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parallel Reconstruction of Quad Only Meshes from Volume Data 15

Parallel Reconstruction of Quad Only Meshes from Volume Data

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15th international Joint conference on Computer Vision, Imaging and Computer Graphics theory and Applications (VISIGRAPP) / 15th international conference on Computer Vision theory and Applications (VISAPP)

作者： Grosso, Roberto Zint, Daniel Friedrich Alexander Univ Erlangen Nurnberg FAU Visual Comp Erlangen Germany

ISBN: (纸本)9789897584022

We present a method to reconstruct quad only meshes from volume data which mainly consists of two steps: reconstruction of a quad only mesh and topological simplification to reduce the number of irregular vertices. A novel algorithm is described that computes Dual Marching Cubes (DMC) meshes without using lookup tables. the meshes are topologically consistent across cell borders, i.e. they are watertight. the output of the algorithm is a quad only mesh stored in a halfedge data structure. Due to the transitions between voxel layers in volume data, meshes have numerous quad elements with vertices of valence 3 - X - 3 - Y, where X, Y >= 5, and 3 - 3 - 3 - 3. Hence, we simplify the mesh by eliminating these elements wherever possible. Finally, we briefly describe a CUDA implementation of the algorithms, which allows processing huge amounts of data on GPU at almost interactive time rates.

关键词： Dual Marching Cubes Quad Meshing parallel Mesh Reconstruction

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Hardware Accelerator for Dual Standard Deblocking Filter

Hardware Accelerator for Dual Standard Deblocking Filter

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international conference on VLSI Design

作者： P. Saravanan B. Syndia Priyadarshini P. Vignesh Kanna P. Vaishnavi PSG College of Technology Coimbatore India

Advanced Video Coding (AVC) also known as H.264 is a popular video coding standard which is used in many image processing applications. In order to fulfill the increasing demand for better video quality with less complex designs, High Efficiency Video Coding (HEVC) also known as H.265 came into picture. HEVC provides better quality with half the bit rate required for processing in comparison with AVC. In case of AVC, the design of deblocking filter is more complex due to the filtering decisions, whereas HEVC is less complex and also supports parallel processing. As there is a need for high throughput and less complex deblocking filter, a novel dual standard deblocking filter is proposed in this work which supports both AVC and HEVC. In particular, a novel filtering order which enables four edges to be processed simultaneously has been proposed for both AVC and HEVC. For AVC, the proposed design takes 18 clock cycles to process a 16 × 16 macroblock and for HEVC standard, the proposed design takes 12 clock cycles to process a 16 × 16 coding transform unit (CTU). In addition to this, the proposed design supports parallel processing concept in HEVC. the proposed design is synthesized using Xilinx ISE 14.7 and is mapped to xc5vlx30-1ff324 Virtex-5 Field programmable gate array (FPGA). thus it helps in increasing the execution speed of the design and it is also noted that there is a reduction in number of clock cycles needed for processing when compared with the existing architectures. the physical implementation of the proposed architecture is also carried out using Semiconductor Lab (SCL) 180 nm process node.

关键词： Filtering Transforms parallel processing Very large scale integration throughput Standards Field programmable gate arrays

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A New Partially-parallel VLSI-Architecture of Quasi-Cyclic LDPC Decoder for 5G New-Radio 33

A New Partially-Parallel VLSI-Architecture of Quasi-Cyclic L...

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33rd IEEE international conference on VLSI Design / 19th international conference on Embedded Systems

作者： Verma, Anuj Shrestha, Rahul Indian Inst Technol Mandi Sch Comp & Elect Engn Suran Himachal Prades India

ISBN: (数字)9781728157016

ISBN: (纸本)9781728157016

this paper presents partially-parallel low-density parity-check (LDPC) decoder architecture for binary-input additive-white Gaussian-noise (BIAWGN) channel in 5G new-radio cellular-communication technology based on layered min-sum decoding algorithm. this architecture exploits the iterative decomposition of check node and variable-node combined-processing unit which leads to area optimization and lower hardware complexity. the proposed VLSI architecture of LDPC decoder processes quasi-cyclic LDPC encoded information which is received as log-likelihood ratios (LLRs) from soft demodulator at the receiver side. It achieves the adequate bit-error-rate (BER) of 10(-6) between 2 to 4 dB of bit-energy to noise-power-spectral density (E-b/N-o) with 10 decoding iterations for various code rates like 1/3, 2/5, 1/2, 2/3, 3/4, 5/6, and 8/9. the VLSI architecture of 5G new-radio LDPC decoder has been field-programmable logic-array (FPGA) prototyped and its implementation results are compared with state-of-the-art designs where the proposed LDPC decoder shows lower hardware utilization up to 87%. To the best of our knowledge, this is the first VLSI-architecture of LDPC decoder reported for 5G new-radio compliant to the specifications of enhanced mobile broadband (eMBB).

