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检索条件"任意字段=International Conference on Data Compression, Communications and Processing"

共 25987 条记录，以下是41-50 订阅

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Towards a Context-based Mobility Prediction in Smart Cities: First Experimentations 22

Towards a Context-based Mobility Prediction in Smart Cities:...

引用

IEEE 22nd international conference on Trust, Security and Privacy in Computing and communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Hocine, Boukhedouma Abdelkrim, Meziane Slimane, Hammoudi Amel, Benna Allel, Hadjali Res Ctr Sci & Tech Informat Dept Multimedia & Informat Syst Algiers Algeria Univ Sci & Technol Houari Boumediene Dept Comp Sci Algiers Algeria ESEO TECH ERIS TEAM Angers France Univ Poitiers LIAS ISAE ENSMA Chasseneuil France

ISBN: (纸本)9798350381993;9798350382006

This paper addresses the prediction of mobility and emphasizes the main steps of our context-based mobility prediction approach that are trajectory modelling, data processing and prediction process. First, we present our context-based and prediction-oriented trajectory model, which relies on the grid technique for trajectory description. Second, we describe a data processing architecture for data coming from Wifi networks. Third, we focus on our data mining-based prediction process used for the first experimentations. Evaluation of our contributions is performed on a real dataset. First results showed the positive impact of our trajectory model on our mobility prediction process.

关键词： urban human mobility mobility prediction trajectory modeling context region data processing architecture

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Highly Concurrent data processing Design for 5G C-V2X Intelligent Transportation Systems (ITS)

Highly Concurrent Data Processing Design for 5G C-V2X Intell...

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IEEE/CIC international conference on communications in China (ICCC)

作者： Liu, Bo Chen, Weibo Sheng, Zhengguo Barakat, Ahmed Guan, Yong Liang Univ Sussex Sch Engn & Informat Brighton E Sussex England Cohda Wireless Ltd Wayville Australia Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9798350378412

Vehicle-to-Everything (V2X) technology plays a key role in enhancing road traffic safety, increasing transportation efficiency and reducing environmental pollution. The ongoing evolution of this technology suggests that future vehicular communication system will need to adapt to greater connectivity demands and more complex interactive scenarios. Particularly, the introduction of the 3rd Generation Partnership Project (3GPP) Rel. 16 SideLink standard has laid the groundwork for a plethora of enhanced vehicular applications (eV2X). However, there are new challenges brought by the limitations of traditional multi-threading approaches in processing large volumes of data. This study introduces a lightweight and highly concurrent data processing framework that offers a novel approach to enhance the data processing capability and scalability of V2X. Unlike traditional concurrent execution, this framework uses a refined modular design for functionalities and utilizes a collaborative approach between task management and the thread pool. The development and evaluation conducted on an actual vehicular testbed have confirmed that this framework significantly enhances the data processing capacity, stability and robustness of vehicular communications, which results in a significant increase of 41.62% in data processing speed and a substantial reduction of 22.68% in peak processor load, which is particularly suitable for ITS applications that require handling large data volume and demanding real-time performance.

关键词： C-V2X High concurrency Multi-threading OBU RSU

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IMPLICIT NEURAL MULTIPLE DESCRIPTION FOR DNA-BASED data STORAGE 49

IMPLICIT NEURAL MULTIPLE DESCRIPTION FOR DNA-BASED DATA STOR...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Le, Trung Hieu Pic, Xavier Mateos, Jeremy Antonini, Marc Cote dAzur Univ I3S Lab Sophia Antipolis France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9798350344868;9798350344851

DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.

关键词： DNA data storage Multiple Description Coding (MDC) Implicit Neural Network (INR) Quaternary Shannon Fano Entropy Coder (SFC4)

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In-Domain data Augmentation to Enhance Severity Level Classification of Dysarthria from Speech 15

In-Domain Data Augmentation to Enhance Severity Level Classi...

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15th international conference on Signal processing and communications (SPCOM)

作者： Karumuru, Bhanuja Sapkota, Paban Kathania, Hemant Natl Inst Technol Sikkim Dept Elect & Commun Engn Sikkim India

ISBN: (纸本)9798350350463;9798350350456

In this paper, we present our endeavor to construct an automatic dysarthria severity level classification system tailored for low-resource dysarthric speech datasets. The scarcity of available speech data from dysarthric speakers poses a significant challenge to training an effective classification system. Addressing this challenge, we devised a robust baseline system by blending a distinctive set of features, encompassing temporal, prosodic, and spectral information, with the traditional MFCC features. This amalgamation aptly captures the nuanced characteristics of dysarthric speech, facilitating efficient model training. To tackle the constraints of low-resource conditions, we explored four prominent augmentation techniques: Speaking Rate, Pitch, Formant, and Vocal Tract Length Perturbation (VTLP) modification based data augmentation for the task of severity classification. The explored data augmentation gives a significant reduction in the classification error rate (CER), and VTLP-based data augmentation is superior among others. Further, we also investigated combinations of explored data augmentation methods, fortifying the reliability of our dysarthria severity classification system. The combined novel augmentation gives a noteworthy relative improvement of 42.86% over the baseline on the dysarthric severity classification.

