Browsing by Author "Aslan, Muhammet Fatih"

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Advancing Remote Sensing with Few-Shot Learning: A Comprehensive Review of Methods, Challenges, and Future Directions
(Wiley, 2025) Aslan, Muhammet Fatih; Sabanci, Kadir; Durdu, Akif; Kaousar, Rehana
In this review, the details and developments of few-shot learning (FSL) techniques in different remote sensing (RS) studies including change monitoring, disaster management, urban monitoring, and agriculture are discussed in detail. Furthermore, a categorization is made by dividing FSL methods into three categories (metric-based, optimization-based, and transfer learning approaches) and considering hybrid approaches. Special attention is given to episodic training and meta-learning approaches that provide rapid adaptation to new classes with minimal examples. Furthermore, the integration of explainable artificial intelligence (XAI) and its real-time application capabilities are discussed. Important issues such as domain shift, class imbalance, and high dimensionality are discussed. Recent refinements such as task-level learning, data augmentation, and multimodal integration are examined. Finally, a coherent framework is suggested for further studies and practical FSL applications in the context of RS. As a result, it provides a more comprehensive perspective than previous reviews. This review aimed to guide future research in the integration of FSL with RS applications by analyzing the existing literature and pointing out important research gaps.
Citation - WoS: 1
Citation - Scopus: 3
An Approach for Learning From Robots Using Formal Languages and Automata
(EMERALD GROUP PUBLISHING LTD, 2019) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir; Erdogan, Kemal
Purpose In this study, human activity with finite and specific ranking is modeled with finite state machine, and an application for human-robot interaction was realized. A robot arm was designed that makes specific movements. The purpose of this paper is to create a language associated to a complex task, which was then used to teach individuals by the robot that knows the language. Design/methodology/approach Although the complex task is known by the robot, it is not known by the human. When the application is started, the robot continuously checks the specific task performed by the human. To carry out the control, the human hand is tracked. For this, the image processing techniques and the particle filter (PF) based on the Bayesian tracking method are used. To determine the complex task performed by the human, the task is divided into a series of sub-tasks. To identify the sequence of the sub-tasks, a push-down automata that uses a context-free grammar language structure is developed. Depending on the correctness of the sequence of the sub-tasks performed by humans, the robot produces different outputs. Findings This application was carried out for 15 individuals. In total, 11 out of the 15 individuals completed the complex task correctly by following the different outputs. Originality/value This type of study is suitable for applications to improve human intelligence and to enable people to learn quickly. Also, the risky tasks of a person working in a production or assembly line can be controlled with such applications by the robots.
Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data
(2018) Aslan, Muhammet Fatih; Çelik, Yunus; Sabancı, Kadir; Durdu, Akif
Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective these methods are for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.
Citation - WoS: 30
Citation - Scopus: 36
A Cnn-Based Novel Solution for Determining the Survival Status of Heart Failure Patients With Clinical Record Data: Numeric To Image
(ELSEVIER SCI LTD, 2021) Aslan, Muhammet Fatih; Sabancı, Kadir; Durdu, Akif
The aim of this study is to effectively evaluate numerical data, which are frequently encountered in the medical field, with popular deep learning-based Convolutional Neural Network (CNN) models. Heart failure is a common disease worldwide and it is very important to identify patients with a high survival rate and whose condition will deteriorate. A heart failure dataset consisting of numerical values only, needs to be converted into image data for analysis using the advantages of CNN. For this, first all raw data are normalized, then each normalized feature is placed in a region in the grid image. Thus, images with different brightness regions are obtained according to the numerical value of each feature. After the data augmentation step, these images are trained with five different CNN models (GoogleNet, MobileNet v2, ResNet18, ResNet50 and ResNet101) and classified. The highest accuracy of 95.13 % is obtained with the ResNet18 model and this accuracy is superior to studies using previous numerical raw data. The success proves the applicability of the proposed method and shows that numerical data in different fields can be easily classified with CNN models.
