Engineering Commons^™

Maritime Automatic Target Recognition For Ground-Based Scanning Radars By Usingsequential Range Profiles, Baki̇ Bati, Nevci̇han Duru

Turkish Journal of Electrical Engineering and Computer Sciences

Classification of marine targets using radar data products has become an important area for modern researchsociety. However, due to several reasons such as the similarity between ship structures and spatial specifications,classification of marine targets constitutes a challenging problem. In almost all of the studies, this problem has beenhandled by focusing on a single instance of range profiles or synthetic aperture radar data. However, this approachis seen to achieve only a particular success. This study introduces a novel classification approach that is shown toprovide additional classification enhancements by exploiting the extra information extracted from sequential rangeprofiles generated by ground-based marine surveillance …

A Novel Pulse Plethysmograph Signal Analysis Method For Identification Of Myocardial Infarction, Dilated Cardiomyopathy, And Hypertension, Muhammad Umar Khan, Sumair Aziz

Turkish Journal of Electrical Engineering and Computer Sciences

Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detectionof CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosisof CDs using various physiological signals has recently been used by researchers. Since pulse plethysmograph (PuPG)signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system designfor the automatic diagnosis of cardiac disorders like hypertension, dilated cardiomyopathy and myocardial infarctionusing a novel fingertip PuPG signal analysis. The proposed system first performs signal denoising of raw PuPG sensordata using discrete wavelet transform (DWT). After …

Analysis Of Shielding Effectiveness By Optimizing Aperture Dimensions Of Arectangular Enclosure With Genetic Algorithm, Sunay Güler, Si̇bel Yeni̇kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Electromagnetic compatibility (EMC) has now become a substantial challenge more than any other time sincethe number of electric vehicles (EV) increased rapidly. The electric driving system in an EV consists of power electroniccomponents supplied by high voltage battery source. They are both source and victim of potential electromagneticinterference (EMI) since fast switching process occurs inside them. Electromagnetic shielding provides a significantprotection against EMI for any electrical and electronic components inside the vehicle. In this paper, analysis of shieldingeffectiveness (SE) by optimizing aperture dimensions of a rectangular enclosure is investigated. Realistic dimensions of theshielding enclosure of an inverter component are employed. …

Haze-Level Prior Approach To Enhance Object Visibility Under Atmosphericdegradation, Vijaya Lakshmi Thirumala, Venkata Satyanarayana Karanam, Pratap Reddy Lankireddy, Aruna Kumari Kakumani, Rakesh Kumar Yacharam

Turkish Journal of Electrical Engineering and Computer Sciences

Outdoor captured scenes are degraded by atmospheric particles and water droplets. Due to scattering andabsorption effects in the atmosphere, degraded images lose contrast and color fidelity. Performances of the computervision algorithms are bound to suffer from low-contrast scene radiance. In many single-image dehazing models, thelarger the deviation in estimation of the key parameters such as transmission map and atmospheric light, the higherthe halo artifacts and loss of fine details in the dehazed image. The available models assume that the scattering lightis independent of wavelength, as the size of the atmospheric particles is larger compared to the wavelength of light.The model …

Neural Relation Extraction: A Review, Mehmet Aydar, Özge Bozal, Furkan Özbay

Turkish Journal of Electrical Engineering and Computer Sciences

Neural relation extraction discovers semantic relations between entities from unstructured text using deeplearning methods. In this study, we make a clear categorization of the existing relation extraction methods in termsof data expressiveness and data supervision, and present a comprehensive and comparative review. We describe theevaluation methodologies and the datasets used for model assessment. We explicitly state the common challenges inrelation extraction task and point out the potential of the pretrained models to solve them. Accordingly, we investigateadditional research directions and improvement ideas in this field.

A Nonlinear Disturbance Observer Scheme For Discrete Time Control Systems, Mehmet Önder Efe, Coşku Kasnakoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a modification to the original disturbance observer based control (DOBC) scheme byredefining the lowpass filter using a nonlinear element. The proposed technique improves the disturbance predictionperformance for both small and large magnitude disturbance signals. The contribution of the current work is to unfoldthe stability and performance conditions under the proposed modification. A comparative set of simulation studies arediscussed and it is seen that the proposed modification results in smaller disturbance prediction error energy and smallertracking error energy when the plant model is discrete time and uncertain.

