Khan, Muhammad Umer

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Profile URL
https://hdl.handle.net/20.500.14411/6656
Name Variants
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
Job Title
Yardımcı Doçent
Email Address
umer.khan@atilim.edu.tr
Main Affiliation
Mechatronics Engineering
Status
Website
ORCID ID
0000-0002-9195-3477
Scopus Author ID
57209876827
Turkish CoHE Profile ID
277006
Google Scholar ID
8h-JrH8AAAAJ
WoS Researcher ID
N-5478-2016

Sustainable Development Goals

2

ZERO HUNGER
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4

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
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0

Research Products

14

LIFE BELOW WATER
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0

Research Products

6

CLEAN WATER AND SANITATION
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0

Research Products

1

NO POVERTY
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0

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5

GENDER EQUALITY
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0

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

Research Products

17

PARTNERSHIPS FOR THE GOALS
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1

Research Products

15

LIFE ON LAND
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0

Research Products

10

REDUCED INEQUALITIES
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0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
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4

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
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0

Research Products

4

QUALITY EDUCATION
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0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

Research Products

3

GOOD HEALTH AND WELL-BEING
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1

Research Products

13

CLIMATE ACTION
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0

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Documents

37

Citations

625

h-index

13

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Documents

30

Citations

463

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Scholarly Output

36

Articles

14

Views / Downloads

178/1580

Supervised MSc Theses

10

Supervised PhD Theses

0

WoS Citation Count

240

Scopus Citation Count

358

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

6.67

Scopus Citations per Publication

9.94

Open Access Source

10

Supervised Theses

10

Google Analytics Visitor Traffic

JournalCount
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 2 July 2018 through 4 July 2018 -- Oulu -- 1391112
Applied Sciences2
2019 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 6 March 2019 through 7 March 2019 -- Islamabad -- 1469971
2020 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 14 April 2020 through 16 April 2020 -- Antalya -- 1604501
2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 21 April 2021 through 23 April 2021 -- Virtual, Online -- 1767941
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Start date: 2020Subject: [No Keyword Available]

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 31
    Citation - Scopus: 38
    A Computationally Efficient Method for Hybrid Eeg-Fnirs Bci Based on the Pearson Correlation
    (Hindawi Ltd, 2020) Hasan, Mustafa A. H.; Khan, Muhammad U.; Mishra, Deepti
    A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.
  • Thumbnail Image
    Article
    Citation - WoS: 5
    Citation - Scopus: 4
    Avoiding Contingent Incidents by Quadrotors Due To One or Two Propellers Failure
    (Public Library Science, 2023) Altinuc, Kemal Orcun; Khan, Muhammad Umer; Iqbal, Jamshed
    With 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.