Pub Date : 2019-11-01DOI: 10.1109/SMART46866.2019.9117437
Tahmina Tashrif Mim, Md. Helal Sheikh, Roksana Akter Shampa, Md. Shamim Reza, Md. Sanzidul Islam
Today's era is an era of Scientific Development. Where, technologies and new ways of solving real-life problems are being invented every day. With the increasing population of the world, basic need of food is increasing parallelly. That's why agriculture plays an important role all over the world. Throughout the year different crops, vegetables, fruits, fishes, animals are cultivated to fulfill the need of people as well as to gain profit for the people involving in those cultivation. But due to lack of proper cultivating knowledge, experience and sense of disease prediction, sometimes those cultivating crops and grains get damaged partially or even completely. Of course, that ends up with a huge loss for the farmers as well as for the economic growth of the country. So, this research paper tends to merge or combine a part of agricultural sector with science and technology to reduce the loss caused by insect's attack and diseases of plant leaves. More specifically, this research happens to combine agricultural sector with computer science. Since, agriculture is a vast sector to work on, to simplify the work, we are detecting vegetable plant diseases using Artificial Intelligence and computer science. To implement this idea, we have chosen “Tomato” as the core vegetable which's leaf diseases are to be predicted by using the algorithms of Artificial Intelligence, CNN and computer science. Tomato is a very popular vegetable in our country as well as in the world, the main motive is to solve the diseases detection problems that the “Tomato” growers are facing nowadays in their cultivable land especially in Bangladesh. And that is why we have chosen tomatoes leaf diseases prediction which is very important. This research tried to eradicate the harmful side effects of chemicals and pesticides with the help of Image Processing system. In this research 6 classification of tomato leaves disease have been detected including one healthy class. The farmers can input the symptoms in the form of images of affected tomato leaves and it will predict the diseases. The system showed up an accuracy over 96.55% at the end. It is counted as a user-friendly system that will help the vegetable farmers specially the “Tomato” growers to reduce insect suppression by detecting its leaf diseases and increase the yield by creating more opportunities for various vegetable diseases research and professional market place.
{"title":"Leaves Diseases Detection of Tomato Using Image Processing","authors":"Tahmina Tashrif Mim, Md. Helal Sheikh, Roksana Akter Shampa, Md. Shamim Reza, Md. Sanzidul Islam","doi":"10.1109/SMART46866.2019.9117437","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117437","url":null,"abstract":"Today's era is an era of Scientific Development. Where, technologies and new ways of solving real-life problems are being invented every day. With the increasing population of the world, basic need of food is increasing parallelly. That's why agriculture plays an important role all over the world. Throughout the year different crops, vegetables, fruits, fishes, animals are cultivated to fulfill the need of people as well as to gain profit for the people involving in those cultivation. But due to lack of proper cultivating knowledge, experience and sense of disease prediction, sometimes those cultivating crops and grains get damaged partially or even completely. Of course, that ends up with a huge loss for the farmers as well as for the economic growth of the country. So, this research paper tends to merge or combine a part of agricultural sector with science and technology to reduce the loss caused by insect's attack and diseases of plant leaves. More specifically, this research happens to combine agricultural sector with computer science. Since, agriculture is a vast sector to work on, to simplify the work, we are detecting vegetable plant diseases using Artificial Intelligence and computer science. To implement this idea, we have chosen “Tomato” as the core vegetable which's leaf diseases are to be predicted by using the algorithms of Artificial Intelligence, CNN and computer science. Tomato is a very popular vegetable in our country as well as in the world, the main motive is to solve the diseases detection problems that the “Tomato” growers are facing nowadays in their cultivable land especially in Bangladesh. And that is why we have chosen tomatoes leaf diseases prediction which is very important. This research tried to eradicate the harmful side effects of chemicals and pesticides with the help of Image Processing system. In this research 6 classification of tomato leaves disease have been detected including one healthy class. The farmers can input the symptoms in the form of images of affected tomato leaves and it will predict the diseases. The system showed up an accuracy over 96.55% at the end. It is counted as a user-friendly system that will help the vegetable farmers specially the “Tomato” growers to reduce insect suppression by detecting its leaf diseases and increase the yield by creating more opportunities for various vegetable diseases research and professional market place.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124794610","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 : 2019-11-01DOI: 10.1109/smart46866.2019.9117423
{"title":"Message Board","authors":"","doi":"10.1109/smart46866.2019.9117423","DOIUrl":"https://doi.org/10.1109/smart46866.2019.9117423","url":null,"abstract":"","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125560134","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117214
Shovon Ahammed, Mostafizur Rahman, Mahedi Hasan Niloy, S. A. Chowdhury
Hate speech is a crime in all countries. Hate speech can be for women, religions, countries, cultures. The big problem for hate speech is that it entices the evil people. Moreover, it inspires them to spread hatred in the society. Bangla is one of the topmost spoken languages in the world. But hate speech detection in Bangla language is rare. Our purpose is to detect hate speech in Bangla language. To perform the task, we were in need of the Bangla datasets. But the Bangla dataset is not available. So, we have collected data from Facebook. Collecting data from the social site is very hectic. The data contain mixed languages, grammatical mistakes. So, we made a team to collect the data. Another team was to process the data. And finally, we labeled the data as hate speech or not. The team members had enough knowledge about hate speech. They were neutral towards the data. Our data contain hate speech against women, community, culture, ethnicity, race, sex, disability. Machine Learning approach is ideal for our work. We have used the SVM and Naïve Bayes algorithm for our work and got a maximum accuracy of 72%.
