Physical Sciences and Mathematics Commons^™

Fuzzy Genetic Based Dynamic Spectrum Allocation (Fgdsa) Approach For Cognitive Radio Sensor Networks, Ganesan Rajesh, Xavier Mercilin Raajini, Kulandairaj Martin Sagayam, Bharat Bhushan, Utku Kose

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive Radio Sensor Network (CRSN) is known as a distributed network of wireless cognitive radio sensor nodes. Such system senses an event signal and ensures collaborative dynamic communication processes over the spectrum bands. Here, the concept of Dynamic Spectrum Access (DSA) denes the method of reaching progressively to the unused range of spectrum band. As among the essential CRSN user types, the Primary User (PU) has the license to access the spectrum band. On the other hand, the Secondary User (SU) tries to access the unused spectrum eciently, by not disturbing the PU. Considering that issue, this study introduces a …

Wavelength Sensitivity Of Indium Tin Oxide On Surface Plasmon Resonance Angles, Antonio Ruiz, Carlos Villa Angulo, Ivan Olaf Hernandez-Fuentes

Turkish Journal of Electrical Engineering and Computer Sciences

Surface plasmon resonance (SPR) is a charge-density oscillation that occurs when a beam of p-polarized monochromatic light impinges with a greater angle than the critical angle in a dielectric-metal interface. Because of the high losses related to metals, the generated surface plasmon waves propagate with high attenuation in the visible and near-infrared spectral regions in most of the dielectric-metal interfaces. An alternative to reduce such losses is to use a transparent indium tin oxide (ITO) film. In this paper, we compared theoretical calculations and experimental measurements of the SPR angle $\theta_{SPR}$ on the interfaces of a borosilicate prism (Bp) and …

Mlcocoa: A Machine Learning-Based Congestion Control For Coap, Alper Kami̇l Demi̇r, Fati̇h Abut

Turkish Journal of Electrical Engineering and Computer Sciences

Internet of Things (IoT) is a technological invention that has the potential to impact on how we live and how we work by connecting any device to the Internet. Consequently, a vast amount of novel applications will enhance our lives. Internet Engineering Task Force (IETF) standardized the Constrained Application Protocol (CoAP) to accommodate the application layer and network congestion needs of such IoT networks. CoAP is designed to be very simple where it employs a genuine congestion control (CC) mechanism, named as default CoAP CC leveraging basic binary exponential backoff. Yet efficient, default CoAP CC does not always utilize the …

Optimization Of Real-Time Wireless Sensor Based Big Data With Deep Autoencoder Network: A Tourism Sector Application With Distributed Computing, Beki̇r Aksoy, Utku Kose

Turkish Journal of Electrical Engineering and Computer Sciences

Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor …

Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang

Electrical & Computer Engineering Faculty Research

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …

The Picture Fuzzy Distance Measure In Controlling Network Power Consumption, Florentin Smarandache, Ngan Thi Roan, Salvador Coll Arnau, Marina Alonso Diaz, Juan Miguel Martinez Rubio, Pedro Lopez, Fran Andujar, Son Hoang Lee, Manh Van Vu

Branch Mathematics and Statistics Faculty and Staff Publications

In order to solve the complex decision making problems, there are many approaches and systems based on fuzzy theory were proposed.

Pig Pose Estimation Based On Extracted Data Of Mask R-Cnn With Vgg Neural Network For Classifications, Sang Kwan Lee

Electronic Theses and Dissertations

This paper proposes a pig pose estimation operating with Region Proposal Network (RPN) of Mask Region based Convolutional Neural Network (Mask R-CNN) and Visual Geometry Group (VGG) Neural Network (NN). Object pose estimations generates from the associations of different key points. Key points could be explained as specific location of an object such as different joints of a human body or joints of different object. Hourglass network is one of a NN delivering key points of an object. Associating the different key points with the hourglass network results could be represented as instance-level detection [3]. However, the instance-level detection shows …

Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao

Electronic Theses and Dissertations

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC).

