Pub Date : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453025
A. Anand, Aditya Asok, Arpith P, S. S, G. Nandana, Swathy S. Panicker, Baby Sreeja S D, Sreenidhi P R
In this paper, the authors propose efficient Piezo-material with optimized structure body motion based energy harvesting based on the principles of piezoelectric effect. The authors plan to implement a secondary source of energy which can be powered by human motions and does not require replacement of batteries which could otherwise pave way for a cost margin that would amount to a larger cost over a longer period of time. The methods used are relevant to the principles of piezoelectricity which are powered by harnessing natural human vibrations. Nanogenerators are used for this purpose of energy conversion. The studies related to length optimizations, material and resonant frequency are conducted by using COMSOL Multiphysics software. Polyvinylidene Fluoride (PVDF) displayed the best output with regard to the materials studied. By patterning the initial structure, piezoelectric material was optimized to achieve a minimum Eigen frequency value due to the generation of mutual capacitance. The minimization of Eigen frequency value signified its ability to generate adequate output voltages even when exposed to minimal frequency vibrations which it can realize.
{"title":"Optimizing Design of Wearable Energy Generator for Body Motion based Energy Harvesting","authors":"A. Anand, Aditya Asok, Arpith P, S. S, G. Nandana, Swathy S. Panicker, Baby Sreeja S D, Sreenidhi P R","doi":"10.1109/ICOEI51242.2021.9453025","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453025","url":null,"abstract":"In this paper, the authors propose efficient Piezo-material with optimized structure body motion based energy harvesting based on the principles of piezoelectric effect. The authors plan to implement a secondary source of energy which can be powered by human motions and does not require replacement of batteries which could otherwise pave way for a cost margin that would amount to a larger cost over a longer period of time. The methods used are relevant to the principles of piezoelectricity which are powered by harnessing natural human vibrations. Nanogenerators are used for this purpose of energy conversion. The studies related to length optimizations, material and resonant frequency are conducted by using COMSOL Multiphysics software. Polyvinylidene Fluoride (PVDF) displayed the best output with regard to the materials studied. By patterning the initial structure, piezoelectric material was optimized to achieve a minimum Eigen frequency value due to the generation of mutual capacitance. The minimization of Eigen frequency value signified its ability to generate adequate output voltages even when exposed to minimal frequency vibrations which it can realize.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122480935","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452920
G. Manikandan, G. Bhuvaneswari, Suhasini, K. G. Saravanan, M. Parameswari, D.Sterlin Rani
Consistent versatility the board is a capacity to offer the different types of assistance during the correspondence in remote heterogeneous organizations. Because of the irregular versatility of the portable terminals, the availability between various cell phones gets lost. To give the lossless network between the cell phones, the handover from the purpose of current connection to another point is fundamental. To improve the Seamless portability the board and traffic signal, an effective model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is presented in the heterogeneous organization. At the point when a portable hub in the organization moves out of its correspondence range, the sign strength of the hubs is determined. In view of the sign strength assessment, the Generalized Light Gradient Boost Decision Tree classifier orders the versatile hubs into the feeble and solid sign strength with the limit esteem. The boosting calculation at first develops' frail students for example double choice tree to distinguish the frail sign strength of the portable hub. At that point the group classifier joins the consequences of frail students and limits the speculation mistake. This assists with playing out the handover just with the powerless sign strength of the hub coming about in limits the repetitive handover. Furthermore, the powerless sign strength of the portable hub from the current connection point handover towards the closest accessible connection highlight improve the consistent information conveyance. Followed by, transmission capacity accessibility is estimated for diminishing the bundle misfortune because of the organization traffic coming about in improves the consistent information conveyance between the hubs. The reenactment is completed to assess the exhibition of the GLGBDT-TASM model with two related methodologies. The outcomes show that the GLGBDT-TASM model viably improved traffic-mindful consistent versatility in a heterogeneous organization with least deferral and bundle misfortune just as a higher information conveyance rate when contrasted with best in class techniques.