{"title":"Implementation of Machine Learning to Detect Hate Speech in Bangla Language","authors":"Shovon Ahammed, Mostafizur Rahman, Mahedi Hasan Niloy, S. A. Chowdhury","doi":"10.1109/SMART46866.2019.9117214","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117214","url":null,"abstract":"Hate speech is a crime in all countries. Hate speech can be for women, religions, countries, cultures. The big problem for hate speech is that it entices the evil people. Moreover, it inspires them to spread hatred in the society. Bangla is one of the topmost spoken languages in the world. But hate speech detection in Bangla language is rare. Our purpose is to detect hate speech in Bangla language. To perform the task, we were in need of the Bangla datasets. But the Bangla dataset is not available. So, we have collected data from Facebook. Collecting data from the social site is very hectic. The data contain mixed languages, grammatical mistakes. So, we made a team to collect the data. Another team was to process the data. And finally, we labeled the data as hate speech or not. The team members had enough knowledge about hate speech. They were neutral towards the data. Our data contain hate speech against women, community, culture, ethnicity, race, sex, disability. Machine Learning approach is ideal for our work. We have used the SVM and Naïve Bayes algorithm for our work and got a maximum accuracy of 72%.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889373","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117493
P. Singh, S. Sharma, Punit Kalia, P. Goswami
Design of a frequency reconfigurable patch antenna is proposed in this paper. The designed antenna is compact in size with overall dimension of 25 mm×25 mm on FR-4 substrate with a thickness of 1.6 mm. antenna covers 5 different frequency bands and operates on 4.80 GHz, 5.32 GHz, 6.01 GHz, 6.22 GHz, and 6.41 GHz. Frequency reconfigurability is achieved by PIN diodes. Two PIN diodes are used as switches to switch the frequency. Also the ground is modified with defected ground structure (DGS). Antenna is simulated and analyzed using HFSS software using FR-4 as substrate having thickness of 1.6 mm. Parameters such as S11, VSWR, gain and radiation patterns of antenna are analyzed and discussed in this paper. Designed antenna is useful for WLAN, Wi-Max, and C-band applications. This antenna having the advantage to be compact, easy to fabricate, and also it is not much complex.
{"title":"A Frequency Reconfigurable Circular Microstrip Patch Antenna using PIN Diode","authors":"P. Singh, S. Sharma, Punit Kalia, P. Goswami","doi":"10.1109/SMART46866.2019.9117493","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117493","url":null,"abstract":"Design of a frequency reconfigurable patch antenna is proposed in this paper. The designed antenna is compact in size with overall dimension of 25 mm×25 mm on FR-4 substrate with a thickness of 1.6 mm. antenna covers 5 different frequency bands and operates on 4.80 GHz, 5.32 GHz, 6.01 GHz, 6.22 GHz, and 6.41 GHz. Frequency reconfigurability is achieved by PIN diodes. Two PIN diodes are used as switches to switch the frequency. Also the ground is modified with defected ground structure (DGS). Antenna is simulated and analyzed using HFSS software using FR-4 as substrate having thickness of 1.6 mm. Parameters such as S11, VSWR, gain and radiation patterns of antenna are analyzed and discussed in this paper. Designed antenna is useful for WLAN, Wi-Max, and C-band applications. This antenna having the advantage to be compact, easy to fabricate, and also it is not much complex.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253370","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117427
Mayank P. Mohta, Nidhi Soni, T. Choudhury, Vivek Kumar
Emergence of new industries from evolving technologies is critical to the global economy. Internet of thing is a boom to IT industry. A succession of LED market has advanced the technology with Li-Fi as one of the emerging technology. Lightning speed and security of Internet has fascinated many researchers towards Li-Fi. Li-Fi provides speed of 224Gbps. LEDs are installed in almost each and every town of the world and installing a micro chip with these LEDs can convert each light source into Data source which can lead to transmission of data. In Li-Fi, light emitting diode is used in order to emit the data which gives us more speed and flexibility than the Wi-Fi technology. Innovations like Li-Fi make this planet a greener as well as more secure and economical way of communication. In this paper, an attempt has been made to prove how bigger and better is the market of the Li-Fi technology.