关键词： Wireless communication LDPC code channel decoder layered min-sum decoding 5G new radio digital very-large-scale-integration (VLSI) architectures and field-programmable gate-array (FPGA)

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IoT Analytics architectures: Challenges, Solution Proposals and Future Research Directions 14th

IoT Analytics Architectures: Challenges, Solution Proposals ...

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14th international conference on Research Challenges in Information Science (RCIS)

作者： Zschoernig, theo Wehlitz, Robert Franczyk, Bogdan Inst Appl Informat InfAI Goerdelerring 9 D-04109 Leipzig Germany Univ Leipzig Inst Informat Syst Grimmaische Str 12 D-04109 Leipzig Germany Wroclaw Univ Econ Business Informat Inst Ul Komandorska 118-120 PL-53345 Wroclaw Poland

ISBN: (纸本)9783030503154;9783030503161

the Internet of things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of data analytics, e.g. algorithms or system architectures, as well as their scientific investigation play a pivotal role in the advancement of the IoT. therefore, past research has presented a multitude of architectural approaches to enable data processing and analytics in various IoT domains, addressing different architectural challenges. In this paper, we identify and present an overview of these challenges as well as existing architectural proposals. Furthermore, we categorize found architectural proposals along various dimensions in order to highlight the evolution of research in this field and pinpoint architectural shortcomings. the results of this paper show that several challenges have been addressed by a large number of IoT system architectures for data analytics while others are either not relevant for certain domains or need further investigation. Finally, we offer points of reference for future research based on the findings of this paper.

关键词： Internet of things IoT analytics Data analytics Analytics architectures

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Fulfilling the IoT Vision: Arewe there Yet? 5

Fulfilling the IoT Vision: Arewe there Yet?

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5th international conference on Internet of things, Big Data and Security, IoTBDS 2020

作者： Gaudio, Daniel Del Hirmer, Pascal Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstraße 38 Stuttgart Germany

ISBN: (纸本)9789897584268

the vision of the Internet of things is enabling self-controlled and decentralized environments, in which hardware devices, equipped with sensors and actuators communicate with each other trough standardized internet protocols to reach common goals. the device-to-device communication should be decentralized and should not necessarily require human interaction. However, enabling such complex IoT applications, e.g., connected cars, is a big challenge, since many requirements need to be fulfilled. these requirements include, for example, security, privacy, timely data processing, uniform communication standards, or location-awareness. Based on an intensive literature review, in this overview paper, we define requirements for such environments and, in addition, we discuss whether they are fulfilled by state-of-the-art approaches or whether there still has to be work done in the future. We conclude this paper by illustrating research gaps that have to be filled in order to realize the IoT vision. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Internet of things

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NUIG-Panlingua-KMI Hindi ↔Marathi MT Systems for Similar Language Translation Task @ WMT 2020 5

NUIG-Panlingua-KMI Hindi ↔Marathi MT Systems for Similar La...

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5th conference on Machine Translation, WMT 2020

作者： Ojha, Atul Kr. Rani, Priya Bansal, Akanksha Chakravarthi, Bharathi Raja Kumar, Ritesh McCrae, John P. Data Science Institute NUIG Galway Ireland Panlingua Language Processing LLP New Delhi India Dr. Bhimrao Ambedkar University Agra India

ISBN: (纸本)9781948087810

NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in the Similar language translation task for the Hindi ↔ Marathi language pair. As part of these efforts, we conducted a series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared for this task, 1 PBSMT systems were prepared for Hindi ↔ Marathi each and 1 NMT systems were developed for Hindi ↔ Marathi using Byte Pair Encoding (BPE) of subwords. the results show that different architectures in NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task. © 2020 Association for Computational Linguistics

关键词： Translation (languages)

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Analysis and Evaluation of PHEV Development and Technical Routes of Major Manufacturers in China

Analysis and Evaluation of PHEV Development and Technical Ro...

