2020 7th International Conference on Smart Structures and Systems (ICSSS)最新文献_第4页

A Compact Reconfigurable UWB Antenna For Short-Range Wireless Applications 一种用于短程无线应用的紧凑型可重构超宽带天线

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9201957

P. N, B. R, Vivek, G. Hemanth, B. S, MD Yasin Faisal

引用次数: 0

Voltage Doubler Design for RF Energy Harvesting System 射频能量收集系统的倍压设计

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9201998

T. Sathiyapriya, V. Gurunathan, T. Vimala, K. Prasad, T. Kumar

引用次数: 2

Underwater Image Detection using Laplacian and Gaussian Technique 基于拉普拉斯和高斯技术的水下图像检测

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202077

R. Nandhini, T. Sivasakthi

引用次数: 4

Investigation of High Directional Gain Pentagonal Shaped Patch Antenna 高定向增益五边形贴片天线的研究

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202223

D. Kumar, B. Caroline, K. Sagadevan, G. Sakthiganesh, R. Saravanan

引用次数: 5

SAR Analysis of Microstrip Patch Antenna in Human Head 头部微带贴片天线的SAR分析

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202240

K. Jayabharathy, S. Ilakkia

引用次数: 3

Australian Bushfire Detection Using Machine Learning and Neural Networks 利用机器学习和神经网络检测澳大利亚丛林大火

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202238

N. Kumar, Aditya Kumar

{"title":"Australian Bushfire Detection Using Machine Learning and Neural Networks","authors":"N. Kumar, Aditya Kumar","doi":"10.1109/ICSSS49621.2020.9202238","DOIUrl":"https://doi.org/10.1109/ICSSS49621.2020.9202238","url":null,"abstract":"Forest fires are increasingly one of the most predominant and alarming disasters in the planet right now and preventing it is very important in order to protect the environment and thousands of animals and plants species that depend on it. The 2019–20 Australian bushfire caused serious uncontrolled fires throughout the summer which burnt millions of hectares of land, destroyed thousands of buildings and killed many people. It has also been estimated to have killed about a billion animals and has bought endangered species on the brink of extinction. Such catastrophic events cannot be allowed to be repeated again. The primary goal of this paper is to improve the efficiency of forest fire detection system of Australia. Data mining and machine learning techniques can help to anticipate and quickly detect fires and take immediate action to minimise the damage. In this paper we try to focus on the implementation of a set of well-known classification algorithms (K-NN and Artificial Neural Networks), which can reduce the existing disadvantages of the fire detection systems. Results from the Kaggle dataset infer that our ANN-MLP algorithm (Multilayer Perceptron) yields better performance by calculating confusion matrix that in turn helps us to calculate performance measure as Detection Rate Accuracy. All predictions and calculations are done with the help of data collected by LANCE FIRMS operated by NASA's Earth Science Data and Information System (ESDIS). The training and testing of the model was done using University of Maryland dataset and was implemented using python.","PeriodicalId":286407,"journal":{"name":"2020 7th International Conference on Smart Structures and Systems (ICSSS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114878650","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}

引用次数: 2

Predicting Airline Crash due to Birds Strike Using Machine Learning 利用机器学习预测鸟类撞击造成的飞机失事

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202137

S. Nimmagadda, S. Sivakumar, Naveen Kumar, D. Haritha

{"title":"Predicting Airline Crash due to Birds Strike Using Machine Learning","authors":"S. Nimmagadda, S. Sivakumar, Naveen Kumar, D. Haritha","doi":"10.1109/ICSSS49621.2020.9202137","DOIUrl":"https://doi.org/10.1109/ICSSS49621.2020.9202137","url":null,"abstract":"The objective of this proposed work is to predict whether the airline crash has occurred due to a bird strike or not by using data mining techniques. Risk and safety are not always guaranteed within the field of aircraft. Bird strikes are dangerous for aircraft due to the relative speed of the plane with reference to the bird. The characteristics of aircraft damage from bird strikes, which is critical enough to make a high risk to continue a safe flight, differs in step with the dimensions of aircraft. Data from the National Transportation Safety Board (NTSB), which records all the aircraft accidents, are used as a training data set for the proposed system. Machine learning is the most effective technology to harnessing the useful information and knowledge from big data. The proposed work intended at building a prediction model using machine learning techniques such as decision trees and Bayesian classifications, which can be very useful in the aviation safety system and is utilized to conjecture the air crafts mishaps ahead of time so that there is an extension to the reduction in aircraft crash rate. The prediction results are range between 80% to 90%. The proposed aircraft crash prediction model is also assessed by using synthetic data sets.","PeriodicalId":286407,"journal":{"name":"2020 7th International Conference on Smart Structures and Systems (ICSSS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018919","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}

引用次数: 7

Design of frequency reconfigurable antenna for SDR application SDR应用中频率可重构天线的设计

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202201

P. Devi, A. Shekar, Abhishek, Tarun

引用次数: 2

Performance Analysis of MIMO-FSO communication link with Hybrid Modulation 混合调制下MIMO-FSO通信链路性能分析

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202159

Arjun Dubey, Harmeet Singh

引用次数: 2

Blood Vessel Extraction in Low Contrast Fundus Images having Lesions under Diabetic conditions 糖尿病患者眼底病变低对比图像中的血管提取

2020 7th International Conference on Smart Structures and Systems (ICSSS) Pub Date : 2020-07-01 DOI: 10.1109/ICSSS49621.2020.9202080

Remya K R, M N Giriprasad

{"title":"Blood Vessel Extraction in Low Contrast Fundus Images having Lesions under Diabetic conditions","authors":"Remya K R, M N Giriprasad","doi":"10.1109/ICSSS49621.2020.9202080","DOIUrl":"https://doi.org/10.1109/ICSSS49621.2020.9202080","url":null,"abstract":"The significance of vessel segmentation approach is increasing in recent years. However, it poses various complex challenges which are difficult to handle and causes degradation in the efficiency of the existing models. Since Blood vessels share the same color and intensity information as that of dark lesions, it is mandatory to extract and remove blood vessels in automatic diabetic retinopathy detection. Thus, a high quality vessel segmentation technique is required to handle these challenges. This paper puts forward a segmentation algorithm to evaluate vessel and non-vessel regions of fundus images using high boost filtering and principal curvature analysis followed by morphological operation. Contrast limited adaptive histogram equalization and second order Gaussian filtering is introduced as an intermediate step that will improve over all detection efficiency. Accuracy of fundus images having inadequate contrast is enriched by color normalization followed by contrast enhancement. The proposed technique is evaluated using the DRIVE and MESSIDOR databases. Results from suggested methods are compared with results from prevailing methods to ascertain superiority in segmentation quality.","PeriodicalId":286407,"journal":{"name":"2020 7th International Conference on Smart Structures and Systems (ICSSS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128709245","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}

引用次数: 2