Pub Date : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854837
Renu Siddagangappa, N. K
The Network on Chip (NoC) is a cost-effective alternative to bus-based connectivity in most multi-core networks. The NoC system solves the drawbacks of bus-based networks by providing higher scalability and dependability. The NoCs are modeled synchronously with the help of global clocks in general. These global clocks are disseminated over vast distances in synchronous NoCs with a modest degree of skew. For high-performance NoC designs that need an expensive customized calibration procedure, a significant global tree is required. As a result, asynchronous NoCs provide an alternate solution to the global clock distribution difficulties. NoC is represented using asynchronous circuits and managed through handshake protocols to tackle global clock difficulties. The Quasi-Delay Insensitive (QDI) circuits are different from DI circuits with time relaxation. The wire delays in QDI circuits are rapidly regulated and incorporated in most practical asynchronous systems, unlike DI-based designs. This manuscript discusses existing ANoC based architecture with fault tolerant mechanisms in detail. The Summary of the current approach, and its performance metrics realization, is highlighted. The challenges and possible solutions for ANoC and its fault-tolerant mechanism are discussed.
{"title":"Asynchronous NoC with Fault tolerant mechanism: A Comprehensive Review","authors":"Renu Siddagangappa, N. K","doi":"10.1109/TEECCON54414.2022.9854837","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854837","url":null,"abstract":"The Network on Chip (NoC) is a cost-effective alternative to bus-based connectivity in most multi-core networks. The NoC system solves the drawbacks of bus-based networks by providing higher scalability and dependability. The NoCs are modeled synchronously with the help of global clocks in general. These global clocks are disseminated over vast distances in synchronous NoCs with a modest degree of skew. For high-performance NoC designs that need an expensive customized calibration procedure, a significant global tree is required. As a result, asynchronous NoCs provide an alternate solution to the global clock distribution difficulties. NoC is represented using asynchronous circuits and managed through handshake protocols to tackle global clock difficulties. The Quasi-Delay Insensitive (QDI) circuits are different from DI circuits with time relaxation. The wire delays in QDI circuits are rapidly regulated and incorporated in most practical asynchronous systems, unlike DI-based designs. This manuscript discusses existing ANoC based architecture with fault tolerant mechanisms in detail. The Summary of the current approach, and its performance metrics realization, is highlighted. The challenges and possible solutions for ANoC and its fault-tolerant mechanism are discussed.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742341","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854829
R. Pinky, Sapam Jitu Singh, Chongtham Pankaj
Human activity recognition is the wide range of field of research and challenging task to identify the actions of the human in period of time based on received signal strength data in wireless sensor network. It is important to monitor activity of a person for numerous reasons. Recently, Machine Learning approach shows capable of classifying the actions of the human by automatically using the raw sensor data. In this work, the dataset consists of received signal strength of seven activities using three sensor nodes that are trained by using supervised machine learning algorithms to recognize the actions and random activities are monitored to identify the strange action of the person using unsupervised machine learning. The proposed machine learning based human activity recognition model are evaluated and predict the seven human activities by achieving 90% of accuracy. The model is later improved to recognize the random actions of the human.
{"title":"Human Activities Recognition and Monitoring System Using Machine Learning Techniques","authors":"R. Pinky, Sapam Jitu Singh, Chongtham Pankaj","doi":"10.1109/TEECCON54414.2022.9854829","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854829","url":null,"abstract":"Human activity recognition is the wide range of field of research and challenging task to identify the actions of the human in period of time based on received signal strength data in wireless sensor network. It is important to monitor activity of a person for numerous reasons. Recently, Machine Learning approach shows capable of classifying the actions of the human by automatically using the raw sensor data. In this work, the dataset consists of received signal strength of seven activities using three sensor nodes that are trained by using supervised machine learning algorithms to recognize the actions and random activities are monitored to identify the strange action of the person using unsupervised machine learning. The proposed machine learning based human activity recognition model are evaluated and predict the seven human activities by achieving 90% of accuracy. The model is later improved to recognize the random actions of the human.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126709717","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854846
K. S. Himaja Chowdary, M. Kalaiyarasi, Swaminathan Saravanan
Satellite images are often volumetric, requiring a lot of storage and transmission space and time. In this paper, a Gated Recurrent Unit RNN based NTD method has been proposed for satellite image compression. RNN is used to convert spectral sensor into small scale spectral sensor. Entropy encoding is performed for final compression. The proposed method is compared to the standard NTD in the wavelet domain, the computing efficiency is improved by 56.40% while compromising just -0.58 dB of PSNR.