{"title":"Traffic Control Loss and to Handle Seamless Mobility in a Heterogeneous Network with Lesser Transmission Delay","authors":"G. Manikandan, G. Bhuvaneswari, Suhasini, K. G. Saravanan, M. Parameswari, D.Sterlin Rani","doi":"10.1109/ICOEI51242.2021.9452920","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452920","url":null,"abstract":"Consistent versatility the board is a capacity to offer the different types of assistance during the correspondence in remote heterogeneous organizations. Because of the irregular versatility of the portable terminals, the availability between various cell phones gets lost. To give the lossless network between the cell phones, the handover from the purpose of current connection to another point is fundamental. To improve the Seamless portability the board and traffic signal, an effective model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is presented in the heterogeneous organization. At the point when a portable hub in the organization moves out of its correspondence range, the sign strength of the hubs is determined. In view of the sign strength assessment, the Generalized Light Gradient Boost Decision Tree classifier orders the versatile hubs into the feeble and solid sign strength with the limit esteem. The boosting calculation at first develops' frail students for example double choice tree to distinguish the frail sign strength of the portable hub. At that point the group classifier joins the consequences of frail students and limits the speculation mistake. This assists with playing out the handover just with the powerless sign strength of the hub coming about in limits the repetitive handover. Furthermore, the powerless sign strength of the portable hub from the current connection point handover towards the closest accessible connection highlight improve the consistent information conveyance. Followed by, transmission capacity accessibility is estimated for diminishing the bundle misfortune because of the organization traffic coming about in improves the consistent information conveyance between the hubs. The reenactment is completed to assess the exhibition of the GLGBDT-TASM model with two related methodologies. The outcomes show that the GLGBDT-TASM model viably improved traffic-mindful consistent versatility in a heterogeneous organization with least deferral and bundle misfortune just as a higher information conveyance rate when contrasted with best in class techniques.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633844","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453003
Sumedh Patil, Baba Patra, Neha Goyal, Kapil O. Gupta
Plants play a crucial role in nature and the well-being of the population. They have a significant contribution towards ecological stability and are also sources of our needs like food, medicine, and essential commercial products. As a result of massive scale deforestation, topsoil erosion, and habitat destruction, both the number and type of plants' existing species are steadily declining. So, plantation and identification and classification of plant species are essential for preserving plant species and accelerated farm as it will help in the better understanding of plants. Nevertheless, they are difficult to exercise as plant identification needs domain knowledge and experience. However, due to advances in machine learning and deep learning, this problem is tackled correctly. Various machine Learning and Deep Learning algorithms like Support Vector Machine, Artificial Neural Network, Convolutional Neural Network, Probabilistic Neural Network have successfully experimented on plant leaf images to identify the species with near correct accuracy. This article attempts a comparative analysis of various approaches used for plant identification. Several experiments with Swedish leaves confirm the effectiveness of machine learning and CNN based classification model.
{"title":"Recognizing Plant species using Digitized leaves- A comparative study","authors":"Sumedh Patil, Baba Patra, Neha Goyal, Kapil O. Gupta","doi":"10.1109/ICOEI51242.2021.9453003","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453003","url":null,"abstract":"Plants play a crucial role in nature and the well-being of the population. They have a significant contribution towards ecological stability and are also sources of our needs like food, medicine, and essential commercial products. As a result of massive scale deforestation, topsoil erosion, and habitat destruction, both the number and type of plants' existing species are steadily declining. So, plantation and identification and classification of plant species are essential for preserving plant species and accelerated farm as it will help in the better understanding of plants. Nevertheless, they are difficult to exercise as plant identification needs domain knowledge and experience. However, due to advances in machine learning and deep learning, this problem is tackled correctly. Various machine Learning and Deep Learning algorithms like Support Vector Machine, Artificial Neural Network, Convolutional Neural Network, Probabilistic Neural Network have successfully experimented on plant leaf images to identify the species with near correct accuracy. This article attempts a comparative analysis of various approaches used for plant identification. Several experiments with Swedish leaves confirm the effectiveness of machine learning and CNN based classification model.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263799","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452990
