Pub Date : 2016-04-05DOI: 10.1109/MERCON.2016.7480107
N. De Mel, H. H. Hettiarachchi, W. Madusanka, G. L. Malaka, A. Perera, U. Kohomban
Due to the increase in the number of people participating online on reviewing travel related entities such as hotels, cities and attractions, there is a rich corpus of textual information available online. However, to make a decision on a certain entity, one has to read many such reviews manually, which is inconvenient. To make sense of the reviews, the essential first step is to understand the semantics that lie therein. This paper discusses a system that uses machine learning based classifiers to label the entities found in text into semantic concepts defined in an ontology. A subject classifier with a precision of 0.785 and a sentiment classifier with a correlation coefficient of 0.9423 was developed providing sufficient accuracy for subject categorization and sentiment evaluation in the proposed system.
{"title":"Machine learning approach to recognize subject based sentiment values of reviews","authors":"N. De Mel, H. H. Hettiarachchi, W. Madusanka, G. L. Malaka, A. Perera, U. Kohomban","doi":"10.1109/MERCON.2016.7480107","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480107","url":null,"abstract":"Due to the increase in the number of people participating online on reviewing travel related entities such as hotels, cities and attractions, there is a rich corpus of textual information available online. However, to make a decision on a certain entity, one has to read many such reviews manually, which is inconvenient. To make sense of the reviews, the essential first step is to understand the semantics that lie therein. This paper discusses a system that uses machine learning based classifiers to label the entities found in text into semantic concepts defined in an ontology. A subject classifier with a precision of 0.785 and a sentiment classifier with a correlation coefficient of 0.9423 was developed providing sufficient accuracy for subject categorization and sentiment evaluation in the proposed system.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316289","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480175
B. Balasuriya, B. A. H. Chathuranga, B. Jayasundara, N. Napagoda, S. Kumarawadu, D. P. Chandima, A. Jayasekara
This paper presents the complete methodology followed in designing and implementing a tracked autonomous navigation robot which can navigate through an unknown outdoor environment using ROS (Robot Operating System). The concept is based on the mapping process using SLAM (Simultaneous Localization and Mapping) GMapping Algorithm. Implementation of the robot on the ROS platform is presented in this paper and experimental results are also presented validating the accuracy of the algorithm.
本文介绍了利用ROS(机器人操作系统)设计和实现可在未知室外环境中导航的履带式自主导航机器人的完整方法。该概念基于使用SLAM (Simultaneous Localization and mapping) gmap算法的制图过程。本文给出了机器人在ROS平台上的实现，并给出了实验结果，验证了算法的准确性。
{"title":"Outdoor robot navigation using Gmapping based SLAM algorithm","authors":"B. Balasuriya, B. A. H. Chathuranga, B. Jayasundara, N. Napagoda, S. Kumarawadu, D. P. Chandima, A. Jayasekara","doi":"10.1109/MERCON.2016.7480175","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480175","url":null,"abstract":"This paper presents the complete methodology followed in designing and implementing a tracked autonomous navigation robot which can navigate through an unknown outdoor environment using ROS (Robot Operating System). The concept is based on the mapping process using SLAM (Simultaneous Localization and Mapping) GMapping Algorithm. Implementation of the robot on the ROS platform is presented in this paper and experimental results are also presented validating the accuracy of the algorithm.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124669514","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480106
S. Prakhash, A. Nazick, R. Panchendrarajan, M. Brunthavan, Surangika Ranathunga, A. Pemasiri
There are many aspects such as food, service, and ambience that a customer would look for, when deciding on a restaurant to dine in. Among these aspects, the type of food it sells and the food quality are the most important. Therefore, when automatically rating restaurants based on customer reviews, the food aspect plays a major role. There exists some research on rating individual food items in a restaurant. However, a potential customer requires not the ranking of an individual food item, but the ranking of a particular food category in general. In order to do that, a categorization of food names is required. This paper presents two techniques for food name categorization using document similarity measurements.