Citation - WoS: 237
Citation - Scopus: 311
Cnn-Based Transfer Learning-Bilstm Network: a Novel Approach for Covid-19 Infection Detection
(ELSEVIER, 2021) Aslan, Muhammet Fatih; Ünlerşen, Muhammed Fahri; Sabancı, Kadir; Durdu, Akif
Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success. (C) 2020 Elsevier B.V. All rights reserved.
Comparison of Contourlet and Time-Invariant Contourlet Transform Performance for Different Types of Noises and Images
(2019) Aslan, Muhammet Fatih; Sabancı, Kadir; Durdu, Akif
A noiseless image is desirable for many applications. However, this is not possible. Generally, wavelet-based methods are used to noise reduction. However, due to insufficient performance of wavelet transforms (WT) on images, different multi-resolution analysis methods have been proposed. In this study, one of them is Contourlet Transform (CT) and the Translation-Invariant Contourlet Transform (TICT) which is an improved version of CT is compared using different noises. The fundus images are taken from the DRIVE dataset and benchmark images are used. Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) are used as comparison criteria. The results showed that TICT is better in Gaussian noisy images.
Citation - WoS: 137
Citation - Scopus: 187
A Comprehensive Survey of the Recent Studies With Uav for Precision Agriculture in Open Fields and Greenhouses
(Mdpi, 2022) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir; Ropelewska, Ewa; Gültekin, Seyfettin Sinan
The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.
Citation - WoS: 93
Citation - Scopus: 123
Covid-19 Diagnosis Using State-Of Cnn Architecture Features and Bayesian Optimization
(Pergamon-Elsevier Science Ltd, 2022) Aslan, Muhammet Fatih; Sabancı, Kadir; Durdu, Akif; Ünlerşen, Muhammed Fahri
The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restriction decisions have been taken all over the world. In addition, the number of COVID-19 tests has been increased to quarantine infected people. However, due to the problems encountered in the supply of RTPCR tests and the ease of obtaining Computed Tomography and X-ray images, imaging-based methods have become very popular in the diagnosis of COVID-19. Therefore, studies using these images to classify COVID-19 have increased. This paper presents a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models (AlexNet, ResNet18, ResNet50, Inceptionv3, Densenet201, Inceptionresnetv2, MobileNetv2, GoogleNet). The determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions in this study. First of all, lung segmentation is performed automatically from the raw image with Artificial Neural Networks (ANNs). To ensure data diversity, data augmentation is applied to the COVID-19 classes, which are fewer than the other two classes. Then these images are applied as input to five different CNN models. The features extracted from each CNN model are given as input to four different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Tree (DT) for classification. To achieve the most successful classification accuracy, the hyperparameters of each ML algorithm are determined using Bayesian optimization. With the classification made using these hyperparameters, the highest success is obtained as 96.29% with the DenseNet201 model and SVM algorithm. The Sensitivity, Precision, Specificity, MCC, and F1-Score metric values for this structure are 0.9642, 0.9642, 0.9812, 0.9641 and 0.9453, respectively. These results showed that ML methods with the most optimum hyperparameters can produce successful results.
Citation - WoS: 3
Citation - Scopus: 5
Covid-19 Isolation Control Proposal Via Uav and Ugv for Crowded Indoor Environments: Assistive Robots in the Shopping Malls
(Frontiers Media Sa, 2022) Aslan, Muhammet Fatih; Hasikin, Khairunnisa; Yusefi, Abdullah; Durdu, Akif; Sabancı, Kadir; Azizan, Muhammad Mokhzaini
Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.
Fusion of Ct and Mr Liver Images by Surf-Based Registration
(2019) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir
Medical imaging plays an important role in the diagnosis and treatment of different diseases. Images with more details are obtained by image fusion for more accurate analysis of medical images. In this study, Computed Tomography (CT) and Magnetic Resonance (MR) images of the liver from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) are fused using different combinations of different wavelet types such as daubechies, coiflet and symlet. To accomplish this task, first the preprocessing steps are completed, and then registration is performed using Speed up Robust Features (SURF). As a result, to measure the quality of the obtained fusion image Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measurement (SSIM), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) metrics are used.
