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

共 16774 条记录，以下是701-710 订阅

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Communication-Efficient Federated Learning with Sparsity and Quantization

Communication-Efficient Federated Learning with Sparsity and...

引用

2023 ieee International conference on Dependable, Autonomic and Secure Computing, 2023 International conference on Pervasive Intelligence and Computing, 2023 International conference on Cloud and Big data Computing, 2023 International conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Long, Zhong Chen, Yuling Dou, Hui Luo, Yun Tan, Chaoyue Sun, Yancheng College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350304602

Google proposed federated learning in 2016, which aims to solve the problems of data silos and privacy leakage in machine learning. However, as the difficulty of the target task increases and the model performance requirements progressively improve, these have to consume more communication costs to increase the model parameters. In order to solve this problem, this paper proposes the Federated Learning with Sparsity and Quantization(FedSQ) method, where we select the gradients with a large rate of change in the gradients of the participants in each round for transmission, and utilize binary compression to quantize the selected model parameters, while adopting a suitable aggregation algorithm to replace the original FedAvg, so that it does not excessively impair the model accuracy in the case of high-fold compression. Finally, we verify the effectiveness of the scheme proposed in this paper, which can improve the quality of model training under model compression. © 2023 ieee.

关键词： Learning systems

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Deduplication of Textual data by NLP Approaches 97

Deduplication of Textual Data by NLP Approaches

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97th ieee Vehicular Technology conference (VTC-Spring)

作者： Ghassabi, Kiana Pahlevani, Peyman Lucani, Daniel E. Inst Adv Studies Basic Sci IASBS Dept Comp Sci Zanjan Iran Aalborg Univ Dept Elect Syst Copenhagen Denmark Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark

ISBN: (纸本)9798350311143

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

关键词： CSP Storage Generalized Deduplication

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A Multihypothesis Based Reconstruction Algorithm for Block Compressive Sensed WVSN data 11

A Multihypothesis Based Reconstruction Algorithm for Block C...

引用

11th ieee International conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Priya, G.L. Ghosh, D. Indian Institute of Technology Electronics and Communication Engineering Roorkee India

ISBN: (纸本)9798350308433

Wireless Visual Sensor Networks (WVSNs) have emerged as a prominent technological solution for surveillance and monitoring applications. These networks consist of smart camera sensor nodes, which intermittently capture video frames. These frames are then transmitted to a designated central node known as a sink. Block compressive sensing (BCS) for video compression is a research area that focuses on developing efficient algorithms for compressing video data by exploiting the sparsity of video signals in spatial and temporal domains. The primary goal of BCS-based techniques is to reduce the storage and bandwidth requirements of video data while maintaining acceptable video quality. The associated lightweight encoders and the demand for very less storage space make them suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Reconstruction algorithms play a vital role in maintaining image quality and a prominent one such algorithm is Smooth Projected Landweber (SPL). This paper evaluates the appropriateness of an iterative multihypothesis SPL-based reconstruction strategy in WVSN data aggregation. © 2024 ieee.

关键词： Image compression

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On Dynamic Bitvector Implementations

On Dynamic Bitvector Implementations

引用

data compression conference (DCC)

作者： Donges, Saska Puglisi, Simon J. Raman, Rajeev Univ Helsinki Dept Comp Sci Helsinki Finland Univ Leicester Sch Comp & Math Sci Leicester Leics England

ISBN: (纸本)9781665478939

Bitvectors that support rank and select queries are the workhorses of succinct data structures, implementations of which are now widespread, for example, in bioinformatics software. To date, however, most bitvector implementations are static, thus forcing more complex data structures built from them to be static too. In this paper we explore dynamic bitvectors, which, in addition to rank and select queries, also support update operations, specifically: insert, remove, and modify. We first provide several practical optimizations to the recent B-tree based bitvectors of Prezza (Proc. SEA 2017), including the use of buffers at leaves to speed update operations at the cost of a small overhead to query times. We then consider a common use case of succinct data structures, where queries and updates come in separate batches, and examine the efficacy of query support data structures that are fast to construct and speed rank and select queries, but become out of date when update operations are made. Finally, we explore several methods for leaf compression.