{"title":"Categorizing food names in restaurant reviews","authors":"S. Prakhash, A. Nazick, R. Panchendrarajan, M. Brunthavan, Surangika Ranathunga, A. Pemasiri","doi":"10.1109/MERCON.2016.7480106","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480106","url":null,"abstract":"There are many aspects such as food, service, and ambience that a customer would look for, when deciding on a restaurant to dine in. Among these aspects, the type of food it sells and the food quality are the most important. Therefore, when automatically rating restaurants based on customer reviews, the food aspect plays a major role. There exists some research on rating individual food items in a restaurant. However, a potential customer requires not the ranking of an individual food item, but the ranking of a particular food category in general. In order to do that, a categorization of food names is required. This paper presents two techniques for food name categorization using document similarity measurements.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129931539","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480169
S. Umadaran, P. Somasuntharam, A. Samarasekara
In recent years, polymer based composites have become more popular in many scientific researches as well as many commercial programs. Growing global, environmental and social concern, the high rate of depletion of petroleum resources and new environmental regulations has forced the search for new types of composites and environment friendly materials. The objective of this research is extraction of Cellulose and Hemi-Cellulose from sugarcane waste to develop degradable composite material for packaging applications. The goal of this research is to make a synthetic polymer based composite (Low Density Polyethylene) degradable by adding natural substances (Cellulose and Hemi-Cellulose). Degradability of the developed composited was evaluated by measuring tensile strength, elongation, Fourier-transform infrared spectroscopic evaluation, and water absorption and weight loss properties before and after soil burial test. Experiment results showed that there was a significant degradation of the developed composite material after soil burial test.
{"title":"Preparation and characterization of Cellulose and Hemi-Cellulose based degradable composite material using sugarcane waste","authors":"S. Umadaran, P. Somasuntharam, A. Samarasekara","doi":"10.1109/MERCON.2016.7480169","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480169","url":null,"abstract":"In recent years, polymer based composites have become more popular in many scientific researches as well as many commercial programs. Growing global, environmental and social concern, the high rate of depletion of petroleum resources and new environmental regulations has forced the search for new types of composites and environment friendly materials. The objective of this research is extraction of Cellulose and Hemi-Cellulose from sugarcane waste to develop degradable composite material for packaging applications. The goal of this research is to make a synthetic polymer based composite (Low Density Polyethylene) degradable by adding natural substances (Cellulose and Hemi-Cellulose). Degradability of the developed composited was evaluated by measuring tensile strength, elongation, Fourier-transform infrared spectroscopic evaluation, and water absorption and weight loss properties before and after soil burial test. Experiment results showed that there was a significant degradation of the developed composite material after soil burial test.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124142960","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480155
K. Mallawarachchi, L. R. L. M. Bandara, S. Dilshan, T. Ariyadasa, S. Gunawardena
Beer manufacturers worldwide use adjuncts like rice, wheat and sorghum to reduce the cost of production replacing the starch source malt. In Sri Lankan context, rice can be added as the adjunct. Optimization of mashing process in order to obtain maximum sugar yield at a minimum cost is vitally important for the profitability in beer industry. This research investigates the effectiveness of mashing process at different rice-malt ratios and temperatures, thus identifying the optimum conditions for the mashing process when using rice as the adjunct. According to the statistical model developed using experimental data, up to 30.75% rice can be added as an adjunct without adding enzymes externally, and the optimum mashing temperature is 62 °C.
