Pub Date : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865689
Srinath K S, K. K, Gagan A G, Jyothi D K, P. D. Shenoy, V. K. R.
A major challenge faced by the world today is to identify and rehabilitate various types of mental disorders. World Health Organization (WHO) has identified in its survey, around twenty percent of the world’s teenagers and children are suffering from mental health issues. It is also analyzed that there is a reduction of 10 to 25 years of life expectancy who suffer from a serious mental disorder. /p)(p)Machine learning approach is used in our study to predict mental illness on the data collected using DASS42 questionnaire. Severities between normal to extremely severe for stress, anxiety, and depression are classified using a Tree- based machine learning algorithm i.e. Decision Tree and its ensemble XGBoost. After choosing the right configuration for the algorithm for DASS42 dataset by tuning the hyperparameters, it is observed that the tree based machine learning algorithm gives better accuracy of 98.46% for Anxiety, 98.55% for Depression, and 98.44% for Stress, compared to other ML models.
{"title":"Enhancing Mental Illness Prediction using Tree based Machine Learning Approach","authors":"Srinath K S, K. K, Gagan A G, Jyothi D K, P. D. Shenoy, V. K. R.","doi":"10.1109/CONECCT55679.2022.9865689","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865689","url":null,"abstract":"A major challenge faced by the world today is to identify and rehabilitate various types of mental disorders. World Health Organization (WHO) has identified in its survey, around twenty percent of the world’s teenagers and children are suffering from mental health issues. It is also analyzed that there is a reduction of 10 to 25 years of life expectancy who suffer from a serious mental disorder. /p)(p)Machine learning approach is used in our study to predict mental illness on the data collected using DASS42 questionnaire. Severities between normal to extremely severe for stress, anxiety, and depression are classified using a Tree- based machine learning algorithm i.e. Decision Tree and its ensemble XGBoost. After choosing the right configuration for the algorithm for DASS42 dataset by tuning the hyperparameters, it is observed that the tree based machine learning algorithm gives better accuracy of 98.46% for Anxiety, 98.55% for Depression, and 98.44% for Stress, compared to other ML models.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115133907","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865850
M. Ruchitha, S. Suzain, Rajalekshmi Kishore, Sanjeev Gurugopinath
Practical implementation of internet-of-things (IoT) rely on time-critical status updates. A well-known measure of the time-dependency in IoT data is the age of information (AoI). Most of the existing literature considers a theoretical analysis on AoI. Practical considerations and implications of AoI in IoT and cognitive radio (CR)-IoT scenarios have received less attention. In this paper, we consider an experimental Raspberry Pi-based prototype where two sources transmit their status over a common channel – mimicking CR-IoT, whose freshness and relevance are measured by the AoI.For statistical characterization, we let the status updates in each of the nodes to follow uniform, Poisson, and exponential distributions for the inter-packet arrival rate and transmission delay. Further, through real-time experiments based on ThingSpeak, we highlight the dependency of the average AoI on the inter-generation rates and inter-transmission delays.
{"title":"Real-Time Implementation and Analysis of Age of Information for Cognitive Radio Internet-of-Things","authors":"M. Ruchitha, S. Suzain, Rajalekshmi Kishore, Sanjeev Gurugopinath","doi":"10.1109/CONECCT55679.2022.9865850","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865850","url":null,"abstract":"Practical implementation of internet-of-things (IoT) rely on time-critical status updates. A well-known measure of the time-dependency in IoT data is the age of information (AoI). Most of the existing literature considers a theoretical analysis on AoI. Practical considerations and implications of AoI in IoT and cognitive radio (CR)-IoT scenarios have received less attention. In this paper, we consider an experimental Raspberry Pi-based prototype where two sources transmit their status over a common channel – mimicking CR-IoT, whose freshness and relevance are measured by the AoI.For statistical characterization, we let the status updates in each of the nodes to follow uniform, Poisson, and exponential distributions for the inter-packet arrival rate and transmission delay. Further, through real-time experiments based on ThingSpeak, we highlight the dependency of the average AoI on the inter-generation rates and inter-transmission delays.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420492","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865779
Gauri V. Nair, S. Sai Girish, Vasudev Bm, Y. Jeppu
This paper describes the experiences of teaching software quality as an undergraduate course. The subject was offered at National Institute of Technology Karnataka, Surathkal and the course covered the aerospace standards, safety, software requirements, design, reviews, and testing. Teaching the ARP 4761, ARP 4754, DO 178C and DO 331 to the students was enlightening. The course was completely conducted online on Teams and Moodle and this itself brings out many insights of teaching an industry level training as an undergraduate course. The course covered the basic of these standards and went through a safety analysis process using the System Theoretic Process Analysis. The problem statement was an onboard scanner. This mimicked a radar system but was called a scanner and had a set of functionalities that was simple enough to understand at an undergrad level. A detailed set of requirements was developed during the course. The process of requirement review was done on the requirements to merge the workflow and process defined in the standards with a practical example. The design model and code were developed for the problem statement. This also went through a review process. A formal method approach was used to test the modes of the scanner. Finally, a set of random tests were developed with a set of Orthogonal Array tests to test the system. The use of a problem helped the students to better interact and understand the complexity. The paper describes the curriculum and presents the work done by the students and their experiences during the course.
