Mühendislik Fakültesi / Faculty of Engineering

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Adaptation of Gamification as a Man-Machine Interface to Franchise Management System
(Institute of Electrical and Electronics Engineers Inc., 2021) ALTUNEL, YUSUF; GÜNAYDIN, BURAK; KALABALIKOĞLU, FURKAN
Current man-machine interfaces are far from fitting human expectations in understanding and transmission of noteworthy signs and hints that are natural ingredients of human communication. There are certain indicators and interaction possibilities that can be passed between the two sides but as a result of complex behavior and dynamic changing conditions of environments they can be lost, or at least might require long and complex processing. Gamification is used to enhance the interaction possibilities providing the visual representation of environmental conditions and critical indicators, as well as providing the ability to send and receive requests using suitable techniques for human such as touches, and finger moves on Unity environment. Franchise Management System is selected as a case study and adapted to maintain the interactions between franchiser and franchisee. © 2021 IEEE.
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Adaptive Direction-Guided Structure Tensor Total Variation
(Elsevier, 2021) Kamasak, Mustafa E.; TÜREYEN, EZGİ DEMİRCAN
Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.
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Analyzing the Wastewater Treatment Facility Location/Network Design Problem via System Dynamics: Antalya, Turkey Case
(Academic Press Ltd. - Elsevier Science Ltd., 2022) DEMİREL, DUYGUN FATİH; Gönül-Sezer, Eylül Damla; Pehlivan, Seyda Alperen
Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of waste-water treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015-2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.
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The Art of Machine Learning as Fashion Stylish for Designing Clothes
(Institute of Electrical and Electronics Engineers Inc., 2022) KEYDAL, DUYGU; OYMAK, ERENCAN; DEMİR, KADİR BATUHAN; Yılmaz, Güray; ŞAHİNGÖZ, ÖZGÜR KORAY
Over the years, designers have come to the fore with their originality and personal styles and have shaped the fashion industry with their designs. However, due to the progress of time, designers have become unable to meet the demands of all consumers. Since it takes a lot of time to produce an original design, the production process progresses slowly, and customers are uncomfortable with this situation. As in many other industries, designers are trying to solve this problem with the help of artificial intelligence, which is indispensable in the fields of commerce, art, and security. It first entered the fashion sector with drawing programs in the 1950s and has started to change the fashion sector since the 2000s. In the 1950s, artificial intelligence was used only to create a virtual drawing environment When the designer makes a mistake, he can simply erase the mistake and continue working on the design without having to start the whole design from scratch. These programs have greatly facilitated the work of designers. Designers are now able to draw their designs in a much shorter time. But even this shortened period is not enough for the whole fashion industry. Designers could still not keep up with the demands of all customers. Thanks to researchers who added different perspectives to artificial intelligence in the early 2000s with its usage not only for drawing but also for designing, Therefore, in this paper, it is aimed at producing some original designs by preserving the designer's style with the use of Aí techniques. With the proposed model, it has become able to produce ready-made designs by using features such as object detection and visual processing. The experimental results showed that Aí techniques are very successful for combining different patterns for producing an original fashion style. © 2022 IEEE.
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Biosignal Based Emotion-Oriented Video Summarization
(Springer, 2023) DERDİYOK, ŞEYMA; AKBULUT, FATMA PATLAR
Digital video is a crucial component of multimedia that enhances presentations with accurate, engaging visual and aural data that affects several industries. The transition of video storage from analog to digital is being fueled by a variety of causes. Improved compression methods, cheaper technology, and more network needs are some of these drivers. This paper presents a novel video summarization based on physiological signals provided by emotional stimuli. Through these stimuli, 15 emotions are analyzed using physiological signals. The dataset was gathered from 15 participants who watched 61 episodes of 14 television series while wearing a wristband. We built several deep-learning models for the main purpose of recognizing emotions to summarize video. Among the established networks, the best performance has been obtained with the 1D-CNN, with 92.87% accuracy. This work has been done through a series of empirical experiments; since the frequency of the physiological signals is different, we used models with original and resampled configurations in each experiment. The comprehensive comparison result indicates that the oversampling approach gives the highest accuracy as well as the lowest computational complexity. The performance of the proposed video summarization approach was evaluated by a survey of participants, and the results showed that the summaries contained the critical moments of the video. The proposed approach may be useful and effective in physiological signal-based applications requiring emotion recognition, such as emotion-based video summarization or film genre detection. Additionally, reading such summaries facilitates comprehension of the significance of making rapid judgments regarding likes, ratings, comments, etc.
