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

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Long-term localization with map compression based on solar information 26

Long-term localization with map compression based on solar i...

引用

ieee 26th International conference on Intelligent Transportation Systems (ITSC)

作者： Bouaziz, Youssef Royer, Eric Bresson, Guillaume Dhome, Michel Univ Clermont Auvergne CNRS SIGMA Clermont Inst Pascal F-63000 Clermont Ferrand France Inst VEDECOM 23 Bis Allee Marronniers F-78000 Versailles France

ISBN: (纸本)9798350399462

In this paper we address visual based localization in outdoor environments where the appearance changes dramatically. Such environmental changes result in a substantial transformation of the visual information of the scene, producing a significant impact on the visual based localization performance. Hence, these changes can lead to major difficulties when associating data between the current image and the landmarks in the map. One solution for this problem is to keep adding landmarks to the map in order to cover various environmental conditions. However, this solution leads to a continued growth of the map, which in turn, will result in a costly and resource-intensive localization. In this paper we present a map management approach in which we exploit information related to the suns position to compare resemblance between the traversals in the map and maintain a diverse map that incorporates a minimum amount of data and ensures a reliable localization in different environmental conditions. We evaluated our approach on a dataset that incorporates more than 100 sequences with different environmental conditions and we compared the obtained results with a state of the art approach.

关键词： Information management

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BPG-based compression of Poisson noisy images 13

BPG-based compression of Poisson noisy images

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13th International conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Kovalenko, Bogdan Lukin, Vladimir National Aerospace University - KhAI Dept of Information-Communication Technologies Kharkiv Ukraine

ISBN: (纸本)9798350396119

The article is devoted to the lossy compression of noisy images, considering the currently popular trend of green computing. In this paper, we show how this trend can be incorporated into solving an essential task of image data transferring via communication lines and what benefits it can bring to this area of interest. We consider the case of images corrupted with signal-dependent Poisson-distributed noise, which is quite common in real-life situations. compression of noisy images has several peculiarities, among which there is the existence of optimal operation point (OOP), which is a value of compression control parameter (CCP) where a compressed image is closer to a noise-free image than the original noisy one. We show the possible presence of OOP for various visual quality metrics, both SSIM-based and PSNR-based. The recommendations for compression in OOP and its neighborhood are presented. © 2023 ieee.

关键词： Image compression

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High-fidelity 3D Model compression based on Key Spheres

High-fidelity 3D Model Compression based on Key Spheres

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data compression conference (DCC)

作者： Li, Yuanzhan Liu, Yuqi Lu, Yujie Zhang, Siyu Cai, Shen Zhang, Yanting Donghua Univ Visual & Geometr Percept Lab Shanghai Peoples R China

ISBN: (纸本)9781665478939

In recent years, neural signed distance function (SDF) has become one of the most effective representation methods for 3D models. By learning continuous SDFs in 3D space, neural networks can predict the distance from a given query space point to its closest object surface, whose positive and negative signs denote inside and outside of the object, respectively. Training a specific network for each 3D model, which individually embeds its shape, can realize compressed representation of objects by storing fewer network (and possibly latent) parameters. Consequently, reconstruction through network inference and surface recovery can be achieved. In this paper, we propose an SDF prediction network using explicit key spheres as input. Key spheres are extracted from the internal space of objects, whose centers either have relatively larger SDF values (sphere radii), or are located at essential positions. By inputting the spatial information of multiple spheres which imply different local shapes, the proposed method can significantly improve the reconstruction accuracy with a negligible storage cost. Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction. Experiments conducted on three datasets verify the superior performance of our method.

关键词： Training Surface reconstruction Solid modeling Three-dimensional displays Shape Image color analysis Fitting

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Designing Efficient Pipelined Communication Schemes using compression in MPI Libraries 29

Designing Efficient Pipelined Communication Schemes using Co...

