Multimodal machine translation (MMT) handles extracting information from several modalities, considering the presumption that the extra modalities will include beneficial alternative perspectives of the input data. Regardless of its significant benefits, it is challenging to implement an MMT system for several languages, mainly due to the scarcity of the availability of multimodal datasets. As for the low-resource English-Mizo pair, the standard multimodal corpus is not available. Therefore, in this paper, we have developed a Mizo Visual Genome 1.0 (MVG 1.0) dataset for English-Mizo MMT, including images with corresponding bilingual textual descriptions. According to automated assessment measures, the performance of multimodal neural machine translation (MNMT) is better than text-only neural machine translation. To the best of our knowledge, our English-Mizo MMT system is the pioneering work in this approach, and as such, it can serve as a baseline for future study in MMT for the low-resource English-Mizo language pair.
{"title":"Mizo Visual Genome 1.0 : A Dataset for English-Mizo Multimodal Neural Machine Translation","authors":"Vanlalmuansangi Khenglawt, Sahinur Rahman Laskar, Riyanka Manna, Partha Pakray, Ajoy Kumar Khan","doi":"10.1109/SILCON55242.2022.10028882","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028882","url":null,"abstract":"Multimodal machine translation (MMT) handles extracting information from several modalities, considering the presumption that the extra modalities will include beneficial alternative perspectives of the input data. Regardless of its significant benefits, it is challenging to implement an MMT system for several languages, mainly due to the scarcity of the availability of multimodal datasets. As for the low-resource English-Mizo pair, the standard multimodal corpus is not available. Therefore, in this paper, we have developed a Mizo Visual Genome 1.0 (MVG 1.0) dataset for English-Mizo MMT, including images with corresponding bilingual textual descriptions. According to automated assessment measures, the performance of multimodal neural machine translation (MNMT) is better than text-only neural machine translation. To the best of our knowledge, our English-Mizo MMT system is the pioneering work in this approach, and as such, it can serve as a baseline for future study in MMT for the low-resource English-Mizo language pair.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130776068","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028977
Shilpa De, Vishwas Kumar, R. Reddy
Deep learning is becoming a mainstream technology for speech recognition as well as face recognition at an industrial scale. The ability of devices to respond to spoken commands is basically speech recognition. The main objective of building a voice assistant is using semantic data sources available on the web providing knowledge to the users from the knowledge database. For the security purpose of the voice-triggered device, liveness analysis is required. The objective of this paper is to prevent spoofing attacks on voice assistant devices by introducing a liveness analysis of genuine faces. Different classification algorithms are used for face recognition purposes. Finally, the performance analysis of different classification models is made.
{"title":"Voice-Assistant Liveness Analysis","authors":"Shilpa De, Vishwas Kumar, R. Reddy","doi":"10.1109/SILCON55242.2022.10028977","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028977","url":null,"abstract":"Deep learning is becoming a mainstream technology for speech recognition as well as face recognition at an industrial scale. The ability of devices to respond to spoken commands is basically speech recognition. The main objective of building a voice assistant is using semantic data sources available on the web providing knowledge to the users from the knowledge database. For the security purpose of the voice-triggered device, liveness analysis is required. The objective of this paper is to prevent spoofing attacks on voice assistant devices by introducing a liveness analysis of genuine faces. Different classification algorithms are used for face recognition purposes. Finally, the performance analysis of different classification models is made.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134373020","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028788
Dipan Bandyopadhyay, S. Nag, R. B. Roy
In this research work, a reliable, as well as rapid Ultraviolet-visible (UV-Vis) spectroscopy technique, was employed for assessing syringic acid (SGA) contents in real samples-cauliflower (CLF), oregano (ORG) and black olive (BOL). Data measurements were performed using UV Spectrophotometer, operating in the wavelength range of 200-400 nm. Principal component analysis (PCA) was applied for analyzing and distinguishing different samples. PCA plot confirmed the effective clustering of the samples. A high-class separability index of 313.52 was obtained for the UV-vis absorbance data. Moreover, for prediction and correlation of SGA levels in the samples, principal component regression (PCR) as well as Partial least square regression (PLSR) analysis were performed. These prediction algorithms showed high average prediction accuracy of 99.68% and 99.65% respectively and almost the same correlation factor (CF) as high as 0.99 was obtained for both models. Further, high precision was observed with a low RSD value of 0.33 % for the peak absorbance at around 220nm. The primary investigation results recommend that for detecting and assessing SGA contents in real samples, the UV-Vis spectroscopy technique coupled with multivariate analysis may be a viable approach.