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Power and Energy Engineering (ICPEE), international conference on

作者： Baodi Zhang Fuyu Yang Hewu Wang Hongren Zhao Xin Zhang School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China National International Science and Technology Cooperation Base Beijing Jiaotong University Beijing China State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (纸本)9781665420471

the evolution of PHEV architecture represents the development of the PHEV technical route. In order to explore the evolution process of typical PHEV technical routes in the market, the proportion of Chinese PHEV and the composition of the market segments is analyzed. the attitude of each manufacture towards the development of PHEV is clarified. the detailed PHEV architectures are classified based on different motor positions. the technical route trend of Chinese PHEV manufacturers is analyzed with the unified technical route classification standard. the technical route of each manufacturer is quantitatively evaluated. the fuel economy of each technical route is evaluated from the whole situation. the analysis results show that the number of technical iterations and application models of each manufacture are increasing. It is not obvious which technical route has the best fuel economy. On the whole, the serial-parallel architecture has better fuel economy.

关键词： Analytical models Biological system modeling Companies Fuel economy Market research Hybrid electric vehicles Vehicle dynamics

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the Challenge of Onboard SAR processing: A GPU Opportunity 20th

The Challenge of Onboard SAR Processing: A GPU Opportunity

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20th Annual international conference on Computational Science (ICCS)

作者： Romano, Diego Mele, Valeria Lapegna, Marco CNR Inst High Performance Comp & Networking ICAR Naples Italy Univ Naples Federico II Naples Italy

ISBN: (纸本)9783030504205;9783030504199

Data acquired by a Synthetic Aperture Radar (SAR), onboard a satellite or an airborne platform, must be processed to produce a visible image. For this reason, data must be transferred to the ground station and processed through a time/computing-consuming focusing algorithm. thanks to the advances in avionic technology, now GPUs are available for onboard processing, and an opportunity for SAR focusing opened. Due to the unavailability of avionic platforms for this research, we developed a GPU-parallel algorithm on commercial off-the-shelf graphics cards, and with the help of a proper scaling factor, we projected execution times for the case of an avionic GPU. We evaluated performance using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 on both NVIDIA Kepler and Turing architectures.

关键词： Onboard SAR focusing GPU-parallel Range-Doppler algorithm

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HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics 8

HeAT - a Distributed and GPU-accelerated Tensor Framework fo...

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8th IEEE international conference on Big Data (Big Data)

作者： Goetz, Markus Debus, Charlotte Coquelin, Daniel Krajsek, Kai Comito, Claudia Knechtges, Philipp Hagemeier, Bjorn Tarnawa, Michael Hanselmann, Simon Siggel, Martin Basermann, Achim Streit, Achim German Aerosp Ctr Inst Software Technol SC Cologne Germany Forschungszentrum Julich Inst Rio & Geosci Agrosphere IBG 3 Julich Germany Forschungszentrum Julich FZJ Julich Supercomp Ctr JSC Julich Germany Karlsruhe Inst Technol KIT Steinbuch Ctr Comp SCC Karlsruhe Germany

ISBN: (纸本)9781728162515

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce IleAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly I owering the bartier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.

关键词： IleAT Tensor Framework High-performance Computing PyTorch NumPy Message Passing Interface CPU Rig Data Analytics Machine Learning Dask Model parallelism parallel Application Frameworks

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Pandemic rumor identification on social networking sites: A case study of COVID-19 21

Pandemic rumor identification on social networking sites: A ...

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Proceedings of the 2021 5th international conference on Natural Language processing and Information Retrieval

作者： Mohsan Ali Iqbal Murtza Ayesha Ejaz National Center for Cybersecurity Air University Islamabad Pakistan Department of Creative Technologies Air University Islamabad Pakistan Department of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9781450387354

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. these rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques; logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning; LSTM and LSTM-RNN. the combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. the proposed technique obtains 99.02% accuracy, which shows its effectiveness. the dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods.

关键词： Covid-19 Deep Learning Ensemble Learning Long-short term memory Machine learning Natural language processing

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