Pub Date : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006045
Tarique Rashid, B. Kumar, Shashwat Pathak, Arvind Kumar
This paper presents performance analysis of ZigBee network in intra-hospital environment for multi-patient cardiac monitoring. A telemedicine scenario has been proposed where ECG signals of patients in Cardiac Care Unit (CCU) are being transmitted using ZigBee network. These signals are monitored continuously at Nursing Station (NS) on compact handheld devices like Personal Digital Assistant (PDA). The low power and small size ZigBee devices have the ability to form self configuring networks (Ad-hoc network) that can extend themselves through a hospital network. Performance of the proposed Wireless Body Area Network (WBAN) is evaluated for various routing protocols by varying transmission power and simulation results are obtained in terms of throughput, end to end delay, packet delivery ratio, total power consumed and network lifetime.
{"title":"Performance evaluation of ZigBee network for multi-patient cardiac monitoring in intra-hospital scenario","authors":"Tarique Rashid, B. Kumar, Shashwat Pathak, Arvind Kumar","doi":"10.1109/MEDCOM.2014.7006045","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006045","url":null,"abstract":"This paper presents performance analysis of ZigBee network in intra-hospital environment for multi-patient cardiac monitoring. A telemedicine scenario has been proposed where ECG signals of patients in Cardiac Care Unit (CCU) are being transmitted using ZigBee network. These signals are monitored continuously at Nursing Station (NS) on compact handheld devices like Personal Digital Assistant (PDA). The low power and small size ZigBee devices have the ability to form self configuring networks (Ad-hoc network) that can extend themselves through a hospital network. Performance of the proposed Wireless Body Area Network (WBAN) is evaluated for various routing protocols by varying transmission power and simulation results are obtained in terms of throughput, end to end delay, packet delivery ratio, total power consumed and network lifetime.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475474","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006038
Abhinav Hans, S. Kalra
Cloud computing is one of the vast field which provide the users with software's on lease. Cloud computing not only benefiting the computer science world but also it is beneficial for biomedical stream too. Cloud based e-health services can provide a better environment to the patient where he/she can get all kind of medical care that will be given in the hospital. Therefore it saves a lot time of the users by just sending all kind of necessary real time analyzed data to the doctor via cloud and gets prescribed medicine. In this paper we will study various biomedical services that have been provided on cloud. Also we will make a comparative study of these cloud based biomedical services at the end of the paper.
{"title":"Comparitive analysis of various cloud based biomedcial services","authors":"Abhinav Hans, S. Kalra","doi":"10.1109/MEDCOM.2014.7006038","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006038","url":null,"abstract":"Cloud computing is one of the vast field which provide the users with software's on lease. Cloud computing not only benefiting the computer science world but also it is beneficial for biomedical stream too. Cloud based e-health services can provide a better environment to the patient where he/she can get all kind of medical care that will be given in the hospital. Therefore it saves a lot time of the users by just sending all kind of necessary real time analyzed data to the doctor via cloud and gets prescribed medicine. In this paper we will study various biomedical services that have been provided on cloud. Also we will make a comparative study of these cloud based biomedical services at the end of the paper.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054933","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006037
K. Padmavathi, K. R. Krishna
This paper presents Magnitude Squared coherence(MSC) technique and Support Vector Machines (SVM) using kernel function for the classification of Inferior Myocardial Infarction. The coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. MSC technique uses Welch method for calculating PSD. For the detection of normal and IMI beats, MSC technique output values are given as the input features for the SVM classifier. Overall accuracy of SVM classifier is 99.3 percent. The data was collected from MIT/BIH PTB database.
{"title":"Myocardial infarction detection using magnitude squared coherence and Support Vector Machine","authors":"K. Padmavathi, K. R. Krishna","doi":"10.1109/MEDCOM.2014.7006037","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006037","url":null,"abstract":"This paper presents Magnitude Squared coherence(MSC) technique and Support Vector Machines (SVM) using kernel function for the classification of Inferior Myocardial Infarction. The coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. MSC technique uses Welch method for calculating PSD. For the detection of normal and IMI beats, MSC technique output values are given as the input features for the SVM classifier. Overall accuracy of SVM classifier is 99.3 percent. The data was collected from MIT/BIH PTB database.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129575096","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7005986
S. Charan, K. Suma, B. Rao
Nail-fold capillaries exhibit distinctive features which help in early diagnosis of various anomalies like diabetes. The examination of microcirculation of nail-fold capillaries serves as the window to monitor one's health condition as the changes in nail-fold microcirculation often indicate certain clinical disorders. This way of finding disorders is a less harmful technique. Separation of multiple layers of capillary in an image is performed using Speeded Up Robust Features (SURF). The capillary images have lot of smudge and blur in them which makes it difficult to extract the capillaries. This is tackled by using varied metric threshold in SURF features. Cross-correlation between a reference capillary image and the test image is used to separate one layer of capillaries. Extraction of the single capillary is performed via SURF points. This gives the density of capillaries in an image and also talks about the avascularity. The proposed techniques gave satisfactory results in the measurement of density of capillaries and avascularity and hence detecting the anomalies.
