Khan, Muhammad Umer
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Khan, Muhammad Umer
K.,Muhammad Umer
Muhammad Umer, Khan
Khan,Muhammad Umer
M.U.Khan
M., Khan
M.,Khan
Khan U.
Khan M.
Khan,M.U.
M. U. Khan
Umer Khan M.
K., Muhammad Umer
Muhammad Umer Khan
Khan, Umer
Khan, Muhammed Umer
Khan, M. U.
Khan, M.U
K.,Muhammad Umer
Muhammad Umer, Khan
Khan,Muhammad Umer
M.U.Khan
M., Khan
M.,Khan
Khan U.
Khan M.
Khan,M.U.
M. U. Khan
Umer Khan M.
K., Muhammad Umer
Muhammad Umer Khan
Khan, Umer
Khan, Muhammed Umer
Khan, M. U.
Khan, M.U
Job Title
Yardımcı Doçent
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umer.khan@atilim.edu.tr
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Mechatronics Engineering
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Sustainable Development Goals
2
ZERO HUNGER
4
Research Products
7
AFFORDABLE AND CLEAN ENERGY
4
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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17
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1
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Scholarly Output
31
Articles
11
Citation Count
215
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9
30 results
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Now showing 1 - 10 of 30
Master Thesis Bilinmeyen Ortamlarda Robot Sürüleri için Algoritma Planlamada Etkin Bir Yol(2020) Abdı, Mohammed Isam Ismael; Khan, Muhammad Umer; Mechatronics EngineeringBirçok durumda birkaç mobil robot —bağımsız ajan— tek bir robot için gerçekleştirilmesi zor veya imkânsız hedefleri elde etmek amacıyla ekip halinde bir araya gelebilirler. Bu mobil robotlar belli bir görevi yerine getirmek için iş birliği yapabilirler, bu, sürünün büyüklüğüyle tam bir karşılıklı ilişki halindedir. Tek tek her robot sensörlerini kullanarak yerel ortamla karşılıklı olarak etkileşir. Sürü açısından birincil endişe başlangıçtan hedef yere kadar güvenli bir yolun tanımlanması ve izlenmesidir. Literatürde bu hedefin gerçekleştirilmesiyle ilgili Neural Network (Sinir Ağları), Genetic Algorithms (Genetik Algoritmalar), Bacterial Foraging Optimization (Bakteriyel Besin Arama Optimizasyonu), Ant Colony Optimization (Karınca Kolonisi Optimizasyonu), Artificial Potential Field (Yapay Potansiyel Alan), v.b. gibi pek çok algoritma mevcuttur. Bunlar arasında Bacterial Foraging Optimization (BFO) algoritması çalışma ortamında bilinen tüm engelleri göz önüne alarak güvenliği ve hedefin bulunmasını sağlamaktaki etkinliği nedeniyle pek çok bilimcinin dikkatini çekmektedir. Ayrıca, belirlenen yolu keşfeder ve doğru olarak izler. BFO kümeleşme prensiplerini ve doğadaki sosyal davranışlar analojisini kullanan, ilhamını biyolojiden alan doğrudan yaklaşımlı ama güçlü bir optimizasyon yöntemidir. BFO yassı bir yüzey haritası üzerinde engellerin varlığında başlangıçtan hedef noktaya kadar optimal yolu başarıyla araştırır. Ancak bu algoritma, konveks olmayan engellerin işe karışması durumunda yerel asgari şartlara sıkışmak gibi bir zayıflığa sahiptir. Sürünün robotlarından herhangi birinin sıkışıp kalması durumu görevinin tamamının başarısızlığı olarak görülmektedir. Bu araştırma BFO algoritmasının hem konveks olan hem de olmayan niteliklerdeki engellerden başarıyla kaçınılmasını sağlayan iyileştirilmiş bir versiyonunu önermektedir. Önerilen algoritma engele zıt yöndeki belli bir mesafeyi kapsayarak robotun yerel asgari değerlerden kurtulmasına yardım eder. Sert bir açıyla karşılaşıldığında algoritma güvenli bir yol oluşturmak için görsel engeller oluşturmaya başlar. Daha sonra bu bilgi diğer robotlara aktarılarak onların da yerel minimumlardan kaçınmaları sağlanır. Önerilen algoritmanın etkinliğinin test edilmesi için mevcut BFO algoritmasıyla bir karşılaştırma yapılmıştır. Her iki algoritmanın performansı bilinmeyen dinamik ve statik ortamlarda test edilmiştir. Sonuçlara göre, önerilen algoritmanın yerel minimumlardan başarıyla kurtulduğu ve BFO'nun sıkışıp kaldığı gözlenmiştir.Article Citation - WoS: 16Citation - Scopus: 30Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, Alok; Software Engineering; Mechatronics EngineeringComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Conference Object Citation - Scopus: 4Convolution Neural Network (cnn) Based Automatic Sorting of Cherries(Institute of Electrical and Electronics Engineers Inc., 2021) Park,H.; Khan,M.U.; Mechatronics