{"title":"Teaching Aerospace Software Quality at an Undergrad Level in the Pandemic – Experiences","authors":"Gauri V. Nair, S. Sai Girish, Vasudev Bm, Y. Jeppu","doi":"10.1109/CONECCT55679.2022.9865779","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865779","url":null,"abstract":"This paper describes the experiences of teaching software quality as an undergraduate course. The subject was offered at National Institute of Technology Karnataka, Surathkal and the course covered the aerospace standards, safety, software requirements, design, reviews, and testing. Teaching the ARP 4761, ARP 4754, DO 178C and DO 331 to the students was enlightening. The course was completely conducted online on Teams and Moodle and this itself brings out many insights of teaching an industry level training as an undergraduate course. The course covered the basic of these standards and went through a safety analysis process using the System Theoretic Process Analysis. The problem statement was an onboard scanner. This mimicked a radar system but was called a scanner and had a set of functionalities that was simple enough to understand at an undergrad level. A detailed set of requirements was developed during the course. The process of requirement review was done on the requirements to merge the workflow and process defined in the standards with a practical example. The design model and code were developed for the problem statement. This also went through a review process. A formal method approach was used to test the modes of the scanner. Finally, a set of random tests were developed with a set of Orthogonal Array tests to test the system. The use of a problem helped the students to better interact and understand the complexity. The paper describes the curriculum and presents the work done by the students and their experiences during the course.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947237","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865808
Nagaraju Y, Venkatesh, V. K. R.
Strawberry Guava is a fruit that is high in nutrients. Manually harvesting this fruit is a time-consuming and labor-intensive task. The characteristics of ripened strawberry fruit need automated harvesting, as matured strawberries are unfit for consumption within two days. Deep learning-based approaches have arisen as answers to many issues in recent years. They offer a lot of hope in tricky sectors like agriculture, where they can manage distortion in data more successfully than the typical computer vision approaches. This paper describes a strawberry guava identification algorithm based on semantic segmentation. The modified UNet model was trained to segment ripened strawberry guava fruit with the help of human-annotated images appropriately. To analyze our experimental results on the segmentation of ripened strawberry guava the Dice score measure was used. The validation and test dataset dice scores were 91.04% and 89.72%. The proposed methodology demonstrated that matured strawberry guava could be accurately detected using the modified UNet semantic segmentation model with a few input images.
