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

共 16779 条记录，以下是1181-1190 订阅

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Secured data compression and data Authentication in Internet of Thing Networks Using LZW compression Based X.509 Certification

Secured Data Compression and Data Authentication in Internet...

引用

2022 ieee International conference on data Science and Information System, ICDSIS 2022

作者： Karthikeyan, S. Poongodi, T. Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

The Internet of Things (IoT) is a collection of physical entities that forms a huge communication between the IoT devices. The exchange of data between these IoT devices may lead to modification of data and transmission of raw data may easily prone to such modification attacks. In order to mitigate the data modification, it is necessary to compress and authenticate the raw data via devices before encrypting the data via IoT sensors to safeguard it from modification attacks. In this paper, we compress the data using LZW lossless compression scheme and then X.509 certification is used to authenticate the data using mutual authentication mechanism. The model is tested in python over a high-end computing engine, where the comparison with existing model shows that the encoded key size after compression of original data gets reduced than other methods with protection against data modification attacks. © 2022 ieee.

关键词： Authentication

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Smart Grid data compression of Power Quality Events using Wavelet Transform 35

Smart Grid Data Compression of Power Quality Events using Wa...

引用

Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Jose, Kripa M. Morsi, Walid G. Ontario Tech Univ UOIT Dept Elect & Comp Engn Smart Grid & EV Res Lab Oshawa ON Canada

ISBN: (数字)9781665484329

ISBN: (纸本)9781665484329

Smart grids typically incorporate a communication networking layer onto the electric power grid to exchange the data and the information between the intelligent electronic devices and the supervisory control and data acquisition (SCADA). The smart grid monitoring and control requires the acquisition and the transmission of a large volume of data over such communication networks. This process sets new requirements for the existing data communication networks within the smart grid regarding the network transmission capacity and the data storage. data compression is an effective mean to compress the power quality (PQ) data and hence saving the cost of data storage while meeting the communication network transmission capacity limits. The previous work on PQ data compression uses wavelets multiresolution signal decomposition into various decomposition levels. Thresholding is then applied on each wavelet decomposition level to capture the features needed for signal reconstruction. The goodness of compression is significantly affected by the choice of the wavelet basic function as well as the choice of the number of decomposition levels. This paper looks into identifying the most suitable wavelet basic function and the suitable number of wavelet decomposition level to achieve high compression of PQ disturbances. The study considered 80 wavelets and 4 categories of PQ disturbances such as sag, swell, interruption and notches. The results have been presented and the conclusions were drawn.

关键词： Power Quality Wavelets transients Multiresolution analysis data compression

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data Transmission Based on RNN Compensation over Mobile Voice Channel

Data Transmission Based on RNN Compensation over Mobile Voic...

引用

2023 ieee/CIC International conference on Communications in China, ICCC 2023

作者： Gao, Xiang Peng, Tao Beijing University of Posts & Telecommunications Wireless Signal Processing and Network Lab Key Laboratory of Universal Wireless Communications Ministry of Education Beijing China

ISBN: (纸本)9798350345384

data transmission over mobile voice channel (DoV) suffers from vocoder compression and smartphone de-noising. Most previous algorithms ignore these two factors leading to the generated signal being distorted severely during real network transmission. This paper proposes a data modulation algorithm that maps data on the amplitude of different frequency components, and combined with interpolation 0 in frequency domain to obtain short-time stability. Then, a recurrent neural network (RNN) based compensation algorithm is proposed to compensate for distorted signal. As a result, this paper achieves 3.1kb/s data rate with an average 4.5% bit error rate (BER) in real network experiments. Numerical results suggest that the proposed algorithm has a considerable advantage in resisting smartphone de-noising and is capable of reducing distortion generated during transmission. © 2023 ieee.

关键词： Smartphones

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Binary Bit Stream compression Based on Mode Encoding Algorithm 3

Binary Bit Stream Compression Based on Mode Encoding Algorit...

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3rd ieee International conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Ding, Jian-Jiun Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350333862

After encoding audio, image, video, or text data, the output always has the form of the bit stream. Therefore, whether the bit stream can be further compressed is an interesting issue. Many ways, including direct encoding and zero-run length coding, can be applied to encode a bit stream. In this study, we propose a new way to encode the bit stream effectively and adaptively and then perform mode encoding. Experimental results show that, when the variance is not small, no matter whether the bitstream has the probability distributions of Gaussian, hyper-Laplacian, Pareto, or uniform, the proposed algorithm achieves a higher coding gain. © 2023 ieee.

