Physical Sciences and Mathematics Commons^™

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 …

A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest …

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 …

Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …

Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual …

Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap

Turkish Journal of Electrical Engineering and Computer Sciences

As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …

Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan

Turkish Journal of Electrical Engineering and Computer Sciences

Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …

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 …

Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni

Turkish Journal of Electrical Engineering and Computer Sciences

The detection of diabetic retinopathy (DR) in millions of diabetic patients across the globe is a challenging problem. Diagnosis of retinopathy is a lengthy and tedious process, requiring a medical professional to assess the individual fundus images of a patient's retina. This process can be automated by applying deep learning (DL) technology given a huge dataset. The problems associated with DL are the unavailability of a large dataset and their higher training time. The DL model's best performance is achieved using set of optimal hyperparameters (OHPs) obtained by performing costly iterations of hyperparameter optimization (HPO). These problems can be addressed …

Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay

Turkish Journal of Electrical Engineering and Computer Sciences

The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …

Transform Based Approaches For The Detection Of Astrophysical Signals, Marwan Mahfud Alkhweldi

Graduate Theses, Dissertations, and Problem Reports

Development of new algorithms for the detection of isolated astrophysical pulses is of interest to radio astronomers. Both Fast Radio Bursts (FRBs) and several Rotating Radio Transients (RRATs) were detected through the application of a single pulse search algorithm. The conventional approach to detect astronomical pulses requires an exhaustive search for the correct dispersion measure. Its accelerated versions involve signal processing in Fourier transform space.

In this dissertation, we present several new transform-based approaches for the detection and analysis of astrophysical signals with the latest being the most effective and advanced of all. It is implemented in several steps. First, …

Using Ai For Management Of Field Emission In Srf Linacs, A. Carpenter, P. Degtiarenko, R. Suleiman, C. Tennant, D. Turner, L. S. Vidyaratne, Khan Iftekharuddin, Md. Monibor Rahman

Electrical & Computer Engineering Faculty Publications

Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high …

Assessment Of Cu(In, Ga)Se₂ Solar Cells Degradation Due To Water Ingress Effect On The Cds Buffer Layer, Deewakar Poudel, Benjamin Belfore, Shankar Karki, Grace Rajan, Sina Soltanmohammad, Angus Rockett, Sylvain Marsillac

Electrical & Computer Engineering Faculty Publications

The effect of water ingress on the surface of the buffer layer of a Cu(In, Ga)Se2 (CIGS) solar cell was studied. Such degradation can occur either during the fabrication process, if it involves a chemical bath as is often the case for CdS, or while the modules are in the field and encapsulants degrade. To simulate the impact of this moisture ingress, devices with a structure sodalime glass/Mo/CIGS/CdS were immersed in deionized water. The thin films were then analyzed both pre and post water soaking. Dynamic secondary ion mass spectroscopy (SIMS) was performed on completed devices to analyze impurity diffusion …

Transient Behavior Of Drift And Ionization In Atmospheric Pressure Nitrogen Discharge, S. K. Dhali

Electrical & Computer Engineering Faculty Publications

The fluid models are frequently used to describe a non-thermal plasma such as a streamer discharge. The required electron transport data and rate coefficients for the fluid model are parametrized using the local field approximation (LFA) in first order models and the local-mean-energy approximation (LMEA) in second order models. We performed Monte Carlo simulations in Nitrogen gas with step changes in the E/N (reduced electric field) to study the behavior of the transport properties in the transient phase. During the transient phase of the simulation, we extract the instantaneous electron mean energy, which is different from the steady state mean …

Rapid Quantification Of Biofouling With An Inexpensive, Underwater Camera And Image Analysis, Matthew R. First, Scott C. Riley, Kazi Aminul Islam, Victoria Hill, Jiang Li, Richard C. Zimmerman, Lisa A. Drake

Electrical & Computer Engineering Faculty Publications

To reduce the transport of potentially invasive species on ships' submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB^©). Initially, an unsupervised clustering model was used, and three …

Formal Power Series Approach To Nonlinear Systems With Static Output Feedback, G.S. Venkatesh, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

The goal of this paper is to compute the generating series of a closed-loop system when the plant is described in terms of a Chen-Fliess series and static output feedback is applied. The first step is to reconsider the so called Wiener-Fliess connection consisting of a Chen-Fliess series followed by a memoryless function. Of particular importance will be the contractive nature of this map, which is needed to show that the closed-loop system has a Chen-Fliess series representation. To explicitly compute the generating series, two Hopf algebras are needed, the existing output feedback Hopf algebra used to describe dynamic output …

Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …

High Voltage Design And Evaluation Of Wien Filters For The Cebaf 200 Kev Injector Upgrade, Gabriel Palacios-Serrano, Helmut Baumgart, C. Hernández-García, P. Adderley, J. Benesch, D. Bullard, J. Grames, A. Hofler, D. Machie, M. Poelker, M. Stutzman, R. Suleiman

Electrical & Computer Engineering Faculty Publications

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require highly spin-polarization electron beams, produced from strained super-lattice GaAs photocathodes, activated to negative electron affinity in a photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector defines the orientation of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming MOLLER experiment requires increasing the electron beam energy to 200 keV, to reduce unwanted helicity correlated intensity and position systematics and provide precise control of the polarization orientation. …

Initial Studies Of Cavity Fault Prediction At Jefferson Laboratory, L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D. Turner, Khan Iftekharuddin, Md. Monibor Rahman

