Pub Date : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986392
Depth estimation from a single RGB picture has emerged as one of the most significant study areas in recent years because of the wide variety of applications, from robotics to medical sciences. Monocular depth estimation has often had low resolution and blurry depth maps, which are not usable for further training of models with specific applications. The main drawback of generic depth estimation models is that they take an object and its environment into consideration. Because of this, traditional deep learning-based systems often experience severe setbacks in forecasting depths. This paper proposes an encoder-decoder network that, using transfer learning, can forecast high-quality depth pictures from a single RGB image. After initialising the encoder using augmentation algorithms and significant feature extraction from pre-trained networks, the decoder predicts the high-end depth maps. We have also used several boundary detection techniques to remove the object from its environment without losing the object's pixel information. Our network performs comparable to the state-of-the-art on two datasets and also generates qualitatively better results that more accurately represent object boundaries which can be further used in 6D pose estimation to perform robotic grasping.
{"title":"Estimating Depth Map of an RGB image using Encoders and Decoders","authors":"","doi":"10.1109/UPCON56432.2022.9986392","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986392","url":null,"abstract":"Depth estimation from a single RGB picture has emerged as one of the most significant study areas in recent years because of the wide variety of applications, from robotics to medical sciences. Monocular depth estimation has often had low resolution and blurry depth maps, which are not usable for further training of models with specific applications. The main drawback of generic depth estimation models is that they take an object and its environment into consideration. Because of this, traditional deep learning-based systems often experience severe setbacks in forecasting depths. This paper proposes an encoder-decoder network that, using transfer learning, can forecast high-quality depth pictures from a single RGB image. After initialising the encoder using augmentation algorithms and significant feature extraction from pre-trained networks, the decoder predicts the high-end depth maps. We have also used several boundary detection techniques to remove the object from its environment without losing the object's pixel information. Our network performs comparable to the state-of-the-art on two datasets and also generates qualitatively better results that more accurately represent object boundaries which can be further used in 6D pose estimation to perform robotic grasping.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"914 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":"123275616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tremendous volume of video data generated by industrial surveillance networks presents a number of difficulties when examining such videos for a variety of purposes, including video summarization (VS), analysis, indexing and retrieval. The task of creating video summaries is extremely difficult because of the huge amount of data, redundancy, interleaved views and light variations. Multiple object detection and identification in video is difficult for machines to recognize and classify. To address all such issues, multiple low-feature and clustering-based machine learning strategies that fail to completely exploit VS are recommended. In this work, we achieved VS by embedding deep neural network-based soft computing methods. Firstly, the objects in extracted frames are detected using YOLOv5, and then the frames without objects (useless frames) are removed. Video summary generation occurs with the help of frames containing Objects. To check the quality of the proposed work Summary length, precision, recall, PR curve, and mean average precision (mAP) are used and system resource utilization during the model training are also tracked. As a result, the proposed work was able to identify the most effective video summarization framework with best summary length under varying conditions.