We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in …

Internet Of Things In Agricultural Innovation And Security, Abdul Salam

Faculty Publications

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the …

Internet Of Things For Water Sustainability, Abdul Salam

Faculty Publications

The water is a finite resource. The issue of sustainable withdrawal of freshwater is a vital concern being faced by the community. There is a strong connection between the energy, food, and water which is referred to as water-food-energy nexus. The agriculture industry and municipalities are struggling to meet the demand of water supply. This situation is particularly exacerbated in the developing countries. The projected increase in world population requires more fresh water resources. New technologies are being developed to reduce water usage in the field of agriculture (e.g., sensor guided autonomous irrigation management systems). Agricultural water withdrawal is also …

Internet Of Things In Water Management And Treatment, Abdul Salam

Faculty Publications

The goal of the water security IoT chapter is to present a comprehensive and integrated IoT based approach to environmental quality and monitoring by generating new knowledge and innovative approaches that focus on sustainable resource management. Mainly, this chapter focuses on IoT applications in wastewater and stormwater, and the human and environmental consequences of water contaminants and their treatment. The IoT applications using sensors for sewer and stormwater monitoring across networked landscapes, water quality assessment, treatment, and sustainable management are introduced. The studies of rate limitations in biophysical and geochemical processes that support the ecosystem services related to water quality …

A Spreadsheet-Based Decision Support System For Examination Timetabling, Mehmet Güray Güler, Ebru Geçi̇ci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Examination timetabling is an inevitable problem of educational institutions. Each institution has its own particular limitations; however, the main structure is the same: assigning exams to time slots and classrooms. Several institutions solve the problem manually, but it becomes more difficult every year with increasing numbers of students and limited resources. There are many studies in the literature addressing the examination timetabling problem (ETP) and providing high quality solutions within reasonable amounts of time. Nevertheless, almost none of them can be used in practice since they are not converted into a decision support system (DSS). Commercial DSSs, on the other …

Adaptive Modified Artificial Bee Colony Algorithms (Amabc) For Optimization Ofcomplex Systems, Rabi̇a Korkmaz Tan, Şebnem Bora

Turkish Journal of Electrical Engineering and Computer Sciences

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in …

Mutant Selection By Using Fourier Expansion, Savaş Takan, Tolga Ayav

Turkish Journal of Electrical Engineering and Computer Sciences

Mutation analysis is a widely used technique to evaluate the effectiveness of test cases in both hardware and software testing. The original model is mutated systematically under certain fault assumptions and test cases are checked against the mutants created to see whether the test cases can detect the faults or not. Mutation analysis is usually a computationally intensive task, particularly in finite state machine (FSM) testing due to a possibly huge amount of mutants. Random selection could be a practical reduction method under the assumption that each mutant is identical in terms of the probability of occurrence of its associating …

Variable Gain High Order Sliding Mode Control Approaches For Pmsg Basedvariable Speed Wind Energy Conversion System, Ameen Ullah, Laiq Khan, Qudrat Khan, Saghir Ahmad

Turkish Journal of Electrical Engineering and Computer Sciences

This research article proposes two different variants of variable gain higher-order sliding mode control (HOSMC) strategy for a variable-speed wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). The main objective is to extract the maximum wind power with reduced chattering and mechanical stress. The main flaw of the classical sliding mode control (SMC) is the high-frequency switching, called chattering, which is alleviated by employing HOSMC strategies. The control law design is based on a super-twisting algorithm (STA) and a real-twisting algorithm (RTA) with variable gains. The proposed control techniques inherit the property of robustness and …

Electric Field Control Of Fixed Magnetic Skyrmions For Energy Efficient Nanomagnetic Memory, Dhritiman Bhattacharya

Theses and Dissertations

To meet the ever-growing demand of faster and smaller computers, increasing number of transistors are needed in the same chip area. Unfortunately, Silicon based transistors have almost reached their miniaturization limits mainly due to excessive heat generation. Nanomagnetic devices are one of the most promising alternatives of CMOS. In nanomagnetic devices, electron spin, instead of charge, is the information carrier. Hence, these devices are non-volatile: information can be stored in these devices without needing any external power which could enable computing architectures beyond traditional von-Neumann computing. Additionally, these devices are also expected to be more energy efficient than CMOS devices …

Developing A Uas-Deployable Methane Sensor Using Low-Cost Modular Open-Source Components, Gavin Demali

