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Triple ID Flexible MAC for Can Security Improvement

IEEE ACCESS 2021年 9卷 126388-126399页

作者： Kim, Yeon-Jin Woo, Samuel Chung, Jin-Gyun Jeonbuk Natl Univ IT Convergence Res Ctr Div Elect Engn Jeonju 54896 South Korea Dankook Univ Dept Software Sci Yongin 16891 Gyeonggi Do South Korea

The development of automotive-information & communication technology (ICT) convergence has resulted in various vehicle-based electrical/electronic (E/E) systems. An automotive E/E system consists of one or more electronic control units (ECUs), sensors and actuators. With the commercialization of connected/autonomous cars, vehicle-based wireless communication systems have appeared, and are expected to grow in popularity. This increases the number of attack surfaces that can potentially threaten in-vehicle controller area network (CAN). To combat the vulnerabilities of the CAN protocol, the CAN data field should be encrypted and transmitted with authentication codes. Recently, a method of transmitting authentication codes by modifying the CAN protocol was proposed. However, changing the original CAN protocol can cause serious problems in CAN systems. In this paper, to enhance CAN security, a data compression algorithm is used to reduce the data frame length so that there is space for a message authentication code (MAC) to be contained inside the data field. The proposed algorithm guarantees that all CAN frames are authenticated by a MAC of at least four bytes without any change of the original CAN protocol. Simulations using CAN data from Kia Sorento, Kia Soul, and LS Mtron vehicles show that the proposed algorithm works successfully with only a slight increase in the peak load.

关键词： Protocols Authentication compression algorithms Data compression Codes Prediction algorithms Licenses CAN data reduction authentication code vehicle communication data rearrangement

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SHyLoC 2.0: A Versatile Hardware Solution for On-Board Data and Hyperspectral Image compression on Future Space Missions

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IEEE ACCESS 2020年 8卷 54269-54287页

作者： Barrios, Yubal Sanchez, Antonio J. Santos, Lucana Sarmiento, Roberto Univ Las Palmas Gran Canaria Inst Appl Microelect Las Palmas Gran Canaria 35001 Spain European Space Agcy European Space Res & Technol Ctr NL-2220 AG Noordwijk Netherlands

In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor. As entropy coder, the sample-adaptive entropy coder defined in the 123.0-B-1 standard or the low-complexity block-adaptive encoder defined by the CCSDS 121.0-B-2 lossless standard could be used. Both IPs, 123.0-B-1 and 121.0-B-2, are part of SHyLoC 2.0, and can be used together for compression of hyperspectral images, being also possible the compression of any kind of data using only the 121-IP. SHyLoC 2.0 improves and extends the capabilities of SHyLoC 1.0, currently available at the ESA IP Cores library, increasing its compression efficiency and throughput, without compromising the resources footprint. Moreover, it incorporates new features, such as the unit-delay predictor option defined by the CCSDS 121.0-B-2 standard, and burst capabilities in the external memory interface of the CCSDS 123-IP, among others. Dedicated architectures have been designed for all the possible input image sample arrangements, in order to maximise throughput and reduce the hardware resources utilization. The design is technology-agnostic, enabling the mapping of the VHDL code in different FPGAs or ASICs. Results are presented for a representative group of well-known space-qualified FPGAs, including the new NanoXplore BRAVE family. A maximum throughput of 150 MSamples/s is obtained for Xilinx Virtex XQR5VFX130 when the SHyLoC 2.0 CCSDS-123 IP is configured in Band-Interleaved by Pixel (BIP) order, using only the 4 & x0025;of LUTs and less than the 1 & x0025;of internal memory.

