WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/2

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Accurate Edge Detection With Support of Reflectance Transformation Imaging
(Institute of Electrical and Electronics Engineers Inc., 2022) Kaya, Burhan; Durdu, Akif
Reflectance Transformation Imaging (RTI) is a method of photographing an object that reveals details that are invisible to the naked eye. The input of RTI consists of a series of images captured by a fixed positioned camera and each illuminated from a known and different direction by lights. Reflection Transform Imaging is widely used to produce quality models from multi-light image data. It is frequently preferred for various studies in the field of cultural heritage. For the first time in this paper, the RTI photographing method has been used outside of its traditional using way. It is used to solve the well-known problem of edge detection. Reflection transform cannot be used actively, because it is difficult to create an RTI experimental environment in daily life. However, under certain conditions, the approaches mentioned in this paper will be used in daily life, from the analysis of images in every field. In this paper, the ideas that it can be applied in every partially controlled area that needs high resolution object detection are discussed. With the method mentioned in this statement, a new approach has been proposed and proven based on RTI basics for edge and corner detections. © 2022 IEEE.
Citation - WoS: 3
Citation - Scopus: 4
Achievable Rate Analysis for Two-Way Relay Non-Orthogonal Multiple Access Systems
(IEEE, 2021) Özdemir, Özgür
This paper investigates the performance of a non-orthogonal multiple access (NOMA) based two-way relaying system where the users want to exchange independent messages with the help of a decode-and-forward relay. We consider transmission over three phases where the first and second phases are allocated to the transmissions of the users and after detection the relay applies superposition coding and transmits the network encoded symbol to the users in the third phase. Exact analytical expressions are derived to characterize the achievable average rate of the system over independent Rayleigh fading channels. Computer simulations are also presented to confirm the theoretical analysis. Analytical and simulation results show that the proposed three-phase two-way relaying scheme with NOMA outperforms the two-phase and four-phase NOMA-based two-way relaying scenarios in terms of achievable average rate.
Citation - Scopus: 1
Achievable Rate of Noma-Based Cooperative Communication Systems With Best Relay Selection Over Cascaded Rayleigh Fading Channels
(IEEE, 2020) Özdemir, Özgür
In this paper, the achievable rate analysis of NOMA-based cooperative communication systems with best relay selection is studied. The cascaded Rayleigh fading channels are considered since investigations have shown that cascaded channel structure agree better with mobile network models such as inter-vehicular communication systems. A cooperative network where a source terminal communicates with a destination directly and through a selected relay among K relays is considered and the achievable average rate of this system using NOMA is found by computer simulations. The obtained results for cascaded Rayleigh fading channels in case of decode and forward protocol have shown that the average rate is decreased as the cascading degree increases. It has been also seen that for a given cascading degree the average rate performance of the system is increased when the number of total relays is increased.
Aligning Objects as Preprocessing Combined With Imitation Learning for Improved Generalization
(Institute of Electrical and Electronics Engineers Inc., 2024) Barstugan, Mucahid; Masuda, Shimpei; Sagawa, Ryusuke; Kanehiro, Fumio
Imitation learning method transfers human behavior to the robots or machines. This method aims to allow robots or machines to learn by observing tasks performed by human operators and imitating these tasks, rather than direct programming. ACT as an imitation learning method shows the high capability for automating dexterous manipulation tasks. From the viewpoint of industrial application, pose of the target object will be varied. However, even if only for the initial object pose variation, imitation learning method like ACT usually needs a lot of demonstration data that covers pose variation to train the policy that can generalize for. Collecting large demonstration dataset takes many efforts. This study created an object pick-and-place controller to eliminate pose variation as a preprocess step with YOLOv8, which is a recent object detection technique. The preprocess step automatically moves the object to a specific position and eliminates the pose variation. We show that our system effectiveness on the randomly placed bag opening task that requires both generalization for object pose variation and dexterous bimanual manipulation. The bag opening task was conducted with ACT and preprocess applied ACT methods, and the results were evaluated to examine the effect of the preprocess method to generalization process.
