Pub Date : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633838
M. Baskaran, K. Jeyapiriya, S. Gayathri
In the recent era, radio communication has a wide range of strategies like RFID schemes and wireless networks. These wireless devices locate a variety of application requests such as monitoring physical and structural quantities. These devices must be energetically autonomous. Various forms of energies like thermal, radio waves, solar etc., are used to power sensors that monitor the quantities. In this work, radio waves are used as source of energy for power sensors. The novelty lies in achieving the plans by two techniques: 1. Wireless Control transfer that involves transmitting electromagnetic energy from intentional or dedicated transmitter and then converting the obtained energy into DC. This is accomplished by using a rectenna (rectifier + antenna). 2. The second part is harvesting the energy that is obtained by using this rectenna. A multiband frequency rectenna is implemented for energy gathering of the spill. The planned rectenna proficiently crops energy at 3 dissimilar incidences. The design of the antenna is pretended using ADS software and is fabricated using FR-4 substratum. The antenna captures the RF signal. The rectifier then converts the signal to DC power. This DC power is utilized to energize low power devices. Here, the power obtained with such a design is found to be very low so the result is depicted using FFT signal. Based on the results, the measured and return loss of the antenna design produces convincing and required results.
{"title":"Implementation and Investigation of Rectenna for Microwave Applications","authors":"M. Baskaran, K. Jeyapiriya, S. Gayathri","doi":"10.1109/ICSES52305.2021.9633838","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633838","url":null,"abstract":"In the recent era, radio communication has a wide range of strategies like RFID schemes and wireless networks. These wireless devices locate a variety of application requests such as monitoring physical and structural quantities. These devices must be energetically autonomous. Various forms of energies like thermal, radio waves, solar etc., are used to power sensors that monitor the quantities. In this work, radio waves are used as source of energy for power sensors. The novelty lies in achieving the plans by two techniques: 1. Wireless Control transfer that involves transmitting electromagnetic energy from intentional or dedicated transmitter and then converting the obtained energy into DC. This is accomplished by using a rectenna (rectifier + antenna). 2. The second part is harvesting the energy that is obtained by using this rectenna. A multiband frequency rectenna is implemented for energy gathering of the spill. The planned rectenna proficiently crops energy at 3 dissimilar incidences. The design of the antenna is pretended using ADS software and is fabricated using FR-4 substratum. The antenna captures the RF signal. The rectifier then converts the signal to DC power. This DC power is utilized to energize low power devices. Here, the power obtained with such a design is found to be very low so the result is depicted using FFT signal. Based on the results, the measured and return loss of the antenna design produces convincing and required results.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"15 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89622935","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633818
Nibir Borpuzari, A. Mahanta
The paper provides a brief outline of the framework established for digitizing, recognizing and proofreading printed Indic documents with Assamese as a target language. The establishment of such a framework is essential as it depicts the workflow for the digitization and archival of the scanned text and it has a high impact on the end result. The main idea behind the framework is to build the foundation for an automated text correction engine which provides suggestions based on the experience set generated using manual text correction procedure and machine learning approaches. Most of the works already done in this domain is based on the dictionary approach which has its own shortcomings like inability to correct real-word errors, redundant queries, large size, non-exhaustive collection etc. Hence, in this research, the dataset will be built from the scratch based on the experience gathered during digitization which in-turn shall contribute in increasing the accuracy of the OCR engine by means of post-processing.
{"title":"A Framework for Pre Processing, Recognizing and Distributed Proofreading of Assamese Printed Text","authors":"Nibir Borpuzari, A. Mahanta","doi":"10.1109/ICSES52305.2021.9633818","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633818","url":null,"abstract":"The paper provides a brief outline of the framework established for digitizing, recognizing and proofreading printed Indic documents with Assamese as a target language. The establishment of such a framework is essential as it depicts the workflow for the digitization and archival of the scanned text and it has a high impact on the end result. The main idea behind the framework is to build the foundation for an automated text correction engine which provides suggestions based on the experience set generated using manual text correction procedure and machine learning approaches. Most of the works already done in this domain is based on the dictionary approach which has its own shortcomings like inability to correct real-word errors, redundant queries, large size, non-exhaustive collection etc. Hence, in this research, the dataset will be built from the scratch based on the experience gathered during digitization which in-turn shall contribute in increasing the accuracy of the OCR engine by means of post-processing.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"1 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90045262","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633807
S. Marlin, S. Jebaseelan
Improving the voltage profile and controlling the losses during transmission will be achieve by using FACTS devices. In this effort, two FACTS devices for a better output. The motivation of this work is simulation proram for this thirty-bus system with combined UPFC and TCSC by carrying out using MATLAB. Here, UPFC is used since it has the potential to control real and reactive power and also for reducing the losses in this system. Since, there is a great demand of adequate transmission capacity to maintain the transmission network we use flexible AC transmission system which provides enough transmission capacity and controls the flow of power. The optimisation technique is used in finding the best position of the FACTS devices by improving the voltage magnitude.