关键词： Dysarthria severity classification speech modification speech augmentation

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A Reconstructed Autoencoder Design for CSI processing in Massive MIMO Systems 59

A Reconstructed Autoencoder Design for CSI Processing in Mas...

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59th Annual IEEE international conference on communications (IEEE ICC)

作者： Kumar, Venkataramani Mercado-Perez, Dalyana Ye, Feng Hu, Rose Qingyang Qian, Yi Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA Univ Wisconsin Madison Dept Elect & Comp Engn Madison WI USA Utah State Univ Dept Elect & Comp Engn Logan UT USA Univ Nebraska Lincoln Dept Elect & Comp Engn Omaha NE USA

ISBN: (纸本)9781728190549

Massive multiple input multiple output (MIMO) systems are integral to next-generation wireless technologies due to their ability to meet the growing demands of throughput and support a plethora of applications. An efficient operation of massive MIMO requires accurate channel state information (CSI). In a frequency division duplex (FDD) MIMO system, the base station can rely on CSI feedback that user equipment (UE) estimates from downlink CSI from orthogonal pilot sequences. Recently, artificial intelligence (AI), i.e., deep learning approaches, have been introduced to compress and reconstruct CSI matrices at UE and the base station, respectively. However, these existing approaches still rely on channel estimation at the UE side, which introduces additional errors in the autoencoder design. To address these issues, we propose to implement the autoencoder that processes the pilot sequences directly to avoid excessive processing errors. Moreover, a higher compression can be achieved due to the lower error. Evaluation results demonstrate that the proposed scheme can significantly reduce the communication overhead by using a higher compression ratio while maintaining high CSI reconstruction performance in addition to lower bit error rates compared to the existing deep learning approach.

关键词： Base stations

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ByteZip: Efficient Lossless compression for Structured Byte Streams Using DNNs

ByteZip: Efficient Lossless Compression for Structured Byte ...

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international Joint conference on Neural Networks (IJCNN)

作者： Ramakrishnan, Parvathy P. Das, Satyajit Indian Inst Technol Palakkad Dept Data Sci Palakkad Kerala India

ISBN: (纸本)9798350359329;9798350359312

data compression plays a key role in efficient data storage, transmission, and processing. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher compression rate. Deep learning-based general-purpose lossless compression techniques are formulated as an autoregressive sequential prediction problem. These methods are state-of-the-art in terms of compression ratio but not practical due to runtime and resource constraints. Recent advances in lossless image compression using non-autoregressive methods for probability modeling prove to be a faster and more practical approach. In this paper, we propose ByteZip, a lossless compression method based on the non-autoregressive approach for known or defined structured byte streams. ByteZip involves hierarchical probabilistic modeling using autoencoders and density mixture models. This approach reduces the overhead of sequential processing. The goal is to design a practical lossless compressor with faster compression and decompression along with a competitive compression ratio. Experiments show that the proposed approach achieves a 64x higher compression speed than the state-of-the-art transformer-based model TRACE with an overhead of only 5% less size reduction on average. Our approach outperforms general-purpose compressors such as Gzip (23% more size reduction on average) and 7z (16% more size reduction on average).

关键词： lossless compression neural networks autoencoders mixture density networks

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EVENT data STREAM compression BASED ON POINT CLOUD REPRESENTATION 30

EVENT DATA STREAM COMPRESSION BASED ON POINT CLOUD REPRESENT...

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30th IEEE international conference on Image processing (ICIP)

作者： Huang, Bowen Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1012 Lausanne Switzerland

ISBN: (纸本)9781728198354

The Dynamic Vision System (DVS) is a novel image acquisition system that works only when there is a brightness change in a pixel, resulting in a stream of events including timestamps, spatial coordinates and the sign of the brightness change (increase or decrease). Although DVS's output data size is much smaller than conventional image systems, it still requires further compression, as the main applications of DVS are embedded systems with limited transmission and storage resources. In this paper, we propose a new method for lossless compression of event data streams based on point cloud representations. The event data stream is organized into a 3D point cloud to which a compression algorithm is applied. In addition, different generation strategies are devised in order to compare the compression performance of the proposed approach. Experimental results show an improved compression ratio of about 22% under lossless conditions.