关键词： Costs data compression data structures Software Bioinformatics Optimization

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Exploring Light-weight Cryptography for Efficient and Secure Lossy data compression

Exploring Light-weight Cryptography for Efficient and Secure...

引用

ieee International conference on Cluster Computing (CLUSTER)

作者： Shan, Ruiwen Di, Sheng Calhoun, Jon C. Cappello, Franck Clemson Univ Clemson SC 29631 USA Argonne Natl Lab Lemont IL USA Univ Illinois Urbana IL USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

The enormous volume of data generated by large-scale instruments and simulations poses significant challenges in archiving, transferring, sharing and analyzing data for various scientific groups. Lossy reduction techniques are vital to reducing data size to acceptable levels. However, putting more information content per bit, increases the severity of loss if perturbed by malicious users or hardware failures. In the worst case, the entire dataset is compromised. Malevolent alteration or destruction of datasets containing crucial discoveries can completely invalidate research outcomes in scientific studies. Therefore, it is critical to integrate compression and encryption to handle data securely and efficiently. The current state-of-the-art combination technique Cmpr-Encr handles compression and encryption as two distinct processes. This reduces the compression ratio and bandwidth, especially for hard-to-compress datasets. In this paper, we propose two data protection strategies that work in conjunction with the lossy compressor SZ: Encr-Quant and Encr-Huffman, and carefully evaluate the overhead they introduce on compression bandwidth and ratio. Based on the results of testing with real-world scientific datasets, we find that the cost of Encr-Quant varies with the dataset's properties and requires cautious selection. Encr-Huffman is able to maintain more than 99% of the original compression ratio while saving 6.5% in compression time compared to SZ. Applying Cmpr-Encr leads to a reduction in compression bandwidth, whereas Encr-Huffman increases bandwidth by 3.1% over the SZ, on average.

关键词： Lossy data compression Encryption data Security High-performance Computing

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MRI Image compression Using Deep Convolutional Adversarial Networks 4

MRI Image Compression Using Deep Convolutional Adversarial N...

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4th ieee International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Mishra, Pawan Kumar Kishore, Jaydeep Bharati, Rakesh Solanki, Surendra Mehra, Pramod Joshi, Hema Graphic Era Deemed to be University Dehradun India Manipal University Department of Aiml School of Computer Science & Engineering Jaipur India Galgotias University School of Computing Science & Engineering Greater Noida India Uttarakhand Technical University Dehradun India

ISBN: (纸本)9798331518981

Image compression is a type of data compression applied to digital images to reduce their cost for storage or transmission. Supervised learning with convolutional networks (CNNs) has seen widespread adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this research work, a class of CNNs called deep convolutional generative adversarial networks (DCGANs) has been developed. These networks have certain architectural constraints and demonstrate that they are strong candidates for unsupervised learning. Training on various image datasets shows convincing evidence that the proposed deep convolutional adversarial pair learns a hierarchy of representations, from object parts to scenes, in both the generator and discriminator. Additionally, this research uses the learned features for novel tasks, demonstrating their applicability as general image representations. © 2024 ieee.

关键词： Convolutional neural networks

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Energy-Efficient IoT data Aggregation Framework Using Low-Power Wide-Area Networks 2

Energy-Efficient IoT Data Aggregation Framework Using Low-Po...

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2nd ieee International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Reddy, K.Niranjan Shukla, Aasheesh Sivasubramanian, S. Rajappa Sakthidharan, Gangadharan Hussein, Layth Vaishnavi, R. CMR Institute of Technology Department of ECE Telangana Hyderabad India GLA University Department of Electronics & Communication Engineering Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India GRIET Bachupally Department of CSE Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Dr. Sagunthala R&D Institute of Science and Technology Vel Tech Rangarajan Avadi Chennai India

