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

共 16774 条记录，以下是561-570 订阅

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POCS-based Image compression: An Empirical Examination 7

POCS-based Image Compression: An Empirical Examination

引用

7th International conference on Green Technology and Sustainable Development, GTSD 2024

作者： Do, Truong-Dong Tran, Le-Anh Nguyen, Thanh-Dat Truong, Nghe-Nhan Park, Dong-Chul Le, My-Ha Sejong University Dept. of Aerospace Systems Engineering Seoul Korea Republic of Myongji University Dept. of Electronics Engineering Yongin Korea Republic of Dept. of Research and Development Ocst Co. Ltd. Seoul Korea Republic of Sejong University Dept. of Computer Science & Engineering Seoul Korea Republic of Hcmc University of Technology & Education Dept. of Electrical & Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems. © 2024 ieee.

关键词： Unsupervised learning

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Communication-Efficient Joint Signal compression and Activity Detection in Cell-Free Massive MIMO

Communication-Efficient Joint Signal Compression and Activit...

引用

ieee International conference on Communications (ieee ICC)

作者： Lin, Qingfeng Li, Yang Kou, Wei-Bin Chang, Tsung-Hui Wu, Yik-Chung Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9781538674628

A great amount of endeavour has recently been devoted to device activity detection in massive machine-type communications. This paper targets at a practical issue: communication-efficient joint signal compression and activity detection in cell-free massive MIMO with capacity-limited front-hauls. To this end, we propose a novel deep learning framework which jointly optimizes the compression modules, quantization modules at the access points, and the decompression module and detection module at the central processing unit. Specifically, deep unfolding is leveraged for designing the detection module in order to inherit the domain knowledge derived from the optimization algorithm, and the other modules are constructed by generic layers for increasing the learning capability. A joint training strategy is proposed to optimize all the modules in an end-to-end manner. Numerical results demonstrate the superiority of the proposed end-to-end learning framework compared with classical optimization methods.

关键词： Activity detection deep learning deep unfolding capacity-limited fronthauls

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Comparison and extension of autoencoder models for uni- and multivariate signal compression in IIoT

Comparison and extension of autoencoder models for uni- and ...

引用

data compression conference (DCC)

作者： Rosenberger, Julia Kuebel, Alexander Rothfuss, Fabian Bosch Rexroth AG Burgermeister Dr Nebel Str 2 D-97816 Lohr Germany CODE Univ Appl Sci Lohmuhlenstr 65 D-12435 Berlin Germany

A convolutional variational autoencoder (AE) CBN-$VAE^{1}$, a recurrent LSTM-$AE^{2}$, and a discrete wavelet transform (DWT) are compared w.r.t compression performance and resource consumption. The existing models fo... 详细信息

ISBN: (纸本)9781665478939

A convolutional variational autoencoder (AE) CBN-$VAE^compression$, a recurrent LSTM-$AE^compression$, and a discrete wavelet transform (DWT) are compared w.r.t compression performance and resource consumption. The existing models for streaming data are slightly adapted to handle both univariate (UTS) and multivariate time-series (MTS). The experiments on two publicly available data sets3 confirm that the machine learning models compress time-series in a more generalized and robust manner than DWT. This is shown by a higher quality score $QS= \\frac{\\text{compression ratio}(CR)}{\\text{reconstruction error}(RE)}$ for both AE models compared to the DWT. The following aspects are observed w.r.t. the AE models:

关键词： Adaptation models Convolution data compression Machine learning data models Discrete wavelet transforms Industrial Internet of Things

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Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via Evolutionary Multi-objective Model compression

Balancing Performance, Efficiency and Robustness in Open-Wor...

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International Joint conference on Neural Networks (IJCNN)

作者： Del Ser, Javier Martinez-Seras, Aitor Bilbao, Miren Nekane Lobo, Jesus L. Lana, Ibai Herrera, Francisco Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

ISBN: (纸本)9798350359329;9798350359312

When deploying machine learning models on resource-constrained hardware, reducing the memory footprint required by the model without compromising its performance is critical. Moreover, in open-world scenarios models often operate in dynamic and unpredictable environments where the data distribution evolves over time. Robust models can generalize well to unforeseen circumstances, including out-of-distribution inputs that may not have been encountered during the training phase. This adaptability is essential to handle the inherent variability of real-world data. This work formulates a multi-objective optimization problem that aims at optimizing the quantization resolution of the parameters of an already trained machine learning model based on three conflicting goals: maximizing the performance of the model on its designated learning task, minimizing the memory footprint of the compressed model, and enhancing its robustness against out-of-distribution data. Given the complexity of the resulting combinatorial optimization problem, we employ multi-objective evolutionary algorithms to efficiently obtain an approximation of the Pareto front balancing among the aforementioned objectives. Experiments with a randomized neural network compressed under the proposed formulation are run over several benchmark classification datasets. Different multi-objective solvers are employed to compare their effectiveness in terms of the convergence and diversity of their produced Pareto estimations. Additionally, we assess the achieved equilibrium between the three objectives against a floating-point implementation of the same model. Our experiments reveal that both the computational resources and the robustness of the model can be optimized via evolutionary quantization without significantly sacrificing its performance for the task at hand.

