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Articles 391 - 420 of 3106
Full-Text Articles in Engineering
Analysis Of Optical Gyroscopes With Vertically Stacked Ring Resonators, Dooyoung Hah
Analysis Of Optical Gyroscopes With Vertically Stacked Ring Resonators, Dooyoung Hah
Turkish Journal of Electrical Engineering and Computer Sciences
Without any moving part, optical gyroscopes exhibit superior reliability and accuracy in comparison to mechanical sensors. Microring-resonator-based optical gyroscopes emerged as alternatives for bulky conventional Sagnac interferometer sensors, especially attractive for applications with limited footprints. Previously, it has been reported that planar incorporation of multiple resonators does not bring about improvement in sensitivity for a given area because the increase in Sagnac phase accumulation does not outrun the increase of area. Therefore, it was naturally suggested to consider vertical stacking of ring resonators because then, the resonators can share the same footprint. In this work, sensitivity performances of such configurations …
Joint Optimization Of Target Wake Time Mechanism And Scheduling For Ieee802.11ax, Mehmet Karaca
Joint Optimization Of Target Wake Time Mechanism And Scheduling For Ieee802.11ax, Mehmet Karaca
Turkish Journal of Electrical Engineering and Computer Sciences
IEEE 802.11ax as the newest wireless local area networks (WLANs) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 WLAN standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called target wake time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go …
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …
Optimal Coordination Of Directional Overcurrent Relay Based On Combination Ofimproved Particle Swarm Optimization And Linear Programming Consideringmultiple Characteristics Curve, Suzana Pil Ramli, Hazlie Mokhlis, Wei Ru Wong, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor, Muhamad Hatta Hussain
Optimal Coordination Of Directional Overcurrent Relay Based On Combination Ofimproved Particle Swarm Optimization And Linear Programming Consideringmultiple Characteristics Curve, Suzana Pil Ramli, Hazlie Mokhlis, Wei Ru Wong, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor, Muhamad Hatta Hussain
Turkish Journal of Electrical Engineering and Computer Sciences
Optimal coordination of directional over-current relays (DOCRs) is a crucial task in ensuring the security and reliability of power system network. In this paper, a hybridization of an improved particle swarm optimization and linear programming (IPSO-LP) is proposed to solve DOCRs coordination problem. The considered decision variables in the optimization are plug setting current, time multiplier setting, type of relay, and type of curve. By considering these parameters in the optimization, the best relay operating time can be determined. Furthermore, the proposed technique also considered the continuous values of pick-up current setting (PSC) and time setting multiplier (TMS). Test on …
Adaptation Of Metaheuristic Algorithms To Improve Training Performance Of Aneszsl Model, Şi̇fa Özsari, Mehmet Serdar Güzel, Gazi̇ Erkan Bostanci, Ayhan Aydin
Adaptation Of Metaheuristic Algorithms To Improve Training Performance Of Aneszsl Model, Şi̇fa Özsari, Mehmet Serdar Güzel, Gazi̇ Erkan Bostanci, Ayhan Aydin
Turkish Journal of Electrical Engineering and Computer Sciences
Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zeroshot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. Metaheuristic algorithms are considered as …
Novel Fast Terminal Sliding Mode Controller With Current Constraint Forpermanent-Magnet Synchronous Motor, Yao Fang, Huifang Kong, Daoyuan Ding
Novel Fast Terminal Sliding Mode Controller With Current Constraint Forpermanent-Magnet Synchronous Motor, Yao Fang, Huifang Kong, Daoyuan Ding
Turkish Journal of Electrical Engineering and Computer Sciences
Under the noncascade structure, the balance between q-axis current constraint and dynamic performance in permanent-magnet synchronous motor system has become a critical problem. On the one hand, large transient current is required to provide high torque to achieve fast dynamic performance. On the other hand, current constraint becomes a state constraint problem, instead of governing q-axis reference current in the cascade structure directly. Aiming at this issue, a novel fast terminal sliding mode control (FTSMC)-based controller with current constraint is developed in this paper. The novelty of this scheme is related to the proposed penalty function based on interior point …
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
A Novel Hybrid Global Optimization Algorithm Having Training Strategy: Hybridtaguchi-Vortex Search Algorithm, Mustafa Saka, Meli̇h Çoban, İbrahi̇m Eke, Suleyman Sungur Tezcan, Müslüm Cengi̇z Taplamacioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a novel hybrid Taguchi-vortex search algorithm (HTVS) is proposed for solving global optimization problems. Taguchi orthogonal approximation and vortex search algorithm (VS) are hybridized in presenting method. In HTVS, orthogonal arrays in the Taguchi method are trained and obtained better solutions are used to find global optima in VS. Thus, HTVS has better relation between exploration and exploitation, and it exhibits more powerful approximation to find global optimum value. Proposed HTVS algorithm is applied to sixteen well-known benchmark optimization test functions with different dimensions. The results are compared with the Taguchi orthogonal array approximation (TOAA), vortex search …
Fpga Implementation Of Lsd-Omp For Real-Time Ecg Signal Reconstruction, Önder Polat, Sema Kayhan
Fpga Implementation Of Lsd-Omp For Real-Time Ecg Signal Reconstruction, Önder Polat, Sema Kayhan
Turkish Journal of Electrical Engineering and Computer Sciences
Compressed sensing is widely used to compress electrocardiogram (ECG) signals, but the major challenges of the compressed sensing algorithms are their highly complex signal reconstruction processes. In this paper, a reconfigurable high-speed and low-power field-programmable gate array (FPGA) implementation of the least support denoising-orthogonal matching pursuit (LSD-OMP) algorithm for the real-time reconstruction of the ECG signals is presented. The contribution of this study is two-fold: Firstly, LSD-OMP can pick more than one element at each iteration and reconstruct the sparse signal using less number of iterations as compared to the standard OMP algorithms. Latency of the proposed design is therefore …
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 …
Deep Learning-Based Covid-19 Detection System Using Pulmonary Ct Scans, Rajit Nair, Adi Alhudhaif, Deepika Koundal, Rumi Iqbal Doewes, Preeti Sharma
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
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
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 …