{"title":"Deep Learning based Semantic Segmentation to Detect Ripened Strawberry Guava Fruits","authors":"Nagaraju Y, Venkatesh, V. K. R.","doi":"10.1109/CONECCT55679.2022.9865808","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865808","url":null,"abstract":"Strawberry Guava is a fruit that is high in nutrients. Manually harvesting this fruit is a time-consuming and labor-intensive task. The characteristics of ripened strawberry fruit need automated harvesting, as matured strawberries are unfit for consumption within two days. Deep learning-based approaches have arisen as answers to many issues in recent years. They offer a lot of hope in tricky sectors like agriculture, where they can manage distortion in data more successfully than the typical computer vision approaches. This paper describes a strawberry guava identification algorithm based on semantic segmentation. The modified UNet model was trained to segment ripened strawberry guava fruit with the help of human-annotated images appropriately. To analyze our experimental results on the segmentation of ripened strawberry guava the Dice score measure was used. The validation and test dataset dice scores were 91.04% and 89.72%. The proposed methodology demonstrated that matured strawberry guava could be accurately detected using the modified UNet semantic segmentation model with a few input images.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124792586","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865734
N. Gupta
This paper presents the results of LQG (Linear Quadratic Gaussian) controller realization in one of the 32m diameter antenna axes at the Indian Deep Space Network (IDSN) near Bengaluru. The dynamic performance results are compared with that of the classical Proportional-Integral (PI) controller as well as the simulation results. Test results validate the simulation predictions, permitting larger gains, bandwidth and improved command response. These studies demonstrate the feasibility and benefits of realizing the LQG controller in the Indian Deep Space Network antenna - IDSN32.
{"title":"Design & Validation of LQG Controller for IDSN32 Antenna Control Servo System","authors":"N. Gupta","doi":"10.1109/CONECCT55679.2022.9865734","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865734","url":null,"abstract":"This paper presents the results of LQG (Linear Quadratic Gaussian) controller realization in one of the 32m diameter antenna axes at the Indian Deep Space Network (IDSN) near Bengaluru. The dynamic performance results are compared with that of the classical Proportional-Integral (PI) controller as well as the simulation results. Test results validate the simulation predictions, permitting larger gains, bandwidth and improved command response. These studies demonstrate the feasibility and benefits of realizing the LQG controller in the Indian Deep Space Network antenna - IDSN32.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125041311","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865775
T. V. Smitha, A. T, Asha S. Manek, Nataraj V
Image processing applications are often affected by noise during the segmentation of curved edges. In this article, a versatile curved triangle mesh development method is proposed for finite element-based image processing applications using MATLAB. This approach first creates meshes using an open-source finite element mesh generator. The proposed technique then generates and visualizes cubic-order curved triangular elements using subparametric transformation. The resulting meshes can be used to solve a variety of problems using finite element analysis for image and video processing applications. The results of the presented work include a cubic-order curved triangular mesh using subparametric transformation, node coordinates, connectivity matrix, and border nodes for finite element implementations. The results obtained by the proposed mesh generator are verified to obtain the most accurate numerical results for an eigenvalue problem by FEM. It can be utilized to improve numerical results very well for various finite element analysis applications. This technology can be utilized in areas such as medical simulation, image processing, material engineering, video processing, computational materials science, optical flow estimation, computational electromagnetics, and so on.
{"title":"A versatile cubic-curved triangle mesh generator using subparametric transformation for FEM and image processing applications","authors":"T. V. Smitha, A. T, Asha S. Manek, Nataraj V","doi":"10.1109/CONECCT55679.2022.9865775","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865775","url":null,"abstract":"Image processing applications are often affected by noise during the segmentation of curved edges. In this article, a versatile curved triangle mesh development method is proposed for finite element-based image processing applications using MATLAB. This approach first creates meshes using an open-source finite element mesh generator. The proposed technique then generates and visualizes cubic-order curved triangular elements using subparametric transformation. The resulting meshes can be used to solve a variety of problems using finite element analysis for image and video processing applications. The results of the presented work include a cubic-order curved triangular mesh using subparametric transformation, node coordinates, connectivity matrix, and border nodes for finite element implementations. The results obtained by the proposed mesh generator are verified to obtain the most accurate numerical results for an eigenvalue problem by FEM. It can be utilized to improve numerical results very well for various finite element analysis applications. This technology can be utilized in areas such as medical simulation, image processing, material engineering, video processing, computational materials science, optical flow estimation, computational electromagnetics, and so on.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126606397","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865829
Rahisha Thottolil, U. Kumar
Mapping building structures in urban areas are fundamental for many urban studies. High-resolution satellite images and machine learning algorithms are often used for semi-automatic and automatic building detection. This study explores the utility of classification algorithm and high-resolution Google Earth images to detect buildings from a complex urban area having heterogeneous building structures of various shapes, sizes and construction materials. The proposed model converts the RGB images to grayscale followed by derivation of several filtered profiles to capture spatial and spectral feature vectors. Differential morphological profiles (DMPs) were constructed from consecutive morphological profiles to identify the structural information of probable buildings present in the image. Consequently, morphological building index was computed by averaging the DMPs that included mislabeled rooftops due to the presence of shadows. Global Otsu thresholding was used to reduce the number of mislabeled buildings. Finally, Random Forest algorithm was used for the classification of buildings based on the computed feature vectors. After noise removal, the quality of final building maps were assessed using evaluation metrics with reference to the ground truth which showed 4.4% improvement (from 81.2 to 85.62 %) over RF model while also revealing the detailed structural information of buildings.