U. Muthuraman, A. Swetha, J. J, P. Annapandi, N. Rajesh, J. A. Nesa Priya
For voltage sag compensation, an effective device used for gaining acceptance is dynamic voltage restorer. Depending upon the high voltage injection and the amount of energy stored in the restorer, the compensation capability of a dynamic voltage restorer (DVR) will occur. In the distribution system, phenomenon that mainly occurs is the voltage sag, which minimizes the RMS voltage for a short stretch of time. In order to compensate the voltage sag, a power electronic device that is DVR is utilized to inoculate a $3 phi$ voltage in series and to synchronise with the distribution feeder voltage. Here the working of DVR is explained. The control methods employed for compensation of sag, swell, harmonics by power circuit of DVR is explained and verified by using simulation. In the distribution system, power quality disturbance is the main concern, which develops tripping and malfunctions in sensitive equipment. The main power quality problems can be cleared by inoculating the real and reactive power to the point of connection (PCC) by DVR. In order to improve the efficiency of the distribution system, the remuneration of voltage sag and swell issues and the elimination of power factor issues and harmonics by DVR are explained in this study and it is done with and without ANN. During power quality events, it is found that, the obtained output in both the cases is the proposed system that readily find the harmonics and eliminates the power quality problems by inoculating the real/reactive power with no/less distortions than traditional system.
{"title":"Power Quality Improvement Based On Artificial Neural Network Controller and Dynamic Voltage Restorer","authors":"U. Muthuraman, A. Swetha, J. J, P. Annapandi, N. Rajesh, J. A. Nesa Priya","doi":"10.1109/ICOEI51242.2021.9452990","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452990","url":null,"abstract":"For voltage sag compensation, an effective device used for gaining acceptance is dynamic voltage restorer. Depending upon the high voltage injection and the amount of energy stored in the restorer, the compensation capability of a dynamic voltage restorer (DVR) will occur. In the distribution system, phenomenon that mainly occurs is the voltage sag, which minimizes the RMS voltage for a short stretch of time. In order to compensate the voltage sag, a power electronic device that is DVR is utilized to inoculate a $3 phi$ voltage in series and to synchronise with the distribution feeder voltage. Here the working of DVR is explained. The control methods employed for compensation of sag, swell, harmonics by power circuit of DVR is explained and verified by using simulation. In the distribution system, power quality disturbance is the main concern, which develops tripping and malfunctions in sensitive equipment. The main power quality problems can be cleared by inoculating the real and reactive power to the point of connection (PCC) by DVR. In order to improve the efficiency of the distribution system, the remuneration of voltage sag and swell issues and the elimination of power factor issues and harmonics by DVR are explained in this study and it is done with and without ANN. During power quality events, it is found that, the obtained output in both the cases is the proposed system that readily find the harmonics and eliminates the power quality problems by inoculating the real/reactive power with no/less distortions than traditional system.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127730441","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452816
B. Sridhar, S. Sridhar, V. Nanchariah, K. Gayatri
The main aim for this research work is to develop an Adaptive BSA (Backtracking Optimized Search Algorithm) method to solve the optimization problem in image segmentation. In adaptive optimization algorithms, the probability of intersection and mutation depends on the value of the appropriate solution to improve convergence performance. Because of its memory function and simple structure, BSA has powerful features to find a globally optimized solution. However, the algorithm is not yet sufficient to strike a balance between exploration and exploitation of a medical image. Therefore, an improved adaptive tracking and search algorithm has been proposed together with morphological operations, where adaptive bilateral filter will improve the sharpness of edges of a unique region for obtaining global digital optimization in order to reach the cluster image segmentation. The proposed work shows better color quality-based image segmentation for the detection of tumors in medical images. The proposed optimization algorithm results show better performance, when compared to the basic BSA optimization method.