{"title":"Combined Facts Devices for Reactive Power Control by Using Optimization Technique","authors":"S. Marlin, S. Jebaseelan","doi":"10.1109/ICSES52305.2021.9633807","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633807","url":null,"abstract":"Improving the voltage profile and controlling the losses during transmission will be achieve by using FACTS devices. In this effort, two FACTS devices for a better output. The motivation of this work is simulation proram for this thirty-bus system with combined UPFC and TCSC by carrying out using MATLAB. Here, UPFC is used since it has the potential to control real and reactive power and also for reducing the losses in this system. Since, there is a great demand of adequate transmission capacity to maintain the transmission network we use flexible AC transmission system which provides enough transmission capacity and controls the flow of power. The optimisation technique is used in finding the best position of the FACTS devices by improving the voltage magnitude.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"442 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90060851","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633920
A. Sapkal, Chhavi, Shashank Sharma, Pradeep Kumar, Sachin Yadav
Keyword spotting is the method of estimating whether the text query occurs in the document or not. The query- by-example model is used in this paper to present an efficient segmentation-free keyword spotting approach that can be applied in historical document collections. For image de-noising and binarization, we use an autoencoder network in our approach. We are using a patch-based system to create patches for the binarized image, followed by a Siamese network. To determine the degree of similarity between two input word images, a Siamese network employs two identical convolutional networks. Once trained, the network can detect not only words from different writing styles and contexts, but also words that are not in the training set. The method proposed is evaluated on the Bengali Handwritten dataset.
{"title":"Keyword spotting in historical document collections withoutsegmentation using the Siamese Network","authors":"A. Sapkal, Chhavi, Shashank Sharma, Pradeep Kumar, Sachin Yadav","doi":"10.1109/ICSES52305.2021.9633920","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633920","url":null,"abstract":"Keyword spotting is the method of estimating whether the text query occurs in the document or not. The query- by-example model is used in this paper to present an efficient segmentation-free keyword spotting approach that can be applied in historical document collections. For image de-noising and binarization, we use an autoencoder network in our approach. We are using a patch-based system to create patches for the binarized image, followed by a Siamese network. To determine the degree of similarity between two input word images, a Siamese network employs two identical convolutional networks. Once trained, the network can detect not only words from different writing styles and contexts, but also words that are not in the training set. The method proposed is evaluated on the Bengali Handwritten dataset.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"11 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88891813","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633863
Suresh Namagiri, P. Pokkunuri, B. Madhav, Kasi Udaykiran, Srilekha Gandu
Wireless implantable body area network (Wi BAN) technologies have improved the health monitoring system for the medical diagnostic of instance heart attack, BP(blood pressure) and breast cancer. The aim of present work, to model a UWB (Ultra-Wideband) implantable microstrip patch antenna resonating at 4.8GHz applicable for Wi BAN applications. The designed antenna is modelled on liquid crystal polymer (LCP) substrate by using high frequency structural simulator (HFSS) software. In this work, rectangular patch with 2 L slots have been proposed to achieve the 4.8GHz operating frequency for Wi BAN applications. The antenna parameters such as RC (reflection coefficient), operating BW (Bandwidth), VSWR (Voltage standing wave ratio) and radiation pattern have been analysed to understand the performance of the antenna. The designed antenna is operating from 4.21-5.67 GHz with the bandwidth of 1.46GHz and resonating at 4.8GHz with the reflection coefficient of −43dB. Gain of designed antenna is 7.77dBi at operating frequency.
