Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673653
Femy Joseph, Ginnes. K. John, P. K
Solar photovoltaics array-based system is receiving wide attention because of it the abundant of solar energy. This paper deals with application of two switch buck-boost converter in solar PV array-based system for DC bus. The topologies of two switch buck boost converters allow a PV array to follow its maximum power point (MPP) regardless of irradiance, load, or temperature. Additionally, the buck boost converter may work in three modes: buck, boost, and buck boost. These converters give good efficiency even in light load periods. For making maximum out of the system adding Energy Sources System (ESS) makes it more reliability. Maximum output of PV array is obtained by using maximum power point tracking techniques that uses Perturb and Observe (P&O) algorithm. The whole system is evaluated in various solar irradiance using MATLAB/ SIMULINK platform.
{"title":"Solar Based Two Switch Buck Boost Converter with Battery as Energy Storage System for a Common DC Bus","authors":"Femy Joseph, Ginnes. K. John, P. K","doi":"10.1109/ICMSS53060.2021.9673653","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673653","url":null,"abstract":"Solar photovoltaics array-based system is receiving wide attention because of it the abundant of solar energy. This paper deals with application of two switch buck-boost converter in solar PV array-based system for DC bus. The topologies of two switch buck boost converters allow a PV array to follow its maximum power point (MPP) regardless of irradiance, load, or temperature. Additionally, the buck boost converter may work in three modes: buck, boost, and buck boost. These converters give good efficiency even in light load periods. For making maximum out of the system adding Energy Sources System (ESS) makes it more reliability. Maximum output of PV array is obtained by using maximum power point tracking techniques that uses Perturb and Observe (P&O) algorithm. The whole system is evaluated in various solar irradiance using MATLAB/ SIMULINK platform.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124154424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673609
M. Suman Menon, Anju George, N. Aswathy
Face recognition is one of the most functional research in present scenario, with many practical and commercial applications including identification, access control, forensics, medical care, human-computer interactions, security, etc. Face recognition technique is rapidly becoming the mainstay of state of the art technological security solution. One of the crucial applications of face recognition in the current scenario is linked with security. Identifying people from a crowd or a group of people require an exceptional algorithm. One of the most arduous tasks about the existing face recognition system is the processing or prediction time. The current systems focus on accuracy than speed, which leads to an increase in the detection time. There are several techniques in machine learning and deep learning. But deep learning is preferred more than machine learning for detection and recognition applications because of the large availability of data. An algorithm for fast real-time object detecting and recognizing application is required. YOLO (you only look once) is a single shot deep learning object detection algorithm. In this work, the working of the YOLO algorithm and implementing multiple face recognition using YOLO version 3 is explained. A custom dataset is created from taken from Kaggle and google. At the time of testing the model, a processing speed of 30 ms was obtained.
人脸识别是目前最具功能性的研究之一，在身份识别、访问控制、取证、医疗、人机交互、安全等领域有着广泛的实际和商业应用。人脸识别技术正迅速成为最先进的安全技术解决方案的支柱。在当前的场景中，人脸识别的关键应用之一与安全有关。从人群或一群人中识别人需要一种特殊的算法。现有的人脸识别系统最艰巨的任务之一是处理或预测时间。目前的系统更注重精度而不是速度，这导致了检测时间的增加。在机器学习和深度学习中有几种技术。但在检测和识别应用中，由于数据的大量可用性，深度学习比机器学习更受欢迎。需要一种快速实时的目标检测和识别算法。YOLO(你只看一次)是一个单镜头深度学习对象检测算法。本文介绍了YOLO算法的工作原理以及使用YOLO version 3实现多人脸识别。一个自定义数据集是从Kaggle和google中获取的。在对模型进行测试时，得到的处理速度为30 ms。
{"title":"Implementation of a Multitudinous Face Recognition using YOLO.V3","authors":"M. Suman Menon, Anju George, N. Aswathy","doi":"10.1109/ICMSS53060.2021.9673609","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673609","url":null,"abstract":"Face recognition is one of the most functional research in present scenario, with many practical and commercial applications including identification, access control, forensics, medical care, human-computer interactions, security, etc. Face recognition technique is rapidly becoming the mainstay of state of the art technological security solution. One of the crucial applications of face recognition in the current scenario is linked with security. Identifying people from a crowd or a group of people require an exceptional algorithm. One of the most arduous tasks about the existing face recognition system is the processing or prediction time. The current systems focus on accuracy than speed, which leads to an increase in the detection time. There are several techniques in machine learning and deep learning. But deep learning is preferred more than machine learning for detection and recognition applications because of the large availability of data. An algorithm for fast real-time object detecting and recognizing application is required. YOLO (you only look once) is a single shot deep learning object detection algorithm. In this work, the working of the YOLO algorithm and implementing multiple face recognition using YOLO version 3 is explained. A custom dataset is created from taken from Kaggle and google. At the time of testing the model, a processing speed of 30 ms was obtained.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673597
