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Articles 61 - 90 of 800
Full-Text Articles in Physical Sciences and Mathematics
Multiscanning Mode Laser Scanning Confocal Microscopy System, Mert Aktürk, Gökhan Gümüş, Baykal Sarioğlu, Yi̇ği̇t Dağhan Gökdel
Multiscanning Mode Laser Scanning Confocal Microscopy System, Mert Aktürk, Gökhan Gümüş, Baykal Sarioğlu, Yi̇ği̇t Dağhan Gökdel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a table-top, reflective mode, laser scanning confocal microscopy system that is capable of scanning the target specimen alternately through various scanning devices and methods is proposed. We have developed a laser scanning confocal microscopy system to utilize combinations of various scanning devices and methods and to be able to characterize the optical performance of different scanners and micromirrors that are frequently used in scanning microscopy systems such as multiphoton microscopy, optical coherence tomography, or confocal microscopy. By integrating the scanner to be characterized on the same optical path with a galvanometric scan mirror, which is the conventional …
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic …
Structure Tensor Adaptive Total Variation For Image Restoration, Surya Prasath, Dang Nh Thanh
Structure Tensor Adaptive Total Variation For Image Restoration, Surya Prasath, Dang Nh Thanh
Turkish Journal of Electrical Engineering and Computer Sciences
Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.
Polyhedral Conic Kernel-Like Functions For Svms, Gürkan Öztürk, Emre Çi̇men
Polyhedral Conic Kernel-Like Functions For Svms, Gürkan Öztürk, Emre Çi̇men
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we propose a new approach that can be used as a kernel-like function for support vector machines (SVMs) in order to get nonlinear classification surfaces. We combined polyhedral conic functions (PCFs) with the SVM method. To get nonlinear classification surfaces, kernel functions are used with SVMs. However, the parameter selection of the kernel function affects the classification accuracy. Generally, in order to get successful classifiers which can predict unknown data accurately, best parameters are explored with the grid search method which is computationally expensive. We solved this problem with the proposed method. There is no need to …
Understanding Attribute And Social Circle Correlation In Social Networks, Pranav Nerurkar, Madhav Chandane, Sunil Bhirud
Understanding Attribute And Social Circle Correlation In Social Networks, Pranav Nerurkar, Madhav Chandane, Sunil Bhirud
Turkish Journal of Electrical Engineering and Computer Sciences
Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained …
A Fast And Memory-Efficient Two-Pass Connected-Component Labeling Algorithm For Binary Images, Bilal Bataineh
A Fast And Memory-Efficient Two-Pass Connected-Component Labeling Algorithm For Binary Images, Bilal Bataineh
Turkish Journal of Electrical Engineering and Computer Sciences
Connected-component labeling is an important process in image analysis and pattern recognition. It aims to deduct the connected components by giving a unique label value for each individual component. Many algorithms have been proposed, but they still face several problems such as slow execution time, falling in the pipeline, requiring a huge amount of memory with high resolution, being noisy, and giving irregular images. In this work, a fast and memory-efficient connected-component labeling algorithm for binary images is proposed. The proposed algorithm is based on a new run-base tracing method with a new resolving process to find the final equivalent …
Power Quality Improvement Of Smart Microgrids Using Ems-Based Fuzzy Controlled Upqc, Ahmed A. Hossam-Eldin, Ahmed A. Mansour, Mohammed El-Gamal, Karim H. Youssef
Power Quality Improvement Of Smart Microgrids Using Ems-Based Fuzzy Controlled Upqc, Ahmed A. Hossam-Eldin, Ahmed A. Mansour, Mohammed El-Gamal, Karim H. Youssef
Turkish Journal of Electrical Engineering and Computer Sciences
The prevalent power quality problems in smart microgrids and power distribution systems are voltage sag, voltage swell, and harmonic distortion. The achievement of pure sinusoidal waveform with proper magnitude and phase is currently a great research and development concern. The aim of this paper is to evaluate and mitigate the smart microgrid harmonics, voltage sag, and voltage swell throughout a 24-h cycle, taking into consideration the variation in solar power generation due to changes in irradiation received by photovoltaic cells, the variation in wind power generation due to changes in wind speed, and the variation of linear and nonlinear load …
A Novel Accuracy Assessment Model For Video Stabilization Approaches Based On Background Motion, Md Alamgir Hossain, Tien-Dung Nguyen, Eui Nam Huh
A Novel Accuracy Assessment Model For Video Stabilization Approaches Based On Background Motion, Md Alamgir Hossain, Tien-Dung Nguyen, Eui Nam Huh
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which …
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Turkish Journal of Electrical Engineering and Computer Sciences
Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer …
Robust Compressed Domain Watermarking Algorithm For Video Protection And Authentication In Noisy Channels, Naveen Cheggoju, Vishal Satpute
