Pub Date : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885301
Subhashree Jayasingh, Subodh Kumar Mohanty
Fault detection in series controlled compensated TCSC line by using decision tree algorithm is presented in this paper. The conventional technique of distance relay for ordinary transmission line is not satisfying for all cases of fault detection and classification. Detection and classification of fault in a controlled series compensated line for distance relay is a challenging task and the proposed technique is more suitable for this work. The proposed decision tree classifier provided with four cycle pre and posts three phase fault current data to test the efficiency of the algorithm. The new approach is tested on a 400 kV, 50 Hz power system network simulated in PSCAD/EMTDC platform. Four cycle pre and post fault current data are retrieved from relaying point in different cases such as variation of distance, fault resistance and different fault inception angle.
{"title":"A Novel Approach to Support Distance relay application in a TCSC compensated line","authors":"Subhashree Jayasingh, Subodh Kumar Mohanty","doi":"10.1109/SPICSCON54707.2021.9885301","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885301","url":null,"abstract":"Fault detection in series controlled compensated TCSC line by using decision tree algorithm is presented in this paper. The conventional technique of distance relay for ordinary transmission line is not satisfying for all cases of fault detection and classification. Detection and classification of fault in a controlled series compensated line for distance relay is a challenging task and the proposed technique is more suitable for this work. The proposed decision tree classifier provided with four cycle pre and posts three phase fault current data to test the efficiency of the algorithm. The new approach is tested on a 400 kV, 50 Hz power system network simulated in PSCAD/EMTDC platform. Four cycle pre and post fault current data are retrieved from relaying point in different cases such as variation of distance, fault resistance and different fault inception angle.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"823 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116797301","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885726
Shruti Jain
The different ECG systems may have different computer programs, each using a certain algorithm(s) to do its interpretation. A physician is only as good as their training and their effort to continually educate and improve themselves. The motivation of this paper is to optimize the Delay and Power constraints in portable ECG devices. In this paper, the authors can get high computational density which helps make Flexible architecture of portable devices. The main aim of this paper is to design and implement a different circuit to denoise ECG signals using an FPGA Zedboard board. The higher sampling rate is the advantage of FPGA for designing a digital filter application over DSP chips and it is also cost-efficient than ASIC for moderate volume application. We can pre-process the ECG signal using the VIVADO tool. In this paper, the authors have used fixed-point representation with variable length.
{"title":"Hardware Simulation of BRAM Digital FIR filter for Noise Removal of ECG Signal","authors":"Shruti Jain","doi":"10.1109/SPICSCON54707.2021.9885726","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885726","url":null,"abstract":"The different ECG systems may have different computer programs, each using a certain algorithm(s) to do its interpretation. A physician is only as good as their training and their effort to continually educate and improve themselves. The motivation of this paper is to optimize the Delay and Power constraints in portable ECG devices. In this paper, the authors can get high computational density which helps make Flexible architecture of portable devices. The main aim of this paper is to design and implement a different circuit to denoise ECG signals using an FPGA Zedboard board. The higher sampling rate is the advantage of FPGA for designing a digital filter application over DSP chips and it is also cost-efficient than ASIC for moderate volume application. We can pre-process the ECG signal using the VIVADO tool. In this paper, the authors have used fixed-point representation with variable length.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"17 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316906","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885474
S. Sahoo, Chinmay Kumar Nayak, S. K. Pattnaik, S. Samal, S. Bandopadhaya, J. Das
In this proposed work, safe routing is guaranteed using the Secured Multicast Routing offered in MANET by utilizing the Ant Colony Optimization (ACO) technique to integrate the QOS-conscious route setup into the route selection. This implies that only the data transmission may select the way to meet the QoS limitations from source to destination. Furthermore, the track reliability is considered by selecting the best path between the source and destination nodes. For the optimization of the best path and its performance, the optimized algorithm called the micro artificial bee colony approach is chosen about the probabilistic ant routing technique. The experimental analysis is carried out in the NS2.34 simulation model and this concludes that the proposed system contributes to increased performance and improved safety.