{"title":"Gated Recurrent Unit RNN based Non-negative Tucker Decomposition for Satellite Image Compression","authors":"K. S. Himaja Chowdary, M. Kalaiyarasi, Swaminathan Saravanan","doi":"10.1109/TEECCON54414.2022.9854846","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854846","url":null,"abstract":"Satellite images are often volumetric, requiring a lot of storage and transmission space and time. In this paper, a Gated Recurrent Unit RNN based NTD method has been proposed for satellite image compression. RNN is used to convert spectral sensor into small scale spectral sensor. Entropy encoding is performed for final compression. The proposed method is compared to the standard NTD in the wavelet domain, the computing efficiency is improved by 56.40% while compromising just -0.58 dB of PSNR.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124631870","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854842
Abhishek Kashyap, Aruna Kumara B
Kannada, A dravidian language of south India that consists of kannada numerals from 0 to 9 and 49 letters that are further classified into swara, vyanjana and yogavahagalu. The task Optical Character Recognition(OCR) is to transform printed or handwritten text into digital form. This technique can be explored to extract kannada numerals and letters from images of handwritten documents, processed using image processing techniques such as segmentation, skewing and slanting using OpenCV. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Convolutional neural network(CNN) is a deep learning technique that can be used to train the model and classify kannada characters using Tensorflow and Keras. Our study has showed that our model has outperformed present methods to classify Kannada numerals and characters with 100% accuracy.
{"title":"OCR of Kannada Characters Using Deep Learning","authors":"Abhishek Kashyap, Aruna Kumara B","doi":"10.1109/TEECCON54414.2022.9854842","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854842","url":null,"abstract":"Kannada, A dravidian language of south India that consists of kannada numerals from 0 to 9 and 49 letters that are further classified into swara, vyanjana and yogavahagalu. The task Optical Character Recognition(OCR) is to transform printed or handwritten text into digital form. This technique can be explored to extract kannada numerals and letters from images of handwritten documents, processed using image processing techniques such as segmentation, skewing and slanting using OpenCV. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Convolutional neural network(CNN) is a deep learning technique that can be used to train the model and classify kannada characters using Tensorflow and Keras. Our study has showed that our model has outperformed present methods to classify Kannada numerals and characters with 100% accuracy.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130170197","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854838
K. U, Varaprasad Janamala
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder ‘Racheruvu 11kV agricultural feeder’ Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system.
{"title":"A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network","authors":"K. U, Varaprasad Janamala","doi":"10.1109/TEECCON54414.2022.9854838","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854838","url":null,"abstract":"Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder ‘Racheruvu 11kV agricultural feeder’ Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776980","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854835
Asif Hamid, Danish Rafiq, S. A. Nahvi, Mohammad Abid Bazaz
The description of coherent features in modern power grids is fundamental in understanding the underlying transient phenomena. While the system dynamics is large-scale and governed by strong nonlinear behavior, an efficient sparse representation can be formulated in a suitable coordinate system. One such representation is given by the Dynamic Mode Decomposition (DMD). In this contribution, we use DMD to obtain low-dimensional reconstructions of power system models from data obtained via a direct numerical simulation or a physical experiment. Notably, we show that DMD can describe the underlying oscillatory swing dynamics captured in data or project the large-scale solution manifold on a system having fewer degrees of freedom.
{"title":"Discovering low-rank representations of large-scale power-grid models using Koopman theory","authors":"Asif Hamid, Danish Rafiq, S. A. Nahvi, Mohammad Abid Bazaz","doi":"10.1109/TEECCON54414.2022.9854835","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854835","url":null,"abstract":"The description of coherent features in modern power grids is fundamental in understanding the underlying transient phenomena. While the system dynamics is large-scale and governed by strong nonlinear behavior, an efficient sparse representation can be formulated in a suitable coordinate system. One such representation is given by the Dynamic Mode Decomposition (DMD). In this contribution, we use DMD to obtain low-dimensional reconstructions of power system models from data obtained via a direct numerical simulation or a physical experiment. Notably, we show that DMD can describe the underlying oscillatory swing dynamics captured in data or project the large-scale solution manifold on a system having fewer degrees of freedom.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322348","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854824
Sanjeeta Dhegaya, Lavi Tanwar
A single layer tri-band frequency selective surface (FSS) is proposed in this paper. It is composed of two transmission poles and one stop band filter, thus behaving as good isolation between two transmission bands .i.e. C and X-band. The design consists of two square slots with a center square patch and two cross dipole patch diagonally arranged in a two-dimensional unit cell. Two band pass filter is at 6.04 GHz and 9.60 GHz resonant frequency with a band width of 0.89 GHz and 0.87 GHz respectively. One stop band filter at 7.6 GHz resonant frequency in between these C-band and X-band play an important role for the good isolation for wireless communication. The size of unit cell FSS is 0.40λ0×0.40λ0 and thickness of 0.016λ0, where λ0 is the first lower resonant frequency. Both pass band resonant frequencies are spaced with a good shielding providing the frequency ratio of 1.57.
