Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687991
Sudeep D. Thepade, Mrunal E. Idhate
The photos were taken in the dark light or poor environment always affects the quality of the images as these images are not able to understand for humane eyes and machines for experimental analysis. These images are hard to understand and identify objects with the help of precise details of the images. Sometimes machines get confused about these details of the images as image quality is degraded due to images taken in a poorly illuminated or dark environment. There are many existing techniques available for the contrast enhancement of the images. Some of these techniques have disadvantages. Disadvantages as a blurred image, a noise present in the image, the image gets distorted, etc. to overcome such disadvantages this paper proposed contrast enhancement techniques based on the simple weight blending of the bright channel prior(BCP) and Fraction-Order Fusion Model (FFM). For this experimentation exclusively dark image dataset is used and for the evaluation of the quality of the images entropy values of images are calculated. The outcomes of this experimentation give a better result compared to the individual output of bright channel prior (BCP), Fraction-Order Fusion Model (FFM), and other existing methods.
{"title":"Improving the Contrast of Dark Images with Fusion Blending of Fraction-Order Fusion Model and Bright Channel Prior","authors":"Sudeep D. Thepade, Mrunal E. Idhate","doi":"10.1109/CENTCON52345.2021.9687991","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687991","url":null,"abstract":"The photos were taken in the dark light or poor environment always affects the quality of the images as these images are not able to understand for humane eyes and machines for experimental analysis. These images are hard to understand and identify objects with the help of precise details of the images. Sometimes machines get confused about these details of the images as image quality is degraded due to images taken in a poorly illuminated or dark environment. There are many existing techniques available for the contrast enhancement of the images. Some of these techniques have disadvantages. Disadvantages as a blurred image, a noise present in the image, the image gets distorted, etc. to overcome such disadvantages this paper proposed contrast enhancement techniques based on the simple weight blending of the bright channel prior(BCP) and Fraction-Order Fusion Model (FFM). For this experimentation exclusively dark image dataset is used and for the evaluation of the quality of the images entropy values of images are calculated. The outcomes of this experimentation give a better result compared to the individual output of bright channel prior (BCP), Fraction-Order Fusion Model (FFM), and other existing methods.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124187541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9687887
Manasa K R, R. Narendra
5G empowers a novel network that is envisioned to unite all the appliances, machines and gadgets effectively. 5G introduces wide bandwidth with the extension of additional frequency spectrum resources. The 5G network is capable of working both in lower bands like S-band and C-bands and also works in millimetre wave spectrum including Ka-band that helps achieve higher data rate, gain, efficiency and reduced delay. High performance, wideband high gain, compact size and low profile millimetre wave antennas are required for 5G enabled applications to achieve interference free communication. This article presents a microstrip array antenna for millimetre wave applications that resonates in dual bands at green flexible radio frequency range. The antenna array uses parallel feeding technique. The artistic slots introduced in the patches, round and opposite corner fillets result in dual frequency resonance at 57GHz and 60GHz. The proposed small array design with size of 2 × 8 and defected ground results in better gain, directivity, efficiency which is suitable for 5G communication.
{"title":"Dual Band Microstrip 2×8 Array Antenna At Green Flexible RF","authors":"Manasa K R, R. Narendra","doi":"10.1109/CENTCON52345.2021.9687887","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9687887","url":null,"abstract":"5G empowers a novel network that is envisioned to unite all the appliances, machines and gadgets effectively. 5G introduces wide bandwidth with the extension of additional frequency spectrum resources. The 5G network is capable of working both in lower bands like S-band and C-bands and also works in millimetre wave spectrum including Ka-band that helps achieve higher data rate, gain, efficiency and reduced delay. High performance, wideband high gain, compact size and low profile millimetre wave antennas are required for 5G enabled applications to achieve interference free communication. This article presents a microstrip array antenna for millimetre wave applications that resonates in dual bands at green flexible radio frequency range. The antenna array uses parallel feeding technique. The artistic slots introduced in the patches, round and opposite corner fillets result in dual frequency resonance at 57GHz and 60GHz. The proposed small array design with size of 2 × 8 and defected ground results in better gain, directivity, efficiency which is suitable for 5G communication.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688283
Vaishnavi Jayaraman, S. S, K. Monica, Arunraj Lakshminarayanan
Agriculture is a detracting sector in the global providence, which is defined as the practice of cultivating crops. Precision agriculture using machine learning algorithms is one of the fast-growing methodologies. It explores the usage of modern technologies to increase the crop yield rate by decreasing the utilization of fertilizers. The main aim of this study is to predict the soil suitability by utilizing the sensors and machine learning techniques. The temperature, humidity, pH and soil moisture were the main sources for plant growth. The nature of the soil would be identified, by measuring the above said entities. This paper analyses the soil suitability using diversified machine learning techniques such as KNN, Support Vector Machine, Random Forest, Naive Bayes, and Extreme Learning Machine. ELM model predicts the soil suitability with 99% of accuracy.
{"title":"Predicting the Soil Suitability using Machine Learning Techniques","authors":"Vaishnavi Jayaraman, S. S, K. Monica, Arunraj Lakshminarayanan","doi":"10.1109/CENTCON52345.2021.9688283","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688283","url":null,"abstract":"Agriculture is a detracting sector in the global providence, which is defined as the practice of cultivating crops. Precision agriculture using machine learning algorithms is one of the fast-growing methodologies. It explores the usage of modern technologies to increase the crop yield rate by decreasing the utilization of fertilizers. The main aim of this study is to predict the soil suitability by utilizing the sensors and machine learning techniques. The temperature, humidity, pH and soil moisture were the main sources for plant growth. The nature of the soil would be identified, by measuring the above said entities. This paper analyses the soil suitability using diversified machine learning techniques such as KNN, Support Vector Machine, Random Forest, Naive Bayes, and Extreme Learning Machine. ELM model predicts the soil suitability with 99% of accuracy.","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121529266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-19DOI: 10.1109/CENTCON52345.2021.9688040
Ankit Vishnoi, A. Aggarwal, A. Prasad, M. Prateek
Data security is an important aspect for datasets of every size and type. Data security refers to provide a secure environment to datasets, web-based datasets, and preventing unauthorized access to the data. The key data security technique is encryption, where the digital data is encrypted. As result, the unauthorized person or hacker gets unreadable data. The computing process generates data that is encrypted by the key and can be decrypted with the correct key only. In this research, an approach is proposed of using transforms to provide a better cryptography process to secure data even during communication. The result of the proposed method indicates that in any condition, the ciphertext will always be the same for each input, but the encryption key will change each time, which makes this encryption complex to break
{"title":"An Encryption Method Involving Homomorphic Transform","authors":"Ankit Vishnoi, A. Aggarwal, A. Prasad, M. Prateek","doi":"10.1109/CENTCON52345.2021.9688040","DOIUrl":"https://doi.org/10.1109/CENTCON52345.2021.9688040","url":null,"abstract":"Data security is an important aspect for datasets of every size and type. Data security refers to provide a secure environment to datasets, web-based datasets, and preventing unauthorized access to the data. The key data security technique is encryption, where the digital data is encrypted. As result, the unauthorized person or hacker gets unreadable data. The computing process generates data that is encrypted by the key and can be decrypted with the correct key only. In this research, an approach is proposed of using transforms to provide a better cryptography process to secure data even during communication. The result of the proposed method indicates that in any condition, the ciphertext will always be the same for each input, but the encryption key will change each time, which makes this encryption complex to break","PeriodicalId":103865,"journal":{"name":"2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129844573","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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