{"title":"Automatic QoS Based Multicast Communication System in MANET","authors":"S. Sahoo, Chinmay Kumar Nayak, S. K. Pattnaik, S. Samal, S. Bandopadhaya, J. Das","doi":"10.1109/SPICSCON54707.2021.9885474","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885474","url":null,"abstract":"In this proposed work, safe routing is guaranteed using the Secured Multicast Routing offered in MANET by utilizing the Ant Colony Optimization (ACO) technique to integrate the QOS-conscious route setup into the route selection. This implies that only the data transmission may select the way to meet the QoS limitations from source to destination. Furthermore, the track reliability is considered by selecting the best path between the source and destination nodes. For the optimization of the best path and its performance, the optimized algorithm called the micro artificial bee colony approach is chosen about the probabilistic ant routing technique. The experimental analysis is carried out in the NS2.34 simulation model and this concludes that the proposed system contributes to increased performance and improved safety.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127740651","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885622
Sadia Enam, M. Rana
This paper proposes the effect of closeness of the human body phantom model to the textile antenna (TA) where fabric materials (wash cotton, curtain cotton, and polycot) have been utilized as substrates. The antennas are designed to operate at 2.45 GHz frequency for on body wireless communication. Here, circular shape TA with curtain cotton provided return loss (−52.43 dB), bandwidth (107.4 MHz at frequency range 2.3982 to 2.5056 GHz), peak directivity 8.83 dBi with a peak gain of 7.11 dBi when body phantom is applied at 2 mm distance from the TA at 2.452 GHz and the radiation efficiency recorded is 68.07%. The overall antenna performance of each antenna without body phantom is satisfactory for the desired application. The specific absorption rate (SAR) for CMPA (curtain cotton) is 0.029 W/kg< 2 W/kg averaged over 10 g tissue.
{"title":"Performance Analysis of Body-Worn Antenna for Wearable Textile Substrates","authors":"Sadia Enam, M. Rana","doi":"10.1109/SPICSCON54707.2021.9885622","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885622","url":null,"abstract":"This paper proposes the effect of closeness of the human body phantom model to the textile antenna (TA) where fabric materials (wash cotton, curtain cotton, and polycot) have been utilized as substrates. The antennas are designed to operate at 2.45 GHz frequency for on body wireless communication. Here, circular shape TA with curtain cotton provided return loss (−52.43 dB), bandwidth (107.4 MHz at frequency range 2.3982 to 2.5056 GHz), peak directivity 8.83 dBi with a peak gain of 7.11 dBi when body phantom is applied at 2 mm distance from the TA at 2.452 GHz and the radiation efficiency recorded is 68.07%. The overall antenna performance of each antenna without body phantom is satisfactory for the desired application. The specific absorption rate (SAR) for CMPA (curtain cotton) is 0.029 W/kg< 2 W/kg averaged over 10 g tissue.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512033","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885517
Farzana Arefin Nazira, Sharna Rani Das, Sadah Anjum Shanto, M. F. Mridha
Facial Expression Recognition (FER) has been an essential field in the Deep Learning area since the arrival of big data. Human psychological activity is reflected by human facial expressions that can provide significant knowledge about human nature. In that time, depression has become a severe mental illness. It is vital to detect depressed people. To detect depression, we have proposed a system based on CNN, OpenCV, Haar Cascade Classifier. Haar Cascade Classifier is a machine learning algorithm used for face detection. Convolution layers are used in the proposed combination technique. Furthermore, no datasets available that represents the natural expressions of the depressed face and can be used to detect depression. So, We have generated a Depressed and Not Depressed (DND) dataset which contains the 5000 images. Our proposed system has been evaluated using the DND dataset and achieved accuracy 81% , precision 87% , recall 88%.
