Pub Date : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986434
Aditya Saxena, Manish Kumar, Pawan Tyagi, Kuldeep Sikarwar, Aman Pathak
Myocardial Infarction term used for heart attack is a condition in which the functioning of heart becomes abnormal due to various factors like blockage of arteries and veins which in turn results in heart attack. Machine Learning became the most suitable approach to develop the models for prediction in various health care sectors. Apart from myocardial infarction prediction ML is useful in predicting various diseases. Predicting heart disease could be risky if the accurate results are not fetched the patient could die. The analytical tools help in predicting various diseases with the help of existing data of health reports. The motive of this research is to predict heart attack with the help of 12 complications that could possibly happen after the first heart attack. The machine learning algorithms used are Support-Vector Machine (SVM), Logistic-Regression which are used in deploying a model to predict heart attack. This study intends to ideate the prediction for chances of occurrence of a heart attack inside the sufferers. The prime objective of this study is for prediction of the myocardial infarction complications in a patient using UCI machine learning repository and various algorithms.
{"title":"Machine Learning based selection of Myocardial Complications to Predict Heart Attack","authors":"Aditya Saxena, Manish Kumar, Pawan Tyagi, Kuldeep Sikarwar, Aman Pathak","doi":"10.1109/UPCON56432.2022.9986434","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986434","url":null,"abstract":"Myocardial Infarction term used for heart attack is a condition in which the functioning of heart becomes abnormal due to various factors like blockage of arteries and veins which in turn results in heart attack. Machine Learning became the most suitable approach to develop the models for prediction in various health care sectors. Apart from myocardial infarction prediction ML is useful in predicting various diseases. Predicting heart disease could be risky if the accurate results are not fetched the patient could die. The analytical tools help in predicting various diseases with the help of existing data of health reports. The motive of this research is to predict heart attack with the help of 12 complications that could possibly happen after the first heart attack. The machine learning algorithms used are Support-Vector Machine (SVM), Logistic-Regression which are used in deploying a model to predict heart attack. This study intends to ideate the prediction for chances of occurrence of a heart attack inside the sufferers. The prime objective of this study is for prediction of the myocardial infarction complications in a patient using UCI machine learning repository and various algorithms.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122341978","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986401
M. Yaswanth, Navin Infant Raj, M. K. Nath
Automobiles are manufactured at an astonishing rate. It is becoming tedious to control, manage and hold accountable to these assets. As the automotive area is vulnerable to theft, accident, and traffic violations. The registration number plates are unique identifiers in tracking the automotive. In modern days, the role of CCTV helps the authorities in monitoring automotive movements. However, the speed of vehicles can escape the human eye. It is practically impossible to rely on the human-intervention for high-speed capturing. The images may get distorted even with advanced camera equipment and this process comes with a trade-off to high processing time with resource utilization. This paper proposes the optimal solution for automated automotive number plate recognition involving fewer computing resources by leveraging Hopfield neural networks. The accuracy obtained with this proposed method is 70.5%.
{"title":"Hopfield Neural Network for Classification of Digits","authors":"M. Yaswanth, Navin Infant Raj, M. K. Nath","doi":"10.1109/UPCON56432.2022.9986401","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986401","url":null,"abstract":"Automobiles are manufactured at an astonishing rate. It is becoming tedious to control, manage and hold accountable to these assets. As the automotive area is vulnerable to theft, accident, and traffic violations. The registration number plates are unique identifiers in tracking the automotive. In modern days, the role of CCTV helps the authorities in monitoring automotive movements. However, the speed of vehicles can escape the human eye. It is practically impossible to rely on the human-intervention for high-speed capturing. The images may get distorted even with advanced camera equipment and this process comes with a trade-off to high processing time with resource utilization. This paper proposes the optimal solution for automated automotive number plate recognition involving fewer computing resources by leveraging Hopfield neural networks. The accuracy obtained with this proposed method is 70.5%.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122586894","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, we present enhance Dynamic Time Warping (DTW) based method on the online Signature Verification (SV) by employing the code-vectors generated from Vector Quantization (VQ) to match the aligned pairs in the warping path. The DTW based method is used to compute the distance score between the test signature and the genuine enrolled signatures, for the decision making. In order to improve the results, we conducted the evaluation using MCYT-100 database and obtained an Equal Error Rate (EER) of 1.55% and SVC-2004 database provides the EER of 2.73%. In this work, we exploit the characteristics of the warping path for online SV and obtained enhanced efficacy of the system.