{"title":"Design and Analysis of a UWB patch antenna for Wireless Implantable Body Area Network (Wi BAN) Applications","authors":"Suresh Namagiri, P. Pokkunuri, B. Madhav, Kasi Udaykiran, Srilekha Gandu","doi":"10.1109/ICSES52305.2021.9633863","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633863","url":null,"abstract":"Wireless implantable body area network (Wi BAN) technologies have improved the health monitoring system for the medical diagnostic of instance heart attack, BP(blood pressure) and breast cancer. The aim of present work, to model a UWB (Ultra-Wideband) implantable microstrip patch antenna resonating at 4.8GHz applicable for Wi BAN applications. The designed antenna is modelled on liquid crystal polymer (LCP) substrate by using high frequency structural simulator (HFSS) software. In this work, rectangular patch with 2 L slots have been proposed to achieve the 4.8GHz operating frequency for Wi BAN applications. The antenna parameters such as RC (reflection coefficient), operating BW (Bandwidth), VSWR (Voltage standing wave ratio) and radiation pattern have been analysed to understand the performance of the antenna. The designed antenna is operating from 4.21-5.67 GHz with the bandwidth of 1.46GHz and resonating at 4.8GHz with the reflection coefficient of −43dB. Gain of designed antenna is 7.77dBi at operating frequency.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"84 6 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74829538","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633886
S. Kavitha, K. Sairam, Ashish Singh
In this manuscript, Nano dipole antenna is designed and simulated using CST MICRO WAVE STUDIO for THz communication. The design is constructed using the noble metals silver and gold as Nano materials. Dispersion properties of gold and silver materials at THz frequency are presented using Drude, Lorentz and Debye models. It is observed that permittivity of a metal becomes complex at the optical frequency and it is frequency dependent. The variation of real and imaginary parts of permittivity for gold and silver materials is plotted. Further it is observed that silver Nano dipole antenna structure has slightly higher resonating frequency compared to gold Nano dipole antenna. It is also noticed that silver has low losses compared to gold at optical frequencies. Directivity of silver Nano dipole antenna is measured to be6. 739dBi and for gold structure it is 6.438 dBi.
{"title":"Silver and Gold Nano Antennas for THz Optical Communication","authors":"S. Kavitha, K. Sairam, Ashish Singh","doi":"10.1109/ICSES52305.2021.9633886","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633886","url":null,"abstract":"In this manuscript, Nano dipole antenna is designed and simulated using CST MICRO WAVE STUDIO for THz communication. The design is constructed using the noble metals silver and gold as Nano materials. Dispersion properties of gold and silver materials at THz frequency are presented using Drude, Lorentz and Debye models. It is observed that permittivity of a metal becomes complex at the optical frequency and it is frequency dependent. The variation of real and imaginary parts of permittivity for gold and silver materials is plotted. Further it is observed that silver Nano dipole antenna structure has slightly higher resonating frequency compared to gold Nano dipole antenna. It is also noticed that silver has low losses compared to gold at optical frequencies. Directivity of silver Nano dipole antenna is measured to be6. 739dBi and for gold structure it is 6.438 dBi.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"40 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79474354","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633875
R. K. Kumar, K. Arunabhaskar
Retinopathy is a serious disease occurred over the retinal area of the eye, in which it is mainly raised based on the Diabetic disease. This kind of retinal disease is named as diabetic retinopathy; it may cause the permanent disorder of an eye. This retinopathy disease affects the blood flow ratio of the retinal veins and cause the blindness to the people as well as it is caused by the irregular blood flow over the veins. This kind of diabetic retinopathy disease results from the damage to the retinal back portion, in which it is caused due to the propensity to the retina. An improper maintenance of Blood Sugar level leads to such risk cases and the diabetic retinopathy can easily be identified by some earlier symptoms such as appearance of floaters, decreased visual acuity, redness, yellow, and orange colors and poor color perception. These are all the common symptoms raised on earlier stages of diabetic retinopathy disease, in which it is recoverable but in case of poor consideration regarding such causes leads to permanent blindness. At the low end of the spectrum, the condition can be managed with careful control of one's diabetes. For more difficult cases, surgery or laser resurfacing may be required. In this paper, a digital image processing logic is utilized to process the retinal images and classify the normal and severe states in clear manner with respect to machine learning principles. This paper introduced a new machine learning strategy by means of combining two powerful machine learning algorithms such as Random Forest Classifier and the AdaBoost Classifier, in which it is integrated together to make a hybrid algorithm called Hybrid Retinal Disease Detection Logic (HRDDL). This proposed approach of HRDDL assures the logic of identifying the retinopathy diseases in clear manner with proper classification logics. The digital retinal image dataset downloaded from Kaggle database is utilized to prove the efficiency of the proposed approach and the resulting scenario is cross-validated with traditional Random Forest Classifier to prove the proposed HRDDL classification accuracy. This paper assures the HRDDL accuracy over prediction of diabetic retinopathy on earlier stages as well as the resulting section shows the clear proof for the identification of disease and the accuracy ratio. The proposed approach of HRDDL provides the accuracy range of 92.5% in results as well as this will be cross-validated with the classical Random Forest classifier to prove the efficiency well.
