VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T04:48:57ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2020-01-19T03:39:02Z2020-01-19T03:39:02Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3924This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/39242020-01-19T03:39:02ZMapping Land Cover Types in Vientiane, Laos Using Multi-Temporal Composite Landsat 8 ImagesPraseuth SanyaTuan Dung PhamDuc Chuc Manchucmd@fimo.edu.vnQuang Hung Buihungbq@vnu.edu.vnThi Nhat Thanh Nguyenthanhntn@vnu.edu.vn2020-01-04T05:40:54Z2020-01-04T05:41:44Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3799This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/37992020-01-04T05:40:54ZEvaluation of Maximum Likelihood Estimation and regression methods for fusion of multiple satellite Aerosol Optical Depth data over VietnamThis paper applied different data fusion methods including Maximum Likelihood Estimation (MLE) and Linear Regression methods on satellite images over Vietnam areas from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. In comparison with ground station Aerosol Robotic Network (AERONET), the regression method is better than Maximum Likelihood Estimator (MLE). Our results show that the fusion methods can improve both data coverage and quality of satellite aerosol optical depth (AOD). Strong correlations were observed between fused AOD and AERONET AOD (R 2 = 0.8118, 0.7511 for Terra regression and MLE method, respectively). This paper presented the evaluation of data fusion algorithm and highlighted its importance on the satellite AOD data coverage and quality methods from multiple sensors.Van Ha Phamhapv@fimo.edu.vnXuan Truong NgoDominique LafflyAstrid JourdanThi Nhat Thanh Nguyenthanhntn@vnu.edu.vn2019-12-10T15:56:44Z2020-01-08T01:10:17Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3798This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/37982019-12-10T15:56:44ZSpatiotemporal analysis of ground and satellite-based aerosol for air quality assessment in the Southeast Asia regionSatellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R2 = 0.81) and VIIRS AOD (R2 = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May–September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.Thi Nhat Thanh Nguyenthanhntn@vnu.edu.vnHa V. PhamKristofer LaskoMai T. BuiDominique LafflyAstrid JourdanQuang Hung Buihungbq@vnu.edu.vn2019-11-27T06:59:08Z2019-11-27T06:59:08Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3588This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35882019-11-27T06:59:08ZA Benchmarking Tool for Elastic MQTT Brokers in IoT ApplicationsCloud computing is an evolution in IT consumption and delivery which makes available self-management on the Internet with a flexible, pay-as-you-go business model. Within the context of Internet of Things, the MQTT (Message Queuing Telemetry Transport) protocol that is implemented broadly by the applications of “Publish-Subscribe” paradigm has a vital role. However, MQTT brokers are saturated easily if they have to cope with huge and speedy data generated by IoT “chatty” devices. With capability of provisioning/deprovisioning granular virtual resources, Cloud computing empowered MQTT brokers by enabling its elasticity feature. Elasticity helps the brokers deal with a very large variety of data integrated into the IoT every single day. However, there was lack of sturdy benchmarking tools that judge all the aspects of MQTT brokers in order to advocate correct elastic decision-making. This article focuses on the work of benchmarking MQTT by introducing a new developed tool called MQTTBrokerBench. With this tool, users not only can benchmark MQTT brokers but also can specify saturation points where the IoT load makes the brokers be saturated. Those saturation points can be used to set thresholds for elastic decision-making. Furthermore, the article also demonstrates the results acquired by this tool through the experiments on Windows Azure Cloud Platform.Manh Linh Phamlinhmp@vnu.edu.vnTruong Thang Nguyenntthang@ioit.ac.vnManh Dong Trandongtm@ioit.ac.vn2019-11-26T07:48:04Z2019-11-26T07:48:04Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3600This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/36002019-11-26T07:48:04ZRapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A