EngineeringCherries are spring fruits enriched with nutrients, and are easily available in food markets around the world. Due to their excess demand, many enterprises solely focused on their processing. Cherries are especially susceptible to pathological-, physiological-diseases and structural degradation due to their soft outer skin. The post-harvest life of the fruit is limited by various characteristics. The agricultural industry has also been at the forefront to get benefits from the advanced machine learning tools. This study presents an image processing-based system for sorting cherries using the convolutional neural network (CNN). For this study, Prunus avium L cherries of export quality, available in Turkey, tagged as ‘0900 Ziraat’, are used. Surprisingly, there exists no dataset for these cherries; hence, we developed our dataset. Through the proposed approach based upon U-Net, the binary classification accuracy of 99% is achieved. Clear identification is demonstrated by the test results of varying mixture ratios of good and bad cherries. It can therefore be said that for cherry sorting and grading, U-Net can be applied as a reliable and promising machine learning tool. ©2021 IEEEMaster Thesis Optı-track Kameraları Kullanarak Birden Fazla Temsilci için Yerelleştirme ve Yol Planlaması(2021) Al-qassab, Ayman; Khan, Muhammad Umer; Mohammadzadeh, Mohammad Hassan Gol; Mechatronics EngineeringFiziksel bir mekandaki nesnelerin veya canlıların hareketlerinin dijital olarak algılanması ve kaydedilmesi işlemi, Opti-Track sistemi gibi hareket yakalama (MoCap) sistemi kullanılarak gerçekleştirilir. Bu çalışmada hareket yakalama sisteminin amacı, hem quadrotor hem de mobil platformun pozlarını sürekli olarak belirlemektir. Konum bilgisi navigasyon sistemi tarafından quadrotor'u hareketli mobil platforma yönlendirmek ve güvenli bir şekilde inmek için kullanılır. İstenen sonuçları iyi bir doğrulukla elde etmek için mobil platformu izlemek için bir Kalman filtresi kullanılır. Ayrıca, mobil platformun gelecekteki konumunu tahmin etmek için başka bir Kalman filtresi kullanılmıştır. Quadrotoru tahmin edilen konuma yönlendirmek için bir model öngörücü kontrolör (MPC) kullanılır. Model öngörücü kontrolü, quadrotor'un istenen yolu izlemesine yardımcı olur. Bu çalışma, hareketli bir mobil platformun gelecekteki konumunu tahmin etmek ve quadrotor'u mobil platforma yönlendirmek için hareket yakalama sisteminin bilgisini ve Kalman filtresini kullanan bir navigasyon sistemi önerdi. Navigasyon sistemi, quadrotor'un kalkışını, seyir yörüngesini ve mobil platforma inişini otonom olarak kontrol eder. Önerilen navigasyon sisteminin performansını ve güvenilirliğini doğrulamak için çeşitli deneyler yapılmıştır. Deneylerin sonuçları, önerilen navigasyon sisteminin istenen sonuçlara ulaşmada etkili olduğunu kanıtladığını göstermektedirConference Object Citation - Scopus: 10Biomechanical Design and Control of Lower Limb Exoskeleton for Sit-To and Stand-To Movements(Institute of Electrical and Electronics Engineers Inc., 2018) Qureshi,M.H.; Masood,Z.; Rehman,L.; Owais,M.; Khan,M.U.; Mechatronics EngineeringIn this paper, we present design and development phase of lower limb robotic exoskeleton that can assist paralyzed individuals. Motion of the human wearing exoskeleton is introduced by actuators. Both exoskeleton legs are attached to the supporting frame by passive universal joints. The exoskeleton provides 3 DOFs per limb of which two joints are active and one passive. The control actions i.e., sit-to-stand and stand-to-sit movements are triggered using Double Pole Double Throw (DPDT) toggle switch. The control scheme is implement using Switch control method and the feedback is provided by means of current measurement. This assistive device can be utilized for the disabled persons. The simulation results are provided that evaluates the performance of the control actions on exoskeleton. © 2018 IEEE.Article Citation - WoS: 21Citation - Scopus: 28Hybrid Eeg-Fnirs Bci Fusion Using Multi-Resolution Singular Value Decomposition (msvd)(Frontiers Media Sa, 2020) Khan, Muhammad Umer; Hasan, Mustafa A. H.; Mechatronics EngineeringBrain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.Article Citation - WoS: 4Citation - Scopus: 3Avoiding Contingent Incidents by Quadrotors Due To One or Two Propellers