关键词： Probability distributions

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ADAPTIVE MULTI-SCALE PROGRESSIVE PROBABILITY MODEL FOR LOSSLESS IMAGE compression 29

ADAPTIVE MULTI-SCALE PROGRESSIVE PROBABILITY MODEL FOR LOSSL...

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ieee International conference on Image Processing (ICIP)

作者： Zhang, Honglei Cricri, Francesco Zou, Nannan Tavakoli, Hamed R. Hannuksela, Miska M. Nokia Technol Espoo Finland

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Domain adaptation is an efficient technique to improve the performance of a system by adapting a pre-trained model to the given input data. The adaptation technique has been generally applied in conventional video codecs. For neural network-based systems, the encoder may adapt the decoder to the input data by fine-tuning a pre-trained model present at the decoder side. The weight update is then transferred to the decoder and the updated model is used to decode the bitstream. However, due to the large number of parameters in deep neural networks, the overhead of the weight update may diminish the gain from the adaptation technique. In recent years, various methods have been proposed to reduce the overhead without significantly compromising the gain. In this paper, we propose an adaptive multi-scale progressive probability model for lossless image compression. The proposed method uses the data that has already been processed at the inference stage to fine-tune the probability model. Importantly, the decoder can apply the fine-tuning by itself resulting a small adaptation overhead to help the decoder in performing the fine-tuning. The proposed method achieves up to 0.28 bits-per-pixel (BPP) reduction on four benchmark datasets compared to the state-of-the-art method.

关键词： domain adaptation lossless image compression probability model video coding

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Real-Time Wavelet-Based data compression in Light of IEC61850 Communication Protocol 35

Real-Time Wavelet-Based Data Compression in Light of IEC6185...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Ayub, Rizwan Morsi, Walid G. Ontario Tech Univ Comp & Software Engn Dept Smart Grid & Elect Vehicles Res Lab Oshawa ON Canada

ISBN: (数字)9781665484329

ISBN: (纸本)9781665484329

This study addresses the problem of data compression in the context of IEC61850 communication protocol used in smart grids. The study presents a new approach based on wavelet compression that uses the predictor importance to identify the wavelet detail level that holds the salient features of the disturbance signal and then applies a hybrid threshold that includes both hard and soft thresholds to the wavelet details. The effectiveness of the proposed approach has been tested and evaluated and the results have shown that the proposed approach was very effective in increasing the compression ratio leading to reduction in the file sizes. Furthermore, the results of testing the proposed approach using a real-time digital simulator in the context of IEC61850 showed that the implementation of the proposed approach not only led to a significant reduction in the number of messages but also leads to a reduction in their sizes while maintaining a high-quality signal reconstruction following the compression process.

关键词： data compression power quality wavelets

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Electric Power Asynchronous Heterogeneous data Accelerated compression for Edge Computing 7

Electric Power Asynchronous Heterogeneous Data Accelerated C...

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7th ieee International conference on Smart Cloud, SmartCloud 2022

作者： Wang, Hongkai Rao, Hanyu Gong, Xiaogang Chen, Zuge Mao, Dong Zhang, Jingyao New Tech. Research Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China Research Center Of Big Data And Computational Intelligence Hangzhou Innovation Institutes Beihang University Hangzhou China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

With the development of edge computing and cloud computing in power scenarios, the cloud center collects a large amount of data from edge nodes every day, and the load of edge nodes is overloaded and the transmission delay increases, making it difficult to store and use data in the cloud center. The communication capabilities, storage capabilities and computing capabilities of nodes face greater challenges. Load data is the most important structural data in the asynchronous heterogeneous data of electric power. In order to reduce the amount of data generated during the transmission process of the edge network, compression technology can be used to effectively compress the load data. Before using the traditional integrated neural network model to compress the time series load data, it is necessary to calculate the variance of the window data, and compare the obtained variance with the empirical threshold, so as to divide the load data into stable data and unstable data. Due to the complex logic of data preprocessing, the overall compression calculation is time-consuming, and the robustness of the data classification algorithm is not high due to the need to manually set empirical parameters. In this paper, the multilayer perceptron is applied to load data classification, combined with the integrated neural network model, to construct an edge-side data compression scheme that can be applied to smart grid scenarios. This scheme achieves faster compression speed on the basis of ensuring the original compression ratio. © 2022 ieee.