关键词： Model compression AI Safety Green AI Out-ofDistribution Detection Multi-objective Evolutionary Algorithms Randomization-based Neural Networks.

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Autoencoder Based Feature compression for Bandwidth-Constrained Wireless Sensor Networks 22

Autoencoder Based Feature Compression for Bandwidth-Constrai...

引用

22nd ieee Consumer Communications and Networking conference, CCNC 2025

作者： Gao, Kang Bhuyan, Amit Kumar Dutta, Hrishikesh Roy, Avirup Lee, Mei-Hua Biswas, Subir Michigan State University Department of Electrical and Computer Engineering East LansingMI United States Michigan State University Department of Kinesiology East LansingMI United States

ISBN: (纸本)9798331508050

This paper introduces an Asymmetric Autoencoder (AAE)-driven data compression framework for efficient management of energy, bandwidth, and transmitter complexity in a Wireless Sensor Network (WSN). WSNs are often limited by their ability to process and transmit high-dimensional data due to various constraints, including available energy, processing cycles, and transmission capacity. Achieving an application-specific downstream task executed in a remote receiver under the influence of such sensor node constraints is the focus of the proposed methodology. It places a data compression encoder and decoder at the transmitter and the receiver, respectively. The architecture of the AAE's encoder and decoder can be asymmetric, and the degree of asymmetry can be adjusted based on the computation and processing abilities of the transmitting node, the available bandwidth, and the performance requirements of a specific downstream task. In the proposed framework, the encoder and decoder are jointly trained, enabling the system to extract downstream task-related information from one or more time series inputs. Verified for Human Activity Recognition (HAR), the framework demonstrates effective feature compression while maintaining efficient task performance. © 2025 ieee.

关键词： Sensor nodes

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Performance Study of Large-Scale GPS Trajectory Query Method and compression Method Based on Spark Platform

Performance Study of Large-Scale GPS Trajectory Query Method...

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2023 ieee International conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Wang, Xin Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350314670

Buses and taxis in cities generate a large amount of GPS trajectory data every day. How to query and manage these large-scale trajectory data is a challenging problem. Based on the RDD component of Spark, a big data computing engine, we design and implement a distributed 3D R-Tree to accelerate trajectory query through global index and local index. The experimental results show that the three queries have the shortest latency under the condition of spatial-temporal grid partition, and the latency of trajectory point query, substring query and region query are 2.33s, 3.33s and 6.88s, respectively. In addition, the massive GPS trajectory data generated by these vehicles bring heavy storage cost pressure to the transportation big data platform. The compression processing of GPS trajectory data is imminent. In this paper, the big data computing engine Spark was used. A set of classical trajectory compression algorithms are implemented in parallel. Finally, the method is verified on a very large-scale real data set. Experimental results on compressed datasets show that the proposed method performs well. It only takes 438 seconds to compress 117.5GB GPS trajectory data of 31000 vehicles for one month on a 14-node Spark cluster. © 2023 ieee.

关键词： Big data

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Event-Driven High-resolution Load data compression via SAX 2

Event-Driven High-resolution Load Data Compression via SAX

引用

2nd Asian conference on Frontiers of Power and Energy (ACFPE)

作者： Zhao, Xueming Hu, Bowei Zhang, Zhigang Liu, Bo Mi, Dongfeng Liu, Hao State Grid Tianjin Elect Power Co Chengdong Power Supply Branch Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Trans Energy Technol Co Ltd Software Dept Tianjin Peoples R China

ISBN: (纸本)9798350303896

With the continuous development of modern power system, more and more advanced metering devices have been deployed and popularized. A large amount of information is generated for power network analysis, load forecasting, state perception and other aspects, providing information support for the related technical development. However, the increasing volume of data also brings huge pressure to the data storage, transmission, analysis and so on. Therefore, how to achieve data compression while reducing the amount of data and retaining the characteristics of data information as much as possible has become a key concern. In this paper, a high-resolution power load data compression method based on symbolic aggregate approximation (SAX) is adopted. The obtained power load data is first divided into event segment and non-event segment. The non-event segment is processed by SAX, and the event segments are divided into long event segments and short event segments. The original data of the short event segment is retained, and the long event segment is also processed by SAX. The comparison testing demonstrates that our proposed method achieves better performance. The average reconstruction accuracy obtained by RMSE index is 99.78%, and the average compression ratio is 21.90, which is promising for future engineering applications.

