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检索条件"任意字段=IEEE Data Compression Conference"

共 16713 条记录，以下是431-440 订阅

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Faster and Stronger Lossless compression with Optimized Autoregressive Framework 60

Faster and Stronger Lossless Compression with Optimized Auto...

引用

60th ACM/ieee Design Automation conference (DAC)

作者： Mao, Yu Li, Jingzong Cui, Yufei Xue, Jason Chun City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (纸本)9798350323481

Neural AutoRegressive (AR) framework has been applied in general-purpose lossless compression recently to improve compression performance. However, this paper found that directly applying the original AR framework causes the duplicated processing problem and the in-batch distribution variation problem, which leads to deteriorated compression performance. The key to address the duplicated processing problem is to disentangle the processing of the history symbol set at the input side. Two new types of neural blocks are first proposed. An individual-block performs separate feature extraction on each history symbol while a mix-block models the correlation between extracted features and estimates the probability. A progressive AR-based compression framework (PAC) is then proposed, which only requires one history symbol from the host at a time rather than the whole history symbol set. In addition, we introduced a trainable matrix multiplication to model the ordered importance, replacing previous hardware-unfriendly Gumble-Softmax sampling. The in-batch distribution variation problem is caused by AR-based compression's structured batch construction. Based on this observation, a batch-location-aware individual block is proposed to capture the heterogeneous in-batch distributions precisely, improving the performance without efficiency losses. Experimental results show the proposed framework can achieve an average of 130% speed improvement with an average of 3% compression ratio gain across data domains compared to the state-of-the-art.

关键词： auto-regressive general-purpose lossless data compression hardware friendly neural networks computational efficient

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Distributed Radiance Fields for Edge Video compression and Metaverse Integration in Autonomous Driving 10

Distributed Radiance Fields for Edge Video Compression and M...

引用

International conference on Smart Computing (SMARTCOMP)

作者： Slapak, Eugen Dopiriak, Matus Al Faruque, Mohammad Abdullah Gazde, Juraj Levorato, Marco Tech Univ Kosice Dept Comp & Informat Kosice Slovakia Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA USA

ISBN: (纸本)9798350349955;9798350349948

The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstruction methods that capture the real world's geometry, appearance, and dynamics. However, sending data for real-time DT updates in the metaverse, such as camera images and videos from connected autonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, and latency, affecting metaverse services. herein, a new method is proposed based on distributed radiance fields (RFs), multi-access edge computing (MEC) network for video compression and metaverse DT updates. RF-based encoder and decoder are used to create and restore representations of camera images. The method is evaluated on a dataset of camera images from the CARLA simulator. data savings of up to 80% were achieved for H.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative metrics for the reconstructed images. Possible uses and challenges for the metaverse and autonomous mobility are also discussed.

关键词： autonomous driving digital twin edge computing metaverse radiance fields video compression

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Research on Power data compression Based on Improved Wavelet Transform Method 7

Research on Power Data Compression Based on Improved Wavelet...

引用

7th ieee International conference on Energy Internet, ICEI 2023

作者： Zheng, Yan Cui, Shu Wang, Hongbin Cong, Qian Ai, Dalin Wang, Zhongwei State Grid Liaoning Electric Power Co. Ltd. Dalian Power Supply Company Dalian China

ISBN: (纸本)9798350386684

Domestic digital power equipment is gradually moving towards the direction of intelligence. As a key operational link of smart grid, the application of various new technologies in intelligent digital power has led to rapid growth in data scale, but bandwidth is always the bottleneck of data transmission. In response to this issue, this article proposes an improved wavelet transform compression method for compressing power data. And it involves comparing experiments with traditional methods and unimproved wavelet filtering compression methods. The experimental results show that the algorithm has good performance in compression ratio, mean square error, signal-to-noise ratio, and energy recovery coefficient. © 2023 ieee.

关键词： Wavelet transforms

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Practical Random Tree Generation: Information-Theoretic Analysis and Applications to IoT

Practical Random Tree Generation: Information-Theoretic Anal...

引用

ieee conference on Standards for Communications and Networking (CSCN)

作者： Farzaneh, Amirmohammad Badiu, Mihai-Alin Coon, Justin P. Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9798350395389

Tree structures make an appearance in many network related problems, such as network routing. Modelling and simulating the appearance of these data structures can be done using random tree generators. However, there has been very little study on random models that are able to capture the dynamics of networks. We introduce the random spanning tree model, which is a random tree generator that is based on generating a tree from an already existing network topology. The Shannon entropy of this model is then analysed, and upper bounds to it are found. As compression can be beneficial because of the complexity of large trees, we then introduce a universal approach to compressing trees generated using the spanning tree model. It will be shown that the proposed method of compression introduces a redundancy that tends to zero for larger trees. We recommend the use of this random model and the proposed compression algorithm in routing protocols of IoT, as it can contribute to saving routing communication overhead and increasing the overall lifetime of the network.

