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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是321-330 订阅

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A Novel 6LowPan-IPv6 based Header compression Algorithm for enhancement of the QoS parameter

A Novel 6LowPan-IPv6 based Header Compression Algorithm for ...

引用

IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： K Kalaivani M Kavyashree V Kiran Vinila Nagaraj R V College of Engineering Bengaluru India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Wireless sensor networks WSNs, are fastest growing ubiquitous networking used for monitoring and managing remote habitats. IEEE 802.15.4 standard was introduced for optimizing power consumption. For improving connectivity and enabling efficient data transfer with minimum modifications of the existing Internet stack, an adaptation layer, 6loWPAN, was introduced by IETF. This paper presents a novel header compression algorithm to combat with the reduction in the Maximum Transmission Unit of 6LoWPAN with respect to Internet. This improves the Quality of Service parameters such as throughput, delay and jitter variations. The size of fragmentation header is reduced by eliding the datagram_size field in in the in between fragments. the first and the last fragment and removing it This results in the reduction of fragment header size by 8 bytes, thereby yielding a reduction of 2.9ms in the transmission time for each IPv6 packet of nominal 1000 bytes payload. Contiki OS was used to record results to validate the effectiveness of the proposed algorithm.

关键词： Wireless sensor networks compression algorithms Protocols Internet IEEE 802.15 Standard Quality of service

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UdeAlignC: Fast Alignment for the compression of DNA Reads

UdeAlignC: Fast Alignment for the Compression of DNA Reads

引用

IEEE Colombian Communications Conference (COLCOM)

作者： Jaime Lotero Andrés Benavides Aníbal Guerra Sebastián Isaza Facultad de Ingeniería Universidad de Antioquia UdeA Calle 70 No. 52-21 Medellín Colombia Facultad de Ciencias y Tecnología FaCyT Universidad de Carabobo UC Valencia Venezuela

Referential compression algorithms are one of the main strategies to cope with the exponential growth of DNA data available to scientists. One of the techniques used to build a referential compression is sequence alignment, which in turn requires a lot of computing. In this article we present UdeAlignC, a fast alignment algorithm for the compression of DNA reads. We demonstrate that our algorithm is 2× faster than prominent state of the art tools while optimality is only reduced by 5.6%. We also implement a GPU-accelerated version and show local speedups of up to 12×. Source code available at https://***/BioMRcomp/udealignc.

关键词： DNA Tools Sensitivity Sequential analysis compression algorithms Indexes Graphics processing units

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HIERARCHICAL SEGMENTATION BASED POINT CLOUD ATTRIBUTE compression

HIERARCHICAL SEGMENTATION BASED POINT CLOUD ATTRIBUTE COMPRE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ke Zhang Wenjie Zhu Yiling Xu Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538646595

With the rapid development of 3D capture techniques, point cloud has attracted significant attentions in recent years. Due to the large data volume of point cloud, efficient compression algorithms are essential for reducing bandwidth and storage consumption. In this paper, we present a novel scheme for point cloud attribute compression based on hierarchical segmentation. In this case, both global segmentation in photometric space and local segmentation in geometric space are analyzed to split point cloud into clusters. An octree based traversal algorithm is introduced to obtain the attribute stream of each cluster. Then, an intra-cluster prediction method is applied to achieve lossless compression. Meanwhile, we map the attribute streams to uniform 2D grids and leverage image coding method to achieve satisfying lossy compression performance. Experimental results demonstrate that our scheme outperforms the previous MPEG scheme in terms of coding efficiency.

关键词： point cloud attribute compression segmentation intra-cluster prediction segmentation CLOUDS Clouds Segmentation compression algorithms compression image coding method

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Flexible Signals and Images Lossless compression Chip Design for IoT and Industry 4.0

Flexible Signals and Images Lossless Compression Chip Design...