{"title":"Cluster Medical Image Segmentation using Morphological Adaptive Bilateral Filter based BSA Algorithm","authors":"B. Sridhar, S. Sridhar, V. Nanchariah, K. Gayatri","doi":"10.1109/ICOEI51242.2021.9452816","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452816","url":null,"abstract":"The main aim for this research work is to develop an Adaptive BSA (Backtracking Optimized Search Algorithm) method to solve the optimization problem in image segmentation. In adaptive optimization algorithms, the probability of intersection and mutation depends on the value of the appropriate solution to improve convergence performance. Because of its memory function and simple structure, BSA has powerful features to find a globally optimized solution. However, the algorithm is not yet sufficient to strike a balance between exploration and exploitation of a medical image. Therefore, an improved adaptive tracking and search algorithm has been proposed together with morphological operations, where adaptive bilateral filter will improve the sharpness of edges of a unique region for obtaining global digital optimization in order to reach the cluster image segmentation. The proposed work shows better color quality-based image segmentation for the detection of tumors in medical images. The proposed optimization algorithm results show better performance, when compared to the basic BSA optimization method.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127746705","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453088
L. Kumari, Mohammad Aatif Jaffery, K. Nigam, G. Manaswi, P. Tharangini
Parkinson's disease is a brain-related disease that is common in every person mainly persons above age 45 years. This disease causes numbness in muscles, swallowing problems, bending of the back, shivering in hands, smell dysfunction, speaking problem, Hearing problem, and many more. Parkinson's disease has to be diagnosed as early as possible since the clinical tests, which take hours to detect, may cost a loss of time and money. An automated model for detecting Parkinson's disease in a person with greater accuracy is proposed in this paper. While several models for detecting Parkinson's disease have been established, they are all less reliable and precise. Our model is created using the gradient boosted decision tree, which not only reliably predicts Parkinson's disease in a human, but also predicts it quickly. The feature set contains 22 parameters of the voice signal, which are given to the XGBoost classifier. The developed model predicts Parkinson's disease with 96.6% of accuracy, 95.6% of sensitivity, 100% of specificity, 100% of Precision, F-Score 97.7%.
{"title":"Detection of Parkinson's Disease using Extreme Gradient Boosting","authors":"L. Kumari, Mohammad Aatif Jaffery, K. Nigam, G. Manaswi, P. Tharangini","doi":"10.1109/ICOEI51242.2021.9453088","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453088","url":null,"abstract":"Parkinson's disease is a brain-related disease that is common in every person mainly persons above age 45 years. This disease causes numbness in muscles, swallowing problems, bending of the back, shivering in hands, smell dysfunction, speaking problem, Hearing problem, and many more. Parkinson's disease has to be diagnosed as early as possible since the clinical tests, which take hours to detect, may cost a loss of time and money. An automated model for detecting Parkinson's disease in a person with greater accuracy is proposed in this paper. While several models for detecting Parkinson's disease have been established, they are all less reliable and precise. Our model is created using the gradient boosted decision tree, which not only reliably predicts Parkinson's disease in a human, but also predicts it quickly. The feature set contains 22 parameters of the voice signal, which are given to the XGBoost classifier. The developed model predicts Parkinson's disease with 96.6% of accuracy, 95.6% of sensitivity, 100% of specificity, 100% of Precision, F-Score 97.7%.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797557","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452861
Y. H. Robinson, R. Babu, K. Narayanan, Raikumar Krishnan, R. Krishnan, M. Paramaivaooan
The identification of hot spots while active transmission in Wireless Sensor Networks (WSNs) is a challenging task. Several location discovery techniques have been focused on the device related localization that finds the terminal target devices. This paper proposes an identification of location using ANN methodology. The RSS signal has the parameter within the gathered data within the communication range is computed. The difference within the values is gathered using this method The non-linear functionality through the coordinate location is the identified output. Whenever the output value is in the monitoring range, the matrix index is used to train the nodes using ANN model, finally the coordinates for location identification may be computed. The mobility framework is implemented through the sensor node that the position of the node has been estimated within the communication range. The repeated data transmission is minimized so that the WSN burdens have been reduced using the node density procedure. The performance evaluation has demonstrated that the proposed method is able to achieve good performance without any particular terminals.