{"title":"A Hybrid Machine Learning Strategy Assisted Diabetic Retinopathy Detection based on Retinal Images","authors":"R. K. Kumar, K. Arunabhaskar","doi":"10.1109/ICSES52305.2021.9633875","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633875","url":null,"abstract":"Retinopathy is a serious disease occurred over the retinal area of the eye, in which it is mainly raised based on the Diabetic disease. This kind of retinal disease is named as diabetic retinopathy; it may cause the permanent disorder of an eye. This retinopathy disease affects the blood flow ratio of the retinal veins and cause the blindness to the people as well as it is caused by the irregular blood flow over the veins. This kind of diabetic retinopathy disease results from the damage to the retinal back portion, in which it is caused due to the propensity to the retina. An improper maintenance of Blood Sugar level leads to such risk cases and the diabetic retinopathy can easily be identified by some earlier symptoms such as appearance of floaters, decreased visual acuity, redness, yellow, and orange colors and poor color perception. These are all the common symptoms raised on earlier stages of diabetic retinopathy disease, in which it is recoverable but in case of poor consideration regarding such causes leads to permanent blindness. At the low end of the spectrum, the condition can be managed with careful control of one's diabetes. For more difficult cases, surgery or laser resurfacing may be required. In this paper, a digital image processing logic is utilized to process the retinal images and classify the normal and severe states in clear manner with respect to machine learning principles. This paper introduced a new machine learning strategy by means of combining two powerful machine learning algorithms such as Random Forest Classifier and the AdaBoost Classifier, in which it is integrated together to make a hybrid algorithm called Hybrid Retinal Disease Detection Logic (HRDDL). This proposed approach of HRDDL assures the logic of identifying the retinopathy diseases in clear manner with proper classification logics. The digital retinal image dataset downloaded from Kaggle database is utilized to prove the efficiency of the proposed approach and the resulting scenario is cross-validated with traditional Random Forest Classifier to prove the proposed HRDDL classification accuracy. This paper assures the HRDDL accuracy over prediction of diabetic retinopathy on earlier stages as well as the resulting section shows the clear proof for the identification of disease and the accuracy ratio. The proposed approach of HRDDL provides the accuracy range of 92.5% in results as well as this will be cross-validated with the classical Random Forest classifier to prove the efficiency well.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79546503","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633904
R. Godfrin, S. Suganthidevi
Retinal image segmentation and classification is a challenge task in diagnosing and treating for Diabetic Retinopathy (DR) over the past decade. Usually, retinal image is used to assess the diabetic diseases, as it offers complementary information for acquiring the retinal image sequences. This long outstanding problem to classify the DR significantly requires more time for a physician. Therefore, developed an automated computational approach for physicians with less time and speed up the diagnosing procedure. The proposed work based on machine learning techniques for achieving blood vessel classification using the optic disc segmented features of retinal image. The segments are generated through the image processing mechanism, which ensure the effectiveness of optimal segment selection that yields to detect the optic disc and blood vessel more accurately. In, this proposed work detailed comparative study for image processing and machine learning techniques in DR are analyzed. Finally, the effectiveness of the proposed work is carried out by using this various machine learning algorithm and attained the better performance value. The proposed work achieves the best results values for blood vessel classification in DR and computed the performance metrics in terms of accuracy, sensitivity and specificity respectively.