Williams Honors College, Honors Research Projects

This project aimed to develop a methane sensor for deployment on an unmanned aerial system (UAS), or drone, platform. This design is centered around low cost, commercially available modular hardware components and open source software libraries. Once successfully developed, this system was deployed at the Bath Nature Preserve in Bath Township, Summit County Ohio in order to detect any potential on site fugitive methane emissions in the vicinity of the oil and gas infrastructure present. The deliverables of this project (i.e. the data collected at BNP) will be given to the land managers there to better inform future management and …

Mac Protocols For Terahertz Communication: A Comprehensive Survey, Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani, Alan Davy

Publications

Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency. However, the unique features of the Terahertz band such as high path loss, scattering, and reflection pose new challenges and results in short communication distance. The antenna directionality, in turn, is required to enhance the communication distance and to overcome the high path loss. However, these features in combine negate the use of traditional medium access protocols (MAC). Therefore, novel MAC protocol designs are required to fully exploit their potential benefits including efficient channel access, control message …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan

All Graduate Theses, Dissertations, and Other Capstone Projects

The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …

Utilizing Machine Learning For Respiratory Rate Detection Via Radar Sensor, Anwar Elhadad

Graduate College Dissertations and Theses

In this research, we investigate a data processing method to capture the respiratory rate of a person by utilizing a doppler radar to monitor their body movement during respiration. We utilize a machine learning algorithm with a radar sensor to capture the chest movement of a person while breathing and determine the respiratory rate according to that movement. We are using a Random Forest classifier to distinguish between different classes of pulses. After that, the algorithm constructs a sinusoidal signal representing the breathing rate of the sample. By applying this technique, we can detect the breathing rate accurately for different …

Energy Efficiency In Cmos Power Amplifier Designs For Ultralow Power Mobile Wireless Communication Systems, Selvakumar Mariappan, Jagadheswaran Rajendran, Norlaili Mohd Noh, Harikrishnan Ramiah, Asrulnizam Abd Manaf

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless communication standards keep evolving so that the requirement for high data rate operation can be fulfilled. This leads to the efforts in designing high linearity and low power consumption radio frequency power amplifier (RFPA) to support high data rate signal transmission and preserving battery life. The percentage of the DC power of the transceiver utilized by the power amplifier (PA) depends on the efficiency of the PA, user data rate, propagation conditions, signal modulations, and communication protocols. For example, the PA of a WLAN transceiver consumes 49 % of the overall efficiency from the transmitter. Hence, operating the PA …

A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …

Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli

Turkish Journal of Electrical Engineering and Computer Sciences

Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …

A New Biometric Identity Recognition System Based On A Combination Of Superior Features In Finger Knuckle Print Images, Hadis Heidari, Abdolah Chalechale

Turkish Journal of Electrical Engineering and Computer Sciences

Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two …

Reliability Comparisons Of Mobile Network Operators: An Experimental Case Study From A Crowdsourced Dataset, Engi̇n Zeydan, Ahmet Yildirim

Turkish Journal of Electrical Engineering and Computer Sciences

It is of great interest for Mobile Network Operators (MNOs) to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss (PL) performance comparisons of MNOs using a previously …

Pulse Width Modulation Control Of Fifteen-Switch Inverter For Four Ac Loads, Gaurav Goyal, Mohan Aware

Turkish Journal of Electrical Engineering and Computer Sciences

In many studies in the literature, various topologies with reduced switch count are proposed. With the use of these topologies, a lower number of semiconductor switches are required to produce a desired set of voltage. This in turn reduces the size and cost of the inverter. This paper proposes a new reduced switch count topology named "fifteen-switch inverter (FSI)" which is experimentally verified. The FSI has five switches in one leg and have three legs for three phases. It is capable of controlling four three-phase ac loads. In this proposed inverter topology, fifteen switches are used against the twenty four …

Performance Improvement Of Induction Motor Drives With Model-Based Predictive Torque Control, Fati̇h Korkmaz

Turkish Journal of Electrical Engineering and Computer Sciences

One of the most important advantages of using modeling and simulation software in design and control engineering is the ability to predict system behavior within specified conditions. This paper presents a novel error vector-based control algorithm that aims to reduce torque ripples predicting flux and torque errors in a conventional vector-controlled induction motor. For this purpose, a new control model has been developed that envisages flux change by applying probabilistic space vectors' torque and flux control. In the proposed predictive control algorithm, flux and torque errors are calculated for each candidate voltage vector. Thus, the optimal output voltage vector that …