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Capacity Loss Analysis Using Machine Learning Regression Algorithms
(IEEE, 2022) Atay, Sergen; AYRANCI, AHMET AYTUĞ; Erkmen, Burcu
In this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using Machine Learning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector Machine (SVM), Linear Regression (LR) and Regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.
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Decentral Smart Grid Control System Stability Analysis Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) AYRANCI, AHMET AYTUĞ; İlhan, Hacı
Electrical Grid Systems transmit power produced from various facilities to end-users. Supply and demand must be in balance to achieve secure and stable use in the power grid. To ensure this stability, the amount of electricity fed into the system must always be the same as the amount of demand. High demand makes electrical grid systems' stability more important than ever. Current electrical infrastructures are hard to adapt to these needs. A smart grid system enables two-way electricity flow according to the demand from end-users. Digital communication in smart grid systems enables the system to detect demands, problems, and changes. Also collects information to ensure stability in the system. This study is using the Electrical Grid Stability data set shared at UC Irvine (UCI) Machine Learning repository. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) Network, K-Nearest Neighbors (K-NN), and Naïve Bayes (NB) Machine Learning (ML) algorithms were used to examine the stability performance of the Smart Grid system. Acquired performance metrics compared using Accuracy, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and F-Score. According to the results obtained, the system and its performance are interpreted. © 2022 IEEE.
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Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction
(Institute of Electrical and Electronics Engineers Inc., 2021) TÜRKMEN, EGEMEN
In recent years, the importance of data has been increasing day by day. This has led companies to choose and use them actively, especially for reaching valuable information. Thanks to the interpretation of data, companies can save both time, labor, and costs for these operations in many application areas such as finance, security, e-commerce, data mining, etc. One critical area focuses on the use of finance, in which if the companies properly interpret and use this data, they can directly achieve more successful results in terms of their offering to customers with more accurate campaigns. In this paper, some deep learning methods (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN)) are used to predict the possibility of subscribing to deposit after the customer is called within the scope of the bank telemarketing campaign. Implemented models are tested with the used dataset and experimental results were compared and interpreted. To improve the obtained accuracy level different approaches are applied to the dataset. Because of the unbalanced structure of the used dataset, SMOTE approach was used to reach more accurate results. After the dataset is processed to be a balanced form, some deep learning methods are applied to it. Obtained results had compared with other proposals. Experimental results showed that the proposed algorithms gave a very acceptable prediction, and it is expected to be used in the finance sector. © 2021 IEEE.
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Deep Sentiment Analysis With Data Augmentation in Distance Education During the Pandemic
(Institute of Electrical and Electronics Engineers Inc., 2022) SOSUN, SERA DENİZ; TAYFUN, BÜLENT; NUKAN, YASEMİN; ALTUN, İREM; ERİK, ELİF BERRA; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR
During the global Covid-19 pandemic, the shutdown of educational institutes has resulted in a phenomenal surge in online learning. Academic activities were shifted to online learning platforms to restrict the influence of COVID-19 and block its spread. For both students and parents, the efficiency of online learning is a major concern, particularly in terms of its suitability for students and teachers, as well as its technological applicability in various social situations. Before the online learning approach can be employed on such a big scale, such challenges must be viewed from different aspects. This study aims to assess the efficiency of online learning by examining individuals' sentiments toward it. Due to social media becoming such an essential form of communication, people's opinions can be observed on platforms like Twitter. The main motivation is to use a Twitter dataset featuring online learning-related tweets. Briefly, we focused on specifying the impact of the Covid-19 pandemic on education in many aspects and parameters by using tweets. We utilized natural language processing models for text classification with a gathered dataset that includes fetching tweets consisting of Covid-19 and education topics. We developed a fine-tuned Long short-term memory (LSTM) model that utilizes data augmentation for classifying the emotional states of individuals. With the deep sentiment analysis model that we proposed, we observed that the negative sentiments were experienced more. © 2022 IEEE.