关键词： High-resolution Load data compression Symbolic Aggregate Approximation Event Detection

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Research on Intelligent Sensor Network for Laboratory data Acquisition and Processing

Research on Intelligent Sensor Network for Laboratory Data A...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Kong, Fei Li, Junmei College of Art Jingchu University of Technology Jingmen China College of Artificial Intelligence Jingchu University of Technology Jingmen China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504205

This paper proposes a laboratory data acquisition and processing system based on intelligent sensor networks, aiming to improve the efficiency and accuracy of experimental data acquisition. The system collects experimental data in real time by deploying distributed sensor nodes, and uses Huffman coding algorithm for data compression and transmission. Huffman coding effectively reduces the amount of data transmission by optimizing the coding length, thereby improving the transmission efficiency of the system and saving network bandwidth. To ensure the efficient operation of the system, this paper designs a multi-level data processing algorithm based on intelligent sensor networks, which can quickly and accurately analyze and process data after data acquisition, thereby realizing efficient experimental data monitoring and control. Simulation results show that the system can significantly improve the speed of data acquisition and processing under different experimental conditions, and effectively reduce the delay of data transmission. When collecting 1000 data points, the data transmission rate after Huffman coding is increased by about 25%, and the transmission delay is reduced from the original 150ms to 120ms. At the same time, the data compression ratio reaches 50%. Compared with traditional transmission methods, the system can significantly reduce the occupancy of network bandwidth. Huffman coding shows high efficiency in compression ratio and decoding speed. In response to the processing needs of large-scale experimental data, Huffman coding effectively improves data processing capabilities and ensures the integrity of data transmission. © 2025 ieee.

关键词： data collection

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Cross-Domain Deterministic Communication System for Differentiated Concurrent Services Based on Intelligent compression 4

Cross-Domain Deterministic Communication System for Differen...

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4th International conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Gao, Chengtao Wu, Runze Guo, Haobo College of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378542

Under the background of the 'dual carbon' goal and related policies and the construction of new power systems, the large-scale deployment of sensing terminals and the explosive growth of the perception amount of data in the distribution system, the low transmission efficiency of the traditional distribution network has been unable to meet the needs of 'comprehensive perception and efficient collaboration'. How to achieve rapid transmission and real-time analysis of massive sensing data has become one of the main challenges of the new distribution network. Adding lossless compression to edge computing provides a way to transmit lightweight data at cloud edge. Considering the QoS requirements of differentiated concurrent services, this paper proposes an intelligent compressed cross-domain deterministic communication system for differentiated concurrent services. First, a cross-domain deterministic communication system model for differentiated concurrent services is constructed. The problem is optimized according to service QoS requirements and model constraints, and the service compression ratio is minimized under the condition that the service requirements are met. Then deep reinforcement learning method is used to solve the problem, and finally the proposed system effectively improves the certainty of massive distributed PV through simulation. © 2024 ieee.

关键词： DC distribution systems

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Transforming Big data: A Novel Arithmetic compression Method Based on Symbol Frequency 18

Transforming Big Data: A Novel Arithmetic Compression Method...

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18th ieee International conference on Computer Science and Information Technologies, CSIT 2023

作者： Yarmilko, Andrii Rozlomii, Inna Mysiura, Yuliia Bohdan Khmelnytsky National University of Cherkasy Department of Automated Systems Software Cherkasy Ukraine Bohdan Khmelnytsky National University of Cherkasy Department of Information Technologies Cherkasy Ukraine

ISBN: (纸本)9798350360462

The article proposes a new method for arithmetic compression of large volumes of data. The used compression approach is based on dividing the original data into slices depending on the frequency of occurrence of symbols. The resulting cuts are subjected to arithmetic compression, which ensures an effective reduction of the volume of data, while maintaining the quality and reliability of information. The new method studies the statistical information on the frequency of occurrence of characters in the data set, which allowed setting the optimal place for dividing the data into slices. The main advantage of the proposed method lies in its ability to adapt to the specific characteristics of the data set, providing optimal partitioning and compression. This makes it possible to achieve a higher degree of stability with traditional arithmetic embossing methods while maintaining data quality. An additional advantage is the possibility of applying the method to various information types of data, which makes it a universal tool for optimizing the storage and transmission of large volumes. © 2023 ieee.

关键词： Big data

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