Pub Date : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863083
Nedim Muzoglu, Ömer Sayli, Ö. Gündoğdu, Melike Kaya Karaaslan, Hakan Oruç
In this study, we present comparison of human and software reading of contrast-detail mammography (CDMAM) phantom images for a digital mammography system. The human readings were conducted through CDMAM images using 3 Megapixel and 5 Megapixel medical monitors. The phantom was positioned between Poly methyl methacrylate (PMMA) blocks of 20mm thickness. A set of 8 digital radiographs were taken using the automated exposure control (AEC-Opdose). The phantom was slightly repositioned between each exposure, always with the smaller details close to the chest wall side. Automated readings were carried out with CDMAM 3.4 Analyser software V.2.8. (CDCOM) and human readings were evaluated by three observers. As a result of this study, CDMAM 3.4 Analyser software is able to provide results significantly better than those estimated by human observers. Additionally, human readings were better for 5 Megapixel medical monitor. We can note that quality control tests of mammography systems and clinical evaluation of mammogram should be evaluated with 5 Megapixel medical monitor.
{"title":"Comparison of automatic and manuel readings for CDMAM phantom images","authors":"Nedim Muzoglu, Ömer Sayli, Ö. Gündoğdu, Melike Kaya Karaaslan, Hakan Oruç","doi":"10.1109/TIPTEKNO.2016.7863083","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863083","url":null,"abstract":"In this study, we present comparison of human and software reading of contrast-detail mammography (CDMAM) phantom images for a digital mammography system. The human readings were conducted through CDMAM images using 3 Megapixel and 5 Megapixel medical monitors. The phantom was positioned between Poly methyl methacrylate (PMMA) blocks of 20mm thickness. A set of 8 digital radiographs were taken using the automated exposure control (AEC-Opdose). The phantom was slightly repositioned between each exposure, always with the smaller details close to the chest wall side. Automated readings were carried out with CDMAM 3.4 Analyser software V.2.8. (CDCOM) and human readings were evaluated by three observers. As a result of this study, CDMAM 3.4 Analyser software is able to provide results significantly better than those estimated by human observers. Additionally, human readings were better for 5 Megapixel medical monitor. We can note that quality control tests of mammography systems and clinical evaluation of mammogram should be evaluated with 5 Megapixel medical monitor.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130839924","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863127
Ahmet Tartar, A. Akan
Recently, the improvement of radiology systems along with the developing technologies has provided an important contribution in the diagnosis and treatment of numerous medical cases. In this study, we propose a new radiation dose measurement approach for patients and radiation workers on X-ray units. The approach is extremely important to correct the shortcomings in this area as well as to provide a “gold standard” for radiation quality control measurement of X-ray units. The implementation of the approach will allow to the elimination of concerns of patients and radiation workers about radiation. Also it will present the opportunity to work in a safe environment for radiologists and radiation workers.
{"title":"A new radiation dose measurement approach for X-ray units on biomedical technical service applications: A study of radiology","authors":"Ahmet Tartar, A. Akan","doi":"10.1109/TIPTEKNO.2016.7863127","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863127","url":null,"abstract":"Recently, the improvement of radiology systems along with the developing technologies has provided an important contribution in the diagnosis and treatment of numerous medical cases. In this study, we propose a new radiation dose measurement approach for patients and radiation workers on X-ray units. The approach is extremely important to correct the shortcomings in this area as well as to provide a “gold standard” for radiation quality control measurement of X-ray units. The implementation of the approach will allow to the elimination of concerns of patients and radiation workers about radiation. Also it will present the opportunity to work in a safe environment for radiologists and radiation workers.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129498978","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863068
Volkan Goreke, E. Uzunhisarcikli, B. Oztoprak
Nowadays the detection of cancer of the breast mass X-ray mammography is widely used by radiologists. This computer-aided system images used by physicians in the interpretation raises the accomplishments of physicians identified masses. Work on computer-aided detection systems consisting of basic image processing and classification section is still in progress. Different methods such as artificial neural networks and support vector machine structure is widely used in mass classification. Previous work in our open access has MIAS containing mass from the database and free mammogram images on image processing techniques and three texture attribute in the second degree by using statistical analysis and derived value statistics of these attributes, attributes, and type-1 fuzzy using Matlab fuzzy toolbox with statistical values inference system is designed. In this study, the standard deviation of the data set using a statistical method on each attribute data set used for type-1 system is calculated. These values are used as the footprint of the uncertainty of the type-2 system parameters. These data sets and data sets related to each piece of histogram chart with type-2 fuzzy inference system was conducted as separate software. We have tested our system type-2 fuzzy inference system has produced more successful than type-1 fuzzy inference system.