Applying Feng Shui to Enhance Interior Quality in Restaurants: A Comparative Study of Spatial Perception Between Architects and Non-Architects
(Emerald Publishing, 2025) Erdogan, E.; Gokdemir, G.; Erdoğan, H.A.
Purpose – This study aims to explore perceptual similarities and differences between architects and non-architects regarding restaurant interiors designed according to the Five Elements principle of Feng Shui through a mixed-method approach. It investigates whether such spaces can achieve a shared aesthetic appeal, supporting the identification of design principles that contribute to creating high-quality interiors broadly appreciated by users. Design/methodology/approach – This study employed a mixed-methods approach integrating qualitative and quantitative analyses. A total of 120 participants (60 architects and 60 non-architects) evaluated 20 visual stimuli, generated from four real restaurant interiors modelled according to Feng Shui principles, based on general aesthetics, liking and warmth. Findings – The results revealed significant divergences between architects and non-architects in their evaluations of general aesthetics, liking and warmth. However, both groups exhibited a notable consensus regarding modern restaurant interiors that incorporated all Five Elements, highlighting the principle’s capacity to promote shared aesthetic appreciation within these settings. Practical implications – The findings demonstrate that balanced Feng Shui principles can enhance user satisfaction, support employee well-being and strengthen restaurant performance, thereby offering applicable design principles for designers and hospitality professionals. Social implications – The findings indicate that designs reinforcing perceptions of comfort, warmth and shared appreciation can support users’ well-being, encourage positive social interactions and enhance spatial experience, particularly in the context of hospitality. Originality/value – This pioneering empirical study in environmental psychology evaluates the influence of Feng Shui on spatial perception, highlighting its potential as a framework for creating high-quality spaces that foster shared appreciation among diverse user groups. By adopting a mixed-methods strategy, it aligns with the framework of evidence-based design (EBD) and extends its application specifically to restaurant interiors. © 2025 Emerald Publishing Limited
Citation - WoS: 11
Citation - Scopus: 19
Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set
(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, Murat
Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.
Citation - WoS: 29
Citation - Scopus: 41
Artificial Potential Field Algorithm for Obstacle Avoidance in Uav Quadrotor for Dynamic Environment
(Ieee, 2021) Ma'arif, Alfian; Rahmaniar, Wahyu; Marquez Vera, Marco Antonio; Nuryono, Aninditya Anggari; Majdoubi, Rania; Cakan, Abdullah
Artificial potential field (APF) is the effective real-time guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve one-obstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON) and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems.
Autogenous Self-Healing Assessment of 1-Year Cementitious Composites
(Springer Science and Business Media B.V., 2021) Yildirim, Gurkan; Ulugol, Huseyin; Ozturk, Oguzhan; Sahmaran, Mustafa
Traditional concrete materials are prone to cracking and as cracks form, durability issues arise which reduce the expected service life of the materials followed by structures incorporating them. This, in many occasions, may lead to repetitive repair and maintenance or even re-construction of certain structural/non-structural sections and structures. Thus, it is highly desirable to reduce the chance and/or further development of cracking. Engineered Cementitious Composites (ECC) are feasible materials to suppress cracking formation and progression through their strain-hardening response under uniaxial tensile loading conditions. Even at the stage of failure, these materials exhibit micron-size cracks which significantly improve the capability to resist against detrimental durability issues. Moreover, these microcracks are constantly reported to be closed through autogenous healing mechanisms with no external interference from outside which significantly improve the mechanical and durability performance and service life of these materials and structures incorporating them. However, the performance of autogenous self-healing in ECC is called into question, especially for late-age specimens since reactions which produce products to plug the micro-size cracks stabilize as the specimens get more and more mature. To clarify this subject, in this study, 1-year-old specimens produced from ECC mixtures incorporated with different mineral admixtures (i.e. Class-F fly ash and ground granulated blast furnace slag) were tested for their self-healing performance. For self-healing evaluation, specimens which were severely preloaded for creating microcracks, were subjected to four different curing conditions which included "Water", "Air", "CO2-water" and "CO2-air" for 90 additional days beyond initial 1 year. Tests used for self-healing assessments were electrical impedance (EI) and rapid chloride permeability (RCP). Results indicate that water is a must-have component for enhanced autogenous self-healing efficiency. "CO2-Water" curing results in the most effective self-healing performance regardless of the composition of ECC mixtures. By properly adjusting mixture proportions and curing conditions, microcracks as large as nearly half a millimeter (458 mu m) can be healed in only 30 days of further curing. Overall, results clearly suggest that late-age autogenous self-healing capability of ECC can be made as effective as the early-age with proper further environmental conditioning and mixture design.