{"title":"Enhanced location identification technique for Wireless Sensor Networks","authors":"Y. H. Robinson, R. Babu, K. Narayanan, Raikumar Krishnan, R. Krishnan, M. Paramaivaooan","doi":"10.1109/ICOEI51242.2021.9452861","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452861","url":null,"abstract":"The identification of hot spots while active transmission in Wireless Sensor Networks (WSNs) is a challenging task. Several location discovery techniques have been focused on the device related localization that finds the terminal target devices. This paper proposes an identification of location using ANN methodology. The RSS signal has the parameter within the gathered data within the communication range is computed. The difference within the values is gathered using this method The non-linear functionality through the coordinate location is the identified output. Whenever the output value is in the monitoring range, the matrix index is used to train the nodes using ANN model, finally the coordinates for location identification may be computed. The mobility framework is implemented through the sensor node that the position of the node has been estimated within the communication range. The repeated data transmission is minimized so that the WSN burdens have been reduced using the node density procedure. The performance evaluation has demonstrated that the proposed method is able to achieve good performance without any particular terminals.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330107","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452987
P. Annapandi, S. Sherlin, J. J. Gnanachandran, A. Ravi, V. Arumugam, A. A. Maneula
The complications related with power quality like swell and sags are presented in this paper. A compensation method of D-STATCOM, which is an electronic device of conventional power is discussed. Moreover, the development and usage of D-STATCOM for swells, voltage sags and the complete outputs are also discussed. The results of simulation proved that the insertion of DSTATCOM reduces the sags in voltage which were caused because of the faults & swell on account of instant load switching in the distribution system. By using Sinusoidal Pulse Width Modulation (SPWM), Voltage Source Convert (VSC) was developed. The control method was found highly robust under the examination of large range of operating conditions in all cases. In D-STATCOM, advanced graphic equipment of MATLAB/SIMULINK is utilized for modelling and simulation. The DC link voltage is controlled through Grey Wolf Optimization algorithm.
{"title":"GWO Optimized DC Link Voltage Control for DSTATCOM Power Module in Power Quality Issues Mitigation","authors":"P. Annapandi, S. Sherlin, J. J. Gnanachandran, A. Ravi, V. Arumugam, A. A. Maneula","doi":"10.1109/ICOEI51242.2021.9452987","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452987","url":null,"abstract":"The complications related with power quality like swell and sags are presented in this paper. A compensation method of D-STATCOM, which is an electronic device of conventional power is discussed. Moreover, the development and usage of D-STATCOM for swells, voltage sags and the complete outputs are also discussed. The results of simulation proved that the insertion of DSTATCOM reduces the sags in voltage which were caused because of the faults & swell on account of instant load switching in the distribution system. By using Sinusoidal Pulse Width Modulation (SPWM), Voltage Source Convert (VSC) was developed. The control method was found highly robust under the examination of large range of operating conditions in all cases. In D-STATCOM, advanced graphic equipment of MATLAB/SIMULINK is utilized for modelling and simulation. The DC link voltage is controlled through Grey Wolf Optimization algorithm.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114629771","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452890
I. Haritha, S. Shareef, Y. Prasanna, JeethuPhilip
Advancements in the zones of reproduction intellect, AI, and clinical imaging innovations has permitted the improvement of the clinical picture handling field by approximately bewildering outcomes over most recent twenty years. Clinicians were able to see the human body in a new light as a result of these advancements or 3-D cross- sectioned cuts, that brought about an expansion in the precision by analysis and the assessment of affected role in a non-obtrusive way. The basic advance for attractive resonance imaging (MRI) mind checks categorizers by capacity to extricate significant highlights. Therefore, numerous works have projected various strategies for highlights extraction to characterize the strange developments in the cerebrum MRI filters. All the more as of late, the use of profound learning calculations to clinical imaging prompts noteworthy execution upgrades in ordering and diagnosing convoluted pathologies, for example, mind tumors. Here a profound learning highlight withdrawal calculation is projected to remove the significant highlights from MRI mind filters. In equal, high quality highlights are removed utilizing the adapted gray level existence matrix (MGLCM) strategy. Hence, the extricated applicable highlights are joined with carefully assembled highlights to progress the grouping cycle of MRI cerebrum examines by support vector machine (SVM) utilized by categorizer. The acquired outcomes demonstrated as mix of the profound learning method and the carefully assembled highlights separated by MGLCM recover the precision of grouping of the SVM categorizer up to 99.30%. The components of your paper [title, text, heads, etc.] are already specified in the style sheet of an electronic document, which is a “live” prototype.