{"title":"Type-2 fuzzy inference system design for computer aided detection in mammogram image","authors":"Volkan Goreke, E. Uzunhisarcikli, B. Oztoprak","doi":"10.1109/TIPTEKNO.2016.7863068","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863068","url":null,"abstract":"Nowadays the detection of cancer of the breast mass X-ray mammography is widely used by radiologists. This computer-aided system images used by physicians in the interpretation raises the accomplishments of physicians identified masses. Work on computer-aided detection systems consisting of basic image processing and classification section is still in progress. Different methods such as artificial neural networks and support vector machine structure is widely used in mass classification. Previous work in our open access has MIAS containing mass from the database and free mammogram images on image processing techniques and three texture attribute in the second degree by using statistical analysis and derived value statistics of these attributes, attributes, and type-1 fuzzy using Matlab fuzzy toolbox with statistical values inference system is designed. In this study, the standard deviation of the data set using a statistical method on each attribute data set used for type-1 system is calculated. These values are used as the footprint of the uncertainty of the type-2 system parameters. These data sets and data sets related to each piece of histogram chart with type-2 fuzzy inference system was conducted as separate software. We have tested our system type-2 fuzzy inference system has produced more successful than type-1 fuzzy inference system.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439287","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863113
Ş. Benli, Semra Içer, Esra Özdemirci, K. Gümüş, S. Doğanay
Schizophrenia is a mental illness which usually begins in adolescence and can display important disorders of feelings, thoughts, and behavior. Delusion, hallucinations, and disorganized speech and behavior are some of the specific symptoms of schizophrenia. It is indicated that schizophrenia can be confused with other psychiatric disorders such as attention deficit hyperactivity disorder and mood disorder. Advanced magnetic resonance (MR) imaging methods are applied in the diagnosis of psychiatric illnesses due to the inadequacy of conventional anatomic imaging in the diagnosis of psychiatric disorders. Functional MRI which is an advanced MR imaging technique enables mapping of brain regions showing activation at rest or during task related events. This preliminary study investigated functional connectivity differences in some of resting state networks (anterior attention network, temporal network, audio network, and the default mode network) in early onset schizophrenia patients and children of control group (typically development) using resting state functional MRI method. This preliminary study aimed to contribute to the characterization of schizophrenia disease by identifying the specific changes that occur in the resting state networks of schizophrenia brain.
{"title":"Obtaining resting state networks in early onset schizophrenia disease by Independent Component Analysis","authors":"Ş. Benli, Semra Içer, Esra Özdemirci, K. Gümüş, S. Doğanay","doi":"10.1109/TIPTEKNO.2016.7863113","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863113","url":null,"abstract":"Schizophrenia is a mental illness which usually begins in adolescence and can display important disorders of feelings, thoughts, and behavior. Delusion, hallucinations, and disorganized speech and behavior are some of the specific symptoms of schizophrenia. It is indicated that schizophrenia can be confused with other psychiatric disorders such as attention deficit hyperactivity disorder and mood disorder. Advanced magnetic resonance (MR) imaging methods are applied in the diagnosis of psychiatric illnesses due to the inadequacy of conventional anatomic imaging in the diagnosis of psychiatric disorders. Functional MRI which is an advanced MR imaging technique enables mapping of brain regions showing activation at rest or during task related events. This preliminary study investigated functional connectivity differences in some of resting state networks (anterior attention network, temporal network, audio network, and the default mode network) in early onset schizophrenia patients and children of control group (typically development) using resting state functional MRI method. This preliminary study aimed to contribute to the characterization of schizophrenia disease by identifying the specific changes that occur in the resting state networks of schizophrenia brain.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116037040","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863093
Veli Baysal, Ergin Yılmaz, M. Özer
In this paper, the effects of autapse (a kind of synapse formed between the axon or soma of a neuron and its own dendrite) on the transmission of weak signal are investigated in scale-free neuronal networks. In the study, we consider that each neuron in the network has an autapse modelled as chemical synapse. Besides, a subthreshold signal are injected to all neurons in the network. When obtained results are analyzed, it is seen that the autaptic conductance has a threshold value. According to this threshold, the effects of autapse on the transmission of weak signal are evaluated within two categories. When conductance of autapse is less than this threshold value, autapses with proper autaptic time delay values slightly increase the transmission of weak signal. When autaptic conductance is bigger than the threshold value, autapses with proper autaptic time deley values prominently block the transmission of weak signal.