Citation - WoS: 48
Citation - Scopus: 58
Goal Distance-Based Uav Path Planning Approach, Path Optimization and Learning-Based Path Estimation: Gdrrt*, Pso-Gdrrt* and Bilstm-Pso
(Elsevier, 2023) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir
The basic conditions for mobile robots to be autonomous are that the mobile robot localizes itself in the environment and knows the geometric structure of the environment (map). After these conditions are met, this mobile robot is given a specific task, but how the robot will navigate for this task is an important issue. Especially for Unmanned Aerial Vehicles (UAV), whose application has increased recently, path planning in a three-dimensional (3D) environment is a common problem. This study performs three experimental applications to discover the most suitable path for UAV in 3D environments with large and many obstacles. Inspired by Rapidly Random-Exploring Tree Star (RRT*), the first implementation develops the Goal Distance-based RRT* (GDRRT*) approach, which performs intelligent sampling taking into account the goal distance. In the second implementation, the path discovered by GDRRT* is shortened using Particle Swarm Optimization (PSO) (PSO-GDRRT*). In the final application, a network with a Bidirectional Long/Short Term Memory (BiLSTM) layer is designed for fast estimation of optimal paths found by PSO-GDRRT* (BiLSTM-PSO-GDRRT*). As a result of these applications, this study provides important novelties: GDRRT* converges to the goal faster than RRT* in large and obstacle-containing 3D environments. To generate groundtruth paths for training the learning-based network, PSO-GDRRT* finds the shortest paths relatively quickly. Finally, BiLSTM-PSO-GDRRT* provides extremely fast path planning for real-time UAV applications. This work is valuable for real-time autonomous UAV applications in a complex and large environment, as the new methods it offers have fast path planning capability.(c) 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 49
Citation - Scopus: 65
Human Action Recognition With Bag of Visual Words Using Different Machine Learning Methods and Hyperparameter Optimization
(SPRINGER LONDON LTD, 2020) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir
Human activity recognition (HAR) has quite a wide range of applications. Due to its widespread use, new studies have been developed to improve the HAR performance. In this study, HAR is carried out using the commonly preferred KTH and Weizmann dataset, as well as a dataset which we created. Speeded up robust features (SURF) are used to extract features from these datasets. These features are reinforced with bag of visual words (BoVW). Different from the studies in the literature that use similar methods, SURF descriptors are extracted from binary images as well as grayscale images. Moreover, four different machine learning (ML) methods such as k-nearest neighbors, decision tree, support vector machine and naive Bayes are used for classification of BoVW features. Hyperparameter optimization is used to set the hyperparameters of these ML methods. As a result, ML methods are compared with each other through a comparison with the activity recognition performances of binary and grayscale image features. The results show that if the contrast of the environment decreases when a human enters the frame, the SURF of the binary image are more effective than the SURF of the gray image for HAR.