Rt Moses, S. Natarajan, Malakreddy A Bharathi
To know the future is to know the past. The ability to properly estimate the future of a system is an elusive problem. Researchers have developed many tools to do just that, but a unified approach does not exist. Intertemporal causalities are main signages for predictions in computational finance. Here, since past value of a variable is highly correlated with the present and future of that variable, time series data analytics is much sought after modality for predictions. For a large temporal data set, time period bias is a very common sampling error, resulting in circumstance-specific unique observations only. Experts cannot extend such observations to a larger industry with wider problem spaces. In this paper, we propose a solution to fit any time series data, with an aim to eliminate the time period bias. In this work, we have created a system that meshes previously created systems such as ARIMA, ARMA, and AR. This helps to create a dynamic system that conforms to the specified time series data and modulates to create a specialized architecture for future prediction. We have taken test cases with varying hyperparameters and found a median accuracy of 94.95 % with a minimum delay in the training of 7 days and a median delay in training the model of 60 days.
{"title":"DIIT: A General Model for Time Series Projections, Proven on NIFTY Index Funds","authors":"Rt Moses, S. Natarajan, Malakreddy A Bharathi","doi":"10.1109/ICMSS53060.2021.9673597","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673597","url":null,"abstract":"To know the future is to know the past. The ability to properly estimate the future of a system is an elusive problem. Researchers have developed many tools to do just that, but a unified approach does not exist. Intertemporal causalities are main signages for predictions in computational finance. Here, since past value of a variable is highly correlated with the present and future of that variable, time series data analytics is much sought after modality for predictions. For a large temporal data set, time period bias is a very common sampling error, resulting in circumstance-specific unique observations only. Experts cannot extend such observations to a larger industry with wider problem spaces. In this paper, we propose a solution to fit any time series data, with an aim to eliminate the time period bias. In this work, we have created a system that meshes previously created systems such as ARIMA, ARMA, and AR. This helps to create a dynamic system that conforms to the specified time series data and modulates to create a specialized architecture for future prediction. We have taken test cases with varying hyperparameters and found a median accuracy of 94.95 % with a minimum delay in the training of 7 days and a median delay in training the model of 60 days.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673630
R. Divya, J. Dinesh Peter
Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.
{"title":"Quantum Machine Learning: A comprehensive review on optimization of machine learning algorithms","authors":"R. Divya, J. Dinesh Peter","doi":"10.1109/ICMSS53060.2021.9673630","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673630","url":null,"abstract":"Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"196 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116794287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673592
R. Rajan, Joshua Antony, Riya Ann Joseph, Jijohn M. Thomas, Chandr Dhanush H, A. V
Listeners browse songs based on artist or genre, but a significant amount of queries are based on emotions like happy, sad, calm etc. and therefore, automatic music mood classification is gaining importance. People search for songs based on the emotions they are feeling or the emotion they hope to feel. Audio-based techniques can achieve satisfying results, but part of the semantic information of songs resides exclusively in the lyrics. In this paper, we present a study on the fusion approach of music mood classification. As both audio and lyrical information is complimentary, creating a hybrid model to classify music based on mood provides enhanced accuracy. Where a single song might fall under two different categories based on audio or lyrical information, a hybrid model helps us achieve more accurate results by merging both the information. In this work, we extracted features using librosa from audio, used TF-IDF for text, and experimented with the Bi-LSTM network. The performance evaluation is done on corpus consists of 776 songs. The multimodal approach achieved average precision, recall and F1-score of 0.66, 0.65 and 0.65 respectively.