{"title":"Blocking of weak signal propagation via autaptic transmission in scale-free neuronal networks","authors":"Veli Baysal, Ergin Yılmaz, M. Özer","doi":"10.1109/TIPTEKNO.2016.7863093","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863093","url":null,"abstract":"In this paper, the effects of autapse (a kind of synapse formed between the axon or soma of a neuron and its own dendrite) on the transmission of weak signal are investigated in scale-free neuronal networks. In the study, we consider that each neuron in the network has an autapse modelled as chemical synapse. Besides, a subthreshold signal are injected to all neurons in the network. When obtained results are analyzed, it is seen that the autaptic conductance has a threshold value. According to this threshold, the effects of autapse on the transmission of weak signal are evaluated within two categories. When conductance of autapse is less than this threshold value, autapses with proper autaptic time delay values slightly increase the transmission of weak signal. When autaptic conductance is bigger than the threshold value, autapses with proper autaptic time deley values prominently block the transmission of weak signal.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522847","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863095
U. Fidan, İsmail Sarı, Raziye Kübra Kumrular
Melanoma arises from cancerous growth in pigmented skin lesion and t is the most deadliest form of skin cancer. Melanoma forms 4% from all skin cancer cases and it accounts for 75% of all skin cancer deaths. Even when the expert dermatologists uses the dermoscopy for diagnosis, the accuracy of melanoma diagnosis is estimated to be about 75–84%. The aim of this work classify skin lesions like normally, atypical and melanoma using artificial intelligence techniques and help to decide of the expert dermatologists in diagnosis for melanoma. Decision support system, which will be held improve both the speed and the accuracy of diagnosis. In this study that done for the classification of skin lesions with ANN, were correctly classified 100% normal skin lesions according to data from the data set PH2. Abnormal and melanoma skin cancers are correctly classified %93.3 with neural network that performed. As a result, the findings that were obtained have indicated the decision support system will be help to the dermatologists in the diagnosis of skin lesions.
{"title":"Classification of skin lesions using ANN","authors":"U. Fidan, İsmail Sarı, Raziye Kübra Kumrular","doi":"10.1109/TIPTEKNO.2016.7863095","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863095","url":null,"abstract":"Melanoma arises from cancerous growth in pigmented skin lesion and t is the most deadliest form of skin cancer. Melanoma forms 4% from all skin cancer cases and it accounts for 75% of all skin cancer deaths. Even when the expert dermatologists uses the dermoscopy for diagnosis, the accuracy of melanoma diagnosis is estimated to be about 75–84%. The aim of this work classify skin lesions like normally, atypical and melanoma using artificial intelligence techniques and help to decide of the expert dermatologists in diagnosis for melanoma. Decision support system, which will be held improve both the speed and the accuracy of diagnosis. In this study that done for the classification of skin lesions with ANN, were correctly classified 100% normal skin lesions according to data from the data set PH2. Abnormal and melanoma skin cancers are correctly classified %93.3 with neural network that performed. As a result, the findings that were obtained have indicated the decision support system will be help to the dermatologists in the diagnosis of skin lesions.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151656","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863118
Fatih Altindis, Bülent Yilmaz
Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method.
{"title":"Feature extraction and classification in a two-state brain-computer interface","authors":"Fatih Altindis, Bülent Yilmaz","doi":"10.1109/TIPTEKNO.2016.7863118","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863118","url":null,"abstract":"Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132880185","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863116
Gokalp Tulum, U. Teomete, T. Ergin, Özgür Dandin, Ferhat Cüce, O. Osman
In this work, we developed a novel method to diagnose traumatized kidney in the presence of laceration, contusion and active bleeding due to abdominal trauma. Our CAD system performs 94,44% of sensitivity at 0.13 false positives per dataset.