Citation - WoS: 36
Citation - Scopus: 42
Hvionet: a Deep Learning Based Hybrid Visual-Inertial Odometry Approach for Unmanned Aerial System Position Estimation
(Pergamon-Elsevier Science Ltd, 2022) Aslan, Muhammet Fatih; Durdu, Akif; Yusefi, Abdullah; Yılmaz, Alper
Sensor fusion is used to solve the localization problem in autonomous mobile robotics applications by integrating complementary data acquired from various sensors. In this study, we adopt Visual- Inertial Odometry (VIO), a low-cost sensor fusion method that integrates inertial data with images using a Deep Learning (DL) framework to predict the position of an Unmanned Aerial System (UAS). The developed system has three steps. The first step extracts features from images acquired from a platform camera and uses a Convolutional Neural Network (CNN) to project them to a visual feature manifold. Next, temporal features are extracted from the Inertial Measurement Unit (IMU) data on the platform using a Bidirectional Long Short Term Memory (BiLSTM) network and are projected to an inertial feature manifold. The final step estimates the UAS position by fusing the visual and inertial feature manifolds via a BiLSTM-based architecture. The proposed approach is tested with the public EuRoC (European Robotics Challenge) dataset and simulation environment data generated within the Robot Operating System (ROS). The result of the EuRoC dataset shows that the proposed approach achieves successful position estimations comparable to previous popular VIO methods. In addition, as a result of the experiment with the simulation dataset, the UAS position is successfully estimated with 0.167 Mean Square Error (RMSE). The obtained results prove that the proposed deep architecture is useful for UAS position estimation. (c) 2022 Elsevier Ltd. All rights reserved.
Kapalı Ortamlarda Otonom İnsansız Hava Sistemlerinin Geliştirilmesi
(Konya Teknik Üniversitesi, 2022) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir
İnsansı görevlerin robotlara yaptırılma ihtiyacı, tıpkı insanlar gibi, kendi kararlarını veren ve buna göre bir görev gerçekleştiren otonom mobil robot uygulamalarını ortaya çıkarmıştır. Bir otonom robot bulunduğu ortamın geometrik yapısını bilmeli, buna göre kendini konumlandırmalı ve son olarak bu bilgilere dayanarak görev noktasına doğru bir hareket yörüngesi oluşturmalıdır. İnsanlarla aynı ortamı paylaşan otonom robotlar geliştirmek için Küresel Konumlandırma Sistemi (Global Positoning System (GPS))'nin yetersiz olduğu iç ortamlarda, farklı sensörlerle mobil robotu konumlandırmaya yönelik Odometri ve Eşzamanlı Konumlandırma ve Haritalama (Simultaneous Localization and Mapping (SLAM)) çalışmaları mevcuttur. Son on yılda araştırmacılar, işlemci hızındaki gelişmeler nedeniyle, maliyet olarak düşük monoküler kameralar ile gerçekleştirilen Görsel Odometri (Visual SLAM (VO)) ve Görsel SLAM (Visual SLAM (VSLAM)) yöntemlerine odaklanmıştır. Ayrıca son zamanlarda, kameralara ek olarak, düşük maliyetli Atalet Ölçü Birimi (Inertial Measurement Unit (IMU)) sensörleri içeren VISLAM ve VIO çözümleri, konumlandırmaya sağladığı katkı nedeni ile sıklıkla tercih edilmeye başlamıştır. Şimdiye kadarki çözümler, genellikle geleneksel geometrik tabanlı çözümler içerirler. Bu klasik yöntemlerle gerçek karmaşık dünyanın iyi temsili çok zor olduğundan, genellikle güvenilir sonuçlar elde edilmez ve ayrıca elle ayarlanan özelliklere çok bağımlıdırlar. Bu nedenle günümüzde geleneksel çözümlerin yerini, farklı ortamlara uyarlanabilmesi ve uygulama kolaylığı sağlaması açısından Yapay Zekâ tabanlı çözümler almaktadır. Bu tez çalışması yukarıda bahsedilen bilgiler ışığında, GPS erişimi olmayan iç ortamlarda otonom bir İnsansız Hava Araçları (İHA) geliştirilmesi için üç farklı uygulama önermektedir. İlk uygulama iç ortamda hareket eden bir İHA'nın konumunu tahmin etmek için derin öğrenme tabanlı hibrit bir mimari ile görsel ve IMU bilgilerine dayalı bir çalışma sunmaktadır. İkinci uygulama IMU bilgisini görüntüye dönüştüren ve İHA'nın konumu yanında açı bilgisini de başarılı bir şekilde tahmin eden yapay zekâ tabanlı farklı bir VIO uygulamasını farklı bir füzyon tekniğiyle gerçekleştirmektedir. Son uygulama bir üç boyutlu ortamda, konum bilgisi bilinen bir İHA için yeni bir yol planlama yöntemi önermektedir. Üstelik yeni bir yol planlama yönteminin yanında, önerilen yöntem için optimizasyon ve Yapay Zeka tabanlı bir uygulama geliştirilmiş, ve sonuçta gerçek zamanlı bir yol planlaması sağlanmıştır. Üç uygulama da iç ortamda otonom bir İHA geliştirilmesi için yeni yöntemler sunmaktadır. Tüm yöntemler önceki çalışmaların büyük bir kısmına üstünlük sağlayacak performans gösterirler. Ayrıca gerçekleştirilen uygulamalar gerçek zamanlı sistemlerde çalışabilecek niteliktedir.