{"title":"Locally Weighted Enhanced DTW Based Online Signature Verification","authors":"Vishwaas Pratap Singh, Prabhnoor Singh, Yash Kamlaskar, Ramesh Kumar Bhukya","doi":"10.1109/UPCON56432.2022.9986409","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986409","url":null,"abstract":"In this paper, we present enhance Dynamic Time Warping (DTW) based method on the online Signature Verification (SV) by employing the code-vectors generated from Vector Quantization (VQ) to match the aligned pairs in the warping path. The DTW based method is used to compute the distance score between the test signature and the genuine enrolled signatures, for the decision making. In order to improve the results, we conducted the evaluation using MCYT-100 database and obtained an Equal Error Rate (EER) of 1.55% and SVC-2004 database provides the EER of 2.73%. In this work, we exploit the characteristics of the warping path for online SV and obtained enhanced efficacy of the system.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700103","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986482
M. M. Khan, Narendra Singh Gurjar
Thulium-doped fiber amplifiers (TDFAs) communicating in S and near-C bands (1460 nm to 1535 nm) can be a potential and effective solution to the existing and narrow C-band (1550 nm) Erbium-doped fiber amplifiers (EDFAs). However, to establish TDFAs in the existing fiber-optic communication system, its performance concerning the optical conversion efficiency has to be optimized. Therefore, this paper focus on the parametric and schematic optimization of TDFAs. The pump schematic with its respective optical power and the signal wavelength have been optimized in this work considering the amplified spontaneous emissions (ASE) at 800 nm and 1800 nm. Additionally, the constructive effects of ion-ion interaction mechanism (IM) involving homogeneous up-conversion (HUC) and pair-induced quenching (PIQ) on the TDFA's gain and noise figure (NF) characteristics have been analyzed. The findings of early population inversion (at 680 mW instead of 700 mW and at 160 mW instead of 300 mW respectively) are owing to IM effects. The increment of 94.57 % in signal gain and 8.43% decrease in NF for the bidirectional pumping scheme has been obtained for the calculated signal wavelength of 1466 nm. Also, a comprehensive simulation analysis has been carried out on comparative forward and backward pumping schemes with the optimum bidirectional pumping scheme.
{"title":"Pump and Signal Optimization in Thulium doped fiber amplifiers for S-Band with Amplified Spontaneous Emission and Ion-Ion Interactions","authors":"M. M. Khan, Narendra Singh Gurjar","doi":"10.1109/UPCON56432.2022.9986482","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986482","url":null,"abstract":"Thulium-doped fiber amplifiers (TDFAs) communicating in S and near-C bands (1460 nm to 1535 nm) can be a potential and effective solution to the existing and narrow C-band (1550 nm) Erbium-doped fiber amplifiers (EDFAs). However, to establish TDFAs in the existing fiber-optic communication system, its performance concerning the optical conversion efficiency has to be optimized. Therefore, this paper focus on the parametric and schematic optimization of TDFAs. The pump schematic with its respective optical power and the signal wavelength have been optimized in this work considering the amplified spontaneous emissions (ASE) at 800 nm and 1800 nm. Additionally, the constructive effects of ion-ion interaction mechanism (IM) involving homogeneous up-conversion (HUC) and pair-induced quenching (PIQ) on the TDFA's gain and noise figure (NF) characteristics have been analyzed. The findings of early population inversion (at 680 mW instead of 700 mW and at 160 mW instead of 300 mW respectively) are owing to IM effects. The increment of 94.57 % in signal gain and 8.43% decrease in NF for the bidirectional pumping scheme has been obtained for the calculated signal wavelength of 1466 nm. Also, a comprehensive simulation analysis has been carried out on comparative forward and backward pumping schemes with the optimum bidirectional pumping scheme.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875494","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986357
Sourabh Jain, Kumar Ujjwaldisha Batra, P. Mishra
In this research work the classification of land cover of Prayagraj region is carried out using Sentinel-1 dual polarimetric C-band SAR (Synthetic Aperture Radar) data. Four classes of land covers namely water, urban, vegetation and bare soil have been considered for classification using 8 polarimetric features which are backscattering coefficients and their ratio ($sigma_{vv},sigma_{vh}, sigma_{vv}/sigma_{vh}$), Radar vegetation index (RVI), Normalized difference polarization index (NDPI) and Eigen value decomposition parameters (Entropy, Anisotropy and Alpha angle). The separability index criterion is applied to determine the best features capable of separating each class. For each of these selected features the threshold value is obtained from our experimental analysis for segregating the classes which are used to develop the algorithm for decision based classifier. Further, a comparison of proposed classifier has been carried out with the three supervised classifiers namely Maximum likelihood classifier, Minimum distance classifier and Random forest classifier along with the accuracy evaluation of each. It has been found that the accuracy of the proposed decision tree classifier is better as compared to the other classifiers which are considered in this work.
