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Modified Adaptive Pulse compression Algorithm for Targets With Range-Straddling

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第5期57卷 3057-3070页

作者： Ning, Chen Tian, Jing Li, Kun Wu, Siliang Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Adaptive pulse compression (APC) has been proposed based on the minimum mean square error (MMSE) criterion to effectively suppress range sidelobes of strong targets and retrieve all targets in multi-target scenarios. However, MMSE-based algorithms suffer from deteriorated performance in the presence of targets with range-straddling due to modelling mismatch. This degradation can be partially compensated by modified MMSE-based algorithms. To further suppress sidelobes when range straddling occurs, we propose a modified APC algorithm robust to targets with range-straddling, namely range-straddling-robust APC (RSR-APC). We first establish the signal model for targets with range-straddling and then derive the expressions of the MMSE filter based on matched filter outputs and the range-straddling offsets based on an improved Rife algorithm. Simulation results show that the proposed algorithm can suppress sidelobes of targets with range-straddling and improve estimation accuracy of target positions in different scenarios.

关键词： Estimation Adaptation models compression algorithms Simulation Signal to noise ratio Mean square error methods Computational modeling Adaptive pulse compression (APC) minimum mean square error range straddling sidelobe suppression

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IPFSz: An Efficient Data compression Scheme in InterPlanetary File System

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IEEE ACCESS 2022年 10卷 122601-122611页

作者： Song, Mansub Han, Jongbeen Eom, Hyeonsang Son, Yongseok Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Chung Ang Univ Dept Comp Sci & Engn Seoul South Korea

InterPlanetary File System (IPFS) is a peer-to-peer (P2P) distributed file system that allows data to be shared and exchanged between connected nodes. Recently, IPFS aims to replace existing protocols used in a centralized system (e.g., HTTP) by overcoming their weaknesses such as a single point of failure and arbitrary control. However, we observe that the performance of IPFS is worse as the data size increases since it demands large numbers of storage and network I/O operations. In this paper, we present an efficient data transmission scheme in IPFS called IPFSz to improve the I/O performance of IPFS. To do this, we extend IPFS by enabling real-time compression functionality while maintaining its existing operations in IPFS. In IPFSz, we compress/decompress the data during I/O operations by using a compression algorithm, manage the states of data (e.g., compressed or decompressed), and provide a compression/decompression interface to applications. Thus, IPFSz can reduce the number of storage and network I/O operations and storage space without sacrificing the integrity of the existing IPFS. We have implemented and evaluated IPFSz on five Amazon EC2 nodes. The experimental results show that IPFSz provides higher performance by up to $7.9\times $ and saves the storage space by up to $6.3\times $ compared with existing IPFS.

关键词： InterPlanetary File System Data compression File systems Servers Metadata compression algorithms Distributed databases InterPlanetary file system data compression decentralized system P2P network distributed file system

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Huffman Deep compression of Edge Node Data for Reducing IoT Network Traffic

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IEEE ACCESS 2024年 12卷 122988-122997页

作者： Said Nasif, Ammar Ali Othman, Zulaiha Samsiah Sani, Nor Kamrul Hasan, Mohammad Abudaqqa, Yousra Univ Kebangsaan Malaysia Fac Informat Sci & Technol Res Ctr Artificial Intelligent Technol Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Res Ctr Cyber Secur Bangi 43600 Selangor Malaysia

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations;HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper introduces a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia's air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value<0.0005), achieving a higher compression ratio (CR) by 1.4x while reducing data size by up to 59%. Furthermore, this achievement is attained while utilizing less than 80 KB of IoT memory and consuming at most 44 mAmps per slide compression. Moreover, the compression performance correlated linearly with the number of patterns in each sliding window. With such excellent performance, using HDC at IoT edge is a considerable solution to reduce the smart-cities network traffic.

关键词： Internet of Things Entropy Random access memory Symbols Deep learning compression algorithms Wireless sensor networks Smart cities Data compression Traffic control compression data traffic deep learning IoT IoT edge node IoT network pooling pruning sensor smart city WSN

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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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THEORETICAL AND EXPERIMENTAL RATE DISTORTION PERFORMANCE IN compression OF AMBULATORY ECGS

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1991年第3期38卷 260-266页

作者： HAMILTON, PS TOMPKINS, WJ UNIV WISCONSIN DEPT ELECT ENGN MADISON WI 53706 USA

We compare ECG data compression algorithms based on signal entropy for a given mean-square-error (MSE) compression distortion. By defining the distortion in terms of the MSE and assuming the ECG signal to be a Gaussian process we are able to estimate theoretical rate distortion bounds from average ECG power spectra. These rate distortion bounds give estimates of the minimum bits per second (bps) required for storage of ECG data with a given MSE regardless of compression method. From average power spectra of the MIT/BIH arrhythmia database we have estimated rate distortion bounds for ambulatory ECG data, both before and after average beat subtraction. These rate distortion estimates indicate that, regardless of distortion, average beat subtraction reduces the theoretical minimum data rate required for ECG storage by approximately 100 bits per second (bps). Our estimates also indicate that practical ambulatory recording requires a compression distortion on the order of 11-mu-V rms. We have compared the performance of common ECG compression algorithms on data from the MIT/BIH database. We sampled and quantized the data to give distortion levels of 2, 5, 8, 11, and 14-mu-V rms. These results indicate that, when sample rates and quantization levels are chosen for optimal rate distortion performance, minimum data rates can be achieved by average beat subtraction followed by first differencing of the residual signal. Achievable data rates approximate our theoretical estimates at low distortion levels and are within 60 bps at higher distortion levels.

