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

共 16774 条记录，以下是711-720 订阅

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Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation 42

Rapid-INR: Storage Efficient CPU-free DNN Training Using Imp...

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42nd ieee/ACM International conference on Computer-Aided Design (ICCAD)

作者： Chen, Hanqiu Yang, Hang Fitzmeyer, Stephen Hao, Cong Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9798350322255

Implicit Neural Representation (INR) is an innovative approach for representing complex shapes or objects without explicitly defining their geometry or surface structure. Instead, INR represents objects as continuous functions. Previous research has demonstrated the effectiveness of using neural networks as INR for image compression, showcasing comparable performance to traditional methods such as JPEG. However, INR holds potential for various applications beyond image compression. This paper introduces Rapid-INR, a novel approach that utilizes INR for encoding and compressing images, thereby accelerating neural network training in computer vision tasks. Our methodology involves storing the whole dataset directly in INR format on a GPU, mitigating the significant data communication overhead between the CPU and GPU during training. Additionally, the decoding process from INR to RGB format is highly parallelized and executed on-the-fly. To further enhance compression, we propose iterative and dynamic pruning, as well as layer-wise quantization, building upon previous work. We evaluate our framework on the image classification task, utilizing the ResNet-18 backbone network and three commonly used datasets with varying image sizes. Rapid-INR reduces memory consumption to only 5% of the original dataset size and achieves a maximum 6x speedup over the PyTorch training pipeline, as well as a maximum 1.2x speedup over the DALI training pipeline, with only a marginal decrease in accuracy. Importantly, Rapid-INR can be readily applied to other computer vision tasks and backbone networks with reasonable engineering efforts. Our implementation code is publicly available at https://***/sharc-lab/Rapid- INR.

关键词： Image compression

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Compressed Image Super Resolution using Convolutional Neural Network 38

Compressed Image Super Resolution using Convolutional Neural...

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38th ieee Region 10 conference, TENCON 2023

作者： Tun, Ei Ei Konkitkriengkrai, Aktanin Ruangsang, Watchara Aramvith, Supavadee Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9798350302196

Image compression is a topic of significant interest as it reduces file sizes in stored data. In this paper, we propose a model that achieves multiple levels of compression, thereby minimizing the storage space required for images, which typically consume substantial amounts of data due to their size and resolution. We combine an image downscaling and upscaling model with an image compression model. By leveraging convolutional techniques to identify image features, we can effectively reduce the size of the image through downscaling and subsequently upscaling it. Additionally, we employ entropy image compression and arithmetic encoding to compress and reconstruct the image while preserving its lossless data. Through experimentation with the Kodak dataset, we observed that our proposed model achieved a compression rate of 96.92%, significantly reducing the data needed for file storage. Moreover, our reconstructed images attained a standardized measure with a signal-to-noise ratio of 33.10 dB and a structural similarity of 0.9219. Notably, the perceptual quality of the images, including intricate details, remained intact to the human eye. © 2023 ieee.

关键词： Image reconstruction

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Replicating Healthy and Metastatic Behavior: A Biomechanical Investigation Using 3D Printed Vertebrae Models 3

Replicating Healthy and Metastatic Behavior: A Biomechanical...

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3rd ieee International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Forni, Riccardo Bruno, Daniela Palanca, Marco Cristofolini, Luca Gargiulo, Paolo Inst. of Biomed. and Neural Engineering Reykjavik University Reykjavik Iceland University of Bologna Industrial Engineering Dep. Bologna Italy

ISBN: (纸本)9798350378009

Understanding the biomechanical impact of metastatic involvement in the spine is critical for patient care and treatment planning. However, limited access to human specimens and ethical concerns hinder research in this field. In this study, we propose a novel approach using multi-material 3D printing to create patient-specific synthetic vertebrae models for investigating metastatic involvement. A pipeline was designed, tested and compared, utilizing quantitative Computer Tomography (qCT) data for model generation. Mechanical properties of healthy and metastatic vertebrae models were evaluated through compression testing. Results showed that qCT-derived models closely mirrored ex-vivo data, demonstrating comparable failure loads and fracture modes. Furthermore, the use of hyperelastic flexible material for simulating metastasis proved effective in replicating failure mechanisms. This innovative approach offers a cost-effective and ethical alternative for studying spinal metastases, with the potential to enhance treatment strategies and patient outcomes. © 2024 ieee.

