Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299305
Elif Cansu Kiliçer, Şevval Ay, V. Akşahi̇n
Systems that determine identity with individual features are called biometric systems. Today, voice, fingerprint, retina/iris, and facial recognition systems are some of the biometric identification methods. These methods have become replicable with the advancement of technology. Accordingly, Electrocardiogram (ECG) signals are universal, unique, easy to measure, and can only be obtained from living people. For this reason, it can be accepted that ECG is an effective method that can be used to prevent counterfeiting among biometric identification methods. In this study, an algorithm that can make identification via ECG is proposed. Within the scope of the study, the time and time-frequency domain analyzes of the ECG signals obtained from the PhsiyoNet database are performed then various features are determined. These determined features were classified using machine learning methods. The performance of the developed algorithm has been calculated as 100% accuracy, 100% specificity, and 100% sensitivity.
{"title":"Identification using ECG Signals","authors":"Elif Cansu Kiliçer, Şevval Ay, V. Akşahi̇n","doi":"10.1109/TIPTEKNO50054.2020.9299305","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299305","url":null,"abstract":"Systems that determine identity with individual features are called biometric systems. Today, voice, fingerprint, retina/iris, and facial recognition systems are some of the biometric identification methods. These methods have become replicable with the advancement of technology. Accordingly, Electrocardiogram (ECG) signals are universal, unique, easy to measure, and can only be obtained from living people. For this reason, it can be accepted that ECG is an effective method that can be used to prevent counterfeiting among biometric identification methods. In this study, an algorithm that can make identification via ECG is proposed. Within the scope of the study, the time and time-frequency domain analyzes of the ECG signals obtained from the PhsiyoNet database are performed then various features are determined. These determined features were classified using machine learning methods. The performance of the developed algorithm has been calculated as 100% accuracy, 100% specificity, and 100% sensitivity.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129469971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299288
Sevilay Çetin, Merve Başaranbilek, Hilal Er, Emel Bakay, M. Seydibeyoğlu, N. Topaloglu
Tissue damage or disruption of tissue continuity is called as wound. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial strain that can cause infection on the wounds, and especially control of the infections plays an important role in the wound healing process. Wound dressings are an alternative method that can be used to shorten wound healing time. At the same time, bacterial infection is tried to be prevented in the wound area by adding antibacterial materials to the contents of the dressing materials. Light applications of certain wavelengths are another method that shows antibacterial effect used in wound infection treatment. In this study, wound dressings were produced by electrospinning method using chitosan (CHT) and thermoplastic polyurethane (TPU) materials. Also, the synergistic antibacterial effects of wound dressings and 808 nm laser light were investigated on Methicillin-resistant Staphylococcus aureus (MRSA). It was observed that the wound dressings produced with TPU and CHT have antibacterial properties and the laser light at 808 nm of wavelength increases the antibacterial efficacy of the dressings. TPU and TPU-CHT wound dressings have a synergistic antibacterial effect when induced with 808 nm laser light. Thus, light induced nanofibers can be an efficient tool to improve the treatments of infected wounds.
{"title":"Light-Induced Bactericidal Effect of Wound Dressings Produced from Thermoplastic Polyurethane and Chitosan","authors":"Sevilay Çetin, Merve Başaranbilek, Hilal Er, Emel Bakay, M. Seydibeyoğlu, N. Topaloglu","doi":"10.1109/TIPTEKNO50054.2020.9299288","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299288","url":null,"abstract":"Tissue damage or disruption of tissue continuity is called as wound. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial strain that can cause infection on the wounds, and especially control of the infections plays an important role in the wound healing process. Wound dressings are an alternative method that can be used to shorten wound healing time. At the same time, bacterial infection is tried to be prevented in the wound area by adding antibacterial materials to the contents of the dressing materials. Light applications of certain wavelengths are another method that shows antibacterial effect used in wound infection treatment. In this study, wound dressings were produced by electrospinning method using chitosan (CHT) and thermoplastic polyurethane (TPU) materials. Also, the synergistic antibacterial effects of wound dressings and 808 nm laser light were investigated on Methicillin-resistant Staphylococcus aureus (MRSA). It was observed that the wound dressings produced with TPU and CHT have antibacterial properties and the laser light at 808 nm of wavelength increases the antibacterial efficacy of the dressings. TPU and TPU-CHT wound dressings have a synergistic antibacterial effect when induced with 808 nm laser light. Thus, light induced nanofibers can be an efficient tool to improve the treatments of infected wounds.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"87 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130667952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299264
Mehmet Akif Ozdemir, Deniz Hande Kisa, Onan Guren, Aytuğ Onan, A. Akan
The Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications.