{"title":"Quantification of Syringic Acid in Real Samples Based on UV-Vis Spectroscopy","authors":"Dipan Bandyopadhyay, S. Nag, R. B. Roy","doi":"10.1109/SILCON55242.2022.10028788","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028788","url":null,"abstract":"In this research work, a reliable, as well as rapid Ultraviolet-visible (UV-Vis) spectroscopy technique, was employed for assessing syringic acid (SGA) contents in real samples-cauliflower (CLF), oregano (ORG) and black olive (BOL). Data measurements were performed using UV Spectrophotometer, operating in the wavelength range of 200-400 nm. Principal component analysis (PCA) was applied for analyzing and distinguishing different samples. PCA plot confirmed the effective clustering of the samples. A high-class separability index of 313.52 was obtained for the UV-vis absorbance data. Moreover, for prediction and correlation of SGA levels in the samples, principal component regression (PCR) as well as Partial least square regression (PLSR) analysis were performed. These prediction algorithms showed high average prediction accuracy of 99.68% and 99.65% respectively and almost the same correlation factor (CF) as high as 0.99 was obtained for both models. Further, high precision was observed with a low RSD value of 0.33 % for the peak absorbance at around 220nm. The primary investigation results recommend that for detecting and assessing SGA contents in real samples, the UV-Vis spectroscopy technique coupled with multivariate analysis may be a viable approach.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463704","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028831
Uzma Ramzan, M. Jamil
Floating photovoltaics (FPV), which involves the placement of PV modules on the water’s surface, solves one of the major difficulties that has arisen as a result of the increased deployment of PV that is land occupancy. Water bodies such as lakes, ponds, and reservoirs can be used to install PV technology without requiring any land, which is a hot topic in India, which is the world’s second most populated country. The goal of this study is to assess the prospective deployment of Floating PV on a lake in order to determine its extent and compare it to a land-based PV system with similar specifications. PVsyst software is utilized to do a techno-economic analysis of a site in India’s Jammu and Kashmir (Wullar lake). Analyzing the effect of enhanced thermal behavior due to the cooling action of water is part of the technical analysis. To compare the two models, FSPV and LBPV (land-based PV), economic criteria such as Levelized Cost of Energy (L.C.O.E), Net Present Value (NPV), and Payback period are employed.
{"title":"Comparative Analysis of Floating Solar Photovoltaic and Land Based Photovoltaic Plant","authors":"Uzma Ramzan, M. Jamil","doi":"10.1109/SILCON55242.2022.10028831","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028831","url":null,"abstract":"Floating photovoltaics (FPV), which involves the placement of PV modules on the water’s surface, solves one of the major difficulties that has arisen as a result of the increased deployment of PV that is land occupancy. Water bodies such as lakes, ponds, and reservoirs can be used to install PV technology without requiring any land, which is a hot topic in India, which is the world’s second most populated country. The goal of this study is to assess the prospective deployment of Floating PV on a lake in order to determine its extent and compare it to a land-based PV system with similar specifications. PVsyst software is utilized to do a techno-economic analysis of a site in India’s Jammu and Kashmir (Wullar lake). Analyzing the effect of enhanced thermal behavior due to the cooling action of water is part of the technical analysis. To compare the two models, FSPV and LBPV (land-based PV), economic criteria such as Levelized Cost of Energy (L.C.O.E), Net Present Value (NPV), and Payback period are employed.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131330809","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028915
Sheikh Masood, S. Begum
One of the challenges in the Student Dropout Prediction (SDP) problem is imbalanced data, which reduces the efficiency of the Machine Learning (ML) classifier when predicting dropout students. The disproportionate distribution of samples between the majority class (more samples) and the minority class (fewer samples) causes the class imbalance problem, which is a significant challenge in classification problems. When a dataset is highly imbalanced, the ML classifiers give high accuracy as they learn mostly from the majority class. Hence, the accuracy may not always give correct insight about the trained model. In this paper, the findings of the study of several resampling techniques for handling imbalanced data at the data preprocessing level are presented. The Machine learning algorithms, viz. Logistic Regression and Support Vector Machine (SVM), over different performance evaluation metrics for binary classification problems, have been used in the present study to predict the minority class. It is found that the Area Under Curve (AUC) score gives the most reliable result amongst the other considered metrics for predicting the minority class, i.e., the dropout rate of the students.