Failure(Public Library Science, 2023) Altinuc, Kemal Orcun; Khan, Muhammad Umer; Iqbal, Jamshed; Mechatronics EngineeringWith the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures.Article Citation - WoS: 2Citation - Scopus: 1Autonomous Landing of a Quadrotor on a Moving Platform Using Motion Capture System(Springer, 2024) Qassab, Ayman; Khan, Muhammad Umer; Irfanoglu, Bulent; Mechatronics Engineering; Department of Mechatronics EngineeringThis paper investigates the challenging problem of the autonomous landing of a quadrotor on a moving platform in a non-cooperative environment. The limited sensing ability of quadrotors often hampers their utilization for autonomous landing, especially in GPS-denied areas. The performance of motion capture systems (MCSs) in many application areas is the motivation to utilize them for the autonomous take-off and landing of the quadrotor in this research. An autonomous closed-loop vision-based navigation, tracking, and control system is proposed for quadrotors to perform landing based upon Model Predictive Control (MPC) by utilizing multi-objective functions. The entire process is posed as a constrained tracking problem to minimize energy consumption and ensure smooth maneuvers. The proposed approach is fully autonomous from take-off to landing; whereas, the movements of the landing platform are pre-defined but still unknown to the quadrotor. The landing performance of the quadrotor is tested and evaluated for three different movement patterns: static, square-shaped, and circular-shaped. Through experimental results, the pose error between the quadrotor and the platform is measured and found to be less than 30 cm. Introducing a holistic vision system for quadrotor navigation, tracking, and landing on stationary/moving platforms. Proposing an energy-efficient, smooth, and stable MPC controller validated by Lyapunov analysis. Validating the adept tracking and safe landings of the quadrotor on stationary/moving platforms through three diverse experiments.Article Utilization of Robotics for Solar Panel Cleaning and Maintenance(2019) Park, Haon; Öztürk, Abdullah; Park, Hajun; Khan, Muhammed Umer; Mechatronics EngineeringIn this study, a portable and low-cost solar panel cleaning robot with a functioning to blowaway dust was designed and built to incur labor costs.The robot is service effective, environmentallyfriendly, energy independent, self-automated, long durable, and cost-effective. After cleaning operation,output voltage and current in solar panel increased by 8.02% and 18.78%, respectively. Moreover, theimage processing with the photos taken by a camera fixed on the robot made classification according tocolor changes in the solar panel. Therefore, it can be concluded that the automated and multifunctionalrobot may facilitate the solar panel surface cleaning and be an applicable maintenance method throughremote monitoring of the surface conditions.Conference Object Citation - Scopus: 6Attitude Control of Quad-Copter Using Deterministic Policy Gradient Algorithms (dpga)(Institute of Electrical and Electronics Engineers Inc., 2019) Ghouri,U.H.; Zafar,M.U.; Bari,S.; Khan,H.; Khan,M.U.; Mechatronics EngineeringIn aerial robotics, intelligent control has been a buzz for the past few years. Extensive research efforts can be witnessed to produce control algorithms for stable flight operation of aerial robots using machine learning. Supervised learning has the tendency but training an agent using supervised learning can be a tedious task. Moreover, the data gathering could be expensive and always prone to inaccuracies due to parametric variations and system dynamics. An alternative approach is to ensure the stability of the aerial robots with the help of Deep Re-inforcement Learning (DRL). This paper deals with the intelligent control of quad-copter using deterministic policy gradient algorithms. In this research, state of the art Deep Deterministic Policy Gradient (DDPG) and Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithms are employed for attitude control of quad-copter. An open source simulation environment GymFC is used for training of quad-copter. The results for comparative analysis of DDPG D4PG algorithms are also presented, highlighting the attitude control performance. © 2019 IEEE.
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