VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T10:38:51ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2023-06-15T04:10:16Z2023-06-15T04:10:16Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4817This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/48172023-06-15T04:10:16ZExtended Upscale and Downscale Representation with Cascade ArrangementSmoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR’s performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of
CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.Quang Manh Doanmdq@vnu.edu.vnTran Hiep Dinhtranhiep.dinh@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vnDiep Nguyen NDiep.Nguyen@uts.edu.auAvinash Kumar Singhavinash.singh@uts.edu.auChin-Teng Linchin-teng.lin@uts.edu.au2022-08-22T04:06:00Z2022-08-22T04:06:00Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4763This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47632022-08-22T04:06:00ZĐánh Giá Hiệu Năng Một Số Kỹ Thuật Học Sâu Cho Phân Vùng Mạch Máu Gan Trong Ảnh Chụp Cắt Lớp Vi TínhQuoc Anh Lequocanh.uet@gmail.comXuan Loc Phamxuanloc97ars@vnu.edu.vnManh Ha Luuhalm@vnu.edu.vn2022-03-21T00:30:45Z2022-03-21T00:30:45Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47122022-03-21T00:30:45ZSparse Subspace Tracking in High DimensionsWe studied the problem of sparse subspace tracking in the
high-dimensional regime where the dimension is comparable
to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm
called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.Trung Thanh Leletrungthanhtbt@gmail.comAbed Meraim Karimkarim.abed-meraim@univ-orleans.frHafiane Adeladel.hafiane@insa-cvl.frLinh Trung Nguyenlinhtrung@vnu.edu.vn2020-12-25T10:22:48Z2020-12-26T05:21:54Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4333This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/43332020-12-25T10:22:48ZA Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing——Part II: Emerging Technologies and Open IssuesThis two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.Thanh Cong NguyenSaputra Yuris MulyaYurisMulya.Saputra@student.uts.edu.auVan Nguyen HuynhNgoc Tan Nguyennguyen.tan170@gmail.comViet Khoa Trankhoatv.uet@vnu.edu.vnTuan Bui Minhtuanbm.uet@vnu.edu.vnNguyen DiepDiep.Nguyen@uts.edu.auThai Hoang DinhHoang.Dinh@uts.edu.auXuan Thang Vuthang.vu85@gmail.comDutkiewicz Erykeryk.dutkiewicz@uts.edu.auChatzinotas SymeonOttersten Bjorn2020-12-18T09:07:40Z2020-12-18T09:07:40Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4288This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/42882020-12-18T09:07:40ZA model for building probabilistic knowledge-based systems using divergence distancesThe knowledge-based systems (KBSs) in general and solving the knowledge merging problem in particular have seen a great surge of research activity in recent years. However, there still exist two main shortcomings that need to be addressed in the probabilistic framework. Firstly, the current methods only deal with the problems in which original probabilistic knowledge bases (PKBs) are required to be consistent and formed in the same structure. It is a very strong requirement and difficult to apply in practice. Secondly, only a few measures of distance between probability distributions have been studied to apply in existing models. To overcome these disadvantages, in this paper, we introduce a novel framework for merging PKBs. To this end, a theoretical model is introduced and several experiments are implemented. In theoretical model, some theorems are pointed out and proved to provide mathematical background to construct the merging model. Moreover, a deep survey on how to employ divergence distance functions (DDFs) between probability distributions to carry out the merging process are performed. In experimental aspect, a consistency recovery algorithm and some merging algorithms based on DDFs are proposed. Through the results of conducted experiments, issues about the time cost of merging process, the number of iterations, and CPU Time Elapsed parameter to solve the class of optimization problems in the merging process are analyzed, compared, and evaluated.Van Tham Nguyenthamnv.nute@gmail.comNgoc Thanh Nguyenngoc-thanh.nguyen@pwr.wroc.plTrong Hieu Tranhieutt@vnu.edu.vn