{"title":"Automatic Building Footprint Extraction using Random Forest Algorithm from High Resolution Google Earth Images: A Feature-Based Approach","authors":"Rahisha Thottolil, U. Kumar","doi":"10.1109/CONECCT55679.2022.9865829","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865829","url":null,"abstract":"Mapping building structures in urban areas are fundamental for many urban studies. High-resolution satellite images and machine learning algorithms are often used for semi-automatic and automatic building detection. This study explores the utility of classification algorithm and high-resolution Google Earth images to detect buildings from a complex urban area having heterogeneous building structures of various shapes, sizes and construction materials. The proposed model converts the RGB images to grayscale followed by derivation of several filtered profiles to capture spatial and spectral feature vectors. Differential morphological profiles (DMPs) were constructed from consecutive morphological profiles to identify the structural information of probable buildings present in the image. Consequently, morphological building index was computed by averaging the DMPs that included mislabeled rooftops due to the presence of shadows. Global Otsu thresholding was used to reduce the number of mislabeled buildings. Finally, Random Forest algorithm was used for the classification of buildings based on the computed feature vectors. After noise removal, the quality of final building maps were assessed using evaluation metrics with reference to the ground truth which showed 4.4% improvement (from 81.2 to 85.62 %) over RF model while also revealing the detailed structural information of buildings.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056245","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}
Dementia is associated to one of the early phases of fatal diseases such as Huntington's disease or, in more severe situations, death. It is a chronic and degenerative disease that affects millions of people throughout the world. Memory loss, difficulty in concentration, mood changes, and being confused are some of the symptoms of dementia. Early detection can prevent or postpone the course of dementia, which is the most common degenerative illness among the elderly. This paper's principal objective is to deploy a variety of machine learning algorithms like Logistic Regression, ExtraTreesClassifier, Random Forest, ExtremeBoost Classifier (XGBoost), Light Gradient Boost (LGBM), Decision Tree, Gradient Boosting, Gaussian Nave Bayes, and Classifier Support Vector Machine (SVM) on selected features from the dataset for multi-class classification of dementia patients as 'demented,' 'non-demented,' and 'converted'. The model evaluation was based on the F1-score, precision and accuracy and it was found that Gradient Boost performed well with an accuracy of 0.96.
{"title":"Classification and Analysis of Dementia using Machine Learning Algorithms","authors":"Aakarsh Arora, Mahendra Kumar Gourisaria, Rajdeep Chatterjee","doi":"10.1109/CONECCT55679.2022.9865789","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865789","url":null,"abstract":"Dementia is associated to one of the early phases of fatal diseases such as Huntington's disease or, in more severe situations, death. It is a chronic and degenerative disease that affects millions of people throughout the world. Memory loss, difficulty in concentration, mood changes, and being confused are some of the symptoms of dementia. Early detection can prevent or postpone the course of dementia, which is the most common degenerative illness among the elderly. This paper's principal objective is to deploy a variety of machine learning algorithms like Logistic Regression, ExtraTreesClassifier, Random Forest, ExtremeBoost Classifier (XGBoost), Light Gradient Boost (LGBM), Decision Tree, Gradient Boosting, Gaussian Nave Bayes, and Classifier Support Vector Machine (SVM) on selected features from the dataset for multi-class classification of dementia patients as 'demented,' 'non-demented,' and 'converted'. The model evaluation was based on the F1-score, precision and accuracy and it was found that Gradient Boost performed well with an accuracy of 0.96.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115517222","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865678
Mohd Ziauddin Jahangir, P. Chandra Sekhar, M. Sikander, J. V. Krishna
This work presents the design and FPGA implementation of a Sine-Wave DDS- All-Digital PLLs for resonant sensors application. The sensor consisting of a mechanical structure. The proposed PLL was designed to track changes in the resonant frequency of a mechanical structure under different spatial orientations. The expected resonant frequency of the mechanical structure under test is less than 10 KHz. As most CMOS PLL ICs operate at very high frequencies, it is not suitable to be used in this application. Additionally, a digital implementation of PLL is preferred due it’s high degree of configurability. In this work, the architecture and design of two different sine wave DDS ADPLLs are presented. A Type-1 sine wave DDS ADPLL was realized on FPGA using SPI ADCs & DACs for interfacing. The important design considerations for realizing proposed ADPLL on the hardware are also presented in this work. The realized ADPLL exhibited a perfect locking behavior around 7KHz center frequency.