A Novel Approach Of Order Diminution Using Time Moment Concept With Routharray And Salp Swarm Algorithm, Nafees Ahamad, Afzal Sikander

Turkish Journal of Electrical Engineering and Computer Sciences

In control engineering, there may be two systems that have the same input-output characteristic with differentdegrees of complexity. This concept leads to order diminution(OD) of a large scale system. In this article, the authorspropose a new hybrid order diminution technique based on the time moment matching method with the Routh arrayconcept and a recently developed fast and accurate salp swarm optimization (SSO) technique. The proposed methodcombines the advantages of both the classical method of OD and the optimization technique. The unknown coe?icient ofthe divisor of the reduced system is obtained by exploring the time moment matching methodology with the Routh …

Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie

Turkish Journal of Electrical Engineering and Computer Sciences

The most challenging objective in machine translation of sign language has been the machine?s inability tolearn interoccluding finger movements during an action process. This work addresses the problem of teaching a deeplearning model to recognize differently oriented skeletal data. The multi-view 2D skeletal sign language video data isobtained using 3D motion-captured system. A total of 9 signer views were used for training the proposed network andthe 6 for testing and validation. In order to obtain multi-view deep features for recognition, we proposed an end-to-endtrainable multistream convolutional neural network (CNN) with late feature fusion. The fused multiview features arethen inputted to …

Analytical Modeling And Study On Noise Characteristics Of Rotor Eccentric Spmsmwith Unequal Magnetic Poles Structure, Pengpeng Xia, Shenbo Yu, Rutong Dou, Fengchen Zhai

Turkish Journal of Electrical Engineering and Computer Sciences

he establishment of the analytical model of the rotor eccentric surface-mounted permanent magnet syn-chronous motor (SPMSM) with unequal magnetic poles structure will be beneficial to calculating the magnetic field andstudying noise characteristics quickly. Based on the equivalent surface current (ESC) method and equivalent boundarymethod, the analytical model of the rotor eccentric SPMSM with unequal magnetic poles structure is proposed. Duringthe modeling process, the magnetic field produced by a permanent magnet (PM) is obtained using the ESC method, andthe effect on the air gap magnetic field, which arised from stator, is replaced with the concentric current sheet (CCS)magnetic field. And, the …

Pfecc: A Precise Feedback-Based Explicit Congestion Control Algorithm In Nameddata Networking, Hui Li

Turkish Journal of Electrical Engineering and Computer Sciences

Named data networking (NDN), as a future Internet architecture that is a promising alternative to TCP/IPnetworks, has the new features of connectionless, in-network cache, and hop-by-hop forwarding, which makes thecongestion control algorithms of traditional TCP/IP networks unable to be directly applied to NDN. In addition, sincethe optimal size of the sending window cannot be determined, the existing window-based congestion control algorithmsgenerally use the AIMD-like window adjustment algorithm, which cannot achieve the optimal throughput. In thispaper, we propose a precise feedback-based, multipath-aware congestion control algorithm PFECC, which is inspiredby Accel-Brake Control algorithm. PFECC considers the influence of Interest flows, uses a …

On The Outage Performance Of Swipt-Noma-Crs With Imperfect Sic And Csi, Ferdi̇ Kara

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a nonorthogonal multiple access based cooperative relaying system (NOMA-CRS) is consideredto increase spectral efficiency. Besides, the simultaneous wireless information and power transfer (SWIPT) is proposedfor the relay in NOMA-CRS. In SWIPT-NOMA-CRS, three different energy harvesting (EH) protocols, power sharing(PS), time sharing (TS) and ideal protocols are implemented. The outage performances of the SWIPT-NOMA-CRSare studied for all three EH protocols. In the analysis, to represent practical/reasonable scenarios, imperfect successiveinterference canceler (SIC) and imperfect channel state information (CSI) are taken into consideration. The derivedoutage probability (OP) expressions are validated via computer simulations. Besides, the OP for the benchmark scheme,NOMA-CRS …

Turkish Sign Language Recognition Based On Multistream Data Fusion, Cemi̇l Gündüz, Hüseyi̇n Polat

Turkish Journal of Electrical Engineering and Computer Sciences

Sign languages are nonverbal, visual languages that hearing- or speech-impaired people use for communication.Aside from hands, other communication channels such as body posture and facial expressions are also valuable insign languages. As a result of the fact that the gestures in sign languages vary across countries, the significance ofcommunication channels in each sign language also differs. In this study, representing the communication channels usedin Turkish sign language, a total of 8 different data streams-4 RGB, 3 pose, 1 optical flow-were analyzed. Inception3D was used for RGB and optical flow; and LSTM-RNN was used for pose data streams. Experiments were conductedby …