Citation - Scopus: 1
Automatic Control of Recirculation System for Respiratory Control
(Springer International Publishing Ag, 2023) Jovanovic, Milos; Dihovicni, Djordje; Aksoy, Muharrem Hilmi
The issue of providing fresh air and technologies for its maintenance within physiologically acceptable references represents an active field of innovative engineering in the field of medicine, industry, army, space program, sports and lately and everyday life of people in cities. The problem-solving approach is generally divided into the construction of personal devices, for living in environments with reduced oxygen concentration and/or increased concentration of harmful elements that would lead to disruption of vital functions and system solutions for air recovery and ventilation in closed indoor units. Although 400 years have passed since the first technical concept, this area is still a challenge to optimize systems and devices and enable people to realize their activities in all potentially and real risk areas, in terms of maintaining respiratory function and metabolism without negative effects and increased efforts as close as possible to the stay in the natural environment for which man is prepared with his biological apparel.
Citation - WoS: 25
Citation - Scopus: 36
Automatic Detection of Power Quality Disturbance Using Convolutional Neural Network Structure With Gated Recurrent Unit
(Hindawi Limited, 2021) Yiğit, E.; Özkaya, U.; Öztürk, Ş.; Singh, D.; Gritli, H.
Power quality disturbance (PQD) is essential for devices consuming electricity and meeting today's energy trends. This study contains an effective artificial intelligence (AI) framework for analyzing single or composite defects in power quality. A convolutional neural network (CNN) architecture, which has an output powered by a gated recurrent unit (GRU), is designed for this purpose. The proposed framework first obtains a matrix using a short-time Fourier transform (STFT) of PQD signals. This matrix contains the representation of the signal in the time and frequency domains, suitable for CNN input. Features are automatically extracted from these matrices using the proposed CNN architecture without preprocessing. These features are classified using the GRU. The performance of the proposed framework is tested using a dataset containing a total of seven single and composite defects. The amount of noise in these examples varies between 20 and 50 dB. The performance of the proposed method is higher than current state-of-the-art methods. The proposed method obtained 98.44% ACC, 98.45% SEN, 99.74% SPE, 98.45% PRE, 98.45% F1-score, 98.19% MCC, and 93.64% kappa metric. A novel power quality disturbance (PQD) system has been proposed, and its application has been represented in our study. The proposed system could be used in the industry and factory. © 2021 Enes Yi?it et al.
Citation - WoS: 1
Citation - Scopus: 1
Bi-Objective Optimization for Joint Production Scheduling and Distribution Problem With Sustainability
(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Yağmur, Ece; Kesen, Saadettin Erhan
This paper considers joint production and distribution planning problem with environmental factors. While the production phase of the problem consists of job shop production environment running under Just-In-Time (JIT) philosophy, the distribution phase involves a heterogeneous fleet of vehicles with regards to capacity and fuel consumption rate. Therefore, we tackle two well-known problems in Operations Research terminology which are called machine scheduling and vehicle routing problems. The joint problem is formulated as a bi-objective structure, the first of which is to minimize the maximum tardiness, the second of which aims to minimize the total amount of CO2 emitted by the vehicles. Orders are required to be consolidated to reduce the traveling time, distance, or cost. An increase in the vehicle capacity results in a higher possibility of consolidation, but in this case, the amount of CO2 emission that the vehicle emits into the air will also increase. Having shown that two objectives are conflicting in an illustrative example, we formulate the problem as amixed integer programming (MIP) formulation and use an Augmented Epsilon Constraint Method (AUGMECON) for solving the bi-objective model. On randomly generated test instances, the applicability of the MIP model through the use of AUGMECON is reported.