{"title":"EMG based Hand Gesture Recognition using Deep Learning","authors":"Mehmet Akif Ozdemir, Deniz Hande Kisa, Onan Guren, Aytuğ Onan, A. Akan","doi":"10.1109/TIPTEKNO50054.2020.9299264","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299264","url":null,"abstract":"The Electromyography (EMG) signal is a nonstationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and amputee device controls. In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 participants. The obtained sEMG signals have been segmented into sections where each movement was found. Then, spectrogram images of the segmented sEMG signals have been created by means of ShortTime Fourier Transform (STFT). The created colored spectrogram images have trained with 50-layer Convolutional Neural Network (CNN) based on Residual Networks (ResNet) architecture. Owing to the proposed method, test accuracy of 99.59% and F1 Score of 99.57% have achieved for 7 different hand gesture classifications.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125140680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299227
Sevde Omeroglu, Rahmetullah Varol, Z. Karavelioglu, Aslıhan Karadağ, Y. Başbınar, Muhammed Enes ORUC, H. Uvet
Knowing the mechanical properties of cells is very important in cell detection, analysis of cell activities, diagnosis and drug treatment. The determination of cell stiffness, which used effectively in cell analysis, is carried out with different measurement techniques. In this study, the stiffness of cells is determined by comparison to the displacement of polystyrene microparticles induced by vibration generated by piezoelectric transducers. The difference of stiffness of the cells and polystyrene microparticles is measured using a digital holographic imaging technique.
{"title":"Determination of Cell Stiffness Using Polymer Microbeads as Reference","authors":"Sevde Omeroglu, Rahmetullah Varol, Z. Karavelioglu, Aslıhan Karadağ, Y. Başbınar, Muhammed Enes ORUC, H. Uvet","doi":"10.1109/TIPTEKNO50054.2020.9299227","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299227","url":null,"abstract":"Knowing the mechanical properties of cells is very important in cell detection, analysis of cell activities, diagnosis and drug treatment. The determination of cell stiffness, which used effectively in cell analysis, is carried out with different measurement techniques. In this study, the stiffness of cells is determined by comparison to the displacement of polystyrene microparticles induced by vibration generated by piezoelectric transducers. The difference of stiffness of the cells and polystyrene microparticles is measured using a digital holographic imaging technique.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128121645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299252
Z. Karavelioglu, Rahmetullah Varol, Sevde Omeroglu, Hanife E. Meco, Yagmur Buyrukbilen, Y. Başbınar, M. E. Oruc, H. Uvet
Cell stiffness that can be measured accordingly elasticity modulus is an important biomechanical feature that plays a one-to-one role on the basic features of the cell, such as migration and proliferation, and this feature is significantly affected by the characteristic of the cytoskeleton. Reactive Oxygen Species (ROS) are side-products formed as a result of the cell’s general metabolic activities. Cells have a very effective antioxidant defense to deactivate the toxic effect of ROS however, oxidative stress at abnormal levels significantly damages cellular balance. Many conditions such as inflammation, neurodegenerative and cardiovascular diseases and aging are associated with oxidative stress. Besides, oxidative stress is one of the parameters that affect the biomechanical behavior of the cell, but the mechanism of this effect still remains a mystery. In this study, oxidative stress was mimicked on Human Umbilical Vein Endothelial (HUVEC) cells by using H2 O2 and the effect of this situation on cell stiffness and morphological structure was investigated interferometrically for the first time. The changes that occurred in the cell stiffness were determined by calculating the elasticity modules of the cells. Cells were exposed to H2 O2 for 24 hours at 0.5 mM and 1 mM concentrations, and as a result, cell stiffness was shown to decrease due to increased H2 O2 concentration.