{"title":"Audio-Mood Classification Using Acoustic-Textual Feature Fusion","authors":"R. Rajan, Joshua Antony, Riya Ann Joseph, Jijohn M. Thomas, Chandr Dhanush H, A. V","doi":"10.1109/ICMSS53060.2021.9673592","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673592","url":null,"abstract":"Listeners browse songs based on artist or genre, but a significant amount of queries are based on emotions like happy, sad, calm etc. and therefore, automatic music mood classification is gaining importance. People search for songs based on the emotions they are feeling or the emotion they hope to feel. Audio-based techniques can achieve satisfying results, but part of the semantic information of songs resides exclusively in the lyrics. In this paper, we present a study on the fusion approach of music mood classification. As both audio and lyrical information is complimentary, creating a hybrid model to classify music based on mood provides enhanced accuracy. Where a single song might fall under two different categories based on audio or lyrical information, a hybrid model helps us achieve more accurate results by merging both the information. In this work, we extracted features using librosa from audio, used TF-IDF for text, and experimented with the Bi-LSTM network. The performance evaluation is done on corpus consists of 776 songs. The multimodal approach achieved average precision, recall and F1-score of 0.66, 0.65 and 0.65 respectively.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126498105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673632
S. Deepika, N. Nishanth, A. Mujeeb
Mobile Ad hoc Networks (MANETs) are decentralized wireless ad hoc networks comprising of self-organizing, self-configuring mobile nodes with constantly varying topology that serves as both the host as well as router. In order to communicate in such a mobile and diverse environment, the network makes use of routing protocols, so as to interconnect nodes which are dynamic and placed arbitrarily. The most predominantly used routing protocol is the Ad hoc On Demand Distance Vector (AODV) routing protocol. However, the constantly varying topology due to node mobility makes routing in MANET a hectic task. Link breakages and node failure in the network can lead to loss of network resources, which makes the optimal path selection between sender and receiver node quite necessary for reducing bandwidth usage, energy consumption and increasing the Quality of Service (QoS). Taking into consideration the routing issues in AODV, five recent AODV extension algorithms have been reviewed in this manuscript for finding their performances and short comings. The algorithms include an Enhanced-Ant-AODV, AODV based on TOPSIS and Fuzzy algorithm, Fungi network-based routing, Dynamic Power AODV (DP-AODV), and Dragon fly algorithm. In this review, some of the network performance parameters like the throughput, Packet Delivery Ratio (PDR), end-to-end delay, and routing overhead of each algorithm are analyzed and compared.
{"title":"An Assessment of Recent Advances in AODV Routing Protocol Path Optimization Algorithms for Mobile Ad hoc Networks","authors":"S. Deepika, N. Nishanth, A. Mujeeb","doi":"10.1109/ICMSS53060.2021.9673632","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673632","url":null,"abstract":"Mobile Ad hoc Networks (MANETs) are decentralized wireless ad hoc networks comprising of self-organizing, self-configuring mobile nodes with constantly varying topology that serves as both the host as well as router. In order to communicate in such a mobile and diverse environment, the network makes use of routing protocols, so as to interconnect nodes which are dynamic and placed arbitrarily. The most predominantly used routing protocol is the Ad hoc On Demand Distance Vector (AODV) routing protocol. However, the constantly varying topology due to node mobility makes routing in MANET a hectic task. Link breakages and node failure in the network can lead to loss of network resources, which makes the optimal path selection between sender and receiver node quite necessary for reducing bandwidth usage, energy consumption and increasing the Quality of Service (QoS). Taking into consideration the routing issues in AODV, five recent AODV extension algorithms have been reviewed in this manuscript for finding their performances and short comings. The algorithms include an Enhanced-Ant-AODV, AODV based on TOPSIS and Fuzzy algorithm, Fungi network-based routing, Dynamic Power AODV (DP-AODV), and Dragon fly algorithm. In this review, some of the network performance parameters like the throughput, Packet Delivery Ratio (PDR), end-to-end delay, and routing overhead of each algorithm are analyzed and compared.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133604008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673638
Aswathy Santhosh, T. Saranya, S. Sundar, S. Natarajan
Deep Learning techniques have remarkably contributed to the advancement of medical image analysis by strengthening prediction accuracy, lead to proper drafting and diagnosis. Automated medical diagnosis using deep learning techniques help doctors, radiologists and clinical experts in the early detection and diagnosis of diseases. The conventional method for detecting the presence of lesions is more time consuming and labour-intensive. In this paper, we focus on reviewing various deep learning-based techniques used in the early identification of the diagnosis of brain tumors. These diagnosis tasks include feature extraction, segmentation, grading, classification, and prediction. This work carried out a detailed review of state-of-the-art innovations performed on each task related to brain tumor images. We summarized and analysed significant contributions over recent years and investigated their extensive advantages, limitations and dataset specification used in the experiments. Eventually, we addressed the ongoing challenges and future research propositions for practitioners in the domain.