Business Models for Electric Vehicle Fixed Charging Station Infrastructure With Commercial and Non-Commercial Uses
(Springer international Publishing Ag, 2023) Erdes, Hakan; Kesen, Saadettin Erhan
This study investigates how the resource utilization of electric vehicle (EV) charging infrastructure can be improved with various business model proposals within the scope of the sharing economy and aims to use resources more efficiently by dividing EVs in three categories: non-commercial, commercial and contracted-commercial. Different charging technologies such as DC charger and Battery Swapping Stations (BSSs) are included in the models presented, and 0-1 MILP formulations are introduced for two business models. Models mainly focus on possible agreements between a Logistics Company (LC) and a Charge Point Operator Company (CPOC) on the basis of certain double-sided conditions. Following the illustrative example given for a better understanding of the problem definition and the problem environment, experimental studies are included. As a result of the experimental studies, sensitivity analysis is given for the effect of executing the agreements under different conditions on the additional cost to be paid. The results revealed that the agreements to be made are shaped according to the different charging technology characteristics, the number of customers and the conditions of the Logistics Companies.
Citation - WoS: 3
Citation - Scopus: 12
Camera/Lidar Sensor Fusion-Based Autonomous Navigation
(Institute of Electrical and Electronics Engineers Inc., 2024) Yusefi, A.; Durdu, A.; Toy, I.
This research presents a novel approach for autonomous navigation of Unmanned Ground Vehicles (UGV) using a camera and LiDAR sensor fusion system. The proposed method is designed to achieve a high rate of obstacle detection, distance estimation, and obstacle avoidance. In order to thoroughly study the form of things and decrease the problem of object occlusion, which frequently happens in camera-based object recognition, the 3D point cloud received from the LiDAR depth sensors is used. The proposed camera and LiDAR sensor fusion design balance the benefits and drawbacks of the two sensors to produce a detection system that is more reliable than others. The UGV's autonomous navigation system is then provided with the region proposal to re-plan its route and navigate appropriately. The experiments were conducted on a UGV system with high obstacle avoidance and fully autonomous navigation capabilities. The outcomes demonstrate that the suggested technique can successfully maneuver the UGV and detect impediments in actual situations. © 2024 IEEE.
Citation - WoS: 5
Cfd Modelling of Industrial Air Curtains With Heating Unit
(E D P SCIENCES, 2019) Aksoy, Muharrem Hilmi; Yağmur, Sercan; Doğan, Sercan
Industrial air curtains are used to prevent air from moving from one space to another space or to environment. The most common used type is downward-facing blower fan mounted over the entrance of a building, or an opening door between two spaces conditioned at different temperatures. In many factories and industrial buildings, heating or cooling applications are difficult due to the huge doors. These huge doors cause heat loses with convection phenomena of the inside air. In this study an air curtain having heater unit is analyzed numerically by CFD. The height of the air curtain from the bottom side is vary between 2.5 m, 3 m, 4m, 5m and 6 m mounted over the entrance door of the conditioned volume. For CFD studies proper mesh structure is created on the flow domain and Shear Stress Transport (SST) k-omega models were used in Unsteady Reynolds Averaged Navier-Stokes (URANS) computations. The blowing temperature of the air curtain has adjusted to 60 degrees C with the inside temperature was aimed to kept at +7 degrees C while the outside temperature was-5 degrees C. It is found that there is less flow occurred to the environment from conditioned volume at 2.5 3, 4 and 5 meter height cases. In these cases, the air curtain also contributes the heating of the conditioned room. But some ratio of the air flows through the atmosphere and the room cannot kept at the +7 degrees C initial temperature at 6 m case. It is also found that the heating ratio at different blowing heights differs between 0,89-1,98 comparing the case without an air curtain.