Citation - WoS: 37
Citation - Scopus: 42
A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest-Damaged Wheat Grain Detection
(Springer, 2022) Sabancı, Kadir; Aslan, Muhammet Fatih; Ropelewska, Ewa; Ünlerşen, Muhammed Fahri; Durdu, Akif
The sunn pest-damaged (SPD) wheat grains negatively affect the flour quality and cause yield loss. This study focuses on the detection of SPD wheat grains using deep learning. With the created image acquisition mechanism, healthy and SPD wheat grains are displayed. Image preprocessing steps are applied to the captured raw images, then data augmentation is performed. The augmented image data is given as an input to two different deep learning architectures. In the first architecture, transfer learning application is made using AlexNet. The second architecture is a hybrid structure, obtained by adding the bidirectional long short-term memory (BiLSTM) layer to the first architecture. In terms of accuracy, the performance of the non-hybrid and hybrid architectures that are presented in the study is determined as 98.50% and 99.50%, respectively. High classification success and innovative deep learning structure are the features of this study that distinguish it from previous studies.
Citation - WoS: 9
Citation - Scopus: 14
Segmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machines
(IEEE, 2018) Aslan, Muhammet Fatih; Ceylan, Murat; Durdu, Akif
The process of obtaining blood vessels from the retinal fundus images plays an important role in the detection of disease in the eye. Analysis of blood vessels provides preliminary information on the presence and treatment of glaucoma, retinopathy, etc. This is why such practices are important. In this study, firstly, features were extracted from color retinal images. Adaptive threshold, Gabor filter and Top-Hat transform were used to make the blood vessel more visible during the feature extraction phase. Subsequently, the acquired features were given as input to the extreme learning machine, and as a result, retinal blood vessel was obtained. At this stage, DRIVE database was used. Twenty colored retinal fundus images were used in the train phase. Thanks to the extreme learning machine, the training process has been carried out quickly (0.42 sec). A high accuracy rate is obtained as %94.59.
Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy
(2020) Durdu, Akif; Orhan, Nuri; Çeltek, Seyit Alperen; Aslan, Muhammet Fatih; Sabancı, Kadir
The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump. The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems. It is important to detect and prevent vortex for the economic life and efficiency of the agriculture pump. The image processing and neuro-fuzzy based novel model is proposed for the detection of a vortex in the deep well pump used in the agriculture system with this paper. The used images and data - submergence, flow rate, the diameter of the pipe, power consumption, pressure values and noise values- is acquired from an experimental pump. The proposed approach consists of three steps; Neuro-Fuzzy Learning, Image Processing and Neuro-Fuzzy Testing. In the first step, the eightytwo data have employed for the training process of the Neuro-Fuzzy. Then, the images derived from a camera placed near the experimental pump are used to detect vortex in the image processing step. Finally, the relevant data to vortex cases have employed for the testing process of the NeuroFuzzy. The result of this study demonstrates that image processing and neuro-fuzzy based design can be successfully used to detect vortex formation. This paper has provided novel contributions in the vortex detection issue such as find out vortex cases by using image processing and NeuroFuzzy. The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.