关键词： Event camera data compression point cloud

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HBC: Combining Lossy and Lossless Hybrid Bilayer compression Framework on Time-Series data 21

HBC: Combining Lossy and Lossless Hybrid Bilayer Compression...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Lu, Wanying Liu, Liang Zhai, Wenbin Chen, Haoyuan Liu, Yulei Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350329223

The popularization and application of the Internet of Things (IoT) technology has brought massive time series data, which puts forward higher requirements for data compression technology. At present, most existing compression methods use only a single lossy or lossless compression algorithm to perform data compression. Furthermore, traditional lossy compression methods usually adopt a fixed error threshold. However, in practical applications, users have different accuracy requirements for time series data in different numerical ranges. In this paper, we design a Hybrid Bilayer compression (HBC) framework, which consists of a data accuracy-aware lossy compression layer and a data feature-aware lossless compression layer. First, the original time series data is lossy compressed on the top layer of HBC, where the error threshold can be adaptively adjusted according to the user's accuracy requirements. Then, based on the features of lossy-compressed data, we use supervised learning to select the optimal lossless compression algorithm from the algorithm pool to further compress the data. Experimental results show that compared with three state-of-the-art compression algorithms, HBC reduces the storage space by 52.1%, 91.66%, and 92.27%, respectively. © 2023 IEEE.

关键词： Digital storage

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Towards Reliable Collaborative data processing Ecosystems: Survey on data Quality Criteria 22

Towards Reliable Collaborative Data Processing Ecosystems: S...

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IEEE 22nd international conference on Trust, Security and Privacy in Computing and communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Sahi, Louis Laborde, Romain Kandi, Mohamed-Ali Sibilla, Michelle Macilotti, Giorgia Abdelmalek, Benzekri Ferreira, Afonso Univ Toulouse III Paul Sabatier Inst Rech Informat Toulouse Toulouse France CNRS Inst Rech Informat Toulouse Toulouse France

ISBN: (纸本)9798350381993;9798350382006

data quality plays a crucial role in the data governance of organizations, as it is essential to ensure that data are fit for the purpose for which they are intended, whether for operational activities, decision-making processes, or strategic planning. As data silos begin to be integrated to form data spaces, guaranteeing data quality becomes a necessity to achieve a reliable collaborative ecosystem. Nevertheless, the concept of data quality remains ambiguous, with various definitions and interpretations offered in the literature, despite its importance. This lack of consensus has led to the need for a thorough review of the different data quality criteria used in scientific work. Therefore, this paper serves as a systematic survey aimed at exploring and consolidating diverse perspectives on data quality. By thoroughly analyzing existing literature, this study compiles a comprehensive set of 30 agreed-upon data quality criteria, with their respective names and definitions. These criteria act as a valuable resource for organizations seeking to establish effective data quality monitoring practices. Then, we expose challenges raised by collaborative data processing and highlight possible research directions where data quality plays a major role.

关键词： data quality data quality criteria trust reliability collaborative data processing decentralized data governance

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FULL-REFERENCE POINT CLOUD QUALITY ASSESSMENT USING SPECTRAL GRAPH WAVELETS 31

FULL-REFERENCE POINT CLOUD QUALITY ASSESSMENT USING SPECTRAL...

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2024 international conference on Image processing

作者： Watanabe, Ryosuke Nonaka, Keisuke Pavez, Eduardo Kobayashi, Tatsuya Ortega, Antonio Univ Southern Calif Los Angeles CA 90007 USA KDDI Res Inc Saitama Japan

ISBN: (纸本)9798350349405;9798350349399

Point clouds in 3D applications frequently experience quality degradation during processing, e.g., scanning and compression. Reliable point cloud quality assessment (PCQA) is important for developing compression algorithms with good bitrate-quality trade-offs and techniques for quality improvement (e.g., denoising). This paper introduces a full-reference (FR) PCQA method utilizing spectral graph wavelets (SGWs). First, we propose novel SGW-based PCQA metrics that compare SGW coefficients of coordinate and color signals between reference and distorted point clouds. Second, we achieve accurate PCQA by integrating several conventional FR metrics and our SGW-based metrics using support vector regression. To our knowledge, this is the first study to introduce SGWs for PCQA. Experimental results demonstrate the proposed PCQA metric is more accurately correlated with subjective quality scores compared to conventional PCQA metrics.

关键词： point cloud quality assessment full reference metric graph signal processing spectral graph wavelet support vector regression

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