{"title":"Interferometric Investigation of Cell Stiffness and Morphology on Oxidative Stress- Induced Human Umbilical Vein Endothelial Cells (HUVEC)","authors":"Z. Karavelioglu, Rahmetullah Varol, Sevde Omeroglu, Hanife E. Meco, Yagmur Buyrukbilen, Y. Başbınar, M. E. Oruc, H. Uvet","doi":"10.1109/TIPTEKNO50054.2020.9299252","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299252","url":null,"abstract":"Cell stiffness that can be measured accordingly elasticity modulus is an important biomechanical feature that plays a one-to-one role on the basic features of the cell, such as migration and proliferation, and this feature is significantly affected by the characteristic of the cytoskeleton. Reactive Oxygen Species (ROS) are side-products formed as a result of the cell’s general metabolic activities. Cells have a very effective antioxidant defense to deactivate the toxic effect of ROS however, oxidative stress at abnormal levels significantly damages cellular balance. Many conditions such as inflammation, neurodegenerative and cardiovascular diseases and aging are associated with oxidative stress. Besides, oxidative stress is one of the parameters that affect the biomechanical behavior of the cell, but the mechanism of this effect still remains a mystery. In this study, oxidative stress was mimicked on Human Umbilical Vein Endothelial (HUVEC) cells by using H2 O2 and the effect of this situation on cell stiffness and morphological structure was investigated interferometrically for the first time. The changes that occurred in the cell stiffness were determined by calculating the elasticity modules of the cells. Cells were exposed to H2 O2 for 24 hours at 0.5 mM and 1 mM concentrations, and as a result, cell stiffness was shown to decrease due to increased H2 O2 concentration.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121385505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299221
Abdullah Nuri Somuncuoğlu, V. Purutçuoğlu, F. Arı, D. Gökçay
Stress is an important problem for people that causes health problems and economic losses. When it becomes chronic, it paves the way for many diseases. Studies in this area have made significant progress in measuring stress levels with the help of data from wearable devices and sensors. In this study, using supervised deep learning methods, we worked on the detection of pupil dilation, which is accepted as one of the stress indicators. In our experiment, two different films containing positive and funny scenes and negative and stressful scenes were shown to the participants. Meanwhile, the pupil diameter was measured continuously. After the obtained signals were cleared of noises, deep learning studies were carried out on them. With these experiments, the effect of different activation functions used in hidden layers along with the different number of hidden layers and neuron numbers on learning were examined. After the trials with Hyperbolic Tangent, ReLU and Swish activation functions, the highest accuracy for classifying the stress of the participants from their pupil responses was obtained with the Swish activation function with 90.79%.
{"title":"Investigation on the Use of Hidden Layers, Different Numbers of Neurons and Different Activation Functions to Detect Pupil Dilation Responses to Stress","authors":"Abdullah Nuri Somuncuoğlu, V. Purutçuoğlu, F. Arı, D. Gökçay","doi":"10.1109/TIPTEKNO50054.2020.9299221","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299221","url":null,"abstract":"Stress is an important problem for people that causes health problems and economic losses. When it becomes chronic, it paves the way for many diseases. Studies in this area have made significant progress in measuring stress levels with the help of data from wearable devices and sensors. In this study, using supervised deep learning methods, we worked on the detection of pupil dilation, which is accepted as one of the stress indicators. In our experiment, two different films containing positive and funny scenes and negative and stressful scenes were shown to the participants. Meanwhile, the pupil diameter was measured continuously. After the obtained signals were cleared of noises, deep learning studies were carried out on them. With these experiments, the effect of different activation functions used in hidden layers along with the different number of hidden layers and neuron numbers on learning were examined. After the trials with Hyperbolic Tangent, ReLU and Swish activation functions, the highest accuracy for classifying the stress of the participants from their pupil responses was obtained with the Swish activation function with 90.79%.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128844501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299223
Gizem Çoban, Faruk Aktas
Smart exercises are reliable and motivating activities that reduce the possibility of injury, and improve the muscle-bone system. When it is made unconscious movements, it can cause either harm to her/his body or slow down the development of exercise. In this study, it is aimed to study both orthopedic patients and athletes. Thanks to the developed algorithm and equipment, the main theme is doing the exercise correctly in given plan within the personal limits. Depending on the purpose of use, according to the program planned by the doctor or trainer, the system gives a notification when a person (after wears the elbow / knee brace) performs the desired movement with the desired degree. The person understands that the movement has been made successfully from this alert. The processes that continue until reaching the result of the exercise can be stored simultaneously on the ThingSpeak Cloud Platform and can be monitored on the linear graph with the via wireless network. With the developed mobile application, the person can instantly see the angle of movement and the application can instantly send and / or call the doctor / trainer. In this system design based on the Internet of Things, the NodeMCU 12E board including ESP8266 Wi-Fi module, knee pad / elbow pad, Flex sensor were used.