引用

IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Shih-Lun Chen Chi-Hao Liao Tsun-Kuang Chi Ting-Lan Lin Chiung-An Chen Department of Electronic Engineering Chung Yuan Christian University Taoyuan City Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City Taiwan

This paper presents a lossless compression algorithm and its chip design for signals and images applied to Internet of Things (IoT) and industry 4.0. Since the sensing signals, such as thermal, photo, gas, etc., are important for IoT and industry 4.0, a lossless signal compression technique based on an adaptive prediction and a hybrid entropy coding is proposed in this study. In addition, since applications of images are more and more important for IoT and industry 4.0, a flexible lossless image compression technique based on pixel restoration, median edge detection, and a hybrid entropy coding was developed for the proposed algorithm. The proposed algorithm can provide lossless compression for images in different types which includes grayscale, color (RGB), color filter array (CFA), and infrared images. In order to meet the demands of tiny and low-power-consumption for IoT, the proposed algorithm was realized by VLSI technique. A hardware sharing and pipeline scheduling techniques were used to reduce hardware cost and improve the operating frequency of the proposed design. The proposed design was synthesized by using a TSMC 0.18-μm CMOS process and it contained 5.46 k gate counts. Compared with previous JPEG-LS based designs, this work has benefits of better compression rates, lower cost, lower memory requirement and more lossless compression functions than previous designs. It is suitable for development of IoT and industry 4.0.

关键词： Image coding Huffman coding Prediction algorithms compression algorithms Image color analysis Prediction methods Image restoration

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A compression Algorithm of Fastq File Based on Distribution Characteristics Analysis

A Compression Algorithm of Fastq File Based on Distribution ...

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International Conference on Computer Science & Education (ICCSE)

作者： Shengyu Lu Hanping Chen Lifa Peng Beizhan Wang Hongji Wang Xiuze Zhou Xiamen University of Software Xiamen China

With the continuous development of sequencing technology scientists in the cost of DNA sequencing in reduce gradually, it also makes the number of DNA sequencing data to increase substantially. While the genome data is need to store, the traditional computer room has not enough to store such large data. Therefore, more and more genome data need to be uploaded to the cloud. Due to the speed of growth of communication have been much faster than the growth of the genomic data, so it is particularly important for genome data compression to reduce the cost of scientific research institutions and it is of great significance to speed up the sharing of genomic data. Fastq file is an important format of genomic data, and now the compression algorithm for fastq files is mainly include of DSRC, FQC, etc. These algorithms are also compressed based on the characteristics of fastq files. In order to improve the rate of compression, we propose an algorithm of DDSRC and establish the statistical models for the distribution characteristics of strings in fastq files to perform more efficient compression algorithms. This paper will explain the algorithm based on the distribution characteristics analysis and compare the results with other compression algorithms.

关键词： Genomics Bioinformatics DNA compression algorithms Standards Huffman coding

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PaSTRI: Error-Bounded Lossy compression for Two-Electron Integrals in Quantum Chemistry

PaSTRI: Error-Bounded Lossy Compression for Two-Electron Int...

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IEEE International Conference on Cluster Computing

作者： Ali Murat Gok Sheng Di Yuri Alexeev Dingwen Tao Vladimir Mironov Xin Liang Franck Cappello Northwestern University USA Argonne National Laboratory USA The University of Alabama USA Lomonosov Moscow State University Russia University of California Riverside USA

Computation of two-electron repulsion integrals is the critical and the most time-consuming step in a typical parallel quantum chemistry simulation. Such calculations have massive computing and storage requirements, which scale as O(N^4) with the size of a chemical system. Compressing the integral's data and storing it on disk can avoid costly recalculation, significantly speeding the overall quantum chemistry calculations; but it requires a fast compression algorithm. To this end, we developed PaSTRI (Pattern Scaling for Two-electron Repulsion Integrals) and implemented the algorithm in the data compression package SZ. PaSTRI leverages the latent pattern features in the integral dataset and optimizes the calculation of the appropriate number of bits required for the storage of the integral. We have evaluated PaSTRI using integral datasets generated by the quantum chemistry program GAMESS. The results show an excellent 16.8 compression ratio with low overhead, while maintaining 10 -10 absolute precision based on user's requirement.