{"title":"Object Detection based Approach for an Efficient Video Summarization with System Statistics over Cloud","authors":"Alok Negi, Krishan Kumar, Parul Saini, Shamal Kashid","doi":"10.1109/UPCON56432.2022.9986376","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986376","url":null,"abstract":"The tremendous volume of video data generated by industrial surveillance networks presents a number of difficulties when examining such videos for a variety of purposes, including video summarization (VS), analysis, indexing and retrieval. The task of creating video summaries is extremely difficult because of the huge amount of data, redundancy, interleaved views and light variations. Multiple object detection and identification in video is difficult for machines to recognize and classify. To address all such issues, multiple low-feature and clustering-based machine learning strategies that fail to completely exploit VS are recommended. In this work, we achieved VS by embedding deep neural network-based soft computing methods. Firstly, the objects in extracted frames are detected using YOLOv5, and then the frames without objects (useless frames) are removed. Video summary generation occurs with the help of frames containing Objects. To check the quality of the proposed work Summary length, precision, recall, PR curve, and mean average precision (mAP) are used and system resource utilization during the model training are also tracked. As a result, the proposed work was able to identify the most effective video summarization framework with best summary length under varying conditions.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"321 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":"122735176","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/UPCON56432.2022.9986399
Sambhavi Tiwari, Manas Gogoi, S. Verma, Krishna Pratap Singh
In this paper, we propose a novel meta-learning method that leverages the advantages of both meta-learning and storage. In meta-learning, the neural network tries to learn parameters distributed across multiple tasks. Meta-learning provides quick learning with unseen meta-testing tasks. In model-based meta-learning methods, an external memory module is used to retain a memory of important parameters from one task to the other, enabling meta-learning. The model proposed in this work consists of a long short-term memory(LSTM) neural network with an external memory network known as Hopfield neural network. Hopfield neural network is a single-layer, non-linear, auto-associative model that uses an external memory network. Unlike previous methods, our proposed model $LSTM_{HAM}$, i.e., long short term memory with Hopfield associative memory focuses on storing knowledge that uses an additional memory network to store and retrieve patterns using different location-based access mechanisms. Our model extends the capabilities of the LSTM and performs meta-learning best on 5-way 10-shot task setting with an average accuracy of approximately 60 percent.
{"title":"Meta-learning with Hopfield Neural Network","authors":"Sambhavi Tiwari, Manas Gogoi, S. Verma, Krishna Pratap Singh","doi":"10.1109/UPCON56432.2022.9986399","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986399","url":null,"abstract":"In this paper, we propose a novel meta-learning method that leverages the advantages of both meta-learning and storage. In meta-learning, the neural network tries to learn parameters distributed across multiple tasks. Meta-learning provides quick learning with unseen meta-testing tasks. In model-based meta-learning methods, an external memory module is used to retain a memory of important parameters from one task to the other, enabling meta-learning. The model proposed in this work consists of a long short-term memory(LSTM) neural network with an external memory network known as Hopfield neural network. Hopfield neural network is a single-layer, non-linear, auto-associative model that uses an external memory network. Unlike previous methods, our proposed model $LSTM_{HAM}$, i.e., long short term memory with Hopfield associative memory focuses on storing knowledge that uses an additional memory network to store and retrieve patterns using different location-based access mechanisms. Our model extends the capabilities of the LSTM and performs meta-learning best on 5-way 10-shot task setting with an average accuracy of approximately 60 percent.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"30 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":"128491371","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/UPCON56432.2022.9986379
Mohammad Zaid, A. Pampori, Y. Chauhan
In this paper, we propose a 16W S-Band Power Amplifier using coupler based design. The design procedure involves the use of power splitting and combining in order to achieve high power of operation. The power amplifier has a measured gain of 14.37 dB at 2.6 GHz, an output power of 42 dBm, and a measured Power Added Efficiency (PAE) of 48.7%. In terms of linearity, the circuit has a measured Output 1-dB compression point OP1dB of 34 dBm and an output Third Order Intercept (OIP3) value of 44.8 dBm.
{"title":"16 Watt S-Band GaN Based Power Amplifier Using Replicating Stages","authors":"Mohammad Zaid, A. Pampori, Y. Chauhan","doi":"10.1109/UPCON56432.2022.9986379","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986379","url":null,"abstract":"In this paper, we propose a 16W S-Band Power Amplifier using coupler based design. The design procedure involves the use of power splitting and combining in order to achieve high power of operation. The power amplifier has a measured gain of 14.37 dB at 2.6 GHz, an output power of 42 dBm, and a measured Power Added Efficiency (PAE) of 48.7%. In terms of linearity, the circuit has a measured Output 1-dB compression point OP1dB of 34 dBm and an output Third Order Intercept (OIP3) value of 44.8 dBm.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"4 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":"129091484","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/UPCON56432.2022.9986455
Nidhi Dubey, S. K.