{"title":"Optimization of mashing process in beer production using rice as an adjunct","authors":"K. Mallawarachchi, L. R. L. M. Bandara, S. Dilshan, T. Ariyadasa, S. Gunawardena","doi":"10.1109/MERCON.2016.7480155","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480155","url":null,"abstract":"Beer manufacturers worldwide use adjuncts like rice, wheat and sorghum to reduce the cost of production replacing the starch source malt. In Sri Lankan context, rice can be added as the adjunct. Optimization of mashing process in order to obtain maximum sugar yield at a minimum cost is vitally important for the profitability in beer industry. This research investigates the effectiveness of mashing process at different rice-malt ratios and temperatures, thus identifying the optimum conditions for the mashing process when using rice as the adjunct. According to the statistical model developed using experimental data, up to 30.75% rice can be added as an adjunct without adding enzymes externally, and the optimum mashing temperature is 62 °C.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131388643","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480129
K. Nanayakkara, T. Peiris
Mathematical knowledge is essential to improve the analytical thinking of engineering undergraduates. Exploring more information from existing academic data is an essential aspect of the educational research. The objective of this study is to explore the impact of mathematics performance on different engineering programs. The study was conducted with 626 engineering students from seven different disciplines at the Faculty of Engineering, University of Moratuwa, Sri Lanka. Canonical Correlation Analysis (CCA) was employed to investigate the relationship between mathematics courses and other engineering courses with respect to their disciplines. Results of CCA revealed that the mathematics performance in both semester 1 and 2 influences significantly on the students' academic performance in Level 2 of the seven engineering disciplines considered. Wilk's lambda test statistic confirmed that only the first canonical variate pair is significant for all disciplines. The squared canonical correlations of first canonical variate pair indicated that the amount of variance between the mathematics performance and academic performance in Level 2 explained varied among seven disciplines from 42% to 68%. The impact is higher from mathematics in semester 2 than that from semester 1 in all disciplines except for Material Science and Engineering discipline. The explainable variability of student academic performance in Level 2 by the canonical variate of mathematics is varied from 27% to 50% among seven disciplines. Based on preliminary analysis, it can be concluded that the performance in mathematics in Level 1 could indicate the trend towards the student academic performance in all engineering programs.
{"title":"Application of Canonical Correlation Analysis to study the influence of mathematics on engineering programs: A case study","authors":"K. Nanayakkara, T. Peiris","doi":"10.1109/MERCON.2016.7480129","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480129","url":null,"abstract":"Mathematical knowledge is essential to improve the analytical thinking of engineering undergraduates. Exploring more information from existing academic data is an essential aspect of the educational research. The objective of this study is to explore the impact of mathematics performance on different engineering programs. The study was conducted with 626 engineering students from seven different disciplines at the Faculty of Engineering, University of Moratuwa, Sri Lanka. Canonical Correlation Analysis (CCA) was employed to investigate the relationship between mathematics courses and other engineering courses with respect to their disciplines. Results of CCA revealed that the mathematics performance in both semester 1 and 2 influences significantly on the students' academic performance in Level 2 of the seven engineering disciplines considered. Wilk's lambda test statistic confirmed that only the first canonical variate pair is significant for all disciplines. The squared canonical correlations of first canonical variate pair indicated that the amount of variance between the mathematics performance and academic performance in Level 2 explained varied among seven disciplines from 42% to 68%. The impact is higher from mathematics in semester 2 than that from semester 1 in all disciplines except for Material Science and Engineering discipline. The explainable variability of student academic performance in Level 2 by the canonical variate of mathematics is varied from 27% to 50% among seven disciplines. Based on preliminary analysis, it can be concluded that the performance in mathematics in Level 1 could indicate the trend towards the student academic performance in all engineering programs.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130430330","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480119
H. M. C. Chandrathilake, H. T. S. Hewawitharana, R. Jayawardana, A. D. D. Viduranga, H. D. Dilum Bandara, S. Marru, S. Perera
Modern weather forecasting models are developed to maximize the accuracy of forecasts by running computationally intensive algorithms with vast volumes of data. Consequently, algorithms take a long time to execute, and it may adversely affect the timeliness of forecast. One solution to this problem is to run the complex weather forecasting models only on the potentially hazardous events, which are pre-identified by a lightweight data filtering algorithm. We propose a Complex Event Processing (CEP) and Machine Learning (ML) based weather monitoring framework using open source resources that can be extended and customized according to the users' requirements. The CEP engine continuously filters out the input weather data stream to identify potentially hazardous weather events, and then generates a rough boundary enclosing all the data points within the Areas of Interest (AOI). Filtered data points are then fed to the machine learner, where the rough boundary gets more refined by clustering it into a set of AOIs. Each cluster is then concurrently processed by complex weather algorithms of the WRF model. This reduces the computational time by ~75%, as resource heavy weather algorithms are executed using a small subset of data that corresponds to only the areas with potentially hazardous weather.