关键词： compression testing

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HOLZ: High-Order Entropy Encoding of Lempel-Ziv Factor Distances

HOLZ: High-Order Entropy Encoding of Lempel-Ziv Factor Dista...

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data compression conference (DCC)

作者： Koppl, Dominik Navarro, Gonzalo Prezza, Nicola TMDU Tokyo Japan Univ Chile Dept Comp Sci Santiago Chile Ca Foscari Univ DAIS Venice Italy

ISBN: (纸本)9781665478939

We propose a new representation of the offsets of the Lempel-Ziv (LZ) factorization based on the co-lexicographic order of the text's prefixes. The selected offsets tend to approach the k-th order empirical entropy. Our evaluations show that this choice is superior to the rightmost and bit-optimal LZ parsings on datasets with small high-order entropy.

关键词： data compression Entropy Encoding

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Model compression by EL-XAI in IoMT Systems

Model Compression by EL-XAI in IoMT Systems

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2024 International conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Vijayalakshmi, S. School of Computational Engineering Takshashila University Tamilnadu Ongur India

ISBN: (纸本)9798331510022

IoMT systems are evolving at a higher rate than other IoT applications. As health is of major concern, more health monitoring sensors and devices are incorporated which helps in the well-being of the people at their comfort zone. The IoMT system needs highly capable sensors and devices to know the exact information of the patients from the remote area. On the other hand, usage of high-resolution devices leads to the higher amount of data packets for communication purposes. This increases the workload of the cloud server that can be faced using compression algorithms. This paper proposes the compression technique for various datasets because the medical devices produce different data format outputs. So, it is difficult for the cloud server to understand and interpret the information from the IoMT devices. Therefore, this paper uses an Explainable Artificial Intelligence based Ensemble learning algorithm using Lempel Ziv, 2D and 3D wavelet compression techniques in the cloud server of the IoMT system. The proposed system utilizes the resource efficiently and shown good results in terms of accuracy, throughput, and storage saving. The results are compared with the conventional EL algorithm to study the proposed system performance. © 2024 ieee.

关键词： Biomedical equipment

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Entropy-Based Analysis of data compression Techniques for Information Efficiency

Entropy-Based Analysis of Data Compression Techniques for In...

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2024 International conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bhuvana, J. Gautam, Chandra Kant Bishnoi, Amit Kumar Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Information compression is a vital approach for optimization and optimization of the scale of a digital record without affecting its content material. This paper offers an entropy-based analysis of two distinct levels of statistics compression strategies, particularly Huffman encoding and mathematics encoding. As a case look at, it analyses the records performance of these encoding strategies on a range of different datasets. The results show that Huffman is a superior approach for encoding excessive-entropy blocks in contrast to arithmetic encoding. At the same time, the latter provides a greater efficient encoding of low-entropy blocks. This perception proves beneficial to practitioners and researchers on the subject of fact compression, providing empirical expertise on ways distinctive coding algorithms are carried out while applied to different fact distributions. Additionally, it affords a precious source of data for choice-makers looking to optimize their facts storage and transmission requirements. © 2024 ieee.

关键词： Analyses Arithmetic Encoding Excessive Expertise Practitioners

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Towards Ultra Low Bit-Rate Digital Human Character Communication via Compact 3D Face Descriptors

Towards Ultra Low Bit-Rate Digital Human Character Communica...

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data compression conference (DCC)

作者： Li, Binzhe Chen, Bolin Wang, Zhao Wang, Shiqi Ye, Yan City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665478939

Recently, there has been a tremendous demand for high-efficiency face video communications, coinciding with the popularization of the digital human character in numerous applications. This paper demonstrates a new communication paradigm of 3D human digital characters in ultra low-bit-rate application scenarios. The paradigm is grounded on the mild assumption of the consistency and persistence of human ap-pearance, such that only the compact features that determine the pose and expression of the 3D character need to be transmitted. The proposed is also expected to benefit virtual-physical world interaction in Metaverse.

关键词： Three-dimensional displays Metaverse data compression Faces

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Furcifer: a Context Adaptive Middleware for Real-world Object Detection Exploiting Local, Edge, and Split Computing in the Cloud Continuum 22

Furcifer: a Context Adaptive Middleware for Real-world Objec...

