering costs organizations billions of dollars a year. Social engineering exploits the weakest link of information security systems, the people who are using them. Phishing is a form of social engineering in which the perpetrator depends on the victim’s instinctual thinking towards an email designed to create a fear or excitement response. It is well-documented in literature that users continue to click on phishing emails costing them and their employers significant monetary resources and data loss. Training does not appear to mitigate the effects of phishing much; other solutions are necessary to mitigate phishing.

Kahneman introduced the concepts of …

An Empirical Assessment Of Users' Information Security Protection Behavior Towards Social Engineering Breaches, Nisha Jatin Patel

CCAC Theses and Dissertations

User behavior is one of the most significant information security risks. Information Security is all about being aware of who and what to trust and behaving accordingly. Due to technology becoming an integral part of nearly everything in people's daily lives, the organization's need for protection from security threats has continuously increased. Social engineering is the act of tricking a user into revealing information or taking action. One of the riskiest aspects of social engineering is that it depends mainly upon user errors and is not necessarily a technology shortcoming. User behavior should be one of the first apprehensions when …

A Study Of Factors That Influence Symbol Selection On Augmentative And Alternative Communication Devices For Individuals With Autism Spectrum Disorder, William Todd Dauterman

CCAC Theses and Dissertations

According to the American Academy of Pediatrics (AAP), 1 in 59 children are diagnosed with Autism Spectrum Disorder (ASD) each year. Given the complexity of ASD and how it is manifested in individuals, the execution of proper interventions is difficult. One major area of concern is how individuals with ASD who have limited communication skills are taught to communicate using Augmentative and Alternative Communication devices (AAC). AACs are portable electronic devices that facilitate communication by using audibles, signs, gestures, and picture symbols. Traditionally, Speech-Language Pathologists (SLPs) are the primary facilitators of AAC devices and help establish the language individuals with …

Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based …

Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M

Turkish Journal of Electrical Engineering and Computer Sciences

The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician?s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such …

A Novel Design Of Current Differencing Transconductance Amplifier With Hightransconductance Gain And Enhanced Bandwidth, Shireesh Kumar Rai, Rishikesh Pandey, Bharat Garg, Sujit Patel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, transconductance gain of current differencing transconductance amplifier (CDTA) has been boosted by using a novel approach. Transconductance is generally varied by two well-known techniques. In the first technique, bias current of differential pair MOSFETs is varied whereas in the second technique, aspect ratios of differential pair MOSFETs are changed. The drawbacks of first technique are limited range of transconductance and higher power dissipation whereas second technique restricts dynamic range, output swing and bandwidth of CDTA. To overcome these drawbacks, 2 new structures of CDTA, namely high transconductance gain CDTAs (HTG-CDTA-I & HTG-CDTA-II) have been proposed. HTG-CDTA-I utilizes …

Mismatch Error Shaping Of Dac Unit Elements In Multibit $\Delta$$\Sigma$ Modulators Using A Novel Unified Adc/Dac, Leila Sharifi, Omid Hashemipour

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a unified analog to digital converter (ADC) and digital to analog converter (DAC) for multibit $\Delta\Sigma$ modulators. The unified ADC/DAC circuit provides error shaping for mismatches between DAC unit elements. Hence, the dynamic element matching (DEM) circuit or digital calibration is not required resulting in the area and power saving as well as the elimination of the excess loop delay introduced by DEM circuit. Incorporating a 6-bit unified ADC/DAC, the $\Delta\Sigma$ modulator achieves 16.15-bit resolution utilizing only a second order loop filter and oversampling ratio of 40. The proposed modulator is simulated in a 65-nm CMOS process. …

Codec-Aware Video Delivery Over Sdns, Obinna Izima, Ruairi Defrein, Ali Malik

Conference papers

To guarantee quality of delivery for video streaming over software defined networks, efficient predictors and adaptive routing frameworks are required. We demonstrate an agent that predicts video quality of delivery metrics in a scalable way using a bespoke codec-aware learning model. We also demonstrate the integration of this agent with an adaptive framework for centrally controlled software-defined networks that re-configures network operational paths in response to the learning agent, ensuring that good quality of delivery of video is maintained during periods of congestion. The demo scenario highlights the feasibility, scalability and accuracy of the framework

Improving Employees’ Compliance With Password Policies, Enas Albataineh

CCAC Theses and Dissertations

Employees’ lack of compliance with password policies increases password susceptibility, which leads to financial damages to the organizations as a result of information disclosure, fraud, and unauthorized transactions. However, few studies have examined what motivates employees to comply with password policies.

The purpose of this quantitative cross-sectional study was to examine what factors influence employees’ compliance with password policies. A theoretical model was developed based on Protection Motivation Theory (PMT), General Deterrence Theory (GDT), Theory of Reasoned Action (TRA), and Psychological Ownership Theory to explain employees’ compliance with password policies.

A non-probability convenience sample was employed. The sample consisted of …

Mixed Carrier Communication For Sixth-Generation Networks :, Ahmed Fahmy Mahmoud Hussein

Legacy Theses & Dissertations (2009 - 2024)

Recently, research on sixth-generation (6G) wireless networks has gained significant interest. By 2030, it is expected that 6G will introduce revolutionary applications and services. Thus, 6G is likely to expand across all available spectrum, including terahertz (THz) and optical frequency bands. Although 5G will offer a massive upgrade to the spectrum, the technology does not provide solutions to support a vast multitude of services and devices simultaneously.Motivated by the heterogeneity of wireless technologies, devices, and services, the Mixed Carrier Communication (MCC) concept is introduced for the first time. MCC is a novel concept that supports the 6G vision by enabling …

Parallel Computation, Mahesh Wosti

Student Academic Conference

Parallel computing has always fascinated me since I became aware of it. I think it is the only path forward if we are to build and make a super-fast computer in days to come. For my senior seminar, I want to build a simple framework of the parallel system and dig deeper into potential benefits and drawbacks of using a parallel system compared to the serial computing that is still pervasive in today’s modern technology.