关键词： Entropy Random Trees Tree coding Tree compression IoT

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Leveraging Compact data Accumulator to Enable In-Network Anomaly Detection in Programmable Switches for Power Grids

Leveraging Compact Data Accumulator to Enable In-Network Ano...

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2024 International conference on Communications Control and Computing Technologies for Smart Grids

作者： Hu, Zheng Lin, Hui Qu, Yanfeng Jin, Dong Univ Arkansas Elect Engn & Comp Sci Fayetteville AR 72701 USA Univ Rhode Isl Elect Comp & Biomed Engn Kingston RI USA

ISBN: (纸本)9798350318562;9798350318555

The escalating cyber-attacks targeting power infrastructure underscore the critical importance of smart grid security. However, existing solutions often struggle with the challenge of balancing security and performance overhead, leading to suboptimal protection or increased operational latency. To address this, we propose an intrusion detection system (IDS) designed to operate within P4-based programmable network devices, enabling real-time identification of critical attacks like distributed denial-of-service (DDoS) and false data injection (FDI). Central to our approach is a novel data structure optimized for time series data, capturing key information such as packet timing and data payload distribution. Leveraging decision trees, a robust machine learning technique, enables effective anomaly detection and prediction. Additionally, we integrate data compression techniques to reduce device memory usage while maintaining detection accuracy. Our evaluation results demonstrate minimal overhead in packet processing speed with 1 to 20 nanoseconds differences per packet, and enhanced data storage efficiency with compression ratios reaching up to 60.9%. Despite these optimizations, there is only a slight decrease in detection accuracy, such as a 2.81% drop in detecting false data injection attack (FDIA).

关键词： data compression Distributed Network Protocol (DNP3) Network Security P4 Programmable Network Supervisory Control and data Acquisition (SCADA) System Smart Grid

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Design and Implementation of Transformer Multi-Physical Field Distribution compression Display System 7

Design and Implementation of Transformer Multi-Physical Fiel...

引用

7th ieee International Electrical and Energy conference, CIEEC 2024

作者： Zhuo, Ran Xu, Kui Gao, Meng Liu, Jun Guo, Baicun Zhao, Sicheng Zeng, Huarong Fu, Mingli Li, Kun Wang, Guoli Csg Electric Power Research Institute China Southern Power Grid Guangzhou China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. China Southern Power Grid Guizhou China School of Electrical Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350359558

At present, the automation and informatization level of the manufacturing, operation and maintenance process of power equipment has gradually improved. However, digital twin technology has not been widely used in the field of power systems with higher realtime requirements for data collection, analysis and feedback. The main research content of this paper is mainly based on the transformer equipment in digital twin power equipment. Through finite element method calculation and correction of measured data, the internal field distribution of the equipment model is obtained, Based on the calculation results, the data is compressed and the three-dimensional model field distribution is plotted, allowing equipment maintenance personnel to observe the internal situation of the transformer equipment in real time through this platform, thereby helping planning decisions and responding quickly to faults. © 2024 ieee.

关键词： data compression

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Experimental Analysis and Assessment of a Real-Life Cloud Mobile Big OLAP System Enhanced with compression and Approximation Paradigms

Experimental Analysis and Assessment of a Real-Life Cloud Mo...

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2024 ieee International conference on Big data, Bigdata 2024

作者： Cuzzocrea, Alfredo Hajian, Mojtaba University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France

ISBN: (纸本)9798350362480

The increasing complexity and volume of data in Cloud-based environments have posed significant challenges for Online Analytical Processing (OLAP), especially on resource-constrained mobile devices. This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, a novel technique for compressing and approximating multidimensional OLAP data cubes. IQTS efficiently handles large data cubes by flattening dimensions, employing Quad-Tree partitioning, and leveraging efficient Indexing methods. These processes significantly reduce storage requirements while ensuring high accuracy in query responses. Our experimental evaluation, utilizing real-world medical datasets, demonstrates that IQTS outperforms existing data compression techniques such as MinSkew, STHoles, and GenHist, delivering superior results in terms of space efficiency and query accuracy. The paper demonstrates the algorithm's potential for mobile Cloud environments, showing its scalability and adaptability for big data applications. © 2024 ieee.