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29th Annual ieee International conference on High Performance Computing, data, and Analytics (HiPC)

作者： Ramesh, Bharath Zhou, Qinghua Shafi, Aamir Abduljabbar, Mustafa Subramoni, Hari Panda, Dhabaleswar K. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665494236

The emergence of trillion-parameter models in AI, and the deployment of dense Graphics Processing Unit (GPU) systems with high-bandwidth inter-GPU and network interconnects underscores the need to design efficient architecture-aware large message communication operations. GPU-based on-the-fly compression communication designs help reduce the amount of data transferred across processes, thereby improving large message communication performance. In this paper, we first analyze bottlenecks in state-of-the-art on-the-fly compressionbased MPI implementations for blocking as well as non-blocking point-to-point communication operations. We then propose efficient point-to-point designs that improve upon state-of-the-art implementations through fine-grained overlap of copy, compression and communication operations. We demonstrate the efficacy of our proposed designs by comparing against state-of-the-art communication runtimes using micro-benchmarks and candidate communication patterns. Our proposed designs deliver 28.7% improvements in latency, 49.7% in bandwidth, and 36% in bidirectional bandwidth using micro-benchmarks, and up to 16.5% improvements for 3D stencil-based communication patterns over state-of-the-art designs.

关键词： HPC Infiniband MPI RDMA compression GPU

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Efficient IoT data Processing Framework For High Velocity data To Non-Intrusively Track Machine Operation Status 4

Efficient IoT Data Processing Framework For High Velocity Da...

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4th ieee Industrial Electronics and Applications conference, IEACon 2023

作者： Goh, Ken Why Ooi, Boon Yaik Kh'ng, Xin Yi Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350347517

Automation is a core component of the Industry 4.0 concept and is gaining popularity among manufacturing firms. This is where the Internet-of- Things (IoT) takes place and machine monitoring system is born. The growth of the Internet of Things (IoT) has led to an enormous number of connected devices being used all over the world. It was noted that the IoT generates a huge amount of data that requires high network bandwidth, energy and large storage space, which are very costly. Therefore, many time-series data reduction methods have been introduced. This work aims to design a sound compression technique that can streamline the process of data acquisition and a sound recognition model that can reduce the network latency and cover all the information needed by the sound recognition model without losing too much accuracy. The proposed method reduces and compresses the sound data, achieving a compression ratio of 22.04. Then, the sound data will be transferred for sound recognition model training. The experiment shows that the model trained using the compressed data can achieve a high accuracy. Although the experimental results show high accuracy, further research and investigation are still needed to provide a better and more mature technique for the use cases. © 2023 ieee.

关键词： Internet of things

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Evaluation of the Quality of compression of Acoustic Signals in the Low-Frequency and Mid-Frequency Sound Range Using a Neural Network Codec

Evaluation of the Quality of Compression of Acoustic Signals...

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2023 International Russian Automation conference, RusAutoCon 2023

作者： Sushkov, Nikita Mironov, Andrey Pacific National University Department of Computer Engineering Khabarovsk Russia

ISBN: (纸本)9798350345551

This article compares the compression algorithm using a neural network codec with modern audio compression algorithms such as MP3, AAC, WMA, ALAC, FLAC. The results of the compression quality evaluation taking into account various parameters of signals before and after compression are presented, as well as a comparative characteristic of the algorithm execution speed on AMD Ryzen 7 5800X, Intel(R) Core(TM) i3-5005U, AMD Ryzen 5 3600, AMD Ryzen 5 2500U, Intel(R) Core(TM) i5 processors-10400F. Experiments have proved that the use of a neural network codec is possible in real-time systems on a single processor core with low performance, but the speed of the neural network codec is many times lower than that of classical algorithms. Also, it was revealed that the compression quality of the neural network codec can compete with lossless compression algorithms. © 2023 ieee.

关键词： data compression

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The High-Resolution Medical Picture compression Using Segmentation-Based Lossless Image Coding 3

The High-Resolution Medical Picture Compression Using Segmen...