关键词： Hyperspectral imaging compression algorithms field programmable gate arrays hardware implementations space missions on-board data processing

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DeepCABAC: A Universal compression Algorithm for Deep Neural Networks

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2020年第4期14卷 700-714页

作者： Wiedemann, Simon Kirchhoffer, Heiner Matlage, Stefan Haase, Paul Marban, Arturo Marinc, Talmaj Neumann, David Nguyen, Tung Schwarz, Heiko Wiegand, Thomas Marpe, Detlev Samek, Wojciech Fraunhofer Heinrich Hertz Inst Machine Learning Grp D-10587 Berlin Germany Fraunhofer Heinrich Hertz Inst D-10587 Berlin Germany Fraunhofer Heinrich Hertz Inst Video Coding Grp D-10587 Berlin Germany

In the past decade deep neural networks (DNNs) have shown state-of-the-art performance on a wide range of complex machine learning tasks. Many of these results have been achieved while growing the size of DNNs, creating a demand for efficient compression and transmission of them. In this work we present DeepCABAC, a universal compression algorithm for DNNs that is based on applying Context-based Adaptive Binary Arithmetic Coder (CABAC) to the DNN parameters. CABAC was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for the lossless compression part of video compression. DeepCABAC applies a novel quantization scheme that minimizes a rate-distortion function while simultaneously taking the impact of quantization to the DNN performance into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for DNN compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 9 MB. The source code for encoding and decoding can be found at https://***/fraunhoferhhi/DeepCABAC.

关键词： Neural networks Source coding Decoding compression algorithms Quantization (signal) Machine learning Deep learning neural network compression efficient representation source coding rate-distortion quantization arithmetic coding

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Reference-Based Framework for Spatio-Temporal Trajectory compression and Query Processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第11期32卷 2227-2240页

作者： Zheng, Kai Zhao, Yan Lian, Defu Zheng, Bolong Liu, Guanfeng Zhou, Xiaofang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Zhejiang Lab Hangzhou 310000 Peoples R China Soochow Univ Sch Comp Sci & Technol Inst Artificial Intelligence Suzhou 215006 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230026 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Univ Queensland Brisbane Qld 4072 Australia

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

关键词： Trajectory Query processing Indexes Measurement Roads Computer science compression algorithms Reference trajectory spatio-temporal trajectory compression

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VLSI Implementation of Lossless ECG compression Algorithm for Low Power Devices

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3317-3321页

作者： Tsai, Tsung-Han Hussain, Muhammad Awais Natl Cent Univ Dept Elect Engn Taoyuan 320 Taiwan

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm(2) in 90 nm CMOS process.

关键词： Electrocardiography Very large scale integration Encoding Hardware compression algorithms Computer architecture Monitoring ECG compression Golomb rice coding VLSI low power design adaptive linear prediction

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2-D Bi-Level Block Coding for Color Image compression and Transmission With Bit-Error Awareness

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IEEE ACCESS 2020年 8卷 110093-110102页

作者： Peng, Xuan Jiang, Jean Tan, Lizhe Hou, Jintao Purdue Univ Northwest Dept Elect & Comp Engn Hammond IN 46323 USA Purdue Univ Northwest Dept Engn Technol Hammond IN 46323 USA

In this paper, we develop a new color image lossless compression algorithm with bit-error awareness based on a general bi-level block coding method. The proposed method contains three stages. First, a color image in the RGB color space is converted to the YCrCb color space by lossless reversible transformations. Next, predictors in the YCrCb color space are applied to generate residue sequences. In order to achieve a better image compression ratio, a particle swarm optimization (PSO) algorithm is adopted to search the best combination from the candidates of color transformations and predictors which generate the minimum residue entropy. Third, a new 2-D bi-level block coding algorithm is developed to further encode the residue sequences. Comparing with the existing residue coding methods, including 1-D bi-level block coding, interval Huffman coding, and standard Huffman coding, the 2-D bi-level block coding algorithm can improve image compression ratio as well as preserving the bit-error resilience. Finally, the key parameters such as color transformation information, predictor parameters and residue coding parameters are protected using (7, 4) Hamming codes in the bit stream before transmission. The performances are validated in terms of compression ratio (CR) and peak signal to noise ratio (PSNR). The compression algorithm with bi-level coding achieves the highest PSNR when the bit-error rate (BER) is larger than 0.001 and maintains an acceptable PSNR for BER less than 0.001. In particular, the compression algorithm using the new 2-D bi-level block coding scheme achieves the highest CR.