An Adaptive Element Division Algorithm For Accurate Evaluation Of Singular Andnear Singular Integrals In 3d, Hakan Bayindir, Besi̇m Baranoğlu, Ali̇ Yazici

Turkish Journal of Electrical Engineering and Computer Sciences

An adaptive algorithm for evaluation of singular and near singular integrals in 3D is presented. The algorithmis based on successive adaptive/selective subdivisions of the element until a prescribed error criteria is met. For evaluatingthe integrals in each subdivision, Gauss quadrature is applied. The method is computationally simple, memory efficientand can be applied for both triangular and quadrilateral elements, including the elements with nonplanar and/or curvedsurfaces. To assess the method, several examples are discussed. It has shown that the algorithm performs well forsingular and near-singular integral examples presented in the paper and evaluates the integrals with very high accuracy.

Dual Bit Control Low-Power Dynamic Content Addressable Memory Design For Iotapplications, V V Satyanarayana Satti, Sridevi Sriadibhatla

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet of things (IoT) is an emerging area in the semiconductor industry for low-power and high-speedapplications. Many search engines of IoT applications require low power consumption and high-speed content addressablememory (CAM) devices for the transmission of data packets between servers and end devices. A CAM is a hardwaredevice used for transfer of packets in a network router with high speed at the cost of power consumption. In this paper,a new dual bit control precharge free (PF) dynamic content addressable memory (DCAM) has been introduced. Theproposed design uses a new charge control circuitry, which is used to control the dual …

Relational-Grid-World: A Novel Relational Reasoning Environment And An Agentmodel For Relational Information Extraction, Faruk Küçüksubaşi, Eli̇f Sürer

Turkish Journal of Electrical Engineering and Computer Sciences

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generallyhave uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms ofgeneralizability and interpretability using symbolic artificial intelligence (AI) tools such as logic programming. Inthis study, we present a model-free RL architecture that is supported with explicit relational representations of theenvironmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problemrather than image-based tasks, and multi-head dot-product attention network (MHDPA) as a baseline for performancecomparisons. We tested two networks in two environments -i.e., the baseline box-world environment and …

A Topological Overview Of Microgrids: From Maturity To The Future, Ayşe Kübra Erenoğlu, Semanur Sancar, Ozan Erdi̇nç, Mustafa Bağriyanik

Turkish Journal of Electrical Engineering and Computer Sciences

The concept of microgrid (MG) has attracted great attention from the system operators for increasing operational effectiveness as well as providing more reliable, sustainable and economic power system. In this paper, a comprehensive investigation is presented to shine new light on evaluating changes in MG operation from maturity to the future. A great deal of literature studies consisting of the traditional MG architecture, encountered challenges and proposed solutions for overcoming them are all examined in detail. Also, the impact of highly integrated renewable-based energy sources into the power system is analysed by current studies. Moreover, modern MG architecture is extensively …

Chaos In Metaheuristic Based Artificial Intelligence Algorithms:A Short Review, Gökhan Atali, İhsan Pehli̇van, Bi̇lal Gürevi̇n, Hali̇l İbrahi̇m Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems- where chaos theory is widely employed is optimization problems. Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already …

Efficient Power Macromodeling Approach For An Ip-Based Soc System Usingdiscrete Water Cycle Algorithm, Faisal Siddiq, Yaseer Arafat Durrani

Turkish Journal of Electrical Engineering and Computer Sciences

Low-power consumption is becoming a crucial concern that cannot be neglected in system-on-chip (SoC) designs. Low-power solutions help designers to provide a powerful methodology to analyze, estimate, and optimize today's power concerns. Early estimation of power at a high level reduces the redesign cycle and turn-around time. Power dissipation is a function of input patterns and their characteristics. This paper describes a power estimation technique by using predefined statistical characteristics-based input patterns, which gives the average power dissipation of individual intellectual property (IP) blocks and interconnects/buses in an SoC system design. During the power estimation phase, the discrete water cycle …

The Effect Of Demand Response Control On Stability Delay Margins Of Loadfrequency Control Systems With Communication Time-Delays, Deni̇z Kati̇poğlu, Şahi̇n Sönmez, Saffet Ayasun, Ausnain Naveed