{"title":"Deep Learning Techniques for Brain Tumor Diagnosis: A Review","authors":"Aswathy Santhosh, T. Saranya, S. Sundar, S. Natarajan","doi":"10.1109/ICMSS53060.2021.9673638","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673638","url":null,"abstract":"Deep Learning techniques have remarkably contributed to the advancement of medical image analysis by strengthening prediction accuracy, lead to proper drafting and diagnosis. Automated medical diagnosis using deep learning techniques help doctors, radiologists and clinical experts in the early detection and diagnosis of diseases. The conventional method for detecting the presence of lesions is more time consuming and labour-intensive. In this paper, we focus on reviewing various deep learning-based techniques used in the early identification of the diagnosis of brain tumors. These diagnosis tasks include feature extraction, segmentation, grading, classification, and prediction. This work carried out a detailed review of state-of-the-art innovations performed on each task related to brain tumor images. We summarized and analysed significant contributions over recent years and investigated their extensive advantages, limitations and dataset specification used in the experiments. Eventually, we addressed the ongoing challenges and future research propositions for practitioners in the domain.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128136041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673641
R. Ram, S. Muhammed, S. M.
The design and deployment challenges for soft grippers include robustness, miniaturization, speed, and control. Bio mimicking micro robots and systems require simplicity, low power, lower computational requirement, and repeatability. The foremost choice for such systems is to shape memory alloy, due to its large strain and reduced size. This paper primarily deals with the study of the performance of a controller for accelerating the speed of the shape memory alloy (SMA) actuator. The temperature control in SMA is achieved using classical joule's heating method. Conventional temperature control in SMA is developed by using sensors like, thermocouple or thermal imaging sensors. But, for submillimetre diameter SMA actuators, this imposes a physical challenge by physically loading the miniature actuator. Here, a sensor-less temperature estimation method is developed by measuring the resistance variation of SMA during actuation. primarily this experiment is to make an actuator for which shall having some significant role in the field of Soft robotic gripper.
{"title":"Sensorless Heating Control of SMA","authors":"R. Ram, S. Muhammed, S. M.","doi":"10.1109/ICMSS53060.2021.9673641","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673641","url":null,"abstract":"The design and deployment challenges for soft grippers include robustness, miniaturization, speed, and control. Bio mimicking micro robots and systems require simplicity, low power, lower computational requirement, and repeatability. The foremost choice for such systems is to shape memory alloy, due to its large strain and reduced size. This paper primarily deals with the study of the performance of a controller for accelerating the speed of the shape memory alloy (SMA) actuator. The temperature control in SMA is achieved using classical joule's heating method. Conventional temperature control in SMA is developed by using sensors like, thermocouple or thermal imaging sensors. But, for submillimetre diameter SMA actuators, this imposes a physical challenge by physically loading the miniature actuator. Here, a sensor-less temperature estimation method is developed by measuring the resistance variation of SMA during actuation. primarily this experiment is to make an actuator for which shall having some significant role in the field of Soft robotic gripper.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115754466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673615
V. Jesline Jeme, S. Albert Jerome
Denoising of MRI images is very essential for the effective diagnosis of various brain diseases. In this paper, a new hybrid ROFTV-Fast ICA algorithm is proposed to enhance the MRI images corrupted by Gaussian noise. The original MRI image is subjected to Gaussian noise. The corrupted brain image is denoised by the combination of both Rudin-Osher- Fatemi (ROF) Total variation filter and Fast-Independent Component Analysis (ICA) algorithms. The total variation in the noisy brain images is minimized by using ROFTV filter. Again, the recovered image is denoised further by Fast ICA algorithm, by separating the noise and noiseless components in the image. The performance of this hybrid ROFTV -Fast ICA filter is evaluated by means of Peak Signal to Noise Ratio (PSNR). The proposed method is also compared with Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF) and Bilateral filter (BF). The result shows that the proposed hybrid algorithm outperforms rest of the filters and smoothens the MRI images very well also preserving the edges and corners.