{"title":"Market Analysis on Li-Fi Technology","authors":"Mayank P. Mohta, Nidhi Soni, T. Choudhury, Vivek Kumar","doi":"10.1109/SMART46866.2019.9117427","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117427","url":null,"abstract":"Emergence of new industries from evolving technologies is critical to the global economy. Internet of thing is a boom to IT industry. A succession of LED market has advanced the technology with Li-Fi as one of the emerging technology. Lightning speed and security of Internet has fascinated many researchers towards Li-Fi. Li-Fi provides speed of 224Gbps. LEDs are installed in almost each and every town of the world and installing a micro chip with these LEDs can convert each light source into Data source which can lead to transmission of data. In Li-Fi, light emitting diode is used in order to emit the data which gives us more speed and flexibility than the Wi-Fi technology. Innovations like Li-Fi make this planet a greener as well as more secure and economical way of communication. In this paper, an attempt has been made to prove how bigger and better is the market of the Li-Fi technology.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117252005","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117477
Md. Majedul Islam, Abu Kaisar Mohammad Masum, Md. Golam Rabbani, Raihana Zannat, Mushfiqur Rahman
The reading newspaper is a common habit in today's life. Before reading news article all are focused on the news headline. Understanding the meaning of news headline everybody can easily identify the news types. That means the containing news article provides positive or negative news. Analysis of the sentiment of the news headline is a good solution for this kind of problem. Sentiment Analysis is a chief part of Natural Language Processing. It mines any kinds of opinion and set the sentiment of any text. We proposed a method for Bengali news headline sentiment measurement with different kinds of the supervised learning algorithm and their performance. Firstly, we set sentiment of each news headline then used the classification method to predicting the news headline which was containing a positive or negative headline. After all, Bengali is one of the most used languages in this world. A lot of research work done previously in a different language but very few in the Bengali language. So, increasing the Bengali language research resource need to develop different kinds of tools and technology.
{"title":"Performance Measurement of Multiple Supervised Learning Algorithms for Bengali News Headline Sentiment Classification","authors":"Md. Majedul Islam, Abu Kaisar Mohammad Masum, Md. Golam Rabbani, Raihana Zannat, Mushfiqur Rahman","doi":"10.1109/SMART46866.2019.9117477","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117477","url":null,"abstract":"The reading newspaper is a common habit in today's life. Before reading news article all are focused on the news headline. Understanding the meaning of news headline everybody can easily identify the news types. That means the containing news article provides positive or negative news. Analysis of the sentiment of the news headline is a good solution for this kind of problem. Sentiment Analysis is a chief part of Natural Language Processing. It mines any kinds of opinion and set the sentiment of any text. We proposed a method for Bengali news headline sentiment measurement with different kinds of the supervised learning algorithm and their performance. Firstly, we set sentiment of each news headline then used the classification method to predicting the news headline which was containing a positive or negative headline. After all, Bengali is one of the most used languages in this world. A lot of research work done previously in a different language but very few in the Bengali language. So, increasing the Bengali language research resource need to develop different kinds of tools and technology.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124185379","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117215
N. Viet, A. Kravets
The wider utilization of information and web technologies, database technologies, development of internet infrastructure has led to the evolution of digital libraries. In particular, digital libraries serve enormous number of various users and play an essential role as repositories and source of investigation and intelligence. With the emergence of IoT (Internet of Things) and different academic open-access digital libraries, the automatic extraction of advantageous knowledge from text data has been more and more a significant subject of research in data mining. In this paper, we perform web scraping system, statistical analyses from the arXiv repository and discuss the results of analyzing recent research trends in this academic open-access digital library.
{"title":"Analyzing Recent Research Trends of Computer Science from Academic Open-access Digital Library","authors":"N. Viet, A. Kravets","doi":"10.1109/SMART46866.2019.9117215","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117215","url":null,"abstract":"The wider utilization of information and web technologies, database technologies, development of internet infrastructure has led to the evolution of digital libraries. In particular, digital libraries serve enormous number of various users and play an essential role as repositories and source of investigation and intelligence. With the emergence of IoT (Internet of Things) and different academic open-access digital libraries, the automatic extraction of advantageous knowledge from text data has been more and more a significant subject of research in data mining. In this paper, we perform web scraping system, statistical analyses from the arXiv repository and discuss the results of analyzing recent research trends in this academic open-access digital library.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134030008","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117307
Ankur Sisodia, Shakti Kundu
Wireless Sensor Networks are the most confined networks somehow it can be resolved by proper use of routing protocols because we know that the performance of sensor networks depends on the routing protocols. In this paper, we try to extend the performance of Wireless Sensor Network Routing Protocols by introducing new element Data Compression in to it and we analyse this statement by studying these Performance Parameters like Throughput, Routing Overhead and End to End Delay with data compression as new element and we hope that this attempt or effort we made also helpful in further research.
