Bilgisayar Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.12491/141

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Nanonetwork-based search and rescue operations in debris areas
(Elsevier, 2023) Şahin, Emre; Akkaş, Mustafa Alper; Dagdeviren, Orhan
Earthquake is one of the most destructive natural disasters. It is vital to carry out search and rescue (SAR) operations in the debris area after such a major disaster. Although there are various technologies to rescue living things in the debris area, reaching narrow gaps in the rubble is a very difficult process. Nanonetworks operating on terahertz (THz) communication channels are composed of thousands of nanodevices that can pass through even ultrasmall gaps. In this paper, we propose an application that utilizes nanodevices for SAR operations in debris areas. To the best of our knowledge, our paper is the first study to provide a detailed and specialized analysis of this application. We work on channel models by extensively analyzing transmittance, propagation loss, absorption loss, path loss, signal-to-noise ratio (SNR), and capacity. Besides, we investigate a physical layer (PHY) model by considering the effects of using directional or omnidirectional antennas to ensure optimal throughput and energy consumption. Moreover, we present an exhaustive performance analysis of the modulation technique with respect to various nanodevice counts and densities.
Characterization of terahertz band transmittance from sea-level to drone altitudes
(Institute of Electrical and Electronics Engineers Inc., 2021) Saeed, Akhtar; Saleem, Ammar; Gürbüz, Özgür; Akkaş, Mustafa Alper
Terahertz (THz) communications has been recognized as a candidate technology for the next generation networks as the THz band offers large bandwidth and data rates, catering for the problem of spectrum scarcity. However, THz band propagation is highly affected by atmospheric absorption due to water vapor molecules, in addition to the high spread loss. Modeling of the absorption loss is essential for a realistic closed form THz path loss model, which can be employed in link level analysis and formulations. For this purpose, in this paper, we characterize the THz transmittance i.e., absorption gain using the data obtained from Line-by-Line Radiative Transfer Model (LBLRTM) tool, considering the available frequency channels selected via water-filling, altitudes from sea-level to drone altitudes and various transmission ranges. We analyze the modeling of transmittance as a function of: (1) Frequency, (2) Distance and (3) Altitude, using different statistical models including, Polynomial, Exponential and Gaussian models. Numerical results depict that modeling transmittance as a function of distance and altitude are feasible approaches using the exponential and the polynomial models, respectively. This work can be extended to characterize the transmittance for all frequencies over the entire THz band, and also for higher altitudes and longer ranges. © 2021 IEEE.
An evaluation of internet of nano-things simulators
(Institute of Electrical and Electronics Engineers Inc., 2021) Şahin, Emre; Dağdeviren, Orhan; Akkaş, Mustafa Alper
Nowadays, distributed systems consisting of a large number of simple devices are used to perform complex operations rather than advanced devices alone. Particularly, networked systems of nanoscale devices are called Internet of Nano-Things (IoNT). This new field has emerged with new problems to be solved where simulation of nanonetworks is one of these problems. In this study, Electromagnetic (EM) communication-based nanonetwork simulators used in IoNT applications are examined in detail to determine the advantages and their drawbacks. Different than the related studies, our contribution is a detailed and up-to-date roadmap for simulator selection for researchers working in different layers of nanonetwork applications such as channel modeling, antenna design, modulation methods, medium access control (MAC) protocols, and routing algorithms. © 2021 IEEE
TeraHertz channel model for search and rescue nanonetwork applications in debris areas
(Institute of Electrical and Electronics Engineers Inc., 2021) Şahin, Emre; Dağdeviren, Orhan; Akkaş, Mustafa Alper
Disasters such as earthquakes cause thousands of deaths every year. Since it is not possible to predict such disasters, research has generally focused on search and rescue operations in the debris area after these disasters. Due to the structures of the debris areas, where there are very tiny gaps between the wracks, the use of large-scale advanced devices is very limited in these operations. On the other hand, nanodevices that can easily pass through these gaps and scatter into the debris area can be used to gather information from under the wreckage. The computing complexities and transmission ranges of nanodevices differ according to their size and the amount of energy they have. In this study, TeraHertz (THz) channel models in debris areas of different densities were investigated for different types of nanodevices. Our contribution is a detailed analysis of channel properties using 0.1-0.35 THz for debris areas in terms of transmittance, absorption loss and path loss. Moreover, a novel research topic is suggested for nanonetwork applications generally associated with biomedical and agricultural studies. © 2021 IEEE.