{"title":"Depression Detection Using Convolutional Neural Networks","authors":"Farzana Arefin Nazira, Sharna Rani Das, Sadah Anjum Shanto, M. F. Mridha","doi":"10.1109/SPICSCON54707.2021.9885517","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885517","url":null,"abstract":"Facial Expression Recognition (FER) has been an essential field in the Deep Learning area since the arrival of big data. Human psychological activity is reflected by human facial expressions that can provide significant knowledge about human nature. In that time, depression has become a severe mental illness. It is vital to detect depressed people. To detect depression, we have proposed a system based on CNN, OpenCV, Haar Cascade Classifier. Haar Cascade Classifier is a machine learning algorithm used for face detection. Convolution layers are used in the proposed combination technique. Furthermore, no datasets available that represents the natural expressions of the depressed face and can be used to detect depression. So, We have generated a Depressed and Not Depressed (DND) dataset which contains the 5000 images. Our proposed system has been evaluated using the DND dataset and achieved accuracy 81% , precision 87% , recall 88%.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000534","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885574
Mohammed Mobasserul Haque, D. Agrawal, Pranat Dixit, B. Bhattacharyya
The 5th generation cellular network works on a user-centric methodology rather than operator-centric as in 3G or service-centric as seen for 4G. Efficient allocation of resources is possible in 5G networks, something which was not feasible in the previous network generations. The network was equally allocated to all users, whereas it should be allocated depending on the usage. A user playing AR/VR games should be given more bandwidth than a user who is just sending text messages. The main objective of this paper is to develop a solution that can manage the network slice for incoming network requests from unidentified devices. We have compared various machine learning algorithms based on their accuracy to predict the network slice. Also, a Hybrid CNN-LSTM Deep Learning model is proposed for understanding user usage patterns and time series based forecasting of slice utilization, active users, resource usage and workload. The Concept of Self-Healing Networks for better Quality of Experience (QoE) and fault detection, anomaly detection diagnosis is discussed.
{"title":"Deep Learning for Network Slicing and Self-Healing in 5G Systems","authors":"Mohammed Mobasserul Haque, D. Agrawal, Pranat Dixit, B. Bhattacharyya","doi":"10.1109/SPICSCON54707.2021.9885574","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885574","url":null,"abstract":"The 5th generation cellular network works on a user-centric methodology rather than operator-centric as in 3G or service-centric as seen for 4G. Efficient allocation of resources is possible in 5G networks, something which was not feasible in the previous network generations. The network was equally allocated to all users, whereas it should be allocated depending on the usage. A user playing AR/VR games should be given more bandwidth than a user who is just sending text messages. The main objective of this paper is to develop a solution that can manage the network slice for incoming network requests from unidentified devices. We have compared various machine learning algorithms based on their accuracy to predict the network slice. Also, a Hybrid CNN-LSTM Deep Learning model is proposed for understanding user usage patterns and time series based forecasting of slice utilization, active users, resource usage and workload. The Concept of Self-Healing Networks for better Quality of Experience (QoE) and fault detection, anomaly detection diagnosis is discussed.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122288012","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885420
Omar Sharif, M. M. Hoque
The phenomenal proliferation of social media platforms has facilitated the spontaneous sharing of expressions, opinions, and emotions in public spaces. Unfortunately, with this rapid rise, these mediums have been repeatedly used to spread propaganda, excite religious and political violence, jeopardize social harmony and disseminate other aggressive content. The pernicious societal effects of such undesired content have become a severe concern for tech giants and government bodies. Studies revealed that the majority of such activities are performed via texts written in regional languages. Developing an automatic system to identify and classify aggressive texts in resource constraint languages like Bengali is monumental. This work proposes an ensemble-based classifier model to compensate for this deficiency. A corpus of 10095 annotated texts (5095 for non-aggressive and 5000 for aggressive) is developed to train the system. Moreover, aggressive texts are classified into four fine-grained categories: political, gendered, verbal and religious using hierarchical annotation schema. This work also investigates 21 standard classifier models developed based on Convolutional Neural Network (CNN), Bidirectional Long Short Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) with different embedding models (i.e., Word2Vec, GloVe, FastText) and ensemble strategies. All the models are trained, tuned and tested on the developed dataset (EATxtC-Extended Aggressive Text Corpus). The experimental result exhibits that the ensemble of CNN and GRU (i.e. CNN+GRU) outperformed the other baseline models with acquiring the highest weighted f1-score of 89.55% (coarse-grained) and 83.77% (fine-grained).