{"title":"Extraction and analysis of nail-fold capillaries","authors":"S. Charan, K. Suma, B. Rao","doi":"10.1109/MEDCOM.2014.7005986","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7005986","url":null,"abstract":"Nail-fold capillaries exhibit distinctive features which help in early diagnosis of various anomalies like diabetes. The examination of microcirculation of nail-fold capillaries serves as the window to monitor one's health condition as the changes in nail-fold microcirculation often indicate certain clinical disorders. This way of finding disorders is a less harmful technique. Separation of multiple layers of capillary in an image is performed using Speeded Up Robust Features (SURF). The capillary images have lot of smudge and blur in them which makes it difficult to extract the capillaries. This is tackled by using varied metric threshold in SURF features. Cross-correlation between a reference capillary image and the test image is used to separate one layer of capillaries. Extraction of the single capillary is performed via SURF points. This gives the density of capillaries in an image and also talks about the avascularity. The proposed techniques gave satisfactory results in the measurement of density of capillaries and avascularity and hence detecting the anomalies.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129607029","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006043
Abhishek Kumar, M. Kolekar
Analysis of EEG is the primary method for diagnosis of epilepsy. In this paper discrete wavelet transform is used for the time-frequency analysis of EEG signal. Using discrete wavelet transform, EEG signal is decomposed into five different frequency bands namely delta, theta, alpha, beta and gamma. Only theta, alpha and beta carry seizure information. Statistical feature like energy, variance and zero crossing rate and nonlinear feature like fractal dimension is extracted from each of the three sub bands and fed to support vector machine classifier. Support vector machine classifies the input EEG signal into seizure free and seizure signal. Experimental results show that the proposed method classifies EEG signals with excellent accuracy, sensitivity and specificity compared to the existing methods.
{"title":"Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals","authors":"Abhishek Kumar, M. Kolekar","doi":"10.1109/MEDCOM.2014.7006043","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006043","url":null,"abstract":"Analysis of EEG is the primary method for diagnosis of epilepsy. In this paper discrete wavelet transform is used for the time-frequency analysis of EEG signal. Using discrete wavelet transform, EEG signal is decomposed into five different frequency bands namely delta, theta, alpha, beta and gamma. Only theta, alpha and beta carry seizure information. Statistical feature like energy, variance and zero crossing rate and nonlinear feature like fractal dimension is extracted from each of the three sub bands and fed to support vector machine classifier. Support vector machine classifies the input EEG signal into seizure free and seizure signal. Experimental results show that the proposed method classifies EEG signals with excellent accuracy, sensitivity and specificity compared to the existing methods.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128511681","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006000
D. K. Rout, Susmita Das
Wireless Body Area Networks (BAN) are being developed to provide health care to patients on the move. Ultra wideband (UWB) is the most preferred candidate for the communication due to its high data rate and lower energy consumption characteristics. Since the data is transmitted wirelessly and narrowband systems are already using frequency bands within the UWB spectrum, a major concern here will be the interference from these existing narrowband wireless networks. The paper presents a novel narrowband interference mitigation method for UWB based Body Area Networks. The technique has been tested in the CM3 channel model for Body Surface communications. Comparison with other techniques demonstrate that the proposed method is far superior and immune to multiple narrowband interferences.