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Design and Realization of an Automatic Optical Inspection System for PCB Solder Joints
(Institute of Electrical and Electronics Engineers Inc., 2021) ÇALIŞKAN, AYHAN; GÜRKAN, GÜRAY
Recent developments in electronics has led to an increase in fabrication and assembly speeds of printed circuit boards (PCBs). In addition, the size of manufactured PCBs and electronic components (e.g. resistors, capacitors, transistors etc.) are becoming much smaller. By increased demand and production speed, the reliability and thus the inspection of manufactured PCB assemblies became an important issue. In the assembly PCB production process, detection of surface mount technology (SMT) solder defects is made with automatic optical inspection (AOI) devices using image processing methods. Besides these expensive device methods, the method by which the controls are made by the operators visually is a low-cost solution used by most of the companies that manufacture printed circuit board assembly. In addition, circuit defects and solder defects cannot be tested and controlled with 100% accuracy due to human error. This paper proposes to detect solder joint defects with machine learning methods using YOLO algorithm to speed up time and increase accuracy in assembly PCB production line. Approximately 40000 images were obtained from the real production line before training with the YOLOv4 algorithm for high accuracy rate. Detection of solder defects of SMT circuit elements in approximately 5K (4056x3040) images resolution can be achieved with 97% accuracy in around 4 seconds. As a result of the use of the system, this proposed method has been proven with the reports received from the production line and precision-recall curves. Thus, it has been observed that the production speed and accuracy rate are increased. © 2021 IEEE.
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Detecting Phishing Websites Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) ALREFAAI, SAFA; ÖZDEMİR, GHINA; MOHAMED, AFNAN
Phishing, a cybercriminal's attempted attack, is a social web-engineering attack in which valuable data or personal information might be stolen from either email addresses or websites. There are many methods available to detect phishing, but new ones are being introduced in an attempt to increase detection accuracy and decrease phishing websites ' success to steal information. Phishing is generally detected using Machine Learning methods with different kinds of algorithms. In this study, our aim is to use Machine Learning to detect phishing websites. We used the data from Kaggle consisting of 86 features and 11,430 total URLs, half of them are phishing and half of them are legitimate. We trained our data using Decision Tree (DT), Random Forest (RF), XGBoost, Multilayer Perceptrons, K-Nearest Neighbors, Naive Bayes, AdaBoost, and Gradient Boosting and reached the highest accuracy of 96.6using X G Boost. © 2022 IEEE.
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Detection of Phishing Websites by Using Machine Learning-Based URL Analysis
(Institute of Electrical and Electronics Engineers Inc., 2020) Korkmaz, Mehmet; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, Banu
In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the 'zero-day' attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.
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Edge Computing and Robotic Applications in Modern Agriculture
(IEEE-Inst Electrical Electronics Engineers Inc., 2024) AYRANCI, AHMET AYTUĞ; Erkmen, Burcu
The modernization of agricultural practices prominently features robotics as a key technology. Efforts are concentrated on achieving automation and enhancing efficiency in agriculture through advancements in robotic applications. The widespread integration of remote sensing systems into agricultural areas facilitates real-time information acquisition, enabling drones and robots to operate with enhanced efficiency and effectiveness. Robotics plays a crucial role in the evolution of agriculture 4.0 and agriculture 5.0 strategies, marking significant strides in agricultural technology. Specifically designed robots for agricultural use are currently employed in tasks like planting, fertilizing, irrigating, pest controlling, and harvesting, proving a certain level of effectiveness. Edge computing is crucial in enhancing efficiency and sustainability within modern agricultural practices. Edge computing can instantly process data from numerous devices, mitigating network congestion effectively. In modern agricultural applications, it is possible to perform multiple tasks in a coordinated and effective manner by using Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) together. These devices can serve as both data collection and edge devices in the network. Multiple agricultural robot applications and benefits of these applications are explained in the study. © 2024 IEEE.