关键词： compression algorithms Shape Chemicals Mathematical model Quantum computing Computational modeling

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Analysis of Content-Aware Image compression with VGG16

Analysis of Content-Aware Image Compression with VGG16

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： Alena Selimović Blaž Meden Peter Peer Aleš Hladnik Faculty of Natural Sciences and Engineering University of Ljubljana Slovenia Faculty of Computer and Information Science University of Ljubljana Slovenia

Content-aware compression based on the use of saliency maps aims to improve the interpretability of an image by encoding the more relevant image regions with a higher quality than the rest of the image. This paper revisits two convolutional neural network (CNN) models based on VGG16, multi-structure region of interest (MS-ROI) and class activation map (CAM), which enable the localization of salient image regions. While the MS-ROI model allows for the localization of multiple salient image regions, the CAM model, on the other hand, tends to localize only the most relevant class. We use the contextual information provided by the obtained saliency maps to guide the compression. By encoding more important image regions at a higher bitrate and less important ones at a lower bitrate, different qualities of compression for the regions of interest and the background are obtained, while also achieving smooth transitions from salient to non-salient regions. The performance of both models is evaluated on images from the MIT Saliency Benchmark dataset and the General-100 dataset, and the results of the compression are compared to the standard JPEG compression at different quality factors. Experimental results show that for the files of approximately same size, the compression methods based on the two CNN models outperform the standard JPEG compression. When comparing the compression based on the MS-ROI model to the compression based on the CAM model, the former is characterized by a higher PSNR and a better visual quality of the obtained images.

关键词： Image coding Transform coding Standards Heating systems Q-factor compression algorithms Feature extraction

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VLSI Implementation of ECG compression Algorithm Using Golomb Rice Coding

VLSI Implementation of ECG Compression Algorithm Using Golom...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Tsung-Han Tsai Muhammad Awais Hussain Ping-Zen Hao Department of Electrical Engineering National Central University Taiwan

A lossless electrocardiogram compression algorithm has been implemented in VLSI circuit design. This structure is based on memory-less model i.e. no RAM is being used in the design. Adaptive linear prediction has been combined with Golomb Rice coding to achieve the lossless data compression. Overall compression ratio for MIT-BIH Arrhythmia database is 3. The design has been synthesized using TSMC 0.18μm technology. The implementation results show high working speed with low power while working at the processing speed of 90 MHz.

关键词： Electrocardiography Encoding Very large scale integration compression algorithms Power demand Data compression Random access memory

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Trajectory Data compression Using Hopfield Neural Network

Trajectory Data Compression Using Hopfield Neural Network

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Chuanchuan Chen Changqing Xu Chenyang Xu Dongrui Liu Longwei Tian Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong University Shanghai China

Trajectory data can be used to predict traffic congestion and analyze crowd behavior patterns, which makes it more important in daily applications. For a long time, the volume of location data collected by various equipment will obtain an high speed increasing. Hence, it will be a enormous challenge to storage and transmit that big data. Due to the fact that traditional trajectory compression algorithms such as Douglas-Peucker algorithm are fast but not optimal algorithms. So, a kind of parallel algorithm utilizing Hopfield Neural Network is proposed in this paper. The proposed algorithm is an optimization algorithm with parallel computing capability, which satisfies fast processing to some extent. Finally, experience shows that the proposed algorithm can implement trajectory compression with fixed compression ratio and less accuracy loss compared with other existing algorithms.

关键词： Trajectory Neurons Optimization Mathematical model Linear programming Neural networks compression algorithms

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Object-based compression of 3D Animation Geometry

Object-based Compression of 3D Animation Geometry

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International Conference on Signal Processing and Communications, SPCOM

作者： Sanjib Das P. K. Bora Dept. of EEE IIT Guwahati Guwahati India

ISBN: (纸本)9781538638224

In multi-object based three-dimensional (MO-3D) animations, the geometry and connectivity data of the constituent objects change across the frames. These data require large disk space for storage and high bandwidth for real-time transmission over the network compared to single-object based 3D (SO-3D) animations. The existing geometry compression methods for 3D animations consider each frame as a single 3D object for compression. This global consideration of the multiple 3D objects as a single object does not exploit fully the local spatio-temporal redundancies exhibited by the individual 3D objects for the animation duration. This work proposes an efficient geometry compression method for MO-3D animations by segmenting the animation geometry data across the frames into objects followed by applying the principal component analysis (PCA) based compression on the geometry data of the segmented individual 3D objects. To compare the performance of the proposed method, a few MO-3D animations are created using the existing public domain SO-3D animations. The simulation results show that the proposed object-based PCA (OPCA) method gives better performance in terms of objective and subjective quality metrics compared to the other PCA based geometry compression methods applied globally on the MO-3D animations.

关键词： Three-dimensional displays Animation Geometry Principal component analysis compression algorithms Redundancy Face

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