C. Hemalatha, S. Satheesh, N. Kamal, C. Devi, A. Vinothkumar, A. Kannan
In global dermatological conditions, skin lesions are significant. Curable early in the diagnosis, only skin lesions can be accurately identified by highly trained dermatologists. Around 21 million patients are diagnosed with this disease and more than 10.12 million deaths worldwide. This paper presents basic work for the detection and ensuing purpose of the CNN to dermoscopic images of skin lesions with cancerous inclination. The models proposed are trained and evaluated in the 2018 International Skin Imaging Collaboration challenge, comprising 2100 training samples and 750 test samples, on normal benchmark datasets. Skin-injured images were mainly segment based on person thresholds for channel intensity. The images were added to CNN to extract features. The extracted characteristics were then used to classify the associated ANN classification. In the past, many approaches have been used to diagnose subjects with variable success levels. The methodology described in this paper showed associated accuracy of 97.13% in comparison to the previous best of ninety seven.
{"title":"Segmentation of Tissue-Injured Melanoma Convolution Neural Networks","authors":"C. Hemalatha, S. Satheesh, N. Kamal, C. Devi, A. Vinothkumar, A. Kannan","doi":"10.1166/JCTN.2021.9389","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9389","url":null,"abstract":"In global dermatological conditions, skin lesions are significant. Curable early in the diagnosis, only skin lesions can be accurately identified by highly trained dermatologists. Around 21 million patients are diagnosed with this disease and more than 10.12 million deaths worldwide.\u0000 This paper presents basic work for the detection and ensuing purpose of the CNN to dermoscopic images of skin lesions with cancerous inclination. The models proposed are trained and evaluated in the 2018 International Skin Imaging Collaboration challenge, comprising 2100 training samples and\u0000 750 test samples, on normal benchmark datasets. Skin-injured images were mainly segment based on person thresholds for channel intensity. The images were added to CNN to extract features. The extracted characteristics were then used to classify the associated ANN classification. In the past,\u0000 many approaches have been used to diagnose subjects with variable success levels. The methodology described in this paper showed associated accuracy of 97.13% in comparison to the previous best of ninety seven.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1256-1262"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47942060","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}
A rechargeable lithium ion battery has captured prime importance in the modern era due to its cycle life and energy density. In the present study hexagonal shaped 18650 lithium ion cylindrical cell battery pack was designed and fabricated with paraffin wax as a Phase Change Material (PCM). But low thermal conductivity of the PCM causes impediment to the development of Electrical Vehicles (EV’s) which remains as a significant challenge. In the cylindrical cell battery module the maximum temperature is obtained in the mid region which causes uneven temperature distribution among cell. In order to overcome the limitation and to achieve efficient performance of battery module, the nano enhanced Phase Change Material (Ne-PCM) was incorporated in middle four cells by using graphene platelet nano powder (GPN), Multi Walled Carbon Nano Tube (MWCNT) and Graphite Synthetic Powder (GSP). Experiments on the battery module were conducted without any cooling, with PCM cooling and with Ne-PCM cooling. The results revealed that Battery pack with Ne-PCM has shown successful performance by minimizing the temperature below 50 °C in all considered discharge rates i.e., (1C, 2C and 3C) and maintained even temperature distribution among cells.
{"title":"An Experimental Investigation on Cooling Performance of Battery Pack by Using Nano-Enhanced Phase Change Material","authors":"G. Murali, G. Sravya, A. Srinath, J. Jaya","doi":"10.1166/JCTN.2021.9380","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9380","url":null,"abstract":"A rechargeable lithium ion battery has captured prime importance in the modern era due to its cycle life and energy density. In the present study hexagonal shaped 18650 lithium ion cylindrical cell battery pack was designed and fabricated with paraffin wax as a Phase Change Material\u0000 (PCM). But low thermal conductivity of the PCM causes impediment to the development of Electrical Vehicles (EV’s) which remains as a significant challenge. In the cylindrical cell battery module the maximum temperature is obtained in the mid region which causes uneven temperature distribution\u0000 among cell. In order to overcome the limitation and to achieve efficient performance of battery module, the nano enhanced Phase Change Material (Ne-PCM) was incorporated in middle four cells by using graphene platelet nano powder (GPN), Multi Walled Carbon Nano Tube (MWCNT) and Graphite Synthetic\u0000 Powder (GSP). Experiments on the battery module were conducted without any cooling, with PCM cooling and with Ne-PCM cooling. The results revealed that Battery pack with Ne-PCM has shown successful performance by minimizing the temperature below 50 °C in all considered discharge rates\u0000 i.e., (1C, 2C and 3C) and maintained even temperature distribution among cells.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1213-1220"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44634724","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}
Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing algorithms.