{"title":"Design of Dual Band Pass and Band Stop Frequency Selective Surface: For Wireless Communication","authors":"Sanjeeta Dhegaya, Lavi Tanwar","doi":"10.1109/TEECCON54414.2022.9854824","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854824","url":null,"abstract":"A single layer tri-band frequency selective surface (FSS) is proposed in this paper. It is composed of two transmission poles and one stop band filter, thus behaving as good isolation between two transmission bands .i.e. C and X-band. The design consists of two square slots with a center square patch and two cross dipole patch diagonally arranged in a two-dimensional unit cell. Two band pass filter is at 6.04 GHz and 9.60 GHz resonant frequency with a band width of 0.89 GHz and 0.87 GHz respectively. One stop band filter at 7.6 GHz resonant frequency in between these C-band and X-band play an important role for the good isolation for wireless communication. The size of unit cell FSS is 0.40λ0×0.40λ0 and thickness of 0.016λ0, where λ0 is the first lower resonant frequency. Both pass band resonant frequencies are spaced with a good shielding providing the frequency ratio of 1.57.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124311167","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854515
P. Pattanaik
Automated segmentation of knee subchondral bone structures such as area and shape using deep learning approaches is a significant task for medical MRI images. However, existing techniques usually suffer from many challenges due to complex tissue structure when utilized in 3D due to their large memory requirements, and unusual image contrast/ brightness. This paper aims to exhibit proof of the concurrent effectiveness and reliability of the dynamic segmentation technique currently used to quantify 3D statistical shape/image-based in knee assessment and to propose suggestions for their utilization in the treatment of osteoarthritis disease. The proposed automated Hybrid UNet+Attention technique involves the enhancement of contrast of knee MRI bone surface images and can process large full-size 3D input samples (no patches) within seconds using the CPU. The overall performance of the proposed technique was estimated against ground truths by computing performance metrics like Intersection over union (IoU), dice similarity coefficient (DSC), precision, and recall.
{"title":"Automated Segmentation for Knee Joint MRI Images Using Hybrid UNet+Attention","authors":"P. Pattanaik","doi":"10.1109/TEECCON54414.2022.9854515","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854515","url":null,"abstract":"Automated segmentation of knee subchondral bone structures such as area and shape using deep learning approaches is a significant task for medical MRI images. However, existing techniques usually suffer from many challenges due to complex tissue structure when utilized in 3D due to their large memory requirements, and unusual image contrast/ brightness. This paper aims to exhibit proof of the concurrent effectiveness and reliability of the dynamic segmentation technique currently used to quantify 3D statistical shape/image-based in knee assessment and to propose suggestions for their utilization in the treatment of osteoarthritis disease. The proposed automated Hybrid UNet+Attention technique involves the enhancement of contrast of knee MRI bone surface images and can process large full-size 3D input samples (no patches) within seconds using the CPU. The overall performance of the proposed technique was estimated against ground truths by computing performance metrics like Intersection over union (IoU), dice similarity coefficient (DSC), precision, and recall.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122832812","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 : 2022-05-26DOI: 10.1109/TEECCON54414.2022.9854839
Swamy Jakkula, Jayaram Nakka, P. S. V. Kishore, J. Rajesh, Sukanta Halder
In this article, a novel nine-level inverter with quadruple boosting capability is proposed. The suggested topology is based on the switched capacitor approach and employs two capacitors, fourteen switches, and one DC source to provide nine output voltage levels. It features self-balancing of capacitor voltages and polarity is created inherently without the usage of H-bridge. For the creation of gate pulses, the level shifted pulse width modulation (LSPWM) scheme is employed, and voltage stress analysis is performed on all switches at each voltage level. Simulations based on MATLAB/Simulink are used to analyze and validate the proposed topology under various parametric changes.
{"title":"A New Nine Level Switched Capacitor-based Inverter with Quadruple Boosting Ability","authors":"Swamy Jakkula, Jayaram Nakka, P. S. V. Kishore, J. Rajesh, Sukanta Halder","doi":"10.1109/TEECCON54414.2022.9854839","DOIUrl":"https://doi.org/10.1109/TEECCON54414.2022.9854839","url":null,"abstract":"In this article, a novel nine-level inverter with quadruple boosting capability is proposed. The suggested topology is based on the switched capacitor approach and employs two capacitors, fourteen switches, and one DC source to provide nine output voltage levels. It features self-balancing of capacitor voltages and polarity is created inherently without the usage of H-bridge. For the creation of gate pulses, the level shifted pulse width modulation (LSPWM) scheme is employed, and voltage stress analysis is performed on all switches at each voltage level. Simulations based on MATLAB/Simulink are used to analyze and validate the proposed topology under various parametric changes.","PeriodicalId":251455,"journal":{"name":"2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127586246","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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