The virtual synchronous generator control is introduced to the power electronic interfaced inverters to control voltage and frequency in the islanded microgrid. In order to establish safe operating conditions for islanded microgrid various values of moment of inertia, damping constant, Q-V droop constant values are taken in this paper to justify the system behavior. Validation of the MATLAB/simulation model is done through the small signal modelling of the electrical system and is verified by the eigen value plot of the system to check the feasibility and correctness of the system. The dynamic performances of the model in terms of current, voltage, active power, reactive power and frequency is analysed.
{"title":"Small signal modelling and Parameter Analysis of Virtual Synchronous Generator Based Control in Isolated Microgrid","authors":"Nidhi Dubey, S. K.","doi":"10.1109/UPCON56432.2022.9986455","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986455","url":null,"abstract":"The virtual synchronous generator control is introduced to the power electronic interfaced inverters to control voltage and frequency in the islanded microgrid. In order to establish safe operating conditions for islanded microgrid various values of moment of inertia, damping constant, Q-V droop constant values are taken in this paper to justify the system behavior. Validation of the MATLAB/simulation model is done through the small signal modelling of the electrical system and is verified by the eigen value plot of the system to check the feasibility and correctness of the system. The dynamic performances of the model in terms of current, voltage, active power, reactive power and frequency is analysed.","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":"129296571","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/UPCON56432.2022.9986479
Aarya Chaumal, Amit M. Joshi
Modern computer architectures are moving towards domain-specific designs of processors. ARM-based processors domi-nate the embedded domain due to their compact and energy-efficient design. x86 processors have higher computing capabilities but at the cost of high energy consumption. This work tries to improve the Network-on-Chip design of x86 processors to have an energy-efficient Chip Multi-Processor configuration with similar computing power. Network-on-Chip is a significant part of modern computer architecture. It helps to efficiently navigate on-chip traffic on current Chip Multi-Processors where the number of cores is increasing rapidly. The topology of a Network-on-Chip significantly impacts system performance as it directly affects the network bandwidth and the area of the system. Therefore, Network-on-Chip topology affects the system's execution time, area, and energy consumption. This work proposes a novel topology to improve performance in terms of energy consumption and execution time, and affects L1D miss rate of the considered specification with few benchmark programs. The proposed topology is inspired from the traditional binary tree topology and tries to overcome its shortcomings to improve the system performance. The experiment results suggest that the proposed topology improves system performance on applications belonging to domains that are suited for embedded class processors.
{"title":"Analyzing Binary Tree based Topology Configuration for Energy Efficient Multicore Architectures","authors":"Aarya Chaumal, Amit M. Joshi","doi":"10.1109/UPCON56432.2022.9986479","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986479","url":null,"abstract":"Modern computer architectures are moving towards domain-specific designs of processors. ARM-based processors domi-nate the embedded domain due to their compact and energy-efficient design. x86 processors have higher computing capabilities but at the cost of high energy consumption. This work tries to improve the Network-on-Chip design of x86 processors to have an energy-efficient Chip Multi-Processor configuration with similar computing power. Network-on-Chip is a significant part of modern computer architecture. It helps to efficiently navigate on-chip traffic on current Chip Multi-Processors where the number of cores is increasing rapidly. The topology of a Network-on-Chip significantly impacts system performance as it directly affects the network bandwidth and the area of the system. Therefore, Network-on-Chip topology affects the system's execution time, area, and energy consumption. This work proposes a novel topology to improve performance in terms of energy consumption and execution time, and affects L1D miss rate of the considered specification with few benchmark programs. The proposed topology is inspired from the traditional binary tree topology and tries to overcome its shortcomings to improve the system performance. The experiment results suggest that the proposed topology improves system performance on applications belonging to domains that are suited for embedded class processors.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"41 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":"121662733","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/UPCON56432.2022.9986446
Prashant Singh, N. Singh, A. K. Singh
The whole world is going through electrical fuel transition, from traditional to renewable energy (RE) sources. Natural resources like coal, natural gas, fossil fuels are still dominant energy sources to produce electrical energy throughout the world. If the switching towards RE source does not take place, these natural sources will deplete sooner, and heavy energy crises will come into picture. The paper addresses the issue of forecasting short-term renewable energy supply. The stochastic nature of RE sources has an impact on power system planning procedures, lowering the reliability as well as security of power supply for end users [1]. In this paper solar photovoltaic (PV) energy forecasting is performed using two dependent data variables such as (a) solar irradiance and (b) temperature, and past solar PV energy output using machine learning and deep learning (DL) algorithms. DL is a kind of complex learning inspired by human learning. Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network are the examples of it. The paper investigates the issue of identifying features and determining suitable error metrics. DL model was developed and tested on real solar PV energy produced on MNNIT Allahabad, India campus. The forecasting performance of developed models is evaluated in terms of three important measures, (a) mean absolute error (MAE), (b) mean squared error (MSE), and (c) root mean square error (RMSE).