Robust Compressed Domain Watermarking Algorithm For Video Protection And Authentication In Noisy Channels, Naveen Cheggoju, Vishal Satpute
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a robust and noise-resilient compressed domain video watermarking technique for data authentication and copyright protection. In recent years, watermarking has emerged as an essential technique to be equipped with data transmission. The main challenge pertaining to transmission is to protect the watermark from noise introduced by the channel. Here, we address this issue by watermark replication and by using the independent pass coding (INPAC) algorithm for compression. A replicated watermark is embedded into the video by the proposed blind video watermarking algorithm and then the watermarked video is compressed by the INPAC algorithm. The compressed video is …
Speech Enhancement Using Adaptive Thresholding Based On Gamma Distribution Of Teager Energy Operated Intrinsic Mode Functions, Özkan Arslan, Erkan Zeki̇ Engi̇n
Speech Enhancement Using Adaptive Thresholding Based On Gamma Distribution Of Teager Energy Operated Intrinsic Mode Functions, Özkan Arslan, Erkan Zeki̇ Engi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a new speech enhancement algorithm based on the adaptive threshold of intrinsic mode functions (IMFs) of noisy signal frames extracted by empirical mode decomposition. Adaptive threshold values are estimated by using the gamma statistical model of Teager energy operated IMFs of noisy speech and estimated noise based on symmetric Kullback--Leibler divergence. The enhanced speech signal is obtained by a semisoft thresholding function, which is utilized by threshold IMF coefficients of noisy speech. The method is tested on the NOIZEUS speech database and the proposed method is compared with wavelet-shrinkage and EMD-shrinkage methods in terms of segmental SNR …
Online Network Coding-Based Multicast Routing In Multichannel Multiradio Wireless Mesh Networks, Leili Farzinvash
Online Network Coding-Based Multicast Routing In Multichannel Multiradio Wireless Mesh Networks, Leili Farzinvash
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we consider the problem of online multicast routing in multichannel multiradio wireless mesh networks (WMNs). We propose an efficient online algorithm, namely zone-based multicast routing (ZBMR), which exploits network coding and wireless broadcast advantage. In the proposed algorithm, to investigate the acceptance of an arrived session in polynomial time, the WMN is divided into some zones. The derived zones are processed sequentially, where the zone processing is defined as connecting the receivers in a given zone to the session. The main challenge in this scheme is to enable data transmission to the receivers in each zone. If …
Low-Latency And Energy-Efficient Scheduling In Fog-Based Iot Applications, Dadmehr Rahbari, Mohsen Nickray
Low-Latency And Energy-Efficient Scheduling In Fog-Based Iot Applications, Dadmehr Rahbari, Mohsen Nickray
Turkish Journal of Electrical Engineering and Computer Sciences
In today's world, the internet of things (IoT) is developing rapidly. Wireless sensor network (WSN) as an infrastructure of IoT has limitations in the processing power, storage, and delay for data transfer to cloud. The large volume of generated data and their transmission between WSNs and cloud are serious challenges. Fog computing (FC) as an extension of cloud to the edge of the network reduces latency and traffic; thus, it is very useful in IoT applications such as healthcare applications, wearables, intelligent transportation systems, and smart cities. Resource allocation and task scheduling are the NP-hard issues in FC. Each application …
Farsi Document Image Recognition System Using Word Layout Signature, Cem Ergün, Sajedeh Norozpour
Farsi Document Image Recognition System Using Word Layout Signature, Cem Ergün, Sajedeh Norozpour
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new representation of Farsi words is proposed to present the keyword spotting problems in Farsi document image retrieval. In this regard, we define a signature for each Farsi word based on the word connected component layout. The mentioned signature is shown as boxes, and then, by sketching vertical and horizontal lines, we construct a grid of each word to provide a new descriptor. One of the advantages of this method is that it can be used for both handwritten and machine-printed texts. Finally, to evaluate the performance of our system in comparison to other methods, a …
Dynamic Physarum Solver: A Bio-Inspired Shortest Path Method Of Dynamically Changing Graphs, Hi̇lal Arslan
Dynamic Physarum Solver: A Bio-Inspired Shortest Path Method Of Dynamically Changing Graphs, Hi̇lal Arslan
Turkish Journal of Electrical Engineering and Computer Sciences
In dynamic graphs, edge weights of the graph change with time and solving the shortest path problem in such graphs is an important real-world problem. The studies in the literature require excessive computational time for computing the dynamic shortest path since determining changing edge weights is difficult especially when the graph size becomes large. In this paper, we propose a dynamic bio-inspired algorithm for finding the dynamic shortest path for large graphs based on Physarum Solver, which is a shortest path algorithm for static graphs. The proposed method is evaluated using three different large graph models representing diverse real-life applications. …