A stack based multimodal machine learning model for breast cancer diagnosis
(Institute of Electrical and Electronics Engineers Inc., 2022) Kayıkçı, Şafak; Khoshgoftaar, Taghi
Breast cancer is the most frequent type of cancer, and it has a dismal prognosis. It represents about 30 % (or 1 in 3) of all new female cancers each year. As a result, there is a pressing need to create efficient and quick computational approaches for breast cancer prognosis. In this study, a multimodal deep learning model that enables decision-making on data from multiple data sources is proposed and used with three different classifiers. We achieved 82% accuracy in decision trees, 90% in random forests and 88% in support vector machines. We have seen that the results we get from the combined data are more successful than the distinct convolutional neural network models we have run separately before. Combining diverse data sources for the successful application of multimodal deep learning algorithms appears to be an effective strategy to improve human breast cancer prediction performance. © 2022 IEEE.
Investigation of environmental effects in terahertz band vehicular communication
(Institute of Electrical and Electronics Engineers Inc., 2022) Körpe, Enis; Akkaş, Mustafa Alper; Sokullu, Radosveta
Terahertz (THz) waves are electromagnetic waves that vary in the range of 0.1-10 THz in the electromagnetic spectrum. It is a band gap that is underexplored and open to new research topics. Filling the gap between radio and optical frequency ranges, THz technology is a promising technology for next-generation wireless networks. It causes less damage to tissues than X-rays. When it comes to vehicular communication, the technologies used for vehicular communication today have begun to be insufficient in the rapidly increasing data traffic. New technologies are needed to support less latency, higher reliability, and more users. THz technology is considered to be a candidate technology that can meet these specifications. In this paper, the effects of environmental factors such as reflection, scattering and absorption on the transmission in different specific THz vehicular communication scenarios are investigated. The opportunities and drawbacks brought by the THz band for vehicular communication are also introduced. © 2022 IEEE.
Classification of freezing of gait in Parkinson's disease using machine learning algorithms
(Institute of Electrical and Electronics Engineers Inc., 2023) Önder, Mithat; Şentürk, Ümit; Polat, Kemal; Paulraj, D.
Freezing of gait (FoG) is a prevalent and incapacitating symptom that affects individuals diagnosed with Parkinson's disease (PD) and other movement disorders. Detecting FoG is crucial for accurate diagnosis, fall prevention, and providing objective measurements, all of which are essential for optimizing treatment strategies and improving the quality of life for individuals with FoG. In this study, FoG has been detected using three different classification algorithms: Medium Gaussian Support Vector Machine (SVM), Medium K-Nearest Neighbor (KNN), and Boosted Trees. The process starts with data segmentation, where the dataset is divided into smaller segments. Then, feature extraction is performed on each segment to obtain various statistical measures such as mean, root mean square, maximum, standard deviation, kurtosis, skewness, and peak of root mean square. To ensure a robust and reliable analysis, the dataset is resampled using bootstrapping, a statistical technique that involves drawing random samples from the dataset with replacement. This leads to a more representative sample and reduces the impact of outliers or imbalanced data. The next step is to split the resampled dataset into three different approaches for the classification algorithm: In 5-FCV, the dataset is divided into five equal-sized subsets. Similarly, 10-FCV splits the dataset into ten subsets and follows the same process. Finally, the Medium Gaussian SVM, Medium KNN, and Boosted Trees classification algorithms are applied to the FoG dataset. The classification accuracy achieved is 86.9%, 87.6%, and 92.7% with 10-fold cross-validation, indicating that these algorithms are effective in accurately classifying FoG. © 2023 IEEE.