{"title":"Enrichment of Performance of Operation based Routing Protocols of WSN using Data Compression","authors":"Ankur Sisodia, Shakti Kundu","doi":"10.1109/SMART46866.2019.9117307","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117307","url":null,"abstract":"Wireless Sensor Networks are the most confined networks somehow it can be resolved by proper use of routing protocols because we know that the performance of sensor networks depends on the routing protocols. In this paper, we try to extend the performance of Wireless Sensor Network Routing Protocols by introducing new element Data Compression in to it and we analyse this statement by studying these Performance Parameters like Throughput, Routing Overhead and End to End Delay with data compression as new element and we hope that this attempt or effort we made also helpful in further research.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132961202","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117466
Mehtab Mehdi, K. Pahwa, Bharti Sharma
Fundamentally, machine learning is the part of data science which is nothing but AI. We use machine learning algorithms for predicting the future results after analyzing the past data. This technique of data processing is called data analytics. Machine Learning algorithms are divided in three sections: Supervised, Unsupervised and Reinforcement. These algorithms are further subdivided in other sections. In this paper we are comparing these algorithms by which in future we could easily update the accuracy level of the ML algorithms. For doing this we used the healthcare data which has been uploaded on the kaggle. We implemented the machine learning algorithm using python programming language and calculated the accuracy level of each algorithm.
{"title":"Comparison of Data Mining Algorithms for Predicting the Cancer Disease Using Python","authors":"Mehtab Mehdi, K. Pahwa, Bharti Sharma","doi":"10.1109/SMART46866.2019.9117466","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117466","url":null,"abstract":"Fundamentally, machine learning is the part of data science which is nothing but AI. We use machine learning algorithms for predicting the future results after analyzing the past data. This technique of data processing is called data analytics. Machine Learning algorithms are divided in three sections: Supervised, Unsupervised and Reinforcement. These algorithms are further subdivided in other sections. In this paper we are comparing these algorithms by which in future we could easily update the accuracy level of the ML algorithms. For doing this we used the healthcare data which has been uploaded on the kaggle. We implemented the machine learning algorithm using python programming language and calculated the accuracy level of each algorithm.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"175 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133157898","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 : 2019-11-01DOI: 10.1109/SMART46866.2019.9117393
Upika Mittal, Snehmani, Sneha Agrawal
In surveillance fields, radar systems are essential as well as critical element for C2ISR i.e. Command, Control Intelligence, Surveillance and Reconnaissance. The performance of radar is severely affected by the terrain and environment conditions. On-field performance evaluation is a costly and time consuming process. This paper presents the in-house developed radar performance simulator to study and evaluate the performance of field surveillance radar system particularly for snow bound regions of India. The paper showcases effect of snow environment on the detection performance of the field surveillance radar. In the input section of the simulator, the characteristic parameters of target, radar system, terrain parameters, and simulation scenario can be set up. During the process of simulation run, state of simulation such as the received signal strength and its probability of detection at various ranges is displayed and plotted. Using the proposed simulator, effect of precarious environment of Western Himalaya on concealment of target has been analysed. Furthermore, the proposed simulator can be used to draw the features of radar based on the information collected and also used to define and design radar systems for future acquisitions.
{"title":"Modelling and Simulation Approach for Performance Evaluation of Field Surveillance Radar System in Snow Bound Region of India","authors":"Upika Mittal, Snehmani, Sneha Agrawal","doi":"10.1109/SMART46866.2019.9117393","DOIUrl":"https://doi.org/10.1109/SMART46866.2019.9117393","url":null,"abstract":"In surveillance fields, radar systems are essential as well as critical element for C2ISR i.e. Command, Control Intelligence, Surveillance and Reconnaissance. The performance of radar is severely affected by the terrain and environment conditions. On-field performance evaluation is a costly and time consuming process. This paper presents the in-house developed radar performance simulator to study and evaluate the performance of field surveillance radar system particularly for snow bound regions of India. The paper showcases effect of snow environment on the detection performance of the field surveillance radar. In the input section of the simulator, the characteristic parameters of target, radar system, terrain parameters, and simulation scenario can be set up. During the process of simulation run, state of simulation such as the received signal strength and its probability of detection at various ranges is displayed and plotted. Using the proposed simulator, effect of precarious environment of Western Himalaya on concealment of target has been analysed. Furthermore, the proposed simulator can be used to draw the features of radar based on the information collected and also used to define and design radar systems for future acquisitions.","PeriodicalId":328124,"journal":{"name":"2019 8th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"74 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629132","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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