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An In-Sight Into How compression Dictionary Architecture Can Affect the Overall Performance in FPGAs

IEEE ACCESS 2020年 8卷 183101-183116页

作者： Bartik, Matej Benes, Tomas Kubalik, Pavel Czech Tech Univ Fac Informat Technol Dept Digital Design Prague 16000 Czech Republic CESNET Dept Technol Network Applicat Prague 16000 Czech Republic

This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a x201C;Distributed Memoryx201D;technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99x0025;of modern FPGAs.

关键词： Dictionaries Field programmable gate arrays compression algorithms Computer architecture Random access memory Throughput IP networks compression algorithm compression dictionary FPGA hash table LZ4 LZ77 memory architecture performance comparison status register

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Large Database compression Based on Perceived Information

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1735-1739页

作者： Maugey, Thomas Toni, Laura Campus Univ Beaulieu Rennes Bretagne Atlantique F-35042 Rennes France UCL Elect & Elect Engn Dept London WC1E 7JE England

Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints. As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space. We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users' preferences while also maximizing the perceived information in the feature domain.

关键词： Covariance matrices compression algorithms Databases Measurement Signal processing algorithms Image coding Entropy Data compression large database repurposing sampling

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DNA Sequences compression by GP² R and Selective Encryption Using Modified RSA Technique

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IEEE ACCESS 2020年 8卷 76880-76895页

作者： Hossein, Syed Mahamud De, Debashis Das Mohapatra, Pradeep Kumar Mondal, Sankar Prasad Ahmadian, Ali Ghaemi, Ferial Senu, Norazak Vidyasagar Univ Dept Comp Sci Midnapore 721102 India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Nadia 741249 India Raiganj Univ Dept Microbiol Raiganj 733134 India Maulana Abul Kalam Azad Univ Technol Dept Appl Sci Nadia 741249 India Natl Univ Malaysia Inst Ind Revolut 4 0 Bangi 43600 Selangor Malaysia Univ Putra Malaysia UPM Inst Math Res INSPEM Serdang 43400 Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43600 Selangor Malaysia

Humans, by nature, have always been fascinated by the possibility of being able to acquire more information in minimum possible time and space. The effective lossless compression method, effective data structure, and DNA (Deoxyribonucleic Acid) data searching are quite essential as they provide a stimulus to easy accessibility and communication. The proposed algorithm is a new Lossless compression algorithm, which compresses data, based on two tiers. Firstly, it searches for the exact Genetic Palindrome(GP), Palindrome(P) and Reverse(R)[GP(2)R] and the substring is reported, which is replaced by the corresponding ASCII character creating a Library file. By using the ASCII code, the Library file acts as a signature as well as provides the security of data. Secondly, modified RSA technique is proposed for the selection encryption purpose. This selection encryption of the modified RSA technique is an approach to lessen computational resources for greatly sized DNA facts. The experimental work shows 44% to 45% original sequence is encrypted where above 95% of the original file is damaged by using this method. This technique can find out the 3.851273 bits per base of the compression rate. The O(n) is the complexity of this algorithm. The running time is a few seconds of this algorithm. This is a hybrid approach to the compression & encryption process. For reducing the compression rate, the first pass output is again compressed by the second pass but it is lossy, This experiment is performed on benchmark DNA order.

关键词： Encryption DNA compression algorithms Libraries Encoding Reverse genetic palindrome palindrome compression ratio rate encryption speed genetic palindrome palindrome and reverse (GP(2)R)

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Research on Efficient Processing Algorithm for Engineering Cost Data Using Cloud Computing Platform