引用

ieee International conference on Pervasive Computing and Communications (PerCom)

作者： Mendula, Matteo Bellavista, Paolo Levorato, Marco Contreras, Sharon Ladron de Guevara Univ Bologna Dept Comp Sci & Engn Bologna Italy Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA USA

ISBN: (纸本)9798350326031;9798350326048

Modern real-time applications widely embed compute intense neural algorithms at their core. Current solutions to support such algorithms either deploy highly-optimized Deep Neural Networks at mobile devices or offload the execution of possibly larger higher-performance neural models to edge servers. While the former solution typically maps to higher energy consumption and lower performance, the latter necessitates the low-latency wireless transfer of high volumes of data. Time-varying variables describing the state of these systems, such as connection quality and system load, determine the optimality of the different computing configurations in terms of energy consumption, task performance, and latency. Herein, we propose Furcifer, a framework capable of dynamically adapting the cloud continuum computing configuration in response to the perceived state of the system. Our container-based approach incorporates low-complexity predictors that generalize well across operating environments. In addition, we develop a highly optimized split Deep Neural Network model, which achieves in-model supervised compression and enhances task offloading. Experimental results for object detection across diverse conditions, environments, and wireless technologies, show Furcifer's remarkable outcomes, including a 2x energy reduction, 30% higher mean Average Precision score than pure local computing, and a notable three-fold increase in frame per second rate compared to static offloading.

关键词： Edge Computing Machine Learning Object Detection Mobile agents Image compression

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A New Parametric Coding Method Combined Linear Microphone Array Topology

A New Parametric Coding Method Combined Linear Microphone Ar...

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data compression conference (DCC)

作者： Zhou, Yao Bao, Changchun Beijing Univ Technol Fac Informat Technol Speech & Audio Signal Proc Lab Beijing 100124 Peoples R China

ISBN: (纸本)9781665478939

Nowadays, microphone array plays an important role in speech signal processing. This paper proposes a new parametric coding scheme to further improve the coding efficiency of linear microphone array. In the coding scheme, Inter-channel Level Difference (ICLD), Inter-channel Time Difference (ICTD) and Inter-channel Coherence (ICC) are used as spatial parameters. As shown in Figure 1, at the encoder, a down-mixed signal is obtained by averaging all signals time-aligned with the reference channel. Only the spatial parameters between the first channel and the last channel are extracted. At the decoder, the speech signal of each channel is reconstructed by the down-mixed signal received, spatial parameters and their interpolation.

关键词： Interpolation Array signal processing data compression Coherence Encoding Microphone arrays Topology

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Detection of compression artifacts in time-series data from continuous glucose monitoring sensors using matched filters 19

Detection of compression artifacts in time-series data from ...

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ieee 19th International conference on Body Sensor Networks (ieee BSN)

作者： Idi, Elena Prendin, Francesco Facchinetti, Andrea Sparacino, Giovanni Del Favero, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9798350338416

Continuous Glucose Monitors are minimally-invasive portable sensors that are revolutionizing the management of Type 1 Diabetes (T1D). A common issue encountered in their daily use is related to the presence of pressure-induced sensor attenuations (PISAs), temporary faults of the devices, resulting in false low blood glucose readings that can impact and compromise the reliability of CGMs. In this work, we explore the application of matched filters (MFs), a powerful pattern recognition technique, for the retrospective identification of PISAs failures. A MF is designed for the detection of a signal with a specific shape, associated with the occurrence of a PISA episode. The proposed algorithm is tested in-silico on a dataset generated with a state-of-art T1D patient simulator. MFs achieve a recall of 0.75 with about 1 false alarm every 5 days, outperforming other state-of-art algorithms proposed for the same purpose, including one based on a Random Forest classifier (RF). Moreover, when embedded as additional feature within a RF it improves the performance by granting a recall of 0.83 and 1 false alarm raised in 10 days. The encouraging outcomes in the simulated scenario pave the way for future investigations involving real-world data, as well as potential enhancements in detecting different types of sensors' failures.

关键词： Type 1 Diabetes Continuous Glucose Monitors compression artifacts Matched filters

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