Citation - WoS: 1
Citation - Scopus: 2
Characterization of Clay Deposits in the Ceyhan Plain (Eastern Cilicia, Turkey): Integrated Petrographic, Mineralogical, Geochemical, and Geospatial Analysis for Provenance Studies of Ancient Ceramics
(Elsevier, 2025) Haciosmanoglu, Sinem; Kibaroglu, Mustafa; Ercan, Hatice Unal; Opitz, Joachim
This study presents the results of a comprehensive multi-analytical investigations of clay deposits from the Ceyhan Plain in Eastern Cilicia, Southern Turkey, aimed at establishing a robust framework for provenance studies of ancient ceramics in the region. A total of 52 clay samples were systematically collected and analyzed using optical microscopy for petrography (OM), X-Ray powder diffraction for mineral phase analysis (XRPD), laser ablation inductively coupled plasma mass spectrometry for geochemical (LA-ICP-MS), and geographic information systems for Geospatial Analysis (GIS) to examine the compositional characteristics and define reference clay groups for archaeometric research. Briquette samples were prepared from the collected clay to facilitate direct geochemical and petrographic comparisons with archaeological ceramics and clay-based artifacts. The results demonstrate that the clay deposits are predominantly calcareous, with contribution from mafic rocks and minor input from ultramafic sources. The compositional diversity is primarily shaped by sedimentation processes associated with the Ceyhan River, while smaller drainage systems influence localized variations. Four distinct clay reference groups were identified: the Ceyhan River Clay Group, Imamoglu-Kozan Clay Group, Kadirli-Savrun Clay group, and Osmaniye-Iliksu Clay Group. These groups serve as reference materials for comparative studies of ancient ceramics enabling deeper insights into local ceramic production strategies and exchange networks within the Eastern Cilicia Plain and its surroundings. They also serve as essential materials for provenance analyses of ceramics and other clay-based artifacts in broader regional contexts, including the Mediterranean, Anatolia, and the Levant. The study further underscores the importance of a multidisciplinary approach and the integration of excavation data with landscape and systematic raw material analysis to achieve a more nuanced understanding of ancient production strategies and resource management.
Citation - WoS: 1
Citation - Scopus: 1
Classification of Mammography Images by Transfer Learning
(IEEE, 2020) Solak, Ahmet; Ceylan, Rahime
Breast cancer is the most common cancer type in women worldwide. Diagnosis and early detection of cancer by mammography images are of great importance in cancer treatment. The use of deep learning in Computer Assisted Diagnostic systems has gained a great momentum especially since 2012. In this study, benign and malignant mass images were reproduced with data augmentation and the data sets obtained were classified with deep learning networks. In this study, a scratch Convolutional Neural Network (CNN) architecture was created and transfer learning was realized with different network models which trained on IMAGENET images. In the transfer learning section, separate training results were obtained by performing feature extraction and fine tuning of network parameters. As a result of the study, the best results were obtained with MobileNet, NASNetLarge and InceptionResNetV2 models which are used in transfer learning models.
Classification of Medical Thermograms Using Transfer Learning
(IEEE, 2020) Örnek, Ahmet Haydar; Ceylan, Murat
Thermal imaging has been used for decades to monitor the health status of neonates as an non-invasive and non-ionizing imaging technique. Applications such as thermal asymmetry and disease analysis can be performed by applying deep learning methods to thermal imaging technique. However, thousands of different images are needed to perform analyzes with deep learning methods. It takes many years to create data sets with thousands of different images due to feeding time, medication time and instant baby care in the neonatal intensive care unit. In this study, a unhealthy-healthy classification was performed using thermal images obtained from the Selcuk University, Faculty of Medicine, Neonatal Intensive Care Unit for one year. Transfer learning method has been used to overcome the lack of data problem. When VGG16 model was used for transfer learning, the results were obtained as 100% sensitivity and 94.73% specificity. This result shows that thermal imaging and transfer learning method can be used in early diagnosis of diseases.