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3rd International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Avni Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Medical imaging serves a crucial role in the diagnosis of illness. The amount of local storage capacity and transfer bandwidth needed by remote medical devices to aid in patient diagnosis and treatment is closely related to the losslessness of the compression used. Losslessness and similarity are two amazing features of medical imaging. compression centres on learning to take use of these two features to decrease the amount of data that must be stored to describe a picture. In this study, we use large dataset mining to create an image catalogue. That is, to identify pictures' elemental parts. For multi-component medical pictures, we offer a soft compression approach that faithfully preserves the images' underlying structure. Our findings demonstrate that our newly developed soft compression approach outperforms the industry standards JPEG2000, and PNG and we accompany this with thorough representation architecture for image compression. © 2023 ieee.

关键词： DCT DWT Huffman Coding Image compression Soft compression

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Proceedings - ITC-Asia 2024: 8th ieee International Test conference in Asia

Proceedings - ITC-Asia 2024: 8th IEEE International Test Con...

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8th ieee International Test conference in Asia, ITC-Asia 2024

ISBN: (纸本)9798331540333

The proceedings contain 48 papers. The topics discussed include: a high performance PODEM algorithm with the improved backtrace process;CRISP: triangle counting acceleration via content addressable memory-integrated 3D-stacked memory;design of ultra-high throughput and resource efficiency TRNG based on NAND-XOR and feedback XOR ring oscillators;advanced DFT clock control architectures with agile development for chisel-based high performance RISC-V processors;efficient functional safety method for gate-level fine-grained digital circuits with ISO-26262;ELSeM: an efficient and lightweight security mechanism for DSP;enhancing subthreshold stuck-at fault testing with polymorphic gates;a brief survey on randomizer design and optimization for efficient stochastic computing;and a scan slice reordering algorithm based on minimizing entropy to enhance test data compression efficiency.

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Deep Learning and its Applications: A Novel Approach to Machine Learning with Encoding Algorithms

Deep Learning and its Applications: A Novel Approach to Mach...

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2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Suresh Babu, Kamalesh Thoppaen Ashok Tingare, Bhagyashree Khedkar, Vijayshri Diwan, Tarun Dhar William, P. Badholia, Abhishek Globallogic Inc 2535 Augustine Drive Santa ClaraCA95054 United States D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Department of Computer Science & Engineering Pune India Atal Bihari Vajpayee University Bilaspur India Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India

ISBN: (纸本)9798350390049

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing. © 2024 ieee.

关键词： data compression Deep Learning Dimensionality Reduction Encoding Algorithms Feature Extraction Hyperspectral Images Model Performance Neural Networks

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Research on Eliminating Redundancy in Electricity Information Transmission Based on Artificial Neural Network Algorithm 6

Research on Eliminating Redundancy in Electricity Informatio...

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6th International conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024

作者： Tao, Ligui Zhou, Yuhuai Su, Zhihao Cao, Hanhua Zhang, Huanping Zhujiang College South China Agricultural University Guangzhou510900 China Guangzhou Xinhua University Guangzhou510520 China

ISBN: (纸本)9798350366143

In order to reduce the storage space occupied by the compressed power information and realize the accurate elimination of redundant data, a method of eliminating the redundancy of power information transmission based on artificial neural network algorithm is proposed. According to the principle of artificial neural network, the transmission format of redundant power information is defined, and the redundant power information can be processed on demand according to the numerical level of information repetition rate index. From the perspectives of redundancy strength and aggregation coefficient, calculate the compression level of redundant power information, and complete the design of power information transmission redundancy elimination method based on artificial neural network algorithm. The results show that under the action of artificial neural network algorithm, the storage space occupied by compressed power consumption information is reduced 0.69× 1012 Mb, which can avoid the repetition of information parameters, so as to realize the accurate elimination of redundant data. © 2024 ieee.

关键词： artificial neural network algorithm compression amount power consumption information redundancy elimination

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