{"title":"Comparative Study and Detection of Diabetic Retinopathy in Retinal Images Using Computational Approach","authors":"R. Godfrin, S. Suganthidevi","doi":"10.1109/ICSES52305.2021.9633904","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633904","url":null,"abstract":"Retinal image segmentation and classification is a challenge task in diagnosing and treating for Diabetic Retinopathy (DR) over the past decade. Usually, retinal image is used to assess the diabetic diseases, as it offers complementary information for acquiring the retinal image sequences. This long outstanding problem to classify the DR significantly requires more time for a physician. Therefore, developed an automated computational approach for physicians with less time and speed up the diagnosing procedure. The proposed work based on machine learning techniques for achieving blood vessel classification using the optic disc segmented features of retinal image. The segments are generated through the image processing mechanism, which ensure the effectiveness of optimal segment selection that yields to detect the optic disc and blood vessel more accurately. In, this proposed work detailed comparative study for image processing and machine learning techniques in DR are analyzed. Finally, the effectiveness of the proposed work is carried out by using this various machine learning algorithm and attained the better performance value. The proposed work achieves the best results values for blood vessel classification in DR and computed the performance metrics in terms of accuracy, sensitivity and specificity respectively.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"13 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78611881","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633947
B. Nithya, J. Jeyachidra
Sensor node localization refers to the knowledge of position information and is a procedural technique for estimating sensor node location. In wireless sensor networks, localization refers to the estimation of sensor node location information. Optimization algorithms are used to determine the position of sensor nodes. Traditional algorithms rely on analytical methods, which increase in computational complexity as the number of sensor nodes grows. Due to resource constraints, cost constraints, and sensor node energy constraints, an algorithm with reduced computational complexity is needed, one that does not need external hardware, needs less run time and memory, is scalable and easy to implement without losing performance, and has improved location estimation accuracy with better convergence. In order to meet these objectives, the proposed to design an optimization technique based on Bat Optimization Algorithm. For each unknown or non-localized node, the algorithm estimates at least 3 reference nodes based on the parameters. Through result it has been proved that this method reduces localization error and delay time and gives better accuracy. Another Important research contribution is this Heterogeneous Wireless Sensor Network (HWSN) utilizes the Natural Language Processing for the performance metric improvement. This HWSN that uses the data in native natural languages processing for localizing speech communication sources and to locate the nodes themselves in the HWSN. Here, performance metrics measured by Time of Arrival and Speed ranging of the nodes from the Speech Acoustic Communication.
{"title":"Optimized Anchor based Localization using Bat Optimization Algorithm for Heterogeneous WSN","authors":"B. Nithya, J. Jeyachidra","doi":"10.1109/ICSES52305.2021.9633947","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633947","url":null,"abstract":"Sensor node localization refers to the knowledge of position information and is a procedural technique for estimating sensor node location. In wireless sensor networks, localization refers to the estimation of sensor node location information. Optimization algorithms are used to determine the position of sensor nodes. Traditional algorithms rely on analytical methods, which increase in computational complexity as the number of sensor nodes grows. Due to resource constraints, cost constraints, and sensor node energy constraints, an algorithm with reduced computational complexity is needed, one that does not need external hardware, needs less run time and memory, is scalable and easy to implement without losing performance, and has improved location estimation accuracy with better convergence. In order to meet these objectives, the proposed to design an optimization technique based on Bat Optimization Algorithm. For each unknown or non-localized node, the algorithm estimates at least 3 reference nodes based on the parameters. Through result it has been proved that this method reduces localization error and delay time and gives better accuracy. Another Important research contribution is this Heterogeneous Wireless Sensor Network (HWSN) utilizes the Natural Language Processing for the performance metric improvement. This HWSN that uses the data in native natural languages processing for localizing speech communication sources and to locate the nodes themselves in the HWSN. Here, performance metrics measured by Time of Arrival and Speed ranging of the nodes from the Speech Acoustic Communication.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"16 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80700871","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633799
Tanya Dinesh, Anala M R, T. T. Newton, Smitha G R
Chatbots or virtual assistants are being used by industries all over the world, they can reduce human intervention and improve efficiency. These days smart-assistants such as Amazon Alexa and Google Assistant help users get quick access to most generic queries within seconds, but when it comes to students and their everyday queries, these assistants fall short in answering the queries they have related to their academic schedules i.e., timetable queries, online classes links, syllabus queries, test dates, etc. The motive behind building this chatbot is to help students get quick and accurate responses to their schedule and syllabus-related queries, this is especially beneficial for students who are taking online classes due to the COVID-19 pandemic and cannot talk to their peers face to face. This chatbot was developed with the Rasa, it is a framework for developing contextual AI assistants and chatbots. Rasa enables the use of components in the NLU pipeline to customize the intent classification, entity extraction, and response selection. This paper goes through the pipeline customizations that were necessary to process the schedule-specific queries from students. It also goes over the stories and custom actions used to generate responses once the intent and entities are extracted. Telegram was used to deploy the chatbot onto the real world to enable students to talk to this chatbot from the comfort of their smartphones.
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