Güneş, Ahmet

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/7663
Name Variants
A.,Gunes
Gunes, Ahmet
G., Ahmet
A., Gunes
Ahmet, Gunes
G.,Ahmet
Gunes,A.
Güneş, Ahmet
Güneş,A.
A.,Güneş
Ahmet, Güneş
Job Title
Doktor Öğretim Üyesi
Email Address
ahmet.gunes@atilim.edu.tr
Main Affiliation
Department of Mechatronics Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
1
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

12

Articles

3

Views / Downloads

38/88

Supervised MSc Theses

0

Supervised PhD Theses

1

WoS Citation Count

42

Scopus Citation Count

55

Patents

0

Projects

0

WoS Citations per Publication

3.50

Scopus Citations per Publication

4.58

Open Access Source

2

Supervised Theses

1

JournalCount
Applied Sciences2
26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY2
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- Izmir -- 1377802
IEEE Microwave and Wireless Components Letters1
International Geoscience and Remote Sensing Symposium (IGARSS) -- 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 -- 28 July 2019 through 2 August 2019 -- Yokohama -- 1547921
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Type: Article

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Pseudospectral Time Domain Method Implementation Using Finite Difference Time Stepping
    (Ieee-inst Electrical Electronics Engineers inc, 2018) Gunes, Ahmet; Aksoy, Serkan
    Lagrange interpolation polynomials-based Cheby-shev pseudospectral time domain (CPSTD) method is an efficient time domain solver for Maxwell equations. Although it has the lowest interpolation error among pseudospectral time domain methods, time derivatives must be calculated using higher order time derivative schemes, such as the Runge-Kutta method. The higher order time derivative methods slow down the computation speed at each step by several folds. In this letter, we show that central finite differences can be used for implementation of time derivatives in CPSTD method. Results are verified by a resonator problem.
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 13
    Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
    (Mdpi, 2020) Abdi, Mohammed Isam Ismael; Khan, Muhammad Umer; Gunes, Ahmet; Mishra, Deepti; Ismael Abdi, Mohammed Isam
    The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.
  • Thumbnail Image
    Article
    Citation - WoS: 26
    Citation - Scopus: 35
    Tobset: a New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
    (Mdpi, 2022) Alam, Muhammad Shahab; Khan, Muhammad Umer; Alam, Mansoor; Tufail, Muhammad; Güneş, Ahmet; Khan, Muhammad Umer; Gunes, Ahmet; Salah, Bashir; Khan, Muhammad Tahir; Khan, Muhammad Umer; Güneş, Ahmet; Mechatronics Engineering; Department of Mechatronics Engineering; Mechatronics Engineering; Department of Mechatronics Engineering
    Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.