Tip-I nöronlarda vibrasyonel rezonans vibrational resonance in type-I neurons
(Institute of Electrical and Electronics Engineers Inc., 2022) Çalım, Ali
In this study, vibrational resonance is discussed in the neural system with type-1 excitability. Vibrational resonance is defined as the nonlinear response of an oscillator system to a low frequency subthreshold weak signal with the help of a high frequency signal. Here, at an intermediate amplitude level of the high-frequency signal, the frequency of the oscillations is locked to the weak signal frequency, or the oscillation-signal coherence becomes maximum. In our work, we used Morris-Lecar neuron model as type-I excitable system and global network structure for connectivitiy. As a result of the simulations, it has been seen that in globally connected neural network, signal detection performance can increase more than three times compared to the performance of a single neuron. Different scenarios in synaptic conductivity and the application of high frequency signal are considered. Accordingly, these two parameters play a very active role in the detection and especially the propagation of weak signal or information in the network by means of information encoding of the neuron population based on vibrational resonance. © 2022 IEEE.
Diagnosis of Alzheimer's disease using boosting classification algorithms
(Institute of Electrical and Electronics Engineers Inc., 2023) Önder, Mithat; Şentürk, Ümit; Polat, Kemal; Paulraj, D.
Alzheimer's Disease (AD) is a progressive degenerative disorder of the brain that impacts memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently no cure for Alzheimer's Disease. However, there are available treatments to manage symptoms and slow their advancement. This research conducted a comprehensive study to diagnose AD using four different categorization methods. These methods included XGBoost, GradientBoost, AdaBoost, and voting classification algorithms. To carry out the examination, a high-quality dataset was obtained from the collection of machine learning data of the prestigious University of California. This dataset was carefully selected to ensure accurate and reliable results. The analysis of the collected data revealed some interesting findings. XGBoost exhibited an accuracy rate of 85% in diagnosing Alzheimer's Disease. ADABoost also performed, achieving an accuracy rate of 75%. GradientBoost, similarly, obtained an accuracy rate of 85%. Additionally, the voting classification algorithms showed promise, attaining an accuracy rate of 80%. All these accuracy rates were obtained by implementing a 5-fold cross-validation methodology, which ensured robust and unbiased results. This research contributes to the field of AD diagnosis by providing insights into the effectiveness of different categorization methods. © 2023 IEEE.
Path loss analysis for terahertz band single-lane vehicular communication
(Institute of Electrical and Electronics Engineers Inc., 2023) Körpe, Enis; Akkaş, Mustafa Alper; Sokullu, Radosveta
The Terahertz (THz) band lies between the millimeter wave and the far infrared band (0.1-10 THz) and has an important potential for 5G and 6G communication systems. It is a band gap open to future research as it has not been sufficiently explored. The THz band promises an ultra-high data rate and capacity. Considering vehicular communications, the rapidly increasing data traffic is excessive for current vehicular communication technologies to handle. New technologies and approaches are thus needed to deliver higher capacity and data rates with lower latency. Therefore, it is acknowledged that THz technology is a promising technology that can meet these requirements. In this study, the propagation of the THz signal in single-lane vehicular communication was examined. Respective path loss analyses were performed using the High-Resolution Transmission Molecular Absorption Database (HITRAN). The obtained results were compared with the measurement results in the existing studies. The effect of reflections on the path loss results of the system was also investigated. © 2023 IEEE.
Electricity consumption forecast using machine learning regression models in Turkey
(Institute of Electrical and Electronics Engineers Inc., 2022) Şentürk, Ümit; Beken, Murat; Eyecioğlu, Önder
In today's energy crisis, countries need to know their energy consumption and make their energy investments accordingly. The variability of end users demanding energy makes it difficult to estimate energy needs. In this article, it has been tried to forecast the future consumption from the electrical energy data consumed in Turkey between the years 2016-2022. After the electricity consumption data was converted into daily data, electrical energy consumption estimations were made with machine learning methods such as linear regression, tree regression, voting regression, XGB regression and Artificial neural network (ANN) methods. Estimation results were evaluated with Mean Square Error (MSE) and R2 (coefficient of determination) performance metrics. As a result of the evaluations made with the test data, MSE=0.006 (0-1 min-max normalization dataset) and R2= 82.7 performances, voting regression obtained the best result among the methods used. Accurate estimation of energy consumption will enable energy production to be made at the optimum level. © 2022 IEEE.