{"title":"A Hybrid Filter for Denoising of MRI Brain Images using Fast Independent Component Analysis","authors":"V. Jesline Jeme, S. Albert Jerome","doi":"10.1109/ICMSS53060.2021.9673615","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673615","url":null,"abstract":"Denoising of MRI images is very essential for the effective diagnosis of various brain diseases. In this paper, a new hybrid ROFTV-Fast ICA algorithm is proposed to enhance the MRI images corrupted by Gaussian noise. The original MRI image is subjected to Gaussian noise. The corrupted brain image is denoised by the combination of both Rudin-Osher- Fatemi (ROF) Total variation filter and Fast-Independent Component Analysis (ICA) algorithms. The total variation in the noisy brain images is minimized by using ROFTV filter. Again, the recovered image is denoised further by Fast ICA algorithm, by separating the noise and noiseless components in the image. The performance of this hybrid ROFTV -Fast ICA filter is evaluated by means of Peak Signal to Noise Ratio (PSNR). The proposed method is also compared with Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF) and Bilateral filter (BF). The result shows that the proposed hybrid algorithm outperforms rest of the filters and smoothens the MRI images very well also preserving the edges and corners.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131924002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-18DOI: 10.1109/ICMSS53060.2021.9673635
Renjitha, S. S. Ajitha, D. Vishnu
A patch antenna shows only a minimal range of gain and impedance bandwidth. This can be further improved by using an antenna array. The patch antenna arrays can be used to enhance the properties such as gain, bandwidth, return loss, etc. A $4mathrm{x}1$ circular microstrip patch antenna array with an inset feed line operating in ka band is presented for 5G applications. The bandwidth is enhanced by applying partial ground technique, and gain is elevated by incorporating parasitic patches. The radiating patches are in a non-aligned configuration, which gives symmetry to the pattern formed. The sidelobe levels are alleviated by using strip fences between antenna elements in the array, thereby increasing the front-to-back ratio. Beam steerable antennas have become an essential part of telecommunication. In beam steerable antennas, the radio link is not disrupted if the line of sight is not maintained. Here the beam is steered by changing the phases between the array elements. The simulated results of this work proclaim the proposed work a better option for future $5mathrm{G}$ applications.
{"title":"A 4x1 Circular Patch Antenna Array with Improved Radiation Performance for 5G Applications","authors":"Renjitha, S. S. Ajitha, D. Vishnu","doi":"10.1109/ICMSS53060.2021.9673635","DOIUrl":"https://doi.org/10.1109/ICMSS53060.2021.9673635","url":null,"abstract":"A patch antenna shows only a minimal range of gain and impedance bandwidth. This can be further improved by using an antenna array. The patch antenna arrays can be used to enhance the properties such as gain, bandwidth, return loss, etc. A $4mathrm{x}1$ circular microstrip patch antenna array with an inset feed line operating in ka band is presented for 5G applications. The bandwidth is enhanced by applying partial ground technique, and gain is elevated by incorporating parasitic patches. The radiating patches are in a non-aligned configuration, which gives symmetry to the pattern formed. The sidelobe levels are alleviated by using strip fences between antenna elements in the array, thereby increasing the front-to-back ratio. Beam steerable antennas have become an essential part of telecommunication. In beam steerable antennas, the radio link is not disrupted if the line of sight is not maintained. Here the beam is steered by changing the phases between the array elements. The simulated results of this work proclaim the proposed work a better option for future $5mathrm{G}$ applications.","PeriodicalId":274597,"journal":{"name":"2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134635572","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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