社交媒体平台的惊人增长促进了公共空间中表达、观点和情感的自发分享。不幸的是，随着这种迅速增长，这些媒介一再被用来传播宣传、煽动宗教和政治暴力、危害社会和谐和传播其他侵略性内容。这些不受欢迎的内容的有害社会影响已成为科技巨头和政府机构的严重关切。研究表明，大多数此类活动是通过用区域语言编写的文本进行的。开发一个自动系统来识别和分类孟加拉语等资源受限语言中的攻击性文本是非常重要的。这项工作提出了一个基于集成的分类器模型来弥补这一不足。开发了一个包含10095个注释文本的语料库(5095个为非侵略性文本，5000个为侵略性文本)来训练系统。此外，使用分层注释模式将攻击性文本划分为政治、性别、言语和宗教四种细粒度类别。本研究还研究了基于卷积神经网络(CNN)、双向长短期记忆(BiLSTM)和门控循环单元(GRU)的21个标准分类器模型，这些模型具有不同的嵌入模型(即Word2Vec、GloVe、FastText)和集成策略。所有模型都在开发的数据集(EATxtC-Extended Aggressive Text Corpus)上进行训练、调优和测试。实验结果表明，CNN和GRU的集合(即CNN+GRU)优于其他基线模型，获得了最高的加权f1分数，粗粒度为89.55%，细粒度为83.77%。
{"title":"Align and Conquer: An Ensemble Approach to Classify Aggressive Texts from Social Media","authors":"Omar Sharif, M. M. Hoque","doi":"10.1109/SPICSCON54707.2021.9885420","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885420","url":null,"abstract":"The phenomenal proliferation of social media platforms has facilitated the spontaneous sharing of expressions, opinions, and emotions in public spaces. Unfortunately, with this rapid rise, these mediums have been repeatedly used to spread propaganda, excite religious and political violence, jeopardize social harmony and disseminate other aggressive content. The pernicious societal effects of such undesired content have become a severe concern for tech giants and government bodies. Studies revealed that the majority of such activities are performed via texts written in regional languages. Developing an automatic system to identify and classify aggressive texts in resource constraint languages like Bengali is monumental. This work proposes an ensemble-based classifier model to compensate for this deficiency. A corpus of 10095 annotated texts (5095 for non-aggressive and 5000 for aggressive) is developed to train the system. Moreover, aggressive texts are classified into four fine-grained categories: political, gendered, verbal and religious using hierarchical annotation schema. This work also investigates 21 standard classifier models developed based on Convolutional Neural Network (CNN), Bidirectional Long Short Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) with different embedding models (i.e., Word2Vec, GloVe, FastText) and ensemble strategies. All the models are trained, tuned and tested on the developed dataset (EATxtC-Extended Aggressive Text Corpus). The experimental result exhibits that the ensemble of CNN and GRU (i.e. CNN+GRU) outperformed the other baseline models with acquiring the highest weighted f1-score of 89.55% (coarse-grained) and 83.77% (fine-grained).","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121767344","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885436
Md. Mostakim Rahman, Abu Sonam, Md. Zubaer Mahmud Rony, Ali Karim Adnan
Sustainable development in the electrical sector is a major requirement in the current global situation. Traditional power generation systems around the world rely on fossil fuels, which contribute to global warming by emitting carbon dioxide into the environment. The growing trend of using renewable resources to generate electricity can ensure that energy demand is met. Making use of HOMER, which stands for Hybrid Optimization Model for Electric Renewables, this study examines the renewable and green sources of electricity generation in-depth for the Rohinga Relocation Camp in the remote Bhasan Char area. The suggested hybrid system combines a wind turbine, a diesel generator solar PV module, a tidal turbine, a converter, and a battery to achieve minimum Cost of Energy and minimum Net Present Cost while lowering greenhouse gases.