ImageryThe Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD regionAnh Phananhp@fimo.edu.vnNhat Duong Haduonghn@fimo.edu.vnDuc Chuc Manchucmd@fimo.edu.vnQuang Hung Buihungbq@vnu.edu.vnThi Nhat Thanh Nguyenthanhntn@vnu.edu.vnThanh Thuy Nguyennguyenthanhthuy@vnu.edu.vn2019-11-09T02:56:23Z2019-11-09T02:56:23Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3583This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35832019-11-09T02:56:23ZCau Giay: A Dataset for Very Dense Building Extraction from Google Earth ImageryOne of the major topics in photogrammetry is the automated extraction of building from data acquired by airborne sensors. What makes this task challenging is the very heterogeneous appearance and dense distribution of buildings in urban areas. While many dataset have been established, none of them pay attention to developing cities where buildings are not well planned. To complement the development of building extraction algorithms, a dataset of high resolution satellite image is constructed in this paper covering Cau Giay district, Hanoi, Vietnam. The dataset consists of 2100 images of size 1024 x 1024 pixels extracted from Google Earth. Shape, size, and construction material differ greatly from building to building, thus make it challenging for state-of-the-art algorithm to accurately extract building location. Some baselines are provided using Convolutional Neural Networks (CNNs). Experimental results show that U-Net model trained with Mean Square Error loss is able to achieve comparable results (OA=92.04).Hoang Anh NguyenViet Hung Luuhunglv@fimo.edu.vnAnh Phananhp@fimo.edu.vnQuang Hung Buihungbq@vnu.edu.vnThi Nhat Thanh Nguyenthanhntn@vnu.edu.vn2019-11-07T08:51:13Z2019-11-07T08:51:13Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3582This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/35822019-11-07T08:51:13ZSemi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing DataWhen floods hit populated areas, quick detection of
flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1=96.12) and outperform conventional flood mapping methods.Viet Hung Luuhunglv@fimo.edu.vnMinh Son DaoThi Nhat Thanh Nguyenthanhntn@vnu.edu.vnStuart PerryStuart.Perry@uts.edu.auKoji Zettsu2019-06-20T22:32:58Z2019-06-20T22:32:58Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3499This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34992019-06-20T22:32:58ZAir pollution monitoring network using low-cost sensors, a case study in Hanoi, VietnamAir pollution is a serious problem in Vietnam, especially in urban areas with high pressures of population, traffic, construction, and industrial development. Besides high accurate measurements from automatic and continuous monitoring ground stations and high-cost sensor devices, low-cost sensors have recently utilized to extent air pollution monitoring networks although their data quality is still argumentative. In this paper, we present a low-cost device, named FAirKit, which measured 6 basic air pollutants including PM2.5, PM10, CO, O3, NO2, and SO2, and temperature and relative humidity. The sensors are calibrated with standard devices to improve their data quality. FAirKits are installed and transferred data in real-time to servers where an information system based on Sensor Web Enablement (SWE) standard of Open Geospatial Consortium (OGC) has been developed to store, process, and visualize real-time air pollution information. Currently, the low-cost sensors network has been deploying in Hanoi, Vietnam to enhance public awareness and alert local people to air pollution.Thi Nhat Thanh Nguyenthanhntn@vnu.edu.vnDuc Van Havanhd@fimo.edu.vnThi Nhu Ngoc Dongocdtn@fimo.edu.vnVan Hai Nguyenhainv@fimo.edu.vnXuan Truong Ngotruongnx@fimo.edu.vnVan Ha Phamhapv@fimo.edu.vnNgoc Duc Nguyenducnn@fimo.edu.vnQuang Hung Buihungbq@vnu.edu.vn2019-06-20T22:32:26Z2019-06-20T22:32:26Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3498This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34982019-06-20T22:32:26ZAnalyzing the impacts of urban expansion on air pollution in Vietnam using the SEAP platformThe relationship between urbanization and