ISBN: (纸本)9798350368109

The present work proposes an IoT data aggregation framework for LPWANs that essentially considers the problem areas of energy consumption, data distortion, scalability, and security. The presented framework combines complex aggregation methods with intelligent transmission time division and data compression to maximize the energy consumption and to ensure that data is transmitted accurately and on time. Through online simulations and real-life testing, the potential benefits have been observed to be up to 40% efficiency increment than the conventional aggregation techniques, 99% data reliability, and a high level of efficiency when used in large-scale networks. The aggregated data collected remain secure since the encryption process adopted does not require a huge computational power, hence ensuring maximum level of security with minimal incorporation of additional power. This has been developed to cater a broad network of application in the IoT domain of varying intricacy including environment monitoring, smart city implementations, within a single and efficient platform that is sans in terms of energy usage in restricted networks. © 2024 ieee.

关键词： Adaptive Transmission Scheduling data compression Energy Efficiency Energy Harvesting IoT data Aggregation IoT Scalability LoRaWAN Low-Power Wide-Area Networks (LPWAN) Secure data Transmission Sigfox

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Quantum data compression for Efficient Generation of Control Pulses 23

Quantum Data Compression for Efficient Generation of Control...

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28th Asia and South Pacific Design Automation conference (ASP-DAC)

作者： Volya, Daniel Mishra, Prabhat Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781450397834

In order to physically realize a robust quantum gate, a specifically tailored laser pulse needs to be derived via strategies such as quantum optimal control. Unfortunately, such strategies face exponential complexity with quantum system size and become infeasible even for moderate-sized quantum circuits. In this paper, we propose an automated framework for effective utilization of these quantum resources. Specifically, this paper makes three important contributions. First, we utilize an effective combination of register compression and dimensionality reduction to reduce the area of a quantum circuit. Next, due to the properties of an autoencoder, the compressed gates produced are robust even in the presence of noise. Finally, our proposed compression reduces the computation time of quantum control. Experimental evaluation using popular quantum algorithms demonstrates that our proposed approach can enable efficient generation of noise-resilient control pulses while state-of-the-art fails to handle large-scale quantum systems.

关键词： Quantum optics

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Networked ISAC Beamforming Design with Capacity-Limited Fronthaul Links

Networked ISAC Beamforming Design with Capacity-Limited Fron...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Kexin Xu, Yanqing Chang, Tsung-Hui School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Science & Engineering The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350368741

This study investigates a networked integrated sensing and communication (ISAC) system, where a central processor coordinates multiple ISAC transmitters and a sensing receiver through capacity-limited fronthaul links. The system aims to serve multiple mobile users and simultaneously sense a point target. The primary objective is to minimize the total transmit power while meeting the minimum signal-to-interference-plus-noise ratio (SINR) requirements for both communication and sensing, as well as adhering to the fronthaul capacity constraints. This leads to a complicated joint fronthaul compression and beamforming design (J-FCBD) problem. Although the J-FCBD problem is challenging to solve, we demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers. This novel finding has not been previously reported in the literature. Leveraging this insight, we prove that the remaining problem can be globally solved using the semidefinite relaxation (SDR) technique. Simulation results verify the effectiveness of the proposed design. © 2025 ieee.

关键词： beamforming design fronthaul compression Networked integrated sensing and communication

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings

2025 IEEE International Conference on Acoustics, Speech, and...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

ISBN: (纸本)9798350368741

The proceedings contain 3303 papers. The topics discussed include: object-level data augmentation for visual 3D object detection in autonomous driving;language-queried target sound extraction without parallel training data;ToolFiVe: enhancing tool-augmented LLMs via tool filtering and verification;integrating multi-scale compression attention with edge detection for ultrasound tumor segmentation;G-Depth: an efficient graph method for robust depth completion;kernel-based anomaly detection using generalized hyperbolic processes;enhancing document-level relation extraction through entity-pair-level interaction modeling;towards HRTF personalization using denoising diffusion models;what does an audio deepfake detector focus on? a study in the time domain;enhancing incomplete multimodal learning via modal complementary recovering;and neighborhood attention transformer with progressive channel fusion for speaker verification.

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