{"title":"Land Cover Classification by Decision Based Classifier using Dual Polarimetric SAR Observables","authors":"Sourabh Jain, Kumar Ujjwaldisha Batra, P. Mishra","doi":"10.1109/UPCON56432.2022.9986357","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986357","url":null,"abstract":"In this research work the classification of land cover of Prayagraj region is carried out using Sentinel-1 dual polarimetric C-band SAR (Synthetic Aperture Radar) data. Four classes of land covers namely water, urban, vegetation and bare soil have been considered for classification using 8 polarimetric features which are backscattering coefficients and their ratio ($sigma_{vv},sigma_{vh}, sigma_{vv}/sigma_{vh}$), Radar vegetation index (RVI), Normalized difference polarization index (NDPI) and Eigen value decomposition parameters (Entropy, Anisotropy and Alpha angle). The separability index criterion is applied to determine the best features capable of separating each class. For each of these selected features the threshold value is obtained from our experimental analysis for segregating the classes which are used to develop the algorithm for decision based classifier. Further, a comparison of proposed classifier has been carried out with the three supervised classifiers namely Maximum likelihood classifier, Minimum distance classifier and Random forest classifier along with the accuracy evaluation of each. It has been found that the accuracy of the proposed decision tree classifier is better as compared to the other classifiers which are considered in this work.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322770","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986490
H. Mishra, Sumit Kumar Jha, Amit Dhawan, Manish Tiwari
This paper reviews comparison of different image fusion methods in transform domain. Image fusion is defined as a process of fetching up entire relevant information through multiple photographs and integrating into a single image. This combined image has all the relevant information and is more accurate and informative than any of the original images. The key objective of image fusion is to create a one photograph through all the pertinent data from many images. Hence, compared to any previous photograph, the current one provides a more precise illustration of the arena. The fused picture become more useful and has wide range of application in different areas, so fusion of image is very necessary to find minute information which is present on different picture. The purpose of this paper is to create fused images based on different image fusion techniques and compare the results. Additionally, this paper also provides measurements of quality of the fused images by using two quality indicators namely Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).
{"title":"Comparison of Different-Image Fusion Techniques in Wavelet Domain","authors":"H. Mishra, Sumit Kumar Jha, Amit Dhawan, Manish Tiwari","doi":"10.1109/UPCON56432.2022.9986490","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986490","url":null,"abstract":"This paper reviews comparison of different image fusion methods in transform domain. Image fusion is defined as a process of fetching up entire relevant information through multiple photographs and integrating into a single image. This combined image has all the relevant information and is more accurate and informative than any of the original images. The key objective of image fusion is to create a one photograph through all the pertinent data from many images. Hence, compared to any previous photograph, the current one provides a more precise illustration of the arena. The fused picture become more useful and has wide range of application in different areas, so fusion of image is very necessary to find minute information which is present on different picture. The purpose of this paper is to create fused images based on different image fusion techniques and compare the results. Additionally, this paper also provides measurements of quality of the fused images by using two quality indicators namely Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669127","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986383
Shresth Gupta, Anurag Singh, Abhishek Sharma
The ratio of a variable measurement's maximum and minimum values is known as its dynamic range. This range, as it relates to photography and cinematography, is the proportion between an image's whitest (brightest) and darkest (darkest) values. The main goal here is to enhance the detailed visibility of scenes. In this research, we present a three-adaptive step approach for image dynamic range modification that is both efficient and effective in terms of visibility and ease in use. First, two Gamma functions are adaptively selected on the basis of the histogram of the brightness map independently. Second, to balance the amplification of details in different places, an adaptive fusion technique is presented to integrate the two modified luminance maps. Finally, we propose a method to restore the color to the fused image.