Hybix: A Novel Encoding Bitmap Index For Space- And Time-Efficient Query Processing, Naphat Keawpibal, Ladda Preechaveerakul, Sirirut Vanichayobon
Hybix: A Novel Encoding Bitmap Index For Space- And Time-Efficient Query Processing, Naphat Keawpibal, Ladda Preechaveerakul, Sirirut Vanichayobon
Turkish Journal of Electrical Engineering and Computer Sciences
A bitmap-based index is an effective and efficient indexing method for answering selective queries in a read-only environment. It offers improved query execution time by applying low-cost Boolean operators on the index directly, before accessing raw data. A drawback of the bitmap index is that index size increases with the cardinality of indexed attributes, which additionally has an impact on a query execution time. This impact is related to an increase of query execution time due to the scanning of bitmap vectors to answer the queries. In this paper, we propose a new encoding bitmap index, called the HyBiX bitmap …
A Novel Hybrid Teaching-Learning-Based Optimization Algorithm For The Classification Of Data By Using Extreme Learning Machines, Ender Sevi̇nç, Tansel Dökeroğlu
A Novel Hybrid Teaching-Learning-Based Optimization Algorithm For The Classification Of Data By Using Extreme Learning Machines, Ender Sevi̇nç, Tansel Dökeroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. Teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of …
Triangular Slotted Ground Plane: A Key To Realizing High-Gain, Cross-Polarization-Free Microstrip Antenna With Improved Bandwidth, Abhijyoti Ghosh, Banani Basu
Triangular Slotted Ground Plane: A Key To Realizing High-Gain, Cross-Polarization-Free Microstrip Antenna With Improved Bandwidth, Abhijyoti Ghosh, Banani Basu
Turkish Journal of Electrical Engineering and Computer Sciences
A simple rectangular microstrip antenna with triangular slotted ground plane has been studied both theoretically and experimentally to improve shortcomings like low gain (5 - 6 dBi), narrow bandwidth (3% - 4%), and poor copolarization (CP) to cross-polarization (XP) isolation, i.e. polarization purity (typically 10 - 12 dB), of conventional rectangular microstrip patch antennas. By placing two pairs of triangular shaped slots on the ground plane just below the nonradiating edges of the patch, high gain (around 9 dBi) and more than 22 dB polarization purity over a wide elevation angle has been achieved. The proposed antenna covers almost the …
Invisible Watermarking Framework That Authenticates And Prevents The Visualization Of Anaglyph Images For Copyright Protection, David-Octavio Muñoz-Ramirez, Volodymyr Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos, Beatriz-Paulina Garcia-Salgado
Invisible Watermarking Framework That Authenticates And Prevents The Visualization Of Anaglyph Images For Copyright Protection, David-Octavio Muñoz-Ramirez, Volodymyr Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos, Beatriz-Paulina Garcia-Salgado
Turkish Journal of Electrical Engineering and Computer Sciences
In this work, a watermarking framework to authenticate and protect the copyright that prevents the visualization of nonauthorized anaglyph images is proposed. Designed scheme embeds a binary watermark and the Blue channel of the anaglyph image into the discrete cosine transform domain of the original image. The proposed method applies the quantization index modulation-dither modulation algorithm and a combination of Bose-Chaudhuri-Hocquenghem with repetition codes, which permit to increase the capability in recovering the watermark. Additionally, Hash algorithm is used to scramble the component where the watermark should be embedding, guaranteeing a higher security performance of the scheme. This new technique …
Combined Feature Compression Encoding In Image Retrieval, Lu Huo, Leijie Zhang
Combined Feature Compression Encoding In Image Retrieval, Lu Huo, Leijie Zhang
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral …
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Turkish Journal of Electrical Engineering and Computer Sciences
The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to …
A Hybrid Of Tropical-Singular Value Decomposition Method For Salt And Pepper Noise Removal, Achmad Abdurrazzaq, Ismail Mohd, Ahmad Kadri Junoh, Zainab Yahya
A Hybrid Of Tropical-Singular Value Decomposition Method For Salt And Pepper Noise Removal, Achmad Abdurrazzaq, Ismail Mohd, Ahmad Kadri Junoh, Zainab Yahya
Turkish Journal of Electrical Engineering and Computer Sciences
The unknown information contained in an image that causes the change of information in the image is called noise. In this paper, we propose a new method for removing salt and pepper noise by using singular value decomposition and the concept of tropical algebra operations. To determine the performance of the proposed method, 20 test images are used as samples. Then three different image quality assessments are used: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and image enhancement factor (IEF). In addition, six different filtering methods, i.e. MF, DWMF, PSMF, MDBUTM, NAFSM, and BPDF, are used to compare the performance …