{"title":"Solar Photovoltaic Energy Forecasting Using Machine Learning and Deep Learning Technique","authors":"Prashant Singh, N. Singh, A. K. Singh","doi":"10.1109/UPCON56432.2022.9986446","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986446","url":null,"abstract":"The whole world is going through electrical fuel transition, from traditional to renewable energy (RE) sources. Natural resources like coal, natural gas, fossil fuels are still dominant energy sources to produce electrical energy throughout the world. If the switching towards RE source does not take place, these natural sources will deplete sooner, and heavy energy crises will come into picture. The paper addresses the issue of forecasting short-term renewable energy supply. The stochastic nature of RE sources has an impact on power system planning procedures, lowering the reliability as well as security of power supply for end users [1]. In this paper solar photovoltaic (PV) energy forecasting is performed using two dependent data variables such as (a) solar irradiance and (b) temperature, and past solar PV energy output using machine learning and deep learning (DL) algorithms. DL is a kind of complex learning inspired by human learning. Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network are the examples of it. The paper investigates the issue of identifying features and determining suitable error metrics. DL model was developed and tested on real solar PV energy produced on MNNIT Allahabad, India campus. The forecasting performance of developed models is evaluated in terms of three important measures, (a) mean absolute error (MAE), (b) mean squared error (MSE), and (c) root mean square error (RMSE).","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"22 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":"125259834","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/UPCON56432.2022.9986393
Rituvic Pandey, Tripurari Nath Gupta, M. Rawat
In this work, a 3-phase grid-connected solar energy conversion system is presented, which performs multiple tasks such as mitigation of harmonics and DC offset, reactive power compensation of the nonlinear load, and offers a high power factor at the utility end. A Multi Order Fundamental Signal Extractor (MOFSE) based filtering technique is proposed to achieve the said objective. In this work, a water pumping system is considered as load, which delivers water under rated conditions, irrespective of the solar energy generation. To achieve this, the pump requires constant power input. If the solar power generation exceeds the pump need, the excess power is fed into the grid. In case of deficit generation, the pump draws the remaining power from the grid. Testing of the system is carried out in MATLAB/Simulink environment with varying solar irradiance and highly contaminated grid voltage conditions. The grid current THD is ensured to be as per the IEEE 519-2014 standard.