Citation - Scopus: 11
A Tutorial: Mobile Robotics, Slam, Bayesian Filter, Keyframe Bundle Adjustment and Ros Applications
(Springer Science and Business Media Deutschland GmbH, 2021) Aslan, Muhammet Fatih; Durdu, Akif; Yusefi, A.; Sabancı, Kadir; Sungur, C.
Autonomous mobile robots, an important research topic today, are often developed for smart industrial environments where they interact with humans. For autonomous movement of a mobile robot in an unknown environment, mobile robots must solve three main problems; localization, mapping and path planning. Robust path planning depends on successful localization and mapping. Both problems can be overcome with Simultaneous Localization and Mapping (SLAM) techniques. Since sequential sensor information is required for SLAM, eliminating these sensor noises is crucial for the next measurement and prediction. Recursive Bayesian filter is a statistical method used for sequential state prediction. Therefore, it is an essential method for the autonomous mobile robots and SLAM techniques. This study deals with the relationship between SLAM and Bayes methods for autonomous robots. Additionally, keyframe Bundle Adjustment (BA) based SLAM, which includes state-of-art methods, is also investigated. SLAM is an active research area and new algorithms are constantly being developed to increase accuracy rates, so new researchers need to understand this issue with ease. This study is a detailed and easily understandable resource for new SLAM researchers. ROS (Robot Operating System)-based SLAM applications are also given for better understanding. In this way, the reader obtains the theoretical basis and application experience to develop alternative methods related to SLAM. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 35
Citation - Scopus: 40
Visual-Inertial Image-Odometry Network (viionet): a Gaussian Process Regression-Based Deep Architecture Proposal for Uav Pose Estimation
(Elsevier Sci Ltd, 2022) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir
This study estimates the pose of Unmanned Aerial Vehicle (UAV) through artificial intelligence-based approaches and combines visual-inertial information in a different way than previous studies. For an effective fusion, the inertial data between both frames is normalized after denoising with the Savitzky-Golay technique and finally converted from numerical value to image. To strengthen these inertial image features with the change of motion between two frames, frames of Optical Flow (OF) are obtained and OF frames are combined with inertial images. Simultaneously, a parallel thread combines this OF frame with two consecutive raw frames. After features are extracted from inertial and camera data via Inception-v3, these features are fused and actual UAV poses are estimated via Gaussian Process Regression (GPR). Thanks to the smoothing process applied to these estimated values, a more stable pose estimation is provided. This proposed method is applied to the EuRoC dataset and our dataset produced in the Gazebo environment. The pose estimation results reveal that the proposed method has high performance compared to many previous studies.
Citation - WoS: 18
Citation - Scopus: 21
The Ytu Dataset and Recurrent Neural Network Based Visual-Inertial Odometry
(ELSEVIER SCI LTD, 2021) Gürtürk, Mert; Yusefi, Abdullah; Aslan, Muhammet Fatih; Soycan, Metin; Durdu, Akif; Masiero, Andrea
Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) are fundamental problems to be properly tackled for enabling autonomous and effective movements of vehicles/robots supported by vision -based positioning systems. This study presents a publicly shared dataset for SLAM investigations: a dataset collected at the Yildiz Technical University (YTU) in an outdoor area by an acquisition system mounted on a terrestrial vehicle. The acquisition system includes two cameras, an inertial measurement unit, and two GPS receivers. All sensors have been calibrated and synchronized. To prove the effectiveness of the introduced dataset, this study also applies Visual Inertial Odometry (VIO) on the KITTI dataset. Also, this study proposes a new recurrent neural network-based VIO rather than just introducing a new dataset. In addition, the effectiveness of this proposed method is proven by comparing it with the state-of-the-arts ORB-SLAM2 and OKVIS methods. The experimental results show that the YTU dataset is robust enough to be used for benchmarking studies and the proposed deep learning-based VIO is more successful than the other two traditional methods.