{"title":"Reducing computational time of closed-loop weather monitoring: A Complex Event Processing and Machine Learning based approach","authors":"H. M. C. Chandrathilake, H. T. S. Hewawitharana, R. Jayawardana, A. D. D. Viduranga, H. D. Dilum Bandara, S. Marru, S. Perera","doi":"10.1109/MERCON.2016.7480119","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480119","url":null,"abstract":"Modern weather forecasting models are developed to maximize the accuracy of forecasts by running computationally intensive algorithms with vast volumes of data. Consequently, algorithms take a long time to execute, and it may adversely affect the timeliness of forecast. One solution to this problem is to run the complex weather forecasting models only on the potentially hazardous events, which are pre-identified by a lightweight data filtering algorithm. We propose a Complex Event Processing (CEP) and Machine Learning (ML) based weather monitoring framework using open source resources that can be extended and customized according to the users' requirements. The CEP engine continuously filters out the input weather data stream to identify potentially hazardous weather events, and then generates a rough boundary enclosing all the data points within the Areas of Interest (AOI). Filtered data points are then fed to the machine learner, where the rough boundary gets more refined by clustering it into a set of AOIs. Each cluster is then concurrently processed by complex weather algorithms of the WRF model. This reduces the computational time by ~75%, as resource heavy weather algorithms are executed using a small subset of data that corresponds to only the areas with potentially hazardous weather.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131878731","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480157
S. Ariyarathna, H. P. D. S. N. Siriwardhana, M. Danthurebandara
Rice processing is the major food processing industry in Sri Lanka. Although the process creates immense economic, social and environmental impacts, the studies conducted to analyze those impacts are scarce in the country. Though there are various rice production processes in the world, basically there are two rice production processes as raw rice and parboiled rice in Sri Lanka. Out of these, parboiled rice processing is the most prominent one. But, it creates considerable burden on the environment. Parboiling process can be categorized into two, as conventional and modern based on the processing methods practice in the mills. This paper compares the environmental impact of that conventional and modern rice processing by using life cycle analysis as the assessment tool. The results reveal that the modern rice processing creates elevated environmental impact in terms of all considered impact categories. In addition, soaking, drying and de-husking are identified as the most causative unit operations to the total environmental impact.
{"title":"Life cycle assessment of rice processing in Sri Lanka: Modern and conventional processing","authors":"S. Ariyarathna, H. P. D. S. N. Siriwardhana, M. Danthurebandara","doi":"10.1109/MERCON.2016.7480157","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480157","url":null,"abstract":"Rice processing is the major food processing industry in Sri Lanka. Although the process creates immense economic, social and environmental impacts, the studies conducted to analyze those impacts are scarce in the country. Though there are various rice production processes in the world, basically there are two rice production processes as raw rice and parboiled rice in Sri Lanka. Out of these, parboiled rice processing is the most prominent one. But, it creates considerable burden on the environment. Parboiling process can be categorized into two, as conventional and modern based on the processing methods practice in the mills. This paper compares the environmental impact of that conventional and modern rice processing by using life cycle analysis as the assessment tool. The results reveal that the modern rice processing creates elevated environmental impact in terms of all considered impact categories. In addition, soaking, drying and de-husking are identified as the most causative unit operations to the total environmental impact.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129376851","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480121
Sandareka Wickramanayake, H. D. Dilum Bandara
Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles and preventing fraudulent activities in fleet management. Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. However, not all these factors may be measured or available for the fuel consumption analysis. We consider a case where only a subset of the aforementioned factors is available as a multi-variate time series from a long distance, public bus. Hence, the challenge is to model and/or predict the fuel consumption only with the available data, while still indirectly capturing as much as influences from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in data. In this paper, we compare the predictive ability of three ML techniques in predicting the fuel consumption of the bus, given all available parameters as a time series. Based on the analysis, it can be concluded that the random forest technique produces a more accurate prediction compared to both the gradient boosting and neural networks.