Electrical & Computer Engineering Faculty Publications

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered …

Wavefront-Selective Fano Resonant Metasurface, Adam C. Overvig, Andrea Alù

Publications and Research

Fano resonances are conventionally understood as sharp spectral features with selectivity in the momentum-frequency domain, implying that they can be excited only by plane waves with specific frequencies and incident angles. We demonstrate that Fano resonances can be made generally selective in the space-frequency domain. They can be tailored to resonate only when excited by a frequency, polarization, and wavefront of choice. This generalization reveals that Fano systems are characterized by eigenwaves that scatter to their time-reversed image upon reflection. Although in conventional Fano systems this trivially occurs for normally incident plane waves, we show that, in general, the selected …

A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu

Masters Theses

“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …

Morphology-Dependent Resonances In Two Concentric Spheres With Variable Refractive Index In The Outer Layer: Analytic Solutions, Umaporn Nuntaplook, John A. Adam

Mathematics & Statistics Faculty Publications

In many applications constant or piecewise constant refractive index profiles are used to study the scattering of plane electromagnetic waves by a spherical object. When the structured media has variable refractive indices, this is more of a challenge. In this paper, we investigate the morphology dependent resonances for the scattering of electromagnetic waves from two concentric spheres when the outer shell has a variable refractive index. The resonance analysis is applied to the general solutions of the radial Debye potential for both transverse magnetic and transverse electric modes. Finally, the analytic conditions to determine the resonance locations for this system …

Coordination, Adaptation, And Complexity In Decision Fusion, Weiqiang Dong

Dissertations

A parallel decentralized binary decision fusion architecture employs a bank of local detectors (LDs) that access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H0 (“absent”) or H1 (“present”)). The k 1 LD uses a local decision rule to compress its local observations yk into a binary local decision uk; uk = 0 if the k 1 LD accepts H0 and uk = 1 if it accepts H1. The k 1 LD sends its decision uk over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the …

Drone-Assisted Emergency Communications, Di Wu

Dissertations

Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area …

Countering Internet Packet Classifiers To Improve User Online Privacy, Sina Fathi-Kazerooni

Dissertations

Internet traffic classification or packet classification is the act of classifying packets using the extracted statistical data from the transmitted packets on a computer network. Internet traffic classification is an essential tool for Internet service providers to manage network traffic, provide users with the intended quality of service (QoS), and perform surveillance. QoS measures prioritize a network's traffic type over other traffic based on preset criteria; for instance, it gives higher priority or bandwidth to video traffic over website browsing traffic. Internet packet classification methods are also used for automated intrusion detection. They analyze incoming traffic patterns and identify malicious …

Treated Hfo2 Based Rram Devices With Ru, Tan, Tin As Top Electrode For In-Memory Computing Hardware, Yuvraj Dineshkumar Patel

Theses

The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention …

Preparations And Sodium Storage Properties Of Ni3S2@Cnt Composite, Ming-Tao Duan, Yan-Shuang Meng, Hong-Shuai Zhang

Journal of Electrochemistry

Transition metal sulfides (TMSs)-based electrode materials with highly reversible sodium storage have attracted extensive attentions as one of the most prospective electrode materials for sodium ion batteries (SIBs). However, low cycling stability and rate property caused by large volume expansion and poor electronic conductivity during the electrochemical reaction still hamper their further practical application. In this work, in-situ encapsulated Ni₃S₂ nanoparticles in carbon nanotubes (Ni₃S₂@CNT) have been successfully fabricated as an anode material for high-performance SIBs by a one-step solid-phase calcination process. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), …

Degradation And Thermal Characteristics Of Lini0.8Co0.15Al0.05O2/Graphite Lithium Ion Battery After Different State Of Charge Ranges Cycling, Cun Wang, Wei-Jiang Zhang, Teng-Fei He, Bo Lei, You-Jie Shi, Yao-Dong Zheng, Wei-Lin Luo, Fang-Ming Jiang

Journal of Electrochemistry

The LiNi_0.8Co_0.15Al_0.05O₂ (NCA) cathode exhibits high energy density and large reversible capacity, which plays an essential role in the field of electric vehicles (EVs). However, low capacity retention and poor thermal stability limit its application. Few literatures are found for the capacity degradation mechanism of NCA/graphite batteries at home and abroad. The different state of charge (SOC) ranges cycle degradation behaviors of 18650-type NCA/graphite (2.4 Ah) battery were studied in this paper. The SOC ranges considered were 0% ~ 20% (low), 20% ~ 70% (medium), 70% ~ 100% (high), and 0% ~ 100% …

Preparations And Photoelectrochemical Performances Of Rgo-Tio2 Nanotubes Arrays, Ze-Yang Zhang, Lan Sun, Chang-Jian Lin

Journal of Electrochemistry

Decorating TiO₂ nanotube arrays with RGO to improve the photocatalytic activity of TiO₂ nanotube arrays has been reported. For the reported RGO-TiO₂ nanotube arrays, TiO₂ nanotube arrays were prepared by anodizing the high-purity Ti foil in an organic electrolyte for multiple-step treatments, while RGO were deposited on TiO₂ nanotube arrays by using cyclic voltammetry or other electrical reduction methods. To enhance the reduction degree and the coverage of RGO on the resultant RGO-TiO₂ nanotube arrays, in this work, the one-step electrochemical anodization in hydrofluoric acid was used to fabricate TiO₂ nanotube arrays with …