{"title":"Enhanced Load Balancing Approach for Cloud Data Center Load Optimization","authors":"J. Prassanna, V. Neelanarayanan","doi":"10.1166/JCTN.2021.9399","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9399","url":null,"abstract":"Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce\u0000 the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the\u0000 servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its\u0000 own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing\u0000 algorithms.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1270-1274"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46099422","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}
Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.
{"title":"Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture","authors":"P. Sindhu, G. Indirani, P. Dinadayalan","doi":"10.1166/JCTN.2021.9400","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9400","url":null,"abstract":"Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition\u0000 of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims\u0000 to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used\u0000 to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion\u0000 from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the\u0000 proposed method under all the test images applied.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1194-1200"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41742667","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}
The main objective of the research work is to recognize the rust of the substance with the help of Image Processing. The recognition of the rust portion of an image is carried out by quantizing of image in matrix form. The quantization process helps to perform the fundamental operation on image and also helps to identify the desired oxidation portion of an image. The corrosion portion was identified through the threshold operation, edge detection and segmentation. Threshold value assists to describe the types of the rust. Further the abrupt modification of colour in the images was captured by the edge detection method. Consequently partitioning of an image find the colour changes in the oxidized image. The corrosion portion was recognized by combining the edge recognition and partitioning process. Finally recommended methods provide the 98% accuracy to detect the rust.
{"title":"An Advanced Image Processing Prototype for Corrosion Finding Using Image Processing","authors":"M. Malathi, P. Sinthia","doi":"10.1166/JCTN.2021.9388","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9388","url":null,"abstract":"The main objective of the research work is to recognize the rust of the substance with the help of Image Processing. The recognition of the rust portion of an image is carried out by quantizing of image in matrix form. The quantization process helps to perform the fundamental operation\u0000 on image and also helps to identify the desired oxidation portion of an image. The corrosion portion was identified through the threshold operation, edge detection and segmentation. Threshold value assists to describe the types of the rust. Further the abrupt modification of colour in the\u0000 images was captured by the edge detection method. Consequently partitioning of an image find the colour changes in the oxidized image. The corrosion portion was recognized by combining the edge recognition and partitioning process. Finally recommended methods provide the 98% accuracy to detect\u0000 the rust.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1251-1255"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48123814","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}
Lung cancer (LC) will decrease the yield, which will have a negative impact on the economy. Therefore, primary and accurate the attack finding is a priority for the agro-dependent state. In several modern technologies for early detection of LC, image processing has become a one of the essential tool so that it cannot only early to find the disease accurately, but also successfully measure it. Various approaches have been developed to detect LC based on background modelling. Most of them focus on temporal information but partially or completely ignore spatial information, making it sensitive to noise. In order to overcome these issues an improved hybrid semantic feature descriptor technique is introduced based on Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and histogram of oriented gradients (HOG) feature extraction algorithms. And also to improve the LC segmentation problems a fuzzy c-means clustering algorithm (FCM) is used. Experiments and comparisons on publically available LIDC-IBRI dataset. To evaluate the proposed feature extraction performance three different classifiers are analysed such as artificial neural networks (ANN), recursive neural network and recurrent neural networks (RNNs).
{"title":"Hybrid Semantic Feature Descriptor and Fuzzy C-Means Clustering for Lung Cancer Detection and Classification","authors":"P. Priyadharshini, B. Zoraida","doi":"10.1166/JCTN.2021.9391","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9391","url":null,"abstract":"Lung cancer (LC) will decrease the yield, which will have a negative impact on the economy. Therefore, primary and accurate the attack finding is a priority for the agro-dependent state. In several modern technologies for early detection of LC, image processing has become a one of the\u0000 essential tool so that it cannot only early to find the disease accurately, but also successfully measure it. Various approaches have been developed to detect LC based on background modelling. Most of them focus on temporal information but partially or completely ignore spatial information,\u0000 making it sensitive to noise. In order to overcome these issues an improved hybrid semantic feature descriptor technique is introduced based on Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and histogram of oriented gradients (HOG) feature extraction algorithms. And also\u0000 to improve the LC segmentation problems a fuzzy c-means clustering algorithm (FCM) is used. Experiments and comparisons on publically available LIDC-IBRI dataset. To evaluate the proposed feature extraction performance three different classifiers are analysed such as artificial neural networks\u0000 (ANN), recursive neural network and recurrent neural networks (RNNs).","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1263-1269"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46028819","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}
In this paper, a linear two-dimensional Volterra integral equation of the second kind with the discontinuous kernel is considered. The conditions for ensuring the existence of a unique continuous solution are mentioned. The product Nystrom method, as a well-known method of solving singular integral equations, is presented. Therefore, the Nystrom method is applied to the linear Volterra integral equation with the discontinuous kernel to convert it to a linear algebraic system. Some formulas are expanded in two dimensions. Weights’ functions of the Nystrom method are obtained for kernels of logarithmic and Carleman types. Some numerical applications are presented to show the efficiency and accuracy of the proposed method. Maple18 is used to compute numerical solutions. The estimated error is calculated in each case. The Nystrom method is useful and effective in treating the two-dimensional singular Volterra integral equation. Finally, we conclude that the time factor and the parameter v have a clear effect on the results.