关键词： Image coding Block codes Image color analysis compression algorithms Prediction algorithms Color Resilience Lossless compression bit-error awareness general bi-level block coding particle swarm optimization (7 4) Hamming code

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An Index-Based Provenance compression Scheme for Identifying Malicious Nodes in Multihop IoT Network

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IEEE INTERNET OF THINGS JOURNAL 2020年第5期7卷 4061-4071页

作者： Liu, Zhe Wu, Yuting Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China State Key Lab Cryptol Beijing 100878 Peoples R China

Data provenance is an effective method to evaluate data reliability and has become a research hotspot in recent years. However, the size of the data provenance will increase rapidly as the number of hops experienced increases, which conflicts with the limitations of energy, storage, and resources of IoT devices. The existing DP provenance compression algorithm has a high error rate when decompressing. To solve this problem, we propose an index-based provenance compression algorithm, which adopts the idea of common substring matching, combined with path identifier and path index to represent the path information in the data provenance, thereby achieving the purpose of reducing the size of data provenance. In addition, we extend the data provenance scheme to attack detection and propose a malicious node identification method based on data provenance. The simulation results show that the proposed scheme has a high compression ratio and higher decoding accuracy and has high accuracy in malicious node identification.

关键词： Internet of Things compression algorithms Security Distributed databases Encoding Reliability Error analysis Attack detection compression data provenance security

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Local Decode and Update for Big Data compression

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第9期66卷 5790-5805页

作者： Vatedka, Shashank Tchamkerten, Aslan Indian Inst Technol Dept Elect Engn Hyderabad 502285 India Inst Polytech Paris Dept Communicat & Elect Telecom Paris F-91120 Palaiseau France

This paper investigates data compression that simultaneously allows local decoding and local update. The main result is a universal compression scheme for memoryless sources with the following features. The rate can be made arbitrarily close to the entropy of the underlying source, contiguous fragments of the source can be recovered or updated by probing or modifying a number of codeword bits that is on average linear in the size of the fragment, and the overall encoding and decoding complexity is quasilinear in the blocklength of the source. In particular, the local decoding or update of a single message symbol can be performed by probing or modifying on average a constant number of codeword bits. This latter part improves over previous best known results for which local decodability or update efficiency grows logarithmically with blocklength.

关键词： Decoding Encoding Entropy Complexity theory Computational modeling Cloud computing DNA Data compression source coding compression algorithms big data applications

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Vessel AIS Trajectory Online compression Based on Scan-Pick-Move Algorithm Added Sliding Window

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IEEE ACCESS 2020年 8卷 109350-109359页

作者： Sun, Shuang Chen, Yan Piao, Zaiji Zhang, Jinsong Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

关键词： Trajectory Artificial intelligence compression algorithms Marine vehicles Data compression Microsoft Windows Windows AIS data compression ratio scan-pick-move sliding window vessel trajectory compression

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A Rolling Hash Algorithm and the Implementation to LZ4 Data compression

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IEEE ACCESS 2020年 8卷 35529-35534页

作者： Jiang, Hao Lin, Sian-Jheng Univ Sci & Technol China Sch Informat Sci & Technol CAS Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

LZ77 is a dictionary compression algorithm by replacing the repeating sequence with the addresses of the previous referenced data in the stream. To find out these repetition, the LZ77 encoder maintains a hashing table, which have to frequently calculate hash values during the encoding process. In this paper, we present a class of rolling hash functions, that can calculate multiple hash values via a carry-less multiplication instruction. Then the proposed hash function is implemented in LZ4, which is a derivative of LZ77. The simulation shows that the encoding throughput of LZ4 has 15.7% improvement in average, and the compression ratio is +/- 1% in most cases.

关键词： Encoding Data compression compression algorithms Dictionaries Indexes Image coding Image restoration Carry-less multiplication LZ4 LZ77 rolling hash SIMD

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