{"title":"Power Quality Improvement of 3-phase Solar Energy Conversion System using MOFSE","authors":"Rituvic Pandey, Tripurari Nath Gupta, M. Rawat","doi":"10.1109/UPCON56432.2022.9986393","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986393","url":null,"abstract":"In this work, a 3-phase grid-connected solar energy conversion system is presented, which performs multiple tasks such as mitigation of harmonics and DC offset, reactive power compensation of the nonlinear load, and offers a high power factor at the utility end. A Multi Order Fundamental Signal Extractor (MOFSE) based filtering technique is proposed to achieve the said objective. In this work, a water pumping system is considered as load, which delivers water under rated conditions, irrespective of the solar energy generation. To achieve this, the pump requires constant power input. If the solar power generation exceeds the pump need, the excess power is fed into the grid. In case of deficit generation, the pump draws the remaining power from the grid. Testing of the system is carried out in MATLAB/Simulink environment with varying solar irradiance and highly contaminated grid voltage conditions. The grid current THD is ensured to be as per the IEEE 519-2014 standard.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"19 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":"125994771","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/UPCON56432.2022.9986372
Aayush Sharma, Ashwini Kodipalli, T. Rao
Covid-19 has become a big challenge across the world and there has been an urgent need for breakthroughs in clinical research, vaccine discoveries/trial and pharmaceutical technologies. Symptom identification with the use of machine learning frameworks and strategies can greatly pave way for rapid control and assessments that eventually can help to contain virus outbreaks. We compare performance of two convolutional neural networks namely ResNet-16 and Inception-v4 for classification of X-ray images as Covid-19 or non-Covid-19. Results inferred the model performance is around 83% with Inception-v4, which is considerably a deeper network than ResNet-16
{"title":"Performance of Resnet-16 and Inception-V4 Architecture to Identify Covid-19 from X-Ray Images","authors":"Aayush Sharma, Ashwini Kodipalli, T. Rao","doi":"10.1109/UPCON56432.2022.9986372","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986372","url":null,"abstract":"Covid-19 has become a big challenge across the world and there has been an urgent need for breakthroughs in clinical research, vaccine discoveries/trial and pharmaceutical technologies. Symptom identification with the use of machine learning frameworks and strategies can greatly pave way for rapid control and assessments that eventually can help to contain virus outbreaks. We compare performance of two convolutional neural networks namely ResNet-16 and Inception-v4 for classification of X-ray images as Covid-19 or non-Covid-19. Results inferred the model performance is around 83% with Inception-v4, which is considerably a deeper network than ResNet-16","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":"133977353","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/UPCON56432.2022.9986414
Vaibhav Pratap Singh, Vivek Chand, Yash Pal
On the basis of a previously developed slow wave substrate integrated waveguide, this study offers a new design, simulation, and measurement results of a circularly polarized square slot antenna backed by cavities (SW-SIW). 11.2 GHz is the intended operating frequency for the planned antenna. The size of the proposed antenna has been reduced to 13% when compared to the reference design and 49% when compared to the traditional SIW version of antenna thanks to the internal metallized via that creates the slow wave effect for the physical separation of the electric and magnetic field in the designed antenna structure. The designed antenna has been tested and simulated, and the results show that it performs better than expected in terms of gain, radiation loss, and return loss. According to the suggested design, the maximum return loss achieved by the proposed antenna is 37 dB at 11.2 GHz frequency, and the return loss is less than 10 dB in the frequency range of 11 to 11.3 GHz. With proposed antenna we got the gain of 5.9 dB as compare to reference antenna with 4.8 dBi of gain.
{"title":"Substrate Integrated Waveguide Antenna with Slow Wave Effect to Minimize Dimensions","authors":"Vaibhav Pratap Singh, Vivek Chand, Yash Pal","doi":"10.1109/UPCON56432.2022.9986414","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986414","url":null,"abstract":"On the basis of a previously developed slow wave substrate integrated waveguide, this study offers a new design, simulation, and measurement results of a circularly polarized square slot antenna backed by cavities (SW-SIW). 11.2 GHz is the intended operating frequency for the planned antenna. The size of the proposed antenna has been reduced to 13% when compared to the reference design and 49% when compared to the traditional SIW version of antenna thanks to the internal metallized via that creates the slow wave effect for the physical separation of the electric and magnetic field in the designed antenna structure. The designed antenna has been tested and simulated, and the results show that it performs better than expected in terms of gain, radiation loss, and return loss. According to the suggested design, the maximum return loss achieved by the proposed antenna is 37 dB at 11.2 GHz frequency, and the return loss is less than 10 dB in the frequency range of 11 to 11.3 GHz. With proposed antenna we got the gain of 5.9 dB as compare to reference antenna with 4.8 dBi of gain.","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":"133583159","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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