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Experimental Behaviour and Failure of Beam-Column Joints with Plain Bars, Low-Strength Concrete and Different Anchorage Details
(Pergamon-Elsevier Science Ltd., 2020) Coşgun, Cumhur; TÜRK, AHMET MURAT; Mangir, Atakan; Coşgun, Turgay; Kıymaz, Güven
In framed structures, both steel and reinforced concrete, beam-column joints play a very crucial role in terms of seismic resistance. Under the effects of high lateral seismic loads, beam-column joints are subjected to high forces and moments and their behaviour have a significant influence on the response of the structure. Poor seismic performance of inadequately detailed joints can lead to the total or partial collapse of reinforced concrete frame structures. The use of low strength concrete, plain reinforcing bars, problematic anchorage details and inadequate transverse reinforcement in beam-column joints are the factors increasing the failure risk of the structures during severe earthquakes. In this paper, an experimental study on the cyclic behaviour of reinforced concrete exterior beam-column joints is presented. The study aims at investigating the effects of the longitudinal beam reinforcement anchorage detail on the joint performance and quantifying the level of contribution of retrofitting the joints by fiber reinforced polymer sheets (FRP). Three different details were considered in the test program including the longitudinal reinforcement of the beam being anchored within the joint with 90-degree hooks, 180-degree hooks and straight bar (no hook). All of the test specimens were produced using low strength concrete and plain bars to represent the conditions of joints of existing deficient reinforced concrete building structures. In the first series of tests, four 2/3 scale reinforced concrete beam-column joint specimens were tested by adopting a displacement controlled and quasi-static load application method to assess the performance of joints with the above-mentioned anchorage details. The load was applied in a reversed cyclic fashion. The second series of tests were carried out on two additional specimens with the same details as described above but strengthened using FRP sheets. The response of the specimens were evaluated and compared in terms of load-drift, displacement hysteretic behaviour. It was found out that the problematic anchorage details have a very significant adverse effect on the seismic performance of the joints. On the other hand, FRP retrofitting has resulted in a significant increase in peak loads and sustained ductility particularly for the specimens for which reinforcement slippage was not a governing mode of failure.
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Failure of the Maintenance Gantry of a Metro Crossing Bridge
(Pergamon-Elsevier Science Ltd., 2020) Vatansever, Cüneyt; YAZICI, GÖKHAN; SEÇKİN, EDİP; Alçiçek, Haluk Emre
This paper investigates the failure of the underdeck maintenance gantry of the metro crossing bridge in Istanbul, Turkey on January 4, 2014, which resulted in the injury of four workers. First, a brief description of the gantry and the bridge has been provided, which is followed by the findings of the on-site investigation which involved the review of the video record of the incident, interviews with the field staff, overview of the project drawings and the investigation of the parts of the collapsed gantry. Additionally, the finite element model of the part where the collapse initiated was created and loaded in accordance with the operation procedures of the gantry during the incident. Finally, on-site investigation and finite element analysis results revealed that collapse of the gantry occurred by exceeding the shear capacity of the bolts in the connections. Moreover, the exceedance of the shear capacity was the consequence of unexpected operating practices used by the workers during the incident, which caused unforeseen shear stress concentration in the bolts.