{"title":"Comparison of Resampling Techniques for Imbalanced Datasets in Student Dropout Prediction","authors":"Sheikh Masood, S. Begum","doi":"10.1109/SILCON55242.2022.10028915","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028915","url":null,"abstract":"One of the challenges in the Student Dropout Prediction (SDP) problem is imbalanced data, which reduces the efficiency of the Machine Learning (ML) classifier when predicting dropout students. The disproportionate distribution of samples between the majority class (more samples) and the minority class (fewer samples) causes the class imbalance problem, which is a significant challenge in classification problems. When a dataset is highly imbalanced, the ML classifiers give high accuracy as they learn mostly from the majority class. Hence, the accuracy may not always give correct insight about the trained model. In this paper, the findings of the study of several resampling techniques for handling imbalanced data at the data preprocessing level are presented. The Machine learning algorithms, viz. Logistic Regression and Support Vector Machine (SVM), over different performance evaluation metrics for binary classification problems, have been used in the present study to predict the minority class. It is found that the Area Under Curve (AUC) score gives the most reliable result amongst the other considered metrics for predicting the minority class, i.e., the dropout rate of the students.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471423","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028879
Prameela Madambakam, Shathanaa Rajmohan
Legal Judgment Prediction (LJP) involves examining the given input case document and recommending the judgment prediction such as applicable law sections, charges, and penalties as delivered by the judge in the court. It assists the judges and lawyers in analyzing and resolving the given case. The various steps involved in LJP equip the lawyers with supporting points to argue the case in the court and the parties involved with the probability of winning the case by predicting the judgment outcome. This paper surveys recent state-of-the-art LJP algorithms published between 2018 and 2022 by focusing on various factors such as Deep Learning (DL) and Artificial Intelligence (AI) ambient techniques, civil and criminal case types, evaluation measures, various data sets available, prediction and modelling methods, challenges, and limitations. Based on this study we derived a taxonomy that will organize the collected papers into two channels called criminal and civil cases which are further classified based on the techniques used for prediction.
{"title":"A Study on Legal Judgment Prediction using Deep Learning Techniques","authors":"Prameela Madambakam, Shathanaa Rajmohan","doi":"10.1109/SILCON55242.2022.10028879","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028879","url":null,"abstract":"Legal Judgment Prediction (LJP) involves examining the given input case document and recommending the judgment prediction such as applicable law sections, charges, and penalties as delivered by the judge in the court. It assists the judges and lawyers in analyzing and resolving the given case. The various steps involved in LJP equip the lawyers with supporting points to argue the case in the court and the parties involved with the probability of winning the case by predicting the judgment outcome. This paper surveys recent state-of-the-art LJP algorithms published between 2018 and 2022 by focusing on various factors such as Deep Learning (DL) and Artificial Intelligence (AI) ambient techniques, civil and criminal case types, evaluation measures, various data sets available, prediction and modelling methods, challenges, and limitations. Based on this study we derived a taxonomy that will organize the collected papers into two channels called criminal and civil cases which are further classified based on the techniques used for prediction.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988554","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028784
Divya Gudapati, C. Pravallika, C. Tejaswini, V. Chinnari, B. Divya Meghana, G. Sru Swapna
This article presents a low profile dual polarized multi-layered planar MIMO antenna for Sub 6GHz n78 band 5G base station applications. A conventional solution for the severe overload problem in deploying a more significant number of 3D MIMO antennas for base stations is discussed in this paper. A multi-layered planar MIMO antenna system with an element spacing of 0.5λ0 is designed to operate at 3.5 GHz with a bandwidth of 130MHz and an isolation level of 20dB. A dual polarization technique is employed to combat the problem of interference and multipath fading. The proposed design provides a reasonable gain value of 8.75 dBi for two-element planar MIMO and makes it suitable for 5G Advanced Antenna Systems (AAS). The performance of the MIMO system is validated by calculating the Envelope Correlation Coefficient (ECC) and Diversity Gain (DG).