Turkish Journal of Electrical Engineering and Computer Sciences

This paper studies the effect of dynamic demand response (DR) control on stability delay margins of load frequency control (LFC) systems including communication time-delays. A DR control loop is included in each control area, called as LFC-DR system and Rekasius substitution is utilized to identify stability margins for various proportionalintegral (PI) gains and participation ratios of the secondary and DR control loops. The purpose of Rekasius substitution technique is to obtain purely complex roots on the imaginary axis of the time-delayed LFC-DR system. This substitution first converts the characteristic equation of the LFC-DR system including delay-dependent exponential terms into an …

Using Eeg To Detect Driving Fatigue Based On Common Spatial Pattern Andsupport Vector Machine, Li Wang, David Johnson, Yingzi Lin

Turkish Journal of Electrical Engineering and Computer Sciences

To investigate the correlation between electroencephalogram (EEG) and driving fatigue states, this study used machine learning algorithms to detect driving fatigue based on EEG. 14 channels of EEG data were collected from thirty-four healthy subjects in this research at Northeastern University. Each subject participated in two scenarios (baseline and fatigue scenarios). Subjective ratings of fatigue levels were also obtained from the subjects using the NASA-Task Load Index (TLX). The common spatial pattern (CSP) algorithm was used to extract features from the raw EEG data. The support vector machine (SVM) was used as the classifier in the design of the machine …

Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency, Safety, And Health, Danish Ali

Doctoral Dissertations

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to …

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular apps (with an …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Detection Of Gaussian Attacks In Power Systems Under A Scalable Kalman Consensus Filter Framework, Arnold Fernandes, Rui Bo, Jonathan W. Kimball, Bruce M. Mcmillin

Electrical and Computer Engineering Faculty Research & Creative Works

The dynamic non-linear state-space model of a power-system consisting of synchronous generators, buses, and static loads has been linearized and a linear measurement function has been considered. A distributed dynamic framework for estimating the state vector of the power system has been designed here. This framework employs a type of distributed Kalman filter (DKF) known as a Kalman consensus filter (KCF) which is located at distributed control centers (DCCs) that fuse locally available noise ridden measurements, state vector estimates of neighboring control centers, and a prediction obtained by the linearized model to obtain a filtered state vector estimate. Further, the …

Mental Health And The Covid-19 Pandemic: Analysis Of Twitter Discourse, Omar El-Gayar, Abdullah Wahbeh, Tareq Nasralah, Ahmed El Noshokaty, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications (Archived)

This study analyzed Twitter discourse to understand the association of the COVID-19 pandemic with mental health. The study compared tweets’ volume over time, tweets’ volume per mental health category, emotions, and the top hashtags on mental health before and after November 2019, the month on which the first COVID-19 case was reported. We analyzed a total of 273 million English tweets on mental health collected from 56 million unique users. Results and analysis showed a significant shift in trend for the volume of tweets on mental health over time. There was also a notable increase in the volume of tweets …

Edge Processing Of Image For Uas Sense And Avoidance, Christopher J. Rave

Browse all Theses and Dissertations

Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part …

Arise - Augmented Reality In Surgery And Education, Sadan Suneesh Menon

Browse all Theses and Dissertations

Human errors in healthcare can be fatal. Proper physical assessment of patients to avoid such errors is of paramount importance. Incorrect or insufficient assessment of the patient can cause treatment delays that may lead to negative outcomes. In this dissertation we introduce innovative technology to assist surgeons in patient assessment as well as during the training of nurses in order to enhance learning. Technological advancements have made it possible to visualize overlays of computer-generated 3D models on real-world surfaces. This technology is called augmented reality. Using Steady State Topography (SST) brain imaging to examine the brain activity of people who …

Leveraging Sequential Nature Of Conversations For Intent Classification, Shree Gotteti

Browse all Theses and Dissertations

Conversations are more than just a sequence of text, it is where two or more participants interact in order to achieve their goals. Conversation Understanding (CU) requires all participants to understand each others intent. In the past decade, CU has been extended from automated human-human text processing to build automated conversational agents for human-machine interactions. Despite their popularity, these automated conversational agents (like Siri, Alexa, etc) can't handle more than one or two utterances, and they don't recognize conversations as intents. The development of approaches that extract intents behind an utterance is essential for the advancements of Question Answering (QA) …