{"title":"Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study","authors":"Sandareka Wickramanayake, H. D. Dilum Bandara","doi":"10.1109/MERCON.2016.7480121","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480121","url":null,"abstract":"Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles and preventing fraudulent activities in fleet management. Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. However, not all these factors may be measured or available for the fuel consumption analysis. We consider a case where only a subset of the aforementioned factors is available as a multi-variate time series from a long distance, public bus. Hence, the challenge is to model and/or predict the fuel consumption only with the available data, while still indirectly capturing as much as influences from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in data. In this paper, we compare the predictive ability of three ML techniques in predicting the fuel consumption of the bus, given all available parameters as a time series. Based on the analysis, it can be concluded that the random forest technique produces a more accurate prediction compared to both the gradient boosting and neural networks.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125164900","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 : 2016-04-05DOI: 10.1109/MERCON.2016.7480125
Suranga Handagala, A. Madanayake, L. Belostotski, L. Bruton
A multi-dimensional noise-shaping method based on delta-sigma modulation has been proposed. This method extends delta-sigma modulation into the two-dimensional (2-D) case (space, time). The proposed noise-shaping method employs lossless discrete integrators for realization in microwave and mm-wave array processing systems. The paper shows that 2-D noise-shaping reduces the spectral overlap of a desired array signal with that of noise. By reducing the overlap of the ROSs, 2-D filtering can be used to improve the overall noise figure of the array receiver. A noise figure improvement of 2.6 dB could be simulated for a 4-times spatially over-sampled array with 65 simulated elements for an input signal to noise ratio of 10 dB and LNA noise figure of 5 dB. Simulation results based on wideband signals on 33, 65, 129 and 257 element antenna arrays with 2, 4 and 8 times oversampling show the potential capability of the proposed system in improving overall noise figure. Although mathematical modeling shows potential improvements in receiver noise figure, RF integrated circuit realizations are challenging and have not been attempted yet.
{"title":"Delta-sigma noise shaping in 2D spacetime for uniform linear aperture array receivers","authors":"Suranga Handagala, A. Madanayake, L. Belostotski, L. Bruton","doi":"10.1109/MERCON.2016.7480125","DOIUrl":"https://doi.org/10.1109/MERCON.2016.7480125","url":null,"abstract":"A multi-dimensional noise-shaping method based on delta-sigma modulation has been proposed. This method extends delta-sigma modulation into the two-dimensional (2-D) case (space, time). The proposed noise-shaping method employs lossless discrete integrators for realization in microwave and mm-wave array processing systems. The paper shows that 2-D noise-shaping reduces the spectral overlap of a desired array signal with that of noise. By reducing the overlap of the ROSs, 2-D filtering can be used to improve the overall noise figure of the array receiver. A noise figure improvement of 2.6 dB could be simulated for a 4-times spatially over-sampled array with 65 simulated elements for an input signal to noise ratio of 10 dB and LNA noise figure of 5 dB. Simulation results based on wideband signals on 33, 65, 129 and 257 element antenna arrays with 2, 4 and 8 times oversampling show the potential capability of the proposed system in improving overall noise figure. Although mathematical modeling shows potential improvements in receiver noise figure, RF integrated circuit realizations are challenging and have not been attempted yet.","PeriodicalId":184790,"journal":{"name":"2016 Moratuwa Engineering Research Conference (MERCon)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129946941","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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