Investigating the energy production trends of countries and its relationship between economic development
(Institute of Electrical and Electronics Engineers Inc., 2022) Hangün, Batuhan; Eyecioğlu, Önder; Beken, Murat
There are three main energy resources for energy production that is currently used by the countries can be listed as fossil fuels, renewable energy, and nuclear energy. Since the industrial revolution, the fundamental energy resource of the world is fossil sources. Due to their problems such as greenhouse gas emissions, production cost, etc., fossil fuels are not sustainable sources in terms of world ecology and economy. Developed countries of the world focused on using alternative energy resources like renewable energy and nuclear energy to produce green and sustainable energy for a better future. An assumption might be made about the relationship between the types of energy resources used by a country and its development level. In this study, we investigate the connection between the economic development of the countries and energy consumption patterns using a machine learning approach. We used a custom data set created by the combination of two separate data sets to deduce that relation by using the clustering method. It can be seen that developed countries are prone to cease the usage of fossil fuels and increase the energy produced by renewable and nuclear energy while underdeveloped or developing countries still rely mostly on fossil fuels. This trend among the developed countries has increased by the year. Additionally, we also wanted to see where Turkey stands among the selected countries and make an observation on its energy and economic development progress. © 2022 IEEE.
Estimating the effect of renewable energy policies on the Republic of Turkey's gross national product by using artificial Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2022) Beken, Murat; Kurt, Nursaç; Eyecioğlu, Önder
Today, most of the states in the energy field, especially the developed countries in the world, continue to increase the rate of use of renewable energy. The Republic of Turkey is one of these states. This study aimed to examine the effect of the renewable energy transformation process on the gross national product with artificial intelligence in the action plan of the Republic of Turkey in the process of moving away from fossil fuel-based energy sources. We deployed artificial neural networks to estimate Turkey's gross national product. As a result of the study, because of the policy of increasing the use of renewable energy sources by 7% annually and reducing the use of fossil derivative fuels by 8% annually in the next twenty years, the energy distributions in 2043 and their effect on Turkey's gross national product are shown. © 2022 IEEE.
Beat estimation from musician visual cues
(Sound and Music Computing Network, 2021) Chakraborty, Sutirtha; Aktaş, Senem; Clifford, William; Timoney, Joseph
Musical performance is an expressive art form where musicians interact with each other using auditory and nonverbal information. This paper aims to discover a robust technique that can identify musical phases (beats) through visual cues derived from a musician’s body movements captured through camera sensors.A multi-instrumental dataset was used to carry out a comparative study of two different approaches: (a) motiongram, and (b) pose-estimation, to detect phase from body sway. Decomposition and filtering algorithms were used to clean and fuse multiple signals. The final representations were analysed from which estimates of the beat, based on a’trust factor’, were obtained. The Motiongram and pose estimation were found to demonstrate usefulness depending on the musical instrument as some instrument playing gestures stimulate more movement in the players than others. Overall, the results were most promising using motiongram. It performed well where string instruments were used. The spatial derivative technique based on human pose estimation was consistent with woodwind instruments, where only a small degree of motion was observed. Copyright: © 2021 the Authors.
Quantum computing approach to smart grid stability forecasting
(Institute of Electrical and Electronics Engineers Inc., 2024) Hangün, Batuhan; Eyecioğlu, Önder; Altun, Oğuz
The stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data"dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid's stability into "stable"and "unstable"categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general. © 2024 IEEE.
Stochastic resonance in a single autapse-coupled neuron
(Pergamon-Elsevier Science Ltd, 2023) Baysal, Veli; Çalım, Ali
The signal detection ability of nervous system is highly associated with nonlinear and collective behaviors in neuronal medium. Neuronal noise, which occurs as natural endogenous fluctuations in brain activity, is the most salient factor influencing this ability. Experimental and theoretical research suggests that noise is beneficial, not detrimental, for regular functioning of nervous system. In this regard, there is a general agreement that noise at an adequate intensity can engage rhythmic activity in brain and noise-induced oscillations enhances performance of the weak signal processing, especially when frequency of the signal is around that of the noise-induced rhythmic oscillation. This behavior in biological neural systems is explained by the notion of stochastic resonance. Another factor that plays a key role in regulating neuronal behaviors, including motor and cognitive tasks by maintaining signaling between cells, is characteristics of synapses different in structure and functioning. Here, we study stochastic resonance in Hodgkin-Huxley neuron that has a peculiar synaptic connection called autapse, known as a biophysical feedback mechanism, under presynaptic noise originating from superposition of inhibitory and excitatory Poisson bombardment. Our results show that, under certain conditions, autapse dynamics are able to improve the weak signal detection performance of Hodgkin-Huxley neuron via stochastic resonance. This study provides novel insights into functional role of autapse in neural information processing by revealing a biophysical aspect of stochastic resonance with numerical computations.