关键词： Edge computing

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Video compression and Action Recognition in Self-supervised Learning 5

Video Compression and Action Recognition in Self-supervised ...

引用

5th Asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Hao, Zongbo Hao, Conghui He, Kecheng University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9798350374407

In computer vision, neural network models typically require a large amount of manually annotated images or video data for training. To reduce annotation costs, self-supervised learning has gained significant attention. This paper proposes a self-supervised learning-based method by introducing an auxiliary task involving spatiotemporal context in videos - extracting video keyframes - to guide self-supervised learning. The neural network learns video stream features by completing this task, thereby understanding the high-level semantics of the video. Additionally, this auxiliary task achieves video content redundancy reduction and video compression through keyframe extraction. To quantitatively evaluate the effectiveness of this method, the neural network is transferred to the domain of action recognition and experiments are conducted on the UCF101 dataset, achieving an accuracy of 66.2% with fewer parameters and training data. The validation of the effectiveness of this method as an auxiliary task for video spatiotemporal features contributes to a better understanding of video semantic features by neural networks and facilitates action recognition, thereby expanding the applications of self-supervised learning. © 2024 ieee.

关键词： Self-supervised learning

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Mathematical Modeling and Characterization of a Wearable Soft Robotic Device for Muscle Mechanotherapy 46

Mathematical Modeling and Characterization of a Wearable Sof...

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46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Ticllacuri, Victor Mio, Renato Biomedical Engineering Program PUCP-UPCH Lima Peru Pontificia Universidad Catolica del Peru Laboratory of Biomechanics and Applied Robotics Lima Peru Universidad Peruana Cayetano Heredia Laboratory of Biomedical Engineering Lima Peru University of Bayreuth Digital Health with a Focus on Data Science Germany

ISBN: (纸本)9798350371499

Treatments of disuse-induced muscle atrophy entail unmet clinical needs due to the lack of medical devices capable of mimicking physicians manual therapies. Therefore, in this paper we develop and model a wearable soft pneumatic elastomeric actuator to perform deep cyclic compression stimuli on human soft tissues for muscle rehabilitation by mechanotherapy. Static and dynamic characterization of the prototype demonstrate a 2.5 mm active deformation at 100 kPa with 600 mm3/s and a 5 Hz bandwidth. We estimate the transfer function of the experimentally acquired pressure, flow and deformation signals, processed by a Gaussian kernel-based approach. Our mathematical model accurately describes the actuator behavior and enables to extract its mechanical parameters. Then, through computational simulations, we illustrate its efficacy in emulating multiple complex bio-inspired movements. Our proposed methodology aims to improve the control efficiency in wearable soft robotics for muscle atrophy treatment. © 2024 ieee.

关键词： Pneumatic actuators

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IMPROVED SCREEN CONTENT CODING IN VVC USING SOFT CONTEXT FORMATION 49

IMPROVED SCREEN CONTENT CODING IN VVC USING SOFT CONTEXT FOR...

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49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Och, Hannah Uddehal, Shabhrish Reddy Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Communicat & Signal Proc Erlangen Germany Coburg Univ Appl Sci & Arts Dept Elect Engn & Comp Sci Coburg Germany

ISBN: (纸本)9798350344868;9798350344851

Screen content images typically contain a mix of natural and synthetic image parts. Synthetic sections usually are comprised of uniformly colored areas and repeating colors and patterns. In the VVC standard, these properties are exploited using Intra Block Copy and Palette Mode. In this paper, we show that pixel-wise lossless coding can outperform lossy VVC coding in such areas. We propose an enhanced VVC coding approach for screen content images using the principle of soft context formation. First, the image is separated into two layers in a block-wise manner using a learning-based method with four block features. Synthetic image parts are coded losslessly using soft context formation, the rest with VVC. We modify the available soft context formation coder to incorporate information gained by the decoded VVC layer for improved coding efficiency. Using this approach, we achieve Bjontegaard-Delta-rate gains of 4.98% on the evaluated data sets compared to VVC.

关键词： image compression screen content VVC segmentation soft context formation

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