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Field Simulation of Settlement Analysis for Shallow Foundation Using Cone Penetration Data
(Elsevier Science Ltd., 2021) MERT, AHMET CAN; AREL, ERSİN
This paper deals with the settlement analysis of a vertically loaded strip footing by the use of two-dimensional random field finite element method. Total settlement and rotation of the footing have been calculated by elasto-plastic solution in finite element model. Deformation modulus of soil E-d was assigned conforming to 2D Gaussian random field using Karhunen-Loeve series expansion. Spatial variability of E-d was represented by the horizontal and vertical correlation lengths (theta(vE) and theta(hE)). CPTu database from Adapazari, Turkey has been employed to estimate the correlation lengths. Soil profile was modelled with 4 layers in accordance with random field from in-situ test results, and correlation lengths were assigned to each different layer. Random field realizations were produced by MATLAB code, and probability density functions (PDF) of maximum total settlement and rotation of the footing were constructed using Monte Carlo Simulations by iterative solutions of each realization with finite element analysis. Probabilities of failure (P-f) by settlement and rotation were calculated from PDFs. Initially, the analysis was carried out using average values of each horizontal and vertical correlation lengths that were assigned to every soil layer. Subsequently, analyses were iterated with maximum and minimum values of the correlation lengths in order that both the effect of horizontal and vertical spatial variability can be considered. 1000 calculations were performed for the 5 analysis models, with 200 random field realizations for each model. The effect of varying theta(vE) and theta(hE) on total settlement and rotation of the footing has also been investigated.
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Forecasting of Turkey's Total Electricity Consumption in Sectoral Bases Using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022) HAJJAR, MHD KHAIR; ÜLKÜ, İLAYDA
Electrical energy is a milestone in the economic growth of each country. This study forecasts the sectoral and total electricity consumption in Turkey until the year 2050. This study, utilize two distinct time series forecasting methods namely Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO) as a model to generate the forecasting formulas. The sectoral and total electricity consumption for Turkey from the year 1970 to 2020 was obtained from the Turkish Statistical Institute and fed to the models to forecast the upcoming years. The two models were evaluated and compared using determination coefficient R2 and mean absolute percentage error MAPE. It is found that MLP performed better in forecasting the commercial, governmental, illumination and other sectors and SMO performed better in forecasting the industrial and household sectors alongside the total electricity consumption. © 2022 IEEE.
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Genetic Algorithm Based Optimized Waste Collection in Smart Cities
(Institute of Electrical and Electronics Engineers Inc., 2020) Özmen, Mehmet; Şahin, Hasan; ŞAHİNGÖZ, ÖZGÜR KORAY
In recent years, the concept of smarts cities emerged to cope with the growth that cities around the world are facing. There are lots of problem areas in smart cities such as smart education, health, buildings, shopping, traffic management, etc. Waste management is one complex and effective problems of urbanization that is needed to be solved in smart cities. Route planning for waste collection and garbage trucks is a known issue in waste management. In this project, a genetic algorithm is proposed to address the problem of waste collection route using a truck fleet. The algorithm was tested in a simplified real state in single area and proved to be applicable to real-world scenarios based solely on the actual data of waste collection of cities.
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Global Impact of the Pandemic on Education: A Study of Natural Language Processing
(Institute of Electrical and Electronics Engineers Inc., 2022) AYAZ, TEOMAN BERKAY; USLU, MUHAMMED SAFA; AĞCABAY, İBRAHİM; AHMED, FARUK; KORKMAZ, ÖMER FARUK; KÜREKSİZ, MESUT; ULUÇAM, EMRE; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR
School closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.
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Heterofonik Türk Makam Müziginde İşitsel Melodi Kestirimi
(Enstitute of Electrical and Electronics Engineers Inc., 2021) ŞİMŞEK, BERRAK ÖZTÜRK
In this study, the Improved Variable Mode Decomposition Method (IVMD) is proposed for the estimation of the audio melody in heterophonic works that constitute the general texture of Turkish maqam music. In our study, the fundamental frequencies of the records belonging to huzzam, kurdilihicazkar, ussak, and rast maqams were estimated by using the IVMD method. Since the basis of the heterophonic texture is that the same melody is performed by more than one instrument, the estimated fundamental frequencies are more than one for each time window. After the multiple frequency estimation, in order to obtain the audio melody of the music recording and therefore a single frequency line, the selection of the frequencies belonging to the audio melody line from the fundamental frequencies was made. The study has been compared with the methods widely used in the analysis of polyphonic music works such as YIN and MELODIA. When the comparisons were evaluated on the basis of maqam and mixture according to the MIREX criteria, successful results were obtained with the IVMD method. © 2021 IEEE.

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