Chaotic resonance in an astrocyte-coupled excitable neuron
(Pergamon-Elseiver Science Ltd, 2023) Çalım, Ali; Baysal, Veli
We study the chaotic resonance phenomenon whereby the response of a neuron to a weak signal is amplified with the help of chaotic current stemming from background activity in the brain. This resonance behavior exhibits a bell-shaped curve in terms of detection quality due to increasing chaotic current intensity. Recent experimental studies have shown that astrocytes, which are the most abundant types of glial cells, may be responsible for the regulation of electrophysiological events in neuronal medium. Hence, we consider here a realistic neuronal system which is constituted by a bipartite network consisting of an excitable neuron and an astrocyte. Our analysis reveals that signal detection quality can be greatly enhanced with the astrocyte contribution obtained by appropriate neuronal and astrocytic cell dynamics. We find that depolarization -induced astrocytic glutamate release is able to improve chaotic resonance performance considerably in the presence of an adequately strong interaction between the astrocyte and the excitable neuron receiving a weak signal with a relatively higher frequency. We also show that a moderate production rate of gliotransmitters is required for the astrocyte to affect resonance performance of the neuron. Except for those conditions where the facilitating effect of astrocyte is observed, it can also reduce signal detection performance in the neuron. Furthermore, we demonstrated that intrinsic neuronal excitability is regulated by the astrocyte, via a comparison of resonance behaviors under effects of bias and astrocytic current separately. Taken together, our findings provide a novel insight into the functioning of astrocyte-neuron circuits, in particular the encoding weak signals via chaotic resonance, and suggest that astrocytes play a key role in intrinsic regulation and selectivity in neuronal information processing.
Strain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference System
(Taylor & Francis Inc, 2023) Eyecioğlu, Önder; Kayışlı, Korhan; Beken, Murat
Single Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.
A study on interactions of 14.7-MeV protons and 3.6-MeV alphas in 93Nb target
(Taylor & Francis Ltd, 2024) Yiğit, Mervenur; Kara, Ayhan; Yılmaz, Ali
Niobium is an important alloying material in nuclear reactors because of its enormous strength, low density, low neutron absorption, and high melting point. This study is structured on nuclear data calculations that are based on a Monte Carlo simulation approach. The GEANT4, SRIM, and TALYS codes were used to create a comprehensive simulation of 3.6-MeV alphas and 14.7-MeV protons on a Nb-93 target. We present calculation results on nuclear parameters as ion energy losses, displacements, vacancies, projected ranges, and cross sections. A comparison between the GEANT4 and SRIM codes was made for the projected ranges and ion energy losses. Besides, the calculations of cross sections in the TALYS code were carried out using level densities on the Skyrme energy density functional and the Fermi gas model.
Analyzing the web sites of metropolitan municipalities in Turkey in terms of digital access indicators
(Institute for Local Self-Government Maribor, 2023) Tutar, Hasan; Parlak, İsmail Hakkı; Tuzcuoğlu, Ferruh; Başpınar, Nuran Öztürk; Nam, Selçuk
E-government and e-municipality practices, which have gained critical importance in terms of the spread of citizen-oriented service and participatory management approach in local governments, have become a state policy in many countries. In Turkey, within the framework of restructuring the public administration and the e -transformation strategy, e-municipality practices are being expanded in local governments. One of the critical developments in the field of public administration in Turkey recently is e-government applications and e-municipality, which is its reflection on local governments. E -municipality applications are used as a local government strategy to quickly meet the citizens' municipal needs and reduce service costs. In this study, e-municipality services of metropolitan municipalities in Turkey; It is intended to be analyzed in terms of accessibility, performance, Searches Engine Optimization, and Best Practice indicators. The research findings show that the e-municipal services of metropolitan municipalities in Turkey should be improved significantly, given the mentioned indicators.

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