{"title":"Design and Implementation of FPGA based DDS-ADPLL for Resonant Frequency Tracking in Sensors","authors":"Mohd Ziauddin Jahangir, P. Chandra Sekhar, M. Sikander, J. V. Krishna","doi":"10.1109/CONECCT55679.2022.9865678","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865678","url":null,"abstract":"This work presents the design and FPGA implementation of a Sine-Wave DDS- All-Digital PLLs for resonant sensors application. The sensor consisting of a mechanical structure. The proposed PLL was designed to track changes in the resonant frequency of a mechanical structure under different spatial orientations. The expected resonant frequency of the mechanical structure under test is less than 10 KHz. As most CMOS PLL ICs operate at very high frequencies, it is not suitable to be used in this application. Additionally, a digital implementation of PLL is preferred due it’s high degree of configurability. In this work, the architecture and design of two different sine wave DDS ADPLLs are presented. A Type-1 sine wave DDS ADPLL was realized on FPGA using SPI ADCs & DACs for interfacing. The important design considerations for realizing proposed ADPLL on the hardware are also presented in this work. The realized ADPLL exhibited a perfect locking behavior around 7KHz center frequency.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114416170","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 : 2022-07-08DOI: 10.1109/CONECCT55679.2022.9865693
Urvi Popat, Santanu Sinha
Satellite based Automatic Identification System (AIS) is a potent tool for global maritime surveillance. AIS operates in the Very High Frequency (VHF) band of frequencies over a narrow band width of 25 KHz. The signals transmitted by ships carrying AIS transceivers are received by AIS satellites orbiting in Low Earth Orbit. The band selection in the satellite payload is typically achieved by the use of a precise narrow band Surface Acoustic Wave (SAW) filter. This paper highlights the challenges faced during the design and realization of such a narrow band SAW filter and presents ways to overcome them. Third harmonic technique is used for filter design and its advantages from a filter fabrication point of view are discussed. However, the use of this technique deteriorates the far out of band spurious performance of the filter. Techniques implemented to achieve a spurious suppressed filter response, in order to meet the overall spurious specifications of the payload, are presented.
{"title":"A Spurious free VHF front end SAW Filter for Satellite AIS","authors":"Urvi Popat, Santanu Sinha","doi":"10.1109/CONECCT55679.2022.9865693","DOIUrl":"https://doi.org/10.1109/CONECCT55679.2022.9865693","url":null,"abstract":"Satellite based Automatic Identification System (AIS) is a potent tool for global maritime surveillance. AIS operates in the Very High Frequency (VHF) band of frequencies over a narrow band width of 25 KHz. The signals transmitted by ships carrying AIS transceivers are received by AIS satellites orbiting in Low Earth Orbit. The band selection in the satellite payload is typically achieved by the use of a precise narrow band Surface Acoustic Wave (SAW) filter. This paper highlights the challenges faced during the design and realization of such a narrow band SAW filter and presents ways to overcome them. Third harmonic technique is used for filter design and its advantages from a filter fabrication point of view are discussed. However, the use of this technique deteriorates the far out of band spurious performance of the filter. Techniques implemented to achieve a spurious suppressed filter response, in order to meet the overall spurious specifications of the payload, are presented.","PeriodicalId":380005,"journal":{"name":"2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124759345","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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