Çok Katlı Mekanların Navigasyonu için Bluetooth Tabanlı Beacon Teknolojisi: Pamukkale Üniversitesi Hastanesi Örneği
(2024) Çakır, Recep; Çiçekdemir, Çağrı; Doğanalp, Serkan
İnsanlar alışveriş merkezi, hastane, metro, otopark gibi büyük yapıdaki kapalı alanlarda çoğu kez yönlerini bulmakta zorluk çekmektedirler. Bu tip kapalı alanlarda GNSS teknolojisinin de yetersiz kalmasından dolayı farklı teknolojilerle navigasyon ihtiyacı giderilmektedir. Bu teknolojilerden biri de Bluetooth tabanlı Beacon teknolojisidir. Bu çalışmada Beacon teknolojisi kullanılarak Pamukkale Üniversitesi Hastanesi için navigasyon amaçlı Android ve iOS tabanlı bir mobil uygulama geliştirilmiş ve mobil platformlarda yayınlanarak insanların kullanımına sunulmuştur. Çalışmada hastane katlarının haritası çıkartılarak her bir kata konum belirleme amaçlı Beacon ağı kurulmuştur. Hastanenin kat haritaları, ITRF datumunda ve UTM projeksiyonunda elde edilmiştir. Beacon’lar sinyal yapısına göre her bir kata gruplandırılarak yerleştirilmiştir. Beacon cihazlarının konumlandırılması hastanenin fiziksel durumuna göre 7 ile 12 metre arasında değişkenlik gösteren aralıklarla Yakınlık (Proximity) algoritmasına göre yapılmıştır. Uygulamada Dijkstra algoritması en kısa yol algoritması olarak seçilmiş ve navigasyon testleri gerçekleştirilmiştir. Yapılan navigasyon testlerinde %95 başarı elde edilmiştir.
Comparing Image Similarity Methods for Face Images
(Institute of Electrical and Electronics Engineers Inc., 2021) Örnek, Ahmet Haydar; Çelik, Mustafa; Alper, Ozan Can
In order to realize real-time computer vision projects we need to avoid time consuming operations such as more inference for deep learning. Our current application uses face images to decide whether there is a mask on the face so as to prevent unhealthy situations in view of epidemic. Since frames are sequentially coming it is necessary to eliminate similar frames to avoid more inference. We show how to measure a similarity between two frames by comparing traditional and deep learning based methods in this study. This study shows that deep learning based method is more efficient than traditional methods when comparing images. © 2021 IEEE.
Comparison of Far Field and Near Field Values of Skin Tissue Measured Using Microstrip Antenna Structure
(Institute of Electrical and Electronics Engineers Inc., 2022) Toprak, Rabia; Gültekin, Seyfettin Sinan; Uzer, Dilek
Pathology science has an important place in the medical field. Its importance is increasing day by day because it evaluates the information about diseases at the cellular level. The reports prepared from the tissue samples examined by the pathologists contain very important information for both the patient and the doctor. This information may include the level of the disease and the mode of treatment. Therefore, the time to reach the pathological reports is important. Microstrip patch antennas are used for various purposes in the biomedical field. In this study, the far and near field outputs of the evaluations of the pathological tissue samples were tested with the microstrip patch antenna structure. For this, a microstrip patch antenna with an operating frequency of 2.45 GHz was used. Pathological tissue samples were modeled in the free-space measurement technique created using the antenna structure. The electric field and scattering parameter values obtained as a result of the simulations using the Ansys HFSS program were evaluated for the near and far field. When the evaluation results are examined, it has been shown that near field measurements for electric field data and far field measurements for scattering parameter data are more efficient. © 2022 IEEE.

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