关键词： Rate-distortion Electrocardiography Databases compression algorithms Data compression Entropy Estimation theory Monitoring Solid state circuits Linear predictive coding

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compression and approximate matching

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COMPUTER JOURNAL 1999年第1期42卷 1-10页

作者： Allison, L Powell, D Dix, TI Monash Univ Sch Comp Sci & Software Engn Clayton Vic 3168 Australia

A population of sequences is called non-random if there is a statistical model and an associated compression algorithm that allows members of the population to be compressed, on average. Any available statistical model of a population should be incorporated into algorithms for alignment of the sequences and doing so changes the rank order of possible alignments in general. The model should also be used in deciding if a resulting approximate match between two sequences is significant or not. It is shown how to do this for two plausible interpretations involving pairs of sequences that might or might not be related. Efficient alignment algorithms are described for quite general statistical models of sequences. The new alignment algorithms are more sensitive to what might be termed 'features' of the sequences. A natural significance test is shown to be rarely fooled by apparent similarities between two sequences that are merely typical of all or most members of the population, even unrelated members.

关键词： Image compression Pattern matching Character recognition algorithms Statistical methods Approximate matching Alignment algorithms Longest common subsequence Pattern matching Image compression Sequence Alignment compression approximate match Character recognition Statistical methods statistical models (nuclear) compression algorithms Sequences Alignment Data compression algorithms Rank order

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Lossy compression of Quality Values in Sequencing Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第5期18卷 1958-1969页

作者： Morales, Veronica Suaste Houghten, Sheridan CONABIO Mexico City 14010 DF Mexico Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.

关键词： Sequential analysis DNA Standards Genomics Bioinformatics Data models compression algorithms compression next-generation sequencing quality values SNP calling performance

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Microshift: An Efficient Image compression Algorithm for Hardware

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第11期29卷 3430-3443页

作者： Zhang, Bo Sander, Pedro V. Tsui, Chi-Ying Bermak, Amine Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hamad Bin Khalifa Univ Informat & Comp Technol Div Doha Qatar

In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks.

关键词： Image coding Uncertainty Hardware compression algorithms Quantization (signal) Complexity theory Image quality Microshift MRF model on-chip image compression image sensor FPGA implementation

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compression of low entropy strings with Lempel-Ziv algorithms

Compression of low entropy strings with Lempel-Ziv algorithm...

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compression and Complexity of Sequences Proceedings

作者： S.R. Kosaraju G. Manzini Department of Computer Science Johns Hopkins University Baltimore MD USA Istituto di Matematica Computazionale CNR Italy

We compare the compression ratio of the Lempel-Ziv algorithms with the empirical entropy of the input string, We show that although these algorithms are optimal according to the generally accepted definition, we can find families of low entropy strings which are not compressed optimally. More precisely, we show that the compression ratio achieved by LZ78 (resp. LZ77) can be much higher than the zeroth order entropy H/sub 0/ (resp. the first order entropy H/sub 1/). We present a compression algorithm which combines LZ78 with run length encoding, and we show that for any string s the new algorithm achieves a compression ratio bounded by 3H/sub 0/(s). Finally, we prove that for any string s the compression ratio achieved by LZ77 is bounded by 8H/sub 0/(s).

关键词： Entropy Performance analysis Algorithm design and analysis Probability distribution compression algorithms Encoding Data compression Decoding Random sequences Computer science

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A comparison of algorithms for ambulatory ECG compression with fixed average data rate

A comparison of algorithms for ambulatory ECG compression wi...

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Computers in Cardiology (CinC)

作者： P.S. Hamilton Lafayette College Easton PA USA

The author modified three electrocardiogram (ECG) data compression algorithms, average beat subtraction with residual differencing (ABSURD), SAPA compression, and TRIM compression, to produce specified average data rates. He tested the three algorithms on channel one of the MIT-BIH arrhythmia database sampled at 100 sample/s. With a target data rate of 193 b/s, average data rates for the SAPA and ABSURD algorithms varied from 99.7 to 100.1% of the target data rate. TRIM compression algorithms ranged from 87.3% to 101.7% of the target rate. With the average data rate controlled, ABSURD produced signal-to-compression noise ratios that ranged from 46 to 943. SAPA produced SNRs that ranged from 47 to 436, and TRIM produced SNRs that ranged from 54 to 806.< >

关键词： Electrocardiography Data compression compression algorithms Quantization Signal to noise ratio Solid state circuits Difference equations Educational institutions Testing Databases

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