{"title":"Classification of Handcrafted Image Features for Integrated Deep Learning","authors":"I. Haritha, S. Shareef, Y. Prasanna, JeethuPhilip","doi":"10.1109/ICOEI51242.2021.9452890","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452890","url":null,"abstract":"Advancements in the zones of reproduction intellect, AI, and clinical imaging innovations has permitted the improvement of the clinical picture handling field by approximately bewildering outcomes over most recent twenty years. Clinicians were able to see the human body in a new light as a result of these advancements or 3-D cross- sectioned cuts, that brought about an expansion in the precision by analysis and the assessment of affected role in a non-obtrusive way. The basic advance for attractive resonance imaging (MRI) mind checks categorizers by capacity to extricate significant highlights. Therefore, numerous works have projected various strategies for highlights extraction to characterize the strange developments in the cerebrum MRI filters. All the more as of late, the use of profound learning calculations to clinical imaging prompts noteworthy execution upgrades in ordering and diagnosing convoluted pathologies, for example, mind tumors. Here a profound learning highlight withdrawal calculation is projected to remove the significant highlights from MRI mind filters. In equal, high quality highlights are removed utilizing the adapted gray level existence matrix (MGLCM) strategy. Hence, the extricated applicable highlights are joined with carefully assembled highlights to progress the grouping cycle of MRI cerebrum examines by support vector machine (SVM) utilized by categorizer. The acquired outcomes demonstrated as mix of the profound learning method and the carefully assembled highlights separated by MGLCM recover the precision of grouping of the SVM categorizer up to 99.30%. The components of your paper [title, text, heads, etc.] are already specified in the style sheet of an electronic document, which is a “live” prototype.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260030","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452923
Ankita Deohate, D. Rojatkar
Internet of things is a network that provides ability to control devices and, manage and remotely monitor them. It basically is a platform which creates new serviceable information from enormous streams of real time data. The sensors like RFID, IR, GPS and laser scanners etc. are installed by IoT for everything that we use in our daily life and making connection of them with the internet using certain protocols for interchanging information and communication for obtaining various applications like intelligent recognition, tracking, location, management and monitoring. In internet to things the decisions are being made without any human interaction. With the technical support from IoT, smart agriculture, smart home, smart university and smart city projects have gain momentum. The intelligence of this network of everyday things created by IoT is governs by the software, called middleware. The middleware is one of the enabling technologies of integrating and collecting data from devices interconnected through internet and make decisions based on it by allowing them to communicate among themselves. Middleware is emerges as the software layer between application and communication layer; it creates abstraction such as hiding hardware details. In this paper, we surveyed the main challenges faced by the middleware that needs to be addressed and survey of IoT application protocols and the survey of most popular middleware for internet of things.
物联网是一个网络，它提供了控制设备、管理和远程监控设备的能力。它基本上是一个从海量实时数据流中创建新的可用信息的平台。诸如RFID, IR, GPS和激光扫描仪等传感器由物联网安装，用于我们日常生活中使用的所有东西，并使用某些协议将它们与互联网连接，以交换信息和通信，以获得各种应用，如智能识别，跟踪，定位，管理和监控。在物联网中，决策是在没有任何人类互动的情况下做出的。在物联网的技术支撑下，智慧农业、智慧家居、智慧大学、智慧城市等项目方兴未拟。物联网创建的日常事物网络的智能是由称为中间件的软件控制的。中间件是一种使能技术，用于集成和收集通过internet互联的设备的数据，并允许它们之间进行通信，从而基于这些数据做出决策。中间件作为介于应用层和通信层之间的软件层出现;它创建了抽象，比如隐藏硬件细节。在本文中，我们调查了中间件所面临的需要解决的主要挑战，调查了物联网应用协议和最流行的物联网中间件。
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