air pollution was discovered in many studies. In this study, we analyzed the impacts of urban expansion on air pollution in Vietnam using remotely-sensed data from 2004 to 2015. In this period of time, Vietnam urban square was increased from 4623 km2 in 2004 to 5094 km2 in 2015. Besides, there is a clear difference between the average PM2.5 concentration value of urban areas and non-urban areas in Vietnam, urban PM2.5 values are generally higher than in rural areas for years. In this study, we use the SEAP (big Spatial data Exploration and Analysis Platform) platform to analyze and store data.Tuan Dung Phamdungpt@fimo.edu.vnVan Ha Phamhapv@fimo.edu.vnQuang Thang Luuthanglq@fimo.edu.vnXuan Truong Ngotruongnx@fimo.edu.vnThi Nhat Thanh Nguyenthanhntn@vnu.edu.vnQuang Hung Buihungbq@vnu.edu.vn2019-06-10T01:47:48Z2019-07-05T01:38:50Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3478This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34782019-06-10T01:47:48ZMột công cụ đo kiểm giao thức MQTT cho những ứng dụng IoT phục vụ cho việc ra quyết định co dãn tài nguyên trên đám mâyTrong sự phát triển của Internet vạn vật (IoT), giao thức MQTT (Message Queuing Telemetry Transport) có một vai trò rất quan trọng bởi lẽ nó được sử dụng rộng rãi bởi những ứng dụng IoT thường được triển khai theo mô hình "Xuất bản-Đăng ký". Với khả năng cung ứng và giải phóng những tài nguyên ảo và mịn (chức năng co dãn - elasticity), điện toán đám mây có khả năng làm gia tăng sức mạnh cho những máy chủ môi giới MQTT. Khả năng co dãn giúp các máy chủ môi giới MQTT có thể đương đầu với một khối lượng lớn dữ liệu đang từng ngày được tích hợp vào IoT. Tuy nhiên vẫn có sự thiếu vắng của các công cụ đo kiểm có thể đánh giá đầy đủ tất cả các khía cạnh của MQTT để hỗ trợ việc ra những quyết định co dãn tài nguyên hợp lý và chính xác. Bài báo này tập trung vào chủ đề đo kiểm MQTT thông qua việc giới thiệu MQTTBench một công cụ kiểm thử mới được chúng tôi phát triển cùng với những kết quả ban đầu có được từ việc áp dụng công cụ đo kiểm đó trên một ứng dụng điện toán đám mây giả lập.Manh Linh Phamlinhmp@vnu.edu.vnManh Dong Trandongtm@ioit.ac.vnTruong Thang Nguyenntthang@ioit.ac.vn2019-06-03T04:15:37Z2019-06-03T04:15:37Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3442This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34422019-06-03T04:15:37ZFlexible deployment of component-based distributed applications on the Cloud and beyondIn an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.Manh Linh Phamlinhmp@vnu.edu.vnTruong Thang Nguyenntthang@ioit.ac.vn2019-06-03T04:07:26Z2019-06-03T04:07:26Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3449This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/34492019-06-03T04:07:26ZImproving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classificationIn this letter, we investigate fine-grained classification of arbitrary-oriented ships in very high resolution optical imagery using Bag of Word model with Spatial Pyramid (SP-BoW). Given that based on ‘spatial pyramid’ of the histogram of local features, the final feature vectors not only count the multiplicity of ‘words’ but also represent their spatial topology. We attempt to improve the performance of this model by introducing augmented data for training phase. Our aim is to make the dataset big enough to be able to capture holistic variation of ship orientation. Three data augmentation operations are used including random rotate by an angle of modulo 90°, random flip-left-right, and random flip-top-bottom. Through this procedure, our trained SP-BoW model is able to get better generalization. The proposed approach is validated on the High-Resolution Ship Collections 2016 (HRSC2016) ship dataset. The results indicate that training on augmented data can significantly improve the performance of SP-BoW. Beside, compared to other state-of-the-art convolutional neural network-based approaches, the approach proposed in this research has yielded competitive results and could make it a good baseline for evaluating more sophisticated CNN architecture in the future.Viet Hung Luuhunglv@fimo.edu.vnVan Kiet DinhNguyen Hoang Hoa LuongQuang Hung Buihungbq@vnu.edu.vnThi Nhat Thanh Nguyenthanhntn@vnu.edu.vn