{"title":"IoT-Based Motion Tracking System for Orthopedic Patients and Athletes","authors":"Gizem Çoban, Faruk Aktas","doi":"10.1109/TIPTEKNO50054.2020.9299223","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299223","url":null,"abstract":"Smart exercises are reliable and motivating activities that reduce the possibility of injury, and improve the muscle-bone system. When it is made unconscious movements, it can cause either harm to her/his body or slow down the development of exercise. In this study, it is aimed to study both orthopedic patients and athletes. Thanks to the developed algorithm and equipment, the main theme is doing the exercise correctly in given plan within the personal limits. Depending on the purpose of use, according to the program planned by the doctor or trainer, the system gives a notification when a person (after wears the elbow / knee brace) performs the desired movement with the desired degree. The person understands that the movement has been made successfully from this alert. The processes that continue until reaching the result of the exercise can be stored simultaneously on the ThingSpeak Cloud Platform and can be monitored on the linear graph with the via wireless network. With the developed mobile application, the person can instantly see the angle of movement and the application can instantly send and / or call the doctor / trainer. In this system design based on the Internet of Things, the NodeMCU 12E board including ESP8266 Wi-Fi module, knee pad / elbow pad, Flex sensor were used.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"81 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116341032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299215
Ali Serener, Sertan Serte
COVID-19 is a highly infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2. It can lead to cough and fever and in some cases severe pneumonia. It is generally detected by reverse-transcription polymerase chain reaction and computed tomography scans. However, as it is a lung disease, it has common symptoms with other respiratory diseases. This necessitates us to carefully differentiate COVID-19 from such diseases during the diagnosis. This work aims to do that with the help of several deep learning architectures and chest radiographs. It specifically focuses on differentiating COVID-19 from pneumonia, pleural effusion and lung mass. During this analysis, it is shown that we can differentiate COVID-19 from other respiratory diseases using various deep learning architectures. It is further shown that ResNet-18 architecture produces the best overall performance in three scenarios of experiments.
{"title":"Deep learning to distinguish COVID-19 from other lung infections, pleural diseases, and lung tumors","authors":"Ali Serener, Sertan Serte","doi":"10.1109/TIPTEKNO50054.2020.9299215","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299215","url":null,"abstract":"COVID-19 is a highly infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2. It can lead to cough and fever and in some cases severe pneumonia. It is generally detected by reverse-transcription polymerase chain reaction and computed tomography scans. However, as it is a lung disease, it has common symptoms with other respiratory diseases. This necessitates us to carefully differentiate COVID-19 from such diseases during the diagnosis. This work aims to do that with the help of several deep learning architectures and chest radiographs. It specifically focuses on differentiating COVID-19 from pneumonia, pleural effusion and lung mass. During this analysis, it is shown that we can differentiate COVID-19 from other respiratory diseases using various deep learning architectures. It is further shown that ResNet-18 architecture produces the best overall performance in three scenarios of experiments.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116984018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299283
K. Ateş, Can Yeter, ve Şükrü Özen
With the development of new generation communication systems, antenna technologies used in these areas and their effects on human health are also examined. In this study, a 4×4 MIMO antenna was designed for IEEE 802.11a applications through the finite element method (FEM) based electromagnetic simulation software. Also, its dosimetric effects on tissue were investigated. The proposed antenna includes four antennas operating at 5 GHz. It was designed on a 74 mm ×130 mm dielectric material with a frame height of 5 mm to meet the trend of current phones. The SAR distribution in the head and hand model caused by the antenna model were obtained as 1.4 W/kg and 0.7 W/kg for 1 gr average tissue, respectively.