{"title":"Dual Polarized Planar MIMO Antenna for 5G Base Station Applications","authors":"Divya Gudapati, C. Pravallika, C. Tejaswini, V. Chinnari, B. Divya Meghana, G. Sru Swapna","doi":"10.1109/SILCON55242.2022.10028784","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028784","url":null,"abstract":"This article presents a low profile dual polarized multi-layered planar MIMO antenna for Sub 6GHz n78 band 5G base station applications. A conventional solution for the severe overload problem in deploying a more significant number of 3D MIMO antennas for base stations is discussed in this paper. A multi-layered planar MIMO antenna system with an element spacing of 0.5λ0 is designed to operate at 3.5 GHz with a bandwidth of 130MHz and an isolation level of 20dB. A dual polarization technique is employed to combat the problem of interference and multipath fading. The proposed design provides a reasonable gain value of 8.75 dBi for two-element planar MIMO and makes it suitable for 5G Advanced Antenna Systems (AAS). The performance of the MIMO system is validated by calculating the Envelope Correlation Coefficient (ECC) and Diversity Gain (DG).","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131791738","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028824
N. Tiwari, V. C. Pal
Copper is one of the essential materials in the technological development of modern devices used in engineering and technology. Mining is the first step to obtaining this material. The process starts with mining well-finished copper with the need for energy. Using conventional resources like coal, oil, and others causes too much pollution and are not sustainable energy resources. Therefore, renewable energy resources are another option to address the dangers posed by these scenarios. Solar power can be an appealing and continual energy source since most copper mines are situated in areas with high amounts of radiation. This article outlines present solar techniques, and the method they have used to mark some of the issues the copper mining sector is now experiencing. However, the study is concentrated on the significant copper production country Chile. India has also copper ore reserves contributing about 2% of world reserves. Therefore, it will be beneficial and optimum if solar energy utilizes this process in India. According to the study findings, there are various methods for incorporating solar power into copper mining activity.
{"title":"A Comprehensive review of the utilization of solar energy in the copper mining process","authors":"N. Tiwari, V. C. Pal","doi":"10.1109/SILCON55242.2022.10028824","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028824","url":null,"abstract":"Copper is one of the essential materials in the technological development of modern devices used in engineering and technology. Mining is the first step to obtaining this material. The process starts with mining well-finished copper with the need for energy. Using conventional resources like coal, oil, and others causes too much pollution and are not sustainable energy resources. Therefore, renewable energy resources are another option to address the dangers posed by these scenarios. Solar power can be an appealing and continual energy source since most copper mines are situated in areas with high amounts of radiation. This article outlines present solar techniques, and the method they have used to mark some of the issues the copper mining sector is now experiencing. However, the study is concentrated on the significant copper production country Chile. India has also copper ore reserves contributing about 2% of world reserves. Therefore, it will be beneficial and optimum if solar energy utilizes this process in India. According to the study findings, there are various methods for incorporating solar power into copper mining activity.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124198297","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028944
Nairit Barkataki, Ankur Jyoti Kalita, Utpal Sarma
Ground penetrating radar (GPR) is a preferred non-destructive method to study and identify buried objects in the field of geology, civil engineering, archaeology, military, etc. Landmines are now largely composed of plastic and other non-metallic materials, while archaeologists must deal with buried artefacts such as ceramics, pillars, and walls built of a range of materials. As a result, understanding the material properties of buried artefacts is critical. This study presents an ANN model for automatic classification of buried objects from GPR A-Scan data. The proposed ANN model is trained and validated using a synthetic dataset generated using gprMax. The model performs well in classifying three different object classes of aluminium, iron and limestone, while achieving an overall accuracy of 95%.