A Joint Image Dehazing And Segmentation Model, Haider Ali, Awal Sher, Nosheen Zikria, Lavdi̇e Rada Ülgen
A Joint Image Dehazing And Segmentation Model, Haider Ali, Awal Sher, Nosheen Zikria, Lavdi̇e Rada Ülgen
Turkish Journal of Electrical Engineering and Computer Sciences
Objects and their feature identification in hazy or foggy weather conditions has been of interest in the last decades. Improving image visualization by removing weather influence factors for easy image postprocessing, such as object detection, has benefits for human assistance systems. In this paper, we propose a novel variational model that will be capable of jointly segmenting and dehazing a given image. The proposed model incorporates atmospheric veil estimation and locally computed denoising constrained surfaces into a level set function by performing a robust and efficient image dehazing and segmentation scheme for both gray and color outdoor images. The proposed …
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the …
Low-Cost Multiple Object Tracking For Embedded Vision Applications, Muhammad Imran Shehzad, Fazal Wahab Karam, Shoaib Azmat
Low-Cost Multiple Object Tracking For Embedded Vision Applications, Muhammad Imran Shehzad, Fazal Wahab Karam, Shoaib Azmat
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a low-cost multiple object tracking (MOT) technique by employing a novel appearance update model for object appearance modeling using K-means. The state-of-the-art work has attained a very high accuracy without considering the real-time aspects necessitated by currently trending embedded vision platforms. The major research on multiple object tracking is used to update the appearance model in every frame while discounting its persistent nature. The proposed appearance update model reduces the computational cost of the state-of-the-art MOT 6-fold by exploiting this facet of persistent appearance over the sequence of frames. To ensure accuracy, the proposed model is tested …
An Efficient Retrieval Algorithm Of Encrypted Speech Based On Inverse Fast Fourier Transform And Measurement Matrix, Qiuyu Zhang, Zixian Ge, Liang Zhou, Yongbing Zhang
An Efficient Retrieval Algorithm Of Encrypted Speech Based On Inverse Fast Fourier Transform And Measurement Matrix, Qiuyu Zhang, Zixian Ge, Liang Zhou, Yongbing Zhang
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we present an efficient retrieval algorithm for encrypted speech based on an inverse fast Fourier transform and measurement matrix. Our approach improves query performance, as well as retrieval efficiency and accuracy, compared to existing content-based encrypted speech retrieval methods. Our proposed algorithm constructs a perceptual hash scheme using perceptual hash sequences from original speech files. By classifying the sequences and applying run-length compression, we decrease the cloud storage required for the hash index. We secure the speech database by encrypting it with Henon chaos scrambling, which offers excellent resistance to attacks. Experimental results show that the robustness, …
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …
A Hybrid Sentiment Analysis Method For Turkish, Buket Erşahi̇n, Özlem Aktaş, Deni̇z Kilinç, Mustafa Erşahi̇n
A Hybrid Sentiment Analysis Method For Turkish, Buket Erşahi̇n, Özlem Aktaş, Deni̇z Kilinç, Mustafa Erşahi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the …
Key Word Extraction For Short Text Via Word2vec, Doc2vec, And Textrank, Jun Li, Guimin Huang, Chunli Fan, Zhenglin Sun, Hongtao Zhu
Key Word Extraction For Short Text Via Word2vec, Doc2vec, And Textrank, Jun Li, Guimin Huang, Chunli Fan, Zhenglin Sun, Hongtao Zhu
Turkish Journal of Electrical Engineering and Computer Sciences
Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The data obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separated into clusters correctly, and the right information from these clusters must be obtained. Having the correct clusters depends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods are very important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of the density-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCAN Gaussian means …
Automatic Landing Of A Low-Cost Quadrotor Using Monocular Vision And Kalman Filter In Gps-Denied Environments, Mohammad Fattahi Sani, Maryam Shoaran, Ghader Karimian
Automatic Landing Of A Low-Cost Quadrotor Using Monocular Vision And Kalman Filter In Gps-Denied Environments, Mohammad Fattahi Sani, Maryam Shoaran, Ghader Karimian
Turkish Journal of Electrical Engineering and Computer Sciences
Unmanned aerial vehicles are becoming an important part of the modern life. Despite some recent advances in GPS-aided navigation of quadrotors, the concern of crash and collision still overshadows their reliability and safety, especially in GPS-denied environments. Therefore, the necessity for developing fully automatic methods for safe, accurate, and independent landing of drones increases over time. This paper investigates the autolanding process by focusing on an accurate and continuous position estimation of the drone using a monocular vision system and the fusion with the inertial measurement unit and ultrasonic sensors' data. An ARUCO marker is used as the landing pad, …