{"title":"Dynamic Range Adjustment of HDR Images Using Adaptive Method","authors":"Shresth Gupta, Anurag Singh, Abhishek Sharma","doi":"10.1109/UPCON56432.2022.9986383","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986383","url":null,"abstract":"The ratio of a variable measurement's maximum and minimum values is known as its dynamic range. This range, as it relates to photography and cinematography, is the proportion between an image's whitest (brightest) and darkest (darkest) values. The main goal here is to enhance the detailed visibility of scenes. In this research, we present a three-adaptive step approach for image dynamic range modification that is both efficient and effective in terms of visibility and ease in use. First, two Gamma functions are adaptively selected on the basis of the histogram of the brightness map independently. Second, to balance the amplification of details in different places, an adaptive fusion technique is presented to integrate the two modified luminance maps. Finally, we propose a method to restore the color to the fused image.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115305605","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986487
Satya Prakash, Pooja Pathak, A. S. Jalal
The world is witnessing COVID - 19 Pandemic for quite some time now. India has seen three waves of COVID-19 in the last 700 days. The curiosity still lies in the occurrence and timing of the fourth wave. The current study tries to solve this and predicts the COVID-19 daily incidence in India in the future. State-of-the-art methodologies both from Machine learning (LSTM, KNN, SVR, Random Forest, and Multi Linear Regressor) and Mathematical models (SEIR) have been tried out to train and predict the Daily New Cases of COVID19 in India. Further prediction for the next 200 days has been tried out using the trained models. As per the forecast from most of the models, it is evident that no fourth wave is going to be witnessed in India in the next 200 days.
{"title":"Predicting COVID-19 Fourth Wave Incidence in India Using Machine Learning Algorithms and SEIR Model","authors":"Satya Prakash, Pooja Pathak, A. S. Jalal","doi":"10.1109/UPCON56432.2022.9986487","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986487","url":null,"abstract":"The world is witnessing COVID - 19 Pandemic for quite some time now. India has seen three waves of COVID-19 in the last 700 days. The curiosity still lies in the occurrence and timing of the fourth wave. The current study tries to solve this and predicts the COVID-19 daily incidence in India in the future. State-of-the-art methodologies both from Machine learning (LSTM, KNN, SVR, Random Forest, and Multi Linear Regressor) and Mathematical models (SEIR) have been tried out to train and predict the Daily New Cases of COVID19 in India. Further prediction for the next 200 days has been tried out using the trained models. As per the forecast from most of the models, it is evident that no fourth wave is going to be witnessed in India in the next 200 days.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087058","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986470
Uday Sai Kumar, Ashok Yadav, Vrijendra Singh
Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.
{"title":"Detecting Malware in Android Applications by Using Androguard Tool and XGBoost Algorithm","authors":"Uday Sai Kumar, Ashok Yadav, Vrijendra Singh","doi":"10.1109/UPCON56432.2022.9986470","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986470","url":null,"abstract":"Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214291","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986456
B. Kumar, K. Sarawadekar
Arctangent or inverse tangent function has numerous applications like gradient-based feature extraction, phase noise determination, range rate measurement etc. This paper presents a small area footprint hardware architecture for computing the arctangent of a complex number. The proposed method uses numerical approximation and LUTs used to improve the accuracy of the results obtained. Single-precision floating-point representation is used to implement the proposed design and the results demonstrate very good accuracy with an error of about ±0.0004 radian with $256times 32$ bits memory size. The proposed architecture is implemented on Nexys4 DDR FPGA board using Verilog and it operates at 19.8 MHz. Integrated Logic Analyzer (ILA) is used to debug and validate the proposed design. Further, it is observed that results obtained with the proposed design are in agreement with the Matlab simulation results.
{"title":"Small Area Footprint FPGA Architecture for Approximate atan2(a, b) Algorithm","authors":"B. Kumar, K. Sarawadekar","doi":"10.1109/UPCON56432.2022.9986456","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986456","url":null,"abstract":"Arctangent or inverse tangent function has numerous applications like gradient-based feature extraction, phase noise determination, range rate measurement etc. This paper presents a small area footprint hardware architecture for computing the arctangent of a complex number. The proposed method uses numerical approximation and LUTs used to improve the accuracy of the results obtained. Single-precision floating-point representation is used to implement the proposed design and the results demonstrate very good accuracy with an error of about ±0.0004 radian with $256times 32$ bits memory size. The proposed architecture is implemented on Nexys4 DDR FPGA board using Verilog and it operates at 19.8 MHz. Integrated Logic Analyzer (ILA) is used to debug and validate the proposed design. Further, it is observed that results obtained with the proposed design are in agreement with the Matlab simulation results.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126132683","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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