{"title":"Automatic Material Classification of Targets from GPR Data using Artificial Neural Networks","authors":"Nairit Barkataki, Ankur Jyoti Kalita, Utpal Sarma","doi":"10.1109/SILCON55242.2022.10028944","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028944","url":null,"abstract":"Ground penetrating radar (GPR) is a preferred non-destructive method to study and identify buried objects in the field of geology, civil engineering, archaeology, military, etc. Landmines are now largely composed of plastic and other non-metallic materials, while archaeologists must deal with buried artefacts such as ceramics, pillars, and walls built of a range of materials. As a result, understanding the material properties of buried artefacts is critical. This study presents an ANN model for automatic classification of buried objects from GPR A-Scan data. The proposed ANN model is trained and validated using a synthetic dataset generated using gprMax. The model performs well in classifying three different object classes of aluminium, iron and limestone, while achieving an overall accuracy of 95%.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129969921","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 : 2022-11-04DOI: 10.1109/SILCON55242.2022.10028927
Deborsi Basu, Uttam Ghosh, R. Datta
Revolution in hardware technology immensely enforced the up-gradation of modern communication networks. The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) trigger a massive paradigm shift in industrial automation. As a result, two key complementary technologies are evolving now. They are 6G or 6th generation of wireless communication networks and Industry 5.0 or the 5th Industry insurrection. One drives the latter as far as their wide application area is concerned. The reliability and trust parameters are effectively improved with technology reformations. Smart Cyber-Physical Systems (CPS) are the beneficiary domain of the same. Remote connectivity, enabling smart villages, digital sensing, industrial internet, deep space communication, holographic 3D printing, high-precision manufacturing, asset tracking, quantum computation, logistic supply chains, remote e- healthcare, intelligent and autonomous vehicles, global ground monitoring, immersive interactive experiences like augmented and virtual reality (AR and VR), UAV communication, deepsea communication, etc., are technologies which will reshape the livelihood of the world’s population by combining the 6G with Industry 5.0. 6G along with the 5th Industry revolution are expected to hit the market by the end of 2030. Understanding the fundamental concepts of 6G, industry automation, and their interrelation is extremely necessary. So, our motivation is to provide a base to advance level guidance to young readers, researchers, professionals, or even common learners through a thorough road map. This work explains the open research areas and also points out the broad research scopes.
{"title":"6G for Industry 5.0 and Smart CPS: A Journey from Challenging Hindrance to Opportunistic Future","authors":"Deborsi Basu, Uttam Ghosh, R. Datta","doi":"10.1109/SILCON55242.2022.10028927","DOIUrl":"https://doi.org/10.1109/SILCON55242.2022.10028927","url":null,"abstract":"Revolution in hardware technology immensely enforced the up-gradation of modern communication networks. The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) trigger a massive paradigm shift in industrial automation. As a result, two key complementary technologies are evolving now. They are 6G or 6th generation of wireless communication networks and Industry 5.0 or the 5th Industry insurrection. One drives the latter as far as their wide application area is concerned. The reliability and trust parameters are effectively improved with technology reformations. Smart Cyber-Physical Systems (CPS) are the beneficiary domain of the same. Remote connectivity, enabling smart villages, digital sensing, industrial internet, deep space communication, holographic 3D printing, high-precision manufacturing, asset tracking, quantum computation, logistic supply chains, remote e- healthcare, intelligent and autonomous vehicles, global ground monitoring, immersive interactive experiences like augmented and virtual reality (AR and VR), UAV communication, deepsea communication, etc., are technologies which will reshape the livelihood of the world’s population by combining the 6G with Industry 5.0. 6G along with the 5th Industry revolution are expected to hit the market by the end of 2030. Understanding the fundamental concepts of 6G, industry automation, and their interrelation is extremely necessary. So, our motivation is to provide a base to advance level guidance to young readers, researchers, professionals, or even common learners through a thorough road map. This work explains the open research areas and also points out the broad research scopes.","PeriodicalId":183947,"journal":{"name":"2022 IEEE Silchar Subsection Conference (SILCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132414851","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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