{"title":"Computed aided detection of traumatized kidneys in CT images","authors":"Gokalp Tulum, U. Teomete, T. Ergin, Özgür Dandin, Ferhat Cüce, O. Osman","doi":"10.1109/TIPTEKNO.2016.7863116","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863116","url":null,"abstract":"In this work, we developed a novel method to diagnose traumatized kidney in the presence of laceration, contusion and active bleeding due to abdominal trauma. Our CAD system performs 94,44% of sensitivity at 0.13 false positives per dataset.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318687","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863107
M. Sezdi, Yavuz Üzcan
The operating rooms are known as clean rooms because it must have the high quality air without the dust and the particles. In the clean room, the dust that is sourced from both the personnel and the patient and the machines, causes the risk of infection. By using the temiz-hava system, it is seen that the percent of infection risk decreases. But, this clean air system must be controlled by using the method of “the clean room classification”. The classification of the clean room is determined by the international standard of particle measurements. The main criteria for the classification of clean rooms is particle dimension, particle concentration, measurement points and measurement repetition. By using nonstandard particle measurements, the true classification is not possible because all measurements are taken only from the filters and the main criterias are not considered. The purpose of this study was to develope a standard particle measurement procedure by using measures in accordance with standard, and to achieve standardization for quality studies. In this study, the particles (0.1μm, 0.2μm, 0.3μm, 0.5μm, 1μm, 5μm and 10μm) in the operating rooms of Istanbul Medical Faculty in Istanbul University hospitals were measured by using a particle measurement device in accordance with ISO 14644 and they were interpreted in accordance with the same standard. During the study, a particle measurement procedure was established for Istanbul University Hospitals. Thus, the controls were provided standardly, and the generated particle measurement procedure was applied to all hospitals in Istanbul University (ITF, CTF, Oncology, Cardiology, Dental Health Faculty).
{"title":"Clean room classification in the operating room","authors":"M. Sezdi, Yavuz Üzcan","doi":"10.1109/TIPTEKNO.2016.7863107","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863107","url":null,"abstract":"The operating rooms are known as clean rooms because it must have the high quality air without the dust and the particles. In the clean room, the dust that is sourced from both the personnel and the patient and the machines, causes the risk of infection. By using the temiz-hava system, it is seen that the percent of infection risk decreases. But, this clean air system must be controlled by using the method of “the clean room classification”. The classification of the clean room is determined by the international standard of particle measurements. The main criteria for the classification of clean rooms is particle dimension, particle concentration, measurement points and measurement repetition. By using nonstandard particle measurements, the true classification is not possible because all measurements are taken only from the filters and the main criterias are not considered. The purpose of this study was to develope a standard particle measurement procedure by using measures in accordance with standard, and to achieve standardization for quality studies. In this study, the particles (0.1μm, 0.2μm, 0.3μm, 0.5μm, 1μm, 5μm and 10μm) in the operating rooms of Istanbul Medical Faculty in Istanbul University hospitals were measured by using a particle measurement device in accordance with ISO 14644 and they were interpreted in accordance with the same standard. During the study, a particle measurement procedure was established for Istanbul University Hospitals. Thus, the controls were provided standardly, and the generated particle measurement procedure was applied to all hospitals in Istanbul University (ITF, CTF, Oncology, Cardiology, Dental Health Faculty).","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130852032","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 : 2016-10-01DOI: 10.1109/TIPTEKNO.2016.7863076
S. Kucuk, B. Gungor
In this paper, a new hybrid robot manipulator is proposed for medical purposes. This hybrid robot manipulator has been formed by combination of a serial and a parallel robot manipulator. The inboard joints of new hybrid robot consist of 3-DOF SCARA type robot manipulator that performs positioning of the end effector. The outboard joints of the new hybrid robot consist of Stewart platform parallel manipulator which is used for orientation of the end effector only. Afterwards, the inverse kinematics of this new hybrid robot manipulator is obtained by using DH method. A numerical example is also given in order to demonstrate the working of the model.
{"title":"Inverse kinematics solution of a new hybrid robot manipulator proposed for medical purposes","authors":"S. Kucuk, B. Gungor","doi":"10.1109/TIPTEKNO.2016.7863076","DOIUrl":"https://doi.org/10.1109/TIPTEKNO.2016.7863076","url":null,"abstract":"In this paper, a new hybrid robot manipulator is proposed for medical purposes. This hybrid robot manipulator has been formed by combination of a serial and a parallel robot manipulator. The inboard joints of new hybrid robot consist of 3-DOF SCARA type robot manipulator that performs positioning of the end effector. The outboard joints of the new hybrid robot consist of Stewart platform parallel manipulator which is used for orientation of the end effector only. Afterwards, the inverse kinematics of this new hybrid robot manipulator is obtained by using DH method. A numerical example is also given in order to demonstrate the working of the model.","PeriodicalId":431660,"journal":{"name":"2016 Medical Technologies National Congress (TIPTEKNO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951610","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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