{"title":"Multiple narrowband interference mitigation in UWB body area networks for body surface communications","authors":"D. K. Rout, Susmita Das","doi":"10.1109/MEDCOM.2014.7006000","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006000","url":null,"abstract":"Wireless Body Area Networks (BAN) are being developed to provide health care to patients on the move. Ultra wideband (UWB) is the most preferred candidate for the communication due to its high data rate and lower energy consumption characteristics. Since the data is transmitted wirelessly and narrowband systems are already using frequency bands within the UWB spectrum, a major concern here will be the interference from these existing narrowband wireless networks. The paper presents a novel narrowband interference mitigation method for UWB based Body Area Networks. The technique has been tested in the CM3 channel model for Body Surface communications. Comparison with other techniques demonstrate that the proposed method is far superior and immune to multiple narrowband interferences.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991847","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7005994
J. B. Alonso, Josue Cabrera, Miguel A. Ferrer, J. M. Canino, C. Travieso, M. Dutta, Anushikha Singh
A simple and effective method of automatic discrimination between emotional and unemotional speech is presented. Traditional methods of emotional discrimination use prosodic and paralinguistic features, which are determined by a linguistic segmentation of the locution. However, these methods are not appropriate in real time applications because of their high computational cost and the linguistic segmentation requirement by locutions. This letter proposes a new strategy based on a few prosodic and paralinguistic features set obtained from a temporal segmentation of the speech signal. This new strategy is robust to interfering noises that are present in real environments, offering a low computational cost and improving the performance of a segmentation based on linguistic aspects.
{"title":"Emotional speech characterization for real time applications in real environments","authors":"J. B. Alonso, Josue Cabrera, Miguel A. Ferrer, J. M. Canino, C. Travieso, M. Dutta, Anushikha Singh","doi":"10.1109/MEDCOM.2014.7005994","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7005994","url":null,"abstract":"A simple and effective method of automatic discrimination between emotional and unemotional speech is presented. Traditional methods of emotional discrimination use prosodic and paralinguistic features, which are determined by a linguistic segmentation of the locution. However, these methods are not appropriate in real time applications because of their high computational cost and the linguistic segmentation requirement by locutions. This letter proposes a new strategy based on a few prosodic and paralinguistic features set obtained from a temporal segmentation of the speech signal. This new strategy is robust to interfering noises that are present in real environments, offering a low computational cost and improving the performance of a segmentation based on linguistic aspects.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488361","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006049
Himanshi, V. Bhateja, Abhinav Krishn, Akanksha Sahu
Medical image fusion facilitates the retrieval of complementary information from medical images for diagnostic purposes. This paper presents a combination of Principal Component Analysis (PCA) and Dual Tree Complex Wavelet (DTCWT) as an improved fusion approach for MR and CT-scan images. Unlike real valued discrete wavelet transforms, DTCWT provides shift invariance and improved directionality along with preservation of spectral content. The decomposed images are then processed using PCA a based fusion rule to improve upon the resolution and reduce the redundancy. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics; this further justifies the effectiveness of the proposed approach in comparison to other approaches.
{"title":"An improved medical image fusion approach using PCA and complex wavelets","authors":"Himanshi, V. Bhateja, Abhinav Krishn, Akanksha Sahu","doi":"10.1109/MEDCOM.2014.7006049","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006049","url":null,"abstract":"Medical image fusion facilitates the retrieval of complementary information from medical images for diagnostic purposes. This paper presents a combination of Principal Component Analysis (PCA) and Dual Tree Complex Wavelet (DTCWT) as an improved fusion approach for MR and CT-scan images. Unlike real valued discrete wavelet transforms, DTCWT provides shift invariance and improved directionality along with preservation of spectral content. The decomposed images are then processed using PCA a based fusion rule to improve upon the resolution and reduce the redundancy. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics; this further justifies the effectiveness of the proposed approach in comparison to other approaches.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084519","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7005980
Dixit V. Bhoraniya, R. Kher
Ambulatory ECG signal (A-ECG) is useful when long term cardiac monitoring of a patient is necessary. Ambulatory ECG monitoring provides electrical activity of the heart while a person is involved in doing his or her normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person's body movements during routine activities. This motion artifact has spectral overlap with cardiac signal in 1-10 Hz which corresponds to ECG features like P wave and T wave. These artifacts due to different physical activities (PA) might help in further cardiac diagnosis. For Classification of body movements, first the motion artifacts from A-ECG have been extracted using Adaptive filtering and discrete wavelet transform (DWT) approaches. The statistical parameters such as mean, median, variance, max value of extracted motion artifact signals are calculated. After that feature vector is created by combining principal components and above four parameters of respective motion artifacts signals. These combine features are fed to multilayer feed-forward neural network (MLPFNN) for classification. For this work the ECG signals of six healthy subjects (aged of 19 to 26 years) were recorded while the person performs various body movements activity like (i) up and down movement of left hand, (ii) up and down movement of right hand, (iii) waist twisting movement while standing and (iv) change in position from sitting down on chair to standing up movement in lead I configuration by using BIOPAC MP 36 signal acquiring system.
