Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231178
Matteo Bertolucci, Riccardo Cassettari, L. Fanucci
In recent years, following the rapid innovation guidelines of most space agencies, there have been major advances in satellite transmitter technologies. Released in 2012, the CCSDS 131.2-B-1 is one of the most recent downlink standards, with lacking in-depth research, but strongly endorsed by the European Space Agency (ESA). It seems then important to evaluate the performance of different frequency error detectors (FED) on its specific frame structure. This paper firstly deals with the analysis of the most common FEDs, while in the second part it proposes a lightweight architecture to estimate and compensate the carrier error using different algorithms on the same FPGA implementation. Specifically, the Delay & Multiply, Kay, Fitz, Luise & Reggiannini, Mengali & Morelli, and O'Shea et al. estimators are evaluated for both the estimation range and the accuracy. Following the general trade-offs, the design and implementation of the multi-algorithm estimator are detailed for a single feedback loop receiver. The system implements the Mengali & Morelli algorithm in the initial acquisition phase to exploit its wide estimation range, while it implements the Fitz algorithm for the tracking phase to take advantage of the lower RMS frequency error. The implementation follows a serial pipelined architecture, which can provide a new estimate for both algorithms in 5205 clock cycles using 942 LUT, 918 FF, 2.5 BRAM, and 7 DSP on a Xilinx Virtex 7 FPGA. Together with the frequency error detector specifications, the entire acquisition and tracking loop is reported, which shows an output RMS frequency error of about 1.05 kHz at 8.5 Mbaud and 50 kHz/s Doppler rate, that can be easily compensated by a common pilot-assisted phase estimator.
{"title":"CCSDS 131.2-B-1 Frequency Estimation Trade-Offs and a Novel Multi-Algorithm FPGA Architecture","authors":"Matteo Bertolucci, Riccardo Cassettari, L. Fanucci","doi":"10.1109/iCCECE49321.2020.9231178","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231178","url":null,"abstract":"In recent years, following the rapid innovation guidelines of most space agencies, there have been major advances in satellite transmitter technologies. Released in 2012, the CCSDS 131.2-B-1 is one of the most recent downlink standards, with lacking in-depth research, but strongly endorsed by the European Space Agency (ESA). It seems then important to evaluate the performance of different frequency error detectors (FED) on its specific frame structure. This paper firstly deals with the analysis of the most common FEDs, while in the second part it proposes a lightweight architecture to estimate and compensate the carrier error using different algorithms on the same FPGA implementation. Specifically, the Delay & Multiply, Kay, Fitz, Luise & Reggiannini, Mengali & Morelli, and O'Shea et al. estimators are evaluated for both the estimation range and the accuracy. Following the general trade-offs, the design and implementation of the multi-algorithm estimator are detailed for a single feedback loop receiver. The system implements the Mengali & Morelli algorithm in the initial acquisition phase to exploit its wide estimation range, while it implements the Fitz algorithm for the tracking phase to take advantage of the lower RMS frequency error. The implementation follows a serial pipelined architecture, which can provide a new estimate for both algorithms in 5205 clock cycles using 942 LUT, 918 FF, 2.5 BRAM, and 7 DSP on a Xilinx Virtex 7 FPGA. Together with the frequency error detector specifications, the entire acquisition and tracking loop is reported, which shows an output RMS frequency error of about 1.05 kHz at 8.5 Mbaud and 50 kHz/s Doppler rate, that can be easily compensated by a common pilot-assisted phase estimator.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126931882","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 : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231259
M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou
This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.
{"title":"Preliminary Melanoma Detection Mobile Application using Support Vector Machine Classification","authors":"M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou","doi":"10.1109/iCCECE49321.2020.9231259","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231259","url":null,"abstract":"This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129502095","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 : 2020-05-29DOI: 10.1109/iCCECE49321.2020.9231167
Md Gulzar Hussain, Md. Rashidul Hasan, Mahmuda Rahman, Joy Protim, S. Hasan
Fake or fraudulent news is coming into existence in large numbers for various political and commercial causes, which has become common in internet community. People can easily get tainted by any of these fraudulent news for their falsified words that have tremendous effects on the offline community. Therefore interest has increased in research on this topic. Notable work on the identification of false news in English texts as well as other languages except a few in Bangla Language has been carried out. Our work demonstrates the experimental investigation of detecting fake news from Bangla social media, as this area still requires a lot of concentrate. We have utilized two supervised machine learning techniques throughout this research study, Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB) classifiers to recognize Bangla fake news. Term Frequency - Inverse Document Frequency Vectorizer and CountVectorizer has been used as feature extraction. Our suggested system recognizes fake news according to polarity of the related post. Eventually, our research suggests SVM with linear kernel gives a 96.64 percent accuracy overperforming MNB with a 93.32 percent accuracy.
{"title":"Detection of Bangla Fake News using MNB and SVM Classifier","authors":"Md Gulzar Hussain, Md. Rashidul Hasan, Mahmuda Rahman, Joy Protim, S. Hasan","doi":"10.1109/iCCECE49321.2020.9231167","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231167","url":null,"abstract":"Fake or fraudulent news is coming into existence in large numbers for various political and commercial causes, which has become common in internet community. People can easily get tainted by any of these fraudulent news for their falsified words that have tremendous effects on the offline community. Therefore interest has increased in research on this topic. Notable work on the identification of false news in English texts as well as other languages except a few in Bangla Language has been carried out. Our work demonstrates the experimental investigation of detecting fake news from Bangla social media, as this area still requires a lot of concentrate. We have utilized two supervised machine learning techniques throughout this research study, Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB) classifiers to recognize Bangla fake news. Term Frequency - Inverse Document Frequency Vectorizer and CountVectorizer has been used as feature extraction. Our suggested system recognizes fake news according to polarity of the related post. Eventually, our research suggests SVM with linear kernel gives a 96.64 percent accuracy overperforming MNB with a 93.32 percent accuracy.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122168106","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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