{"title":"Design and Analysis of an Optimized Standalone Micro-grid on Bhasan Char, Bangladesh","authors":"Md. Mostakim Rahman, Abu Sonam, Md. Zubaer Mahmud Rony, Ali Karim Adnan","doi":"10.1109/SPICSCON54707.2021.9885436","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885436","url":null,"abstract":"Sustainable development in the electrical sector is a major requirement in the current global situation. Traditional power generation systems around the world rely on fossil fuels, which contribute to global warming by emitting carbon dioxide into the environment. The growing trend of using renewable resources to generate electricity can ensure that energy demand is met. Making use of HOMER, which stands for Hybrid Optimization Model for Electric Renewables, this study examines the renewable and green sources of electricity generation in-depth for the Rohinga Relocation Camp in the remote Bhasan Char area. The suggested hybrid system combines a wind turbine, a diesel generator solar PV module, a tidal turbine, a converter, and a battery to achieve minimum Cost of Energy and minimum Net Present Cost while lowering greenhouse gases.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124327809","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885579
Mobasshir Mahbub, B. Barua, R. Shubair
For the evolving heterogeneous or multi-tier cellular networks biased factor or power-based user association is one of the most prominent user association schemes to enhance the network coverage. Whereas, device-to-device (D2D) communication enhances the coverage of the cellular network by enabling direct communication between user equipment when sufficient coverage is unavailable in case of a user and initiating communicating with another user having sufficient coverage from the base station. Therefore, the paper presented conceptual research and preliminary analysis considering a three-tier heterogeneous network formed by macro, micro, and femto tiers to analyze the user association with and without enabling D2D communication. The work moreover aimed to measure energy consumption and load distribution i.e. the average number of users in a base station of a selected tier (kth tier).
{"title":"Analysis of the Energy-Cognizant User Association and Load Distribution in a D2D-Enabled 5G Multi-Tier Heterogeneous Cellular Network","authors":"Mobasshir Mahbub, B. Barua, R. Shubair","doi":"10.1109/SPICSCON54707.2021.9885579","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885579","url":null,"abstract":"For the evolving heterogeneous or multi-tier cellular networks biased factor or power-based user association is one of the most prominent user association schemes to enhance the network coverage. Whereas, device-to-device (D2D) communication enhances the coverage of the cellular network by enabling direct communication between user equipment when sufficient coverage is unavailable in case of a user and initiating communicating with another user having sufficient coverage from the base station. Therefore, the paper presented conceptual research and preliminary analysis considering a three-tier heterogeneous network formed by macro, micro, and femto tiers to analyze the user association with and without enabling D2D communication. The work moreover aimed to measure energy consumption and load distribution i.e. the average number of users in a base station of a selected tier (kth tier).","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133868022","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 : 2021-12-03DOI: 10.1109/SPICSCON54707.2021.9885658
Harsh Dungrani, Yagnesh Gotad, U. S. Giridhar, Pallavi Malame
The purpose of the work is to merge two booming fields of science and technology which are machine learning and antenna designing. Genetic algorithm, which belongs to the class of evolutionary algorithms, is inspired by the process of natural selection, where the fittest individuals are selected for reproduction to find the optimal solution for getting the desired output. The proposed U-Slot antennas have their designing parameters determined with the help of genetic algorithms. The proposed dual band antenna has two bands at 0.87–0.92 GHz and 1.14–1.22 GHz. The wideband antenna proposed has the return loss S11 below −10 dB from 0.82 GHz to 1.02 GHz providing a bandwidth of 200 MHz and is suitable for Ultra High Frequency (UHF) Radio Frequency Identification (RFID) applications worldwide. The simulations are performed using the ANSYS Electronics Desktop R19.2 HFSS Software.
{"title":"Application of Genetic Algorithm for Designing of Microstrip Patch Antenna with a U-Slot","authors":"Harsh Dungrani, Yagnesh Gotad, U. S. Giridhar, Pallavi Malame","doi":"10.1109/SPICSCON54707.2021.9885658","DOIUrl":"https://doi.org/10.1109/SPICSCON54707.2021.9885658","url":null,"abstract":"The purpose of the work is to merge two booming fields of science and technology which are machine learning and antenna designing. Genetic algorithm, which belongs to the class of evolutionary algorithms, is inspired by the process of natural selection, where the fittest individuals are selected for reproduction to find the optimal solution for getting the desired output. The proposed U-Slot antennas have their designing parameters determined with the help of genetic algorithms. The proposed dual band antenna has two bands at 0.87–0.92 GHz and 1.14–1.22 GHz. The wideband antenna proposed has the return loss S11 below −10 dB from 0.82 GHz to 1.02 GHz providing a bandwidth of 200 MHz and is suitable for Ultra High Frequency (UHF) Radio Frequency Identification (RFID) applications worldwide. The simulations are performed using the ANSYS Electronics Desktop R19.2 HFSS Software.","PeriodicalId":159505,"journal":{"name":"2021 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124854371","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：