{"title":"Machine intelligence based identification of body movements in Ambulatory ECG (A-ECG)","authors":"Dixit V. Bhoraniya, R. Kher","doi":"10.1109/MEDCOM.2014.7005980","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7005980","url":null,"abstract":"Ambulatory ECG signal (A-ECG) is useful when long term cardiac monitoring of a patient is necessary. Ambulatory ECG monitoring provides electrical activity of the heart while a person is involved in doing his or her normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person's body movements during routine activities. This motion artifact has spectral overlap with cardiac signal in 1-10 Hz which corresponds to ECG features like P wave and T wave. These artifacts due to different physical activities (PA) might help in further cardiac diagnosis. For Classification of body movements, first the motion artifacts from A-ECG have been extracted using Adaptive filtering and discrete wavelet transform (DWT) approaches. The statistical parameters such as mean, median, variance, max value of extracted motion artifact signals are calculated. After that feature vector is created by combining principal components and above four parameters of respective motion artifacts signals. These combine features are fed to multilayer feed-forward neural network (MLPFNN) for classification. For this work the ECG signals of six healthy subjects (aged of 19 to 26 years) were recorded while the person performs various body movements activity like (i) up and down movement of left hand, (ii) up and down movement of right hand, (iii) waist twisting movement while standing and (iv) change in position from sitting down on chair to standing up movement in lead I configuration by using BIOPAC MP 36 signal acquiring system.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123594809","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 : 2014-11-01DOI: 10.1109/MEDCOM.2014.7006052
G. Anand, Shri S. K. Singh
DC power supplies are used in the extreme way in almost all electronic and electrical appliances eg : personal computers, audio sets, TV, Adapters etc. This paper discusses the reduced high conduction losses & increased efficiency of the proposed integrated Bridgeless-Boost PFC converter. By modifying the circuit according to the technology and the necessity the overall power factor can be improved to the expectation of user. The cause of having low power factor in the bridgeless power factor converter is the presence of two energy conversion stages. These stages cause more conduction losses into the circuit. In this paper research is advanced and preceded on the concept of single stage power converter. This highly efficient single stage integrated Bridgeless-Boost power factor correction converter is proposed. Introduction of the Bridgeless-Boost rectifier is given in the first section i.e. SECTION I showing how the BBPFC overcomes the drawbacks of the conventional boost PFC circuit. SECTION II explains the different approaches used for integration of PFC circuits. In SECTION III the proposed circuit is introduced and its complete operating modes are explained. Then finally superiority of the proposed circuit is verified by doing the simulation and verifying the results in the last section. Complete experimental analysis of the circuit is done on 230V ac, 50 Hz. Thus this paper introduces the Integrated Bridgeless-Boost PFC Converter along with the verification with the simulated and experimented results.
{"title":"Design of single stage integrated bridgeless-boost PFC converter","authors":"G. Anand, Shri S. K. Singh","doi":"10.1109/MEDCOM.2014.7006052","DOIUrl":"https://doi.org/10.1109/MEDCOM.2014.7006052","url":null,"abstract":"DC power supplies are used in the extreme way in almost all electronic and electrical appliances eg : personal computers, audio sets, TV, Adapters etc. This paper discusses the reduced high conduction losses & increased efficiency of the proposed integrated Bridgeless-Boost PFC converter. By modifying the circuit according to the technology and the necessity the overall power factor can be improved to the expectation of user. The cause of having low power factor in the bridgeless power factor converter is the presence of two energy conversion stages. These stages cause more conduction losses into the circuit. In this paper research is advanced and preceded on the concept of single stage power converter. This highly efficient single stage integrated Bridgeless-Boost power factor correction converter is proposed. Introduction of the Bridgeless-Boost rectifier is given in the first section i.e. SECTION I showing how the BBPFC overcomes the drawbacks of the conventional boost PFC circuit. SECTION II explains the different approaches used for integration of PFC circuits. In SECTION III the proposed circuit is introduced and its complete operating modes are explained. Then finally superiority of the proposed circuit is verified by doing the simulation and verifying the results in the last section. Complete experimental analysis of the circuit is done on 230V ac, 50 Hz. Thus this paper introduces the Integrated Bridgeless-Boost PFC Converter along with the verification with the simulated and experimented results.","PeriodicalId":246177,"journal":{"name":"2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124967299","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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