{"title":"Nyström Method to Solve Two-Dimensional Volterra Integral Equation with Discontinuous Kernel","authors":"S. Raad, Mariam Mohammed Al-Atawi","doi":"10.1166/JCTN.2021.9718","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9718","url":null,"abstract":"In this paper, a linear two-dimensional Volterra integral equation of the second kind with the discontinuous kernel is considered. The conditions for ensuring the existence of a unique continuous solution are mentioned. The product Nystrom method, as a well-known method of solving singular\u0000 integral equations, is presented. Therefore, the Nystrom method is applied to the linear Volterra integral equation with the discontinuous kernel to convert it to a linear algebraic system. Some formulas are expanded in two dimensions. Weights’ functions of the Nystrom method are obtained\u0000 for kernels of logarithmic and Carleman types. Some numerical applications are presented to show the efficiency and accuracy of the proposed method. Maple18 is used to compute numerical solutions. The estimated error is calculated in each case. The Nystrom method is useful and effective in\u0000 treating the two-dimensional singular Volterra integral equation. Finally, we conclude that the time factor and the parameter v have a clear effect on the results.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1177-1184"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47699477","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}
Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.
{"title":"Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine","authors":"R. Sudha, G. Indirani, S. Selvamuthukumaran","doi":"10.1166/JCTN.2021.9401","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9401","url":null,"abstract":"Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource\u0000 management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for\u0000 the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1275-1281"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48187604","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}
Durai Pandurangan, R. S. Kumar, Lukas Gebremariam, L. Arulmurugan, S. Tamilselvan
Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model. Finally, the enhanced illumination image is blended with original reflectance image to get enhanced color image. This paper shows that the proposed algorithm on various weakly illuminated images is enhanced better and has taken reduced computation time than previous image processing techniques.
{"title":"Combined Gray Level Transformation Technique for Low Light Color Image Enhancement","authors":"Durai Pandurangan, R. S. Kumar, Lukas Gebremariam, L. Arulmurugan, S. Tamilselvan","doi":"10.1166/JCTN.2021.9392","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9392","url":null,"abstract":"Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined\u0000 gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model.\u0000 Finally, the enhanced illumination image is blended with original reflectance image to get enhanced color image. This paper shows that the proposed algorithm on various weakly illuminated images is enhanced better and has taken reduced computation time than previous image processing techniques.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1221-1226"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42357328","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}
R. Ferraz, Raiff Sales da Fonseca, Igor Thonke Rodrigues, Cláudio Bastos da Silva, H. T. Filho
The main goal of this paper is to present the design of a surface electromyography acquisition, processing and amplification system with low power consumption. Based on a micro-controller and a Bluetooth module, it must send the data to a cell phone in real time. The main topology is based on an operational amplifier and passive components in order to produce filters and an instrumentation amplifier applied to Electromyography (EMG). This paper also shows the equations used during design and describes each step of development, from simulations and testing to acquired data and microcontroller programming. In order to produce a low-cost circuit that can be later used as an acquisition tool for portable mechanisms and prosthesis, the design of the main circuit considers the lowest number of components while it does not compromise efficiency.
{"title":"Surface Electromyographic Signal Acquisition System for Real Time Monitoring of Upper Limbs Muscles","authors":"R. Ferraz, Raiff Sales da Fonseca, Igor Thonke Rodrigues, Cláudio Bastos da Silva, H. T. Filho","doi":"10.1166/JCTN.2021.9716","DOIUrl":"https://doi.org/10.1166/JCTN.2021.9716","url":null,"abstract":"The main goal of this paper is to present the design of a surface electromyography acquisition, processing and amplification system with low power consumption. Based on a micro-controller and a Bluetooth module, it must send the data to a cell phone in real time. The main topology is\u0000 based on an operational amplifier and passive components in order to produce filters and an instrumentation amplifier applied to Electromyography (EMG). This paper also shows the equations used during design and describes each step of development, from simulations and testing to acquired data\u0000 and microcontroller programming. In order to produce a low-cost circuit that can be later used as an acquisition tool for portable mechanisms and prosthesis, the design of the main circuit considers the lowest number of components while it does not compromise efficiency.","PeriodicalId":15416,"journal":{"name":"Journal of Computational and Theoretical Nanoscience","volume":"18 1","pages":"1147-1152"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42756888","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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