{"title":"Investigation of the Electromagnetic Dosimetry Characteristics of a 4 × 4 MIMO Antenna for WLAN Applications","authors":"K. Ateş, Can Yeter, ve Şükrü Özen","doi":"10.1109/TIPTEKNO50054.2020.9299283","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299283","url":null,"abstract":"With the development of new generation communication systems, antenna technologies used in these areas and their effects on human health are also examined. In this study, a 4×4 MIMO antenna was designed for IEEE 802.11a applications through the finite element method (FEM) based electromagnetic simulation software. Also, its dosimetric effects on tissue were investigated. The proposed antenna includes four antennas operating at 5 GHz. It was designed on a 74 mm ×130 mm dielectric material with a frame height of 5 mm to meet the trend of current phones. The SAR distribution in the head and hand model caused by the antenna model were obtained as 1.4 W/kg and 0.7 W/kg for 1 gr average tissue, respectively.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"39 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125929699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299219
S. Arıca, Tuğçe Sena Altuntaş, G. Erbay
Cancer is the one of the leading causes of death worldwide, and cancer incidence increases every year. The analysis of lesion margin is quite important to diagnose malignant and benign masses and to detect the presence and the stage of tumor invasion in case of cancer. Accordingly, the aim of the study is to visualize and quantify margin of lesions on radiological images by means of a digital computer. In this study, computed tomography (CT) images of liver have been employed for analysis because the liver has crucial tasks in our body and liver cancer-related deaths is ranked as the forth among the cancer-related deaths. The proposed method consisted of four main steps: image cropping and smoothing, specification of target lesion, the boundary detection of target lesion, and visualization and quantification of margin. First, the images were converted to gray scale. The blank regions surrounding the liver in the CT images were removed before specification of target lesion, and further were smoothed with a bilateral filter. Next, the target region was specified roughly by drawing it manually. The boundary of lesion was more precisely determined with the active contour method employing the sketched borderline as the initial curve. Next, the properties of the target region: the centroid, major axis length, and the orientation values were computed. The intensities along a line passing through the center of the tumor were obtained for eighteen different rotation angles. A pulse model was fit to each of the intensity signal corresponding to a rotation. Then, the intensity change, margin sharpness and width were acquired from the pulse approximation associated to each rotation angle. The level difference provided the intensity change, the slope of edges gave the margin sharpness, and distance between the start and end points of the pulse edge represented margin width. Besides, the inner (core) and outer diameter with respect to angle were also displayed.
{"title":"On Visualization and Quantification of Lesion Margin in CT Liver Images","authors":"S. Arıca, Tuğçe Sena Altuntaş, G. Erbay","doi":"10.1109/TIPTEKNO50054.2020.9299219","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299219","url":null,"abstract":"Cancer is the one of the leading causes of death worldwide, and cancer incidence increases every year. The analysis of lesion margin is quite important to diagnose malignant and benign masses and to detect the presence and the stage of tumor invasion in case of cancer. Accordingly, the aim of the study is to visualize and quantify margin of lesions on radiological images by means of a digital computer. In this study, computed tomography (CT) images of liver have been employed for analysis because the liver has crucial tasks in our body and liver cancer-related deaths is ranked as the forth among the cancer-related deaths. The proposed method consisted of four main steps: image cropping and smoothing, specification of target lesion, the boundary detection of target lesion, and visualization and quantification of margin. First, the images were converted to gray scale. The blank regions surrounding the liver in the CT images were removed before specification of target lesion, and further were smoothed with a bilateral filter. Next, the target region was specified roughly by drawing it manually. The boundary of lesion was more precisely determined with the active contour method employing the sketched borderline as the initial curve. Next, the properties of the target region: the centroid, major axis length, and the orientation values were computed. The intensities along a line passing through the center of the tumor were obtained for eighteen different rotation angles. A pulse model was fit to each of the intensity signal corresponding to a rotation. Then, the intensity change, margin sharpness and width were acquired from the pulse approximation associated to each rotation angle. The level difference provided the intensity change, the slope of edges gave the margin sharpness, and distance between the start and end points of the pulse edge represented margin width. Besides, the inner (core) and outer diameter with respect to angle were also displayed.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124487782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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