关键词： Trees (mathematics)

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4D Image compression in Healthcare Using Convolutional Neural Networks (CNNs) 2

4D Image Compression in Healthcare Using Convolutional Neura...

引用

2nd International conference on Computing and data Analytics, ICCDA 2024

作者： Chitra, P. Gitam University Bangalore India

ISBN: (纸本)9798331542573

In the realm of healthcare, imaging technologies such as MRI, CT scans, and ultrasound are fundamental tools for diagnosis, treatment planning, and monitoring. These technologies generate vast amounts of data, particularly in the form of 4D images, which incorporate a temporal component alongside the three spatial dimensions. Efficient compression of these 4D images is crucial for managing storage, expediting transmission, and facilitating seamless data handling without compromising the high image quality necessary for precise medical analysis. Convolutional Neural Networks (CNNs) have emerged as powerful tools for this purpose, leveraging their ability to learn and exploit spatial hierarchies and patterns within the data. This article explores the application of CNNs in compressing 4D medical images, highlighting key techniques such as spatial and temporal compression, hybrid models, and end-to-end compression frameworks. Additionally, it discusses the practical implications of CNN-based compression in telemedicine, real-time monitoring, and long-term data archiving. A case study on cardiac MRI demonstrates the efficacy of CNN-based 4D image compression in preserving critical diagnostic information while significantly reducing data size. The findings underscore the potential of CNNs to revolutionize 4D image compression in healthcare, enhancing the efficiency and effectiveness of medical imaging and ultimately improving patient outcomes. © 2024 ieee.

关键词： Image compression

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ELiCiT: Effective and Lightweight Lossy compression of Tensors 24

ELiCiT: Effective and Lightweight Lossy Compression of Tenso...

引用

24th ieee International conference on data Mining, ICDM 2024

作者： Ko, Jihoon Kwon, Taehyung Jung, Jinhong Shin, Kijung Kim Jaechul Graduate School of AI KAIST Korea Republic of School of Software Soongsil University Korea Republic of

ISBN: (纸本)9798331506681

Many real-world data from various domains can be represented as tensors, and a significant portion of them is large-scale. Thus, tensor compression is crucial for their storage and transmission. Recently, deep-learning-based methods have emerged to enhance compression performance. However, they require considerable compression time to fulfill their performance. In this work, to achieve both speed and performance, we develop ELICIT, an effective and lightweight lossy tensor compression method. When designing ELICIT, we avoid deep auto-regressive neural networks and index reordering, which incur high computational costs of deep-learning-based tensor compression. Specifically, instead of using the orders of indices as parameters, we introduce a feature-based model for indices, which enhances the model's expressive capacity and simplifies the overall end-to-end training procedure. Moreover, to reduce the size of the parameters and computational cost for inference, we adopt end-to-end clustering-based quantization, as an alter-native to deep auto-regressive architecture. As a result, ELICIT becomes easy to optimize with enhanced expressiveness. We prove that it (partially) generalizes deep-learning-based methods and also traditional ones. Using eight real-world tensors, we show that ELICIT yields compact outputs that fit the input tensor accurately. Compared to the best competitor with similar fitness, it offers 1.51-5.05 × smaller outputs. Moreover, compared to deep-learning-based compression methods, ELICIT is 11.8-96.0 × faster with 5-48% better fitness for a similarly sized output. We also demonstrate that ELICIT is extended to matrix completion and neural network compression, providing the best tradeoffs between model size and application performance. © 2024 ieee.

关键词： data compression

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A Hybrid DCT-DWT Video compression Technique with Motion Estimation and Arithmetic Coding 4

A Hybrid DCT-DWT Video Compression Technique with Motion Est...

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4th International conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chutke, Sravanthi Nandhitha, N.M. Lendale, Praveen Kumar Sathyabama Institute of Science and Technology Chennai India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331529635

The recent technological innovations and the emergence of web-based applications that employ multimedia data transfer limits the network bandwidth. This problem can adequately be solved with the help of effective data compression algorithms. Video data is composed of stream of pictures known as frames, whose compression can be improved by elimination of useless or overlapping information from these frames. Transform-based methods are particularly looked for as they have good performance in image and video frame transmission. This research study the practical application of two transforms: Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The aim of both approaches is to increase the quality of the frames while achieving a high degree of compression which seems to be perfect for transmission of audio and video data. © 2024 ieee.

关键词： Discrete cosine transforms

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