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An Empirical Approach to Monitor the Flood-Prone Regions of North India Using Sentinel-1 Images Mohammed Siddique, Tasneem Ahmed and Mohd Shahid Husain Abstract: Floods in India is among the perilous natural disasters with a high impact on its economic sectors. One of the critical factors to handle such hazardous events is monitoring the affected areas and changes in flood patterns. Flood management is a very complex issue, largely owing to the growing population and investments in flood-affected regions. Satellite images especially Synthetic Aperture Radar (SAR) images are very useful and effective because SAR images are acquired day and night in all types of weather conditions. This research analyzes a combination of machine learning algorithms implemented on Sentinel-1A (SAR) data using supervised classification techniques to monitor the flooded areas in the North Indian region. Random Forest (RF) and the K-nearest neighbour (KNN) classification is applied to classify the different land covers such as water bodies, land, vegetation, and bare soil land covers. The outcomes of the presented work depict that the SAR data provides efficient information that helps in monitoring the flooded extents and the analysis shows that Sentinel-1 images are quite effective to detect changes in flood patterns in urban, vegetation, and regular water areas of the selected regions. The distribution of flooded areas was 16.6% and 16.8% in the respective region which is consistent with the resultant images of the proposed approach using RF and KNN classifiers. The obtained results indicate that both classifiers used in the work generate higher classification accuracy. These classifiers define the potential of multi-polarimetric SAR data in the classification of flood-affected areas. For a thorough evaluation and comparison, the RF and KNN are utilized as benchmarked classifiers. The classification accuracies based on the investigated results from the three SAR images can be improved by incorporating spatial and polarimetric features. In the future, the deep-learning classification techniques using ensemble strategies are expected to achieve an increased accuracy level with an overall classification strategy of urban and vegetation mapping. Keywords: Flood mapping; Image Classification; K-Nearest Neighbour; Random Forest; SAR; Sentinel-1. |
Media Convergence: Path Analysis of Broadcast and Television Media Communication in China Haixia Wu Abstract: With the development of media convergence, the communication mode, characteristics, and path of broadcast and television media have significantly changed. How to achieve better development of broadcast and television media under media convergence has received wide attention from researchers. This paper briefly introduced the features of media convergence and verified the importance of media convergence, taking the Chinese enterprises’ new media index ranking in June 2021 as an example. Then, the communication status of broadcast and television media was analyzed, the current problems of the broadcast and television media in Dazhou city, Sichuan province, were studied, and some suggestions were proposed to perfect and optimize the communication path of broadcast and television media. This paper provides some ideas for the long-term development of broadcast and television media in Dazhou city. Keywords: Broadcast and television media; Communication path; Communication status; Media convergence; Path optimization. |
Research on Music Signal Processing Based on a Blind Source Separation Algorithm Xiaoming Zhao, Qiang Tuo, Ruosi Guo and Tengteng Kong Abstract: The isolation of mixed music signals is beneficial to the extraction and identification of music signal features and to enhance music signal quality. This paper briefly introduced the mathematical model for separating blind source from mixed music signals and the traditional Independent Component Analysis (ICA) algorithm. The separation algorithm was optimized by the complex neural network. The traditional and optimized ICA algorithms were simulated in MATLAB software. It was found that the time-domain waveform of the signal isolated by the improved ICA-based separation algorithm was closer to the source signal. The similarity coefficient matrix, signal-to-interference ratio, performance index, and iteration time of the improved ICA-based algorithm was 62.3, 0.0011, and 0.87 s, respectively, which were all superior to the traditional ICA algorithm. The novelty of this paper is setting the initial iterative matrix of the ICA algorithm with the complex neural network. Keywords: Blind source separation; Complex neural network; Independent component analysis; Mixed music signal, Numerical filter, Short-time Fourier transform. |
Computation and Optimization of Traffic Network Topologies Using Eclipse SUMO Yong H. Chow, Kelvin J. A. Ooi, Mohammad Arif Sobhan Bhuiyan, Mamun B. I. Reaz and Choon W. Yuen Abstract: The advent of modern computational tools in field of transportation can help to forecast the optimized vehicular routes and traffic network topology, using traffic conditions from real world data as inputs. In this study, the topologies of one-way and two-way street networks are analysed using microscopic traffic simulations implemented on the SUMO (Simulation of Urban MObility) platform were performed to analyse the effect of street conversion in Downtown Brickfields, Kuala Lumpur. It was found that one-way streets perform better at the onset of traffic congestion due to their higher capacity, but on average, the four-fold longer travel times make it harder to clear traffic by getting vehicles to their destinations than two-way streets. As time progresses, one-way streets' congestion may become doubly worse than that of two-way streets. This study may contribute to a more holistic assessment of traffic circulation plans designed for smart and liveable cities. Keywords: Eclipse SUMO; Street Conversion; Traffic Congestion; Traffic Simulation. |
Privacy-preserved Secure Medical Data Sharing Using Hierarchical Blockchain in Edge Computing Rasel Iqbal Emon, Md. Mehedi Hassan Onik, Abdullah Al Hussain, Toufiq Ahmed Tanna, Md. Akhtaruzzaman Emon, Muhammad Al Amin Rifat and Mahdi H. Miraz Abstract: A distributed ledger technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Additionally, edge technology is trending rapidly which brings the computing and data storing facility closer to the user end (device), in order to overcome network bottlenecks. This study, therefore, utilises the transparency, security, efficiency of blockchain technology along with the computing and storing facility at the edge level to establish privacy preserved storing and tracking schemes for electronic health records (EHRs). Since the EHR stored in a block is accessible by the peer-to-peer (P2P) nodes, privacy has always been a matter of great concern for any blockchain-based activities. Therefore, to address this privacy issue, multilevel blockchain, which can enforce and preserve complete privacy and security of any blockchain-based application or environment, has become one of the recent blockchain research trends. In this article, we propose an EHR sharing architecture consisting of three different interrelated multilevel or hierarchical chains confined within three different network layers using edge computing. Furthermore, since EHRs are sensitive, a specific data de-identification or anonymisation strategy is also applied to further strengthen the privacy and security of the data shared. Keywords: Blockchain; De-identification; Edge Computing; Health Level Seven (HL7); Medical Data; Privacy; Security. |
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