Research on Efficient Processing Algorithm for Engineering C...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Dantong Li Dalian University of Finance and Economics Dalian China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This paper proposes a research scheme for efficient processing algorithm of engineering cost data based on cloud computing platform, aiming to improve data processing efficiency by utilizing the high-performance computing capability and distributed characteristics of cloud computing. The algorithm design takes distributed compression algorithm as the core, combines data partitioning, parallel computing and storage optimization technology to achieve efficient storage and fast access to large-scale cost data. In the system implementation, a parallel algorithm architecture that adapts to multi-node environment is designed. The algorithm performance is evaluated by building a simulation model. The experimental results show that the method proposed in this paper is superior to traditional algorithms in data compression rate, processing speed and resource utilization. When processing 1TB cost data, the compression rate reaches 85%, and the processing efficiency is improved by about 60%. Data analysis further verifies the stability and scalability of the algorithm, and provides theoretical basis and technical support for intelligent data processing in the field of engineering cost. This study opens up a new path for engineering cost data processing, and the proposed algorithm and platform architecture can be widely used in other complex data scenarios.

关键词： Cloud computing Costs Scalability Distributed databases Computer architecture Stability analysis Partitioning algorithms Resource management compression algorithms System implementation

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Real-time Monitoring System for Large-scale Data Processing in Cloud Computing Environment: A Research Study

Real-time Monitoring System for Large-scale Data Processing ...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Jin Zhang Party School of the CPC Hubei Provincial Committee Hubei Provincial Institute of Public Administration Wuhan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

To meet the real-time processing demands of large-scale e-commerce transaction data, a distributed data processing system was designed and implemented. The system utilizes an improved LZ4 compression algorithm and micro-batch processing mechanism to optimize data transmission efficiency and computing performance. A 25-node cluster environment was set up for performance testing, and the system achieved a single-node processing capacity of 56,700 transactions per second and an overall processing capacity of $\mathbf{8 9, 0 0 0}$ transactions per second. After six months of deployment on a certain e-commerce platform, the system processed an average of 500 million transactions per day, with an $\mathbf{8 5 \%}$ increase in accurate identification of abnormal orders and a system availability of $\mathbf{9 9. 9 9 \%}$, achieving high-efficiency and stable data processing.

关键词： System performance Ecosystems Data processing Real-time systems Electronic commerce Time factors Information technology compression algorithms Optimization Testing

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Two-Stage Debanding Method Based on cGAN Network

Two-Stage Debanding Method Based on cGAN Network

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Hui-Shen Chang Chang Hong Lin Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Banding artifacts, marked by the staircase-like contours in smooth areas, are mainly caused by quantization or compression algorithms and can severely degrade image quality and structure. Traditional debanding methods, such as the dithering quantization, are limited by fixed parameters and struggle with large regional artifacts. Recent techniques using conditional generative adversarial networks (cGANs) aim to address these issues across entire images. However, without proper guidance, cGANs can be disrupted by color distortions in non-flat regions, leading to unnatural colors in flat areas. To overcome this, we propose a two-stage network: first, a flat area segmentation network generates a mask for flat regions, which then guides the debanding network. This approach effectively removes banding in flat areas while avoiding unnatural colors and preserving details in non-flat regions. Our method demonstrates substantial improvements both visually and quantitatively over existing techniques.

关键词： Measurement Image quality Image segmentation Quantization (signal) Image color analysis Generative adversarial networks Distortion Consumer electronics compression algorithms

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Electrocardiogram compression using the nonlinear iterative partial least squares algorithm: a comparison between adaptive and non-adaptive approach

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2020年第4期33卷 367-385页

作者： Ricchetti, Pier Marco Nicolosi, Denys E. C. USP Univ Dante Pazzanese Inst Cardiol Sch Med Ave Dr Dante Pazzanese500 Vila Mariana BR-04012909 Sao Paulo SP Brazil Fundacao Educ Inaciana FEI Ave Humberto Castelo Branco3972 Assuncao BR-09850901 Sao Bernardo Do Campo SP Brazil Sao Judas Univ Rua Taquari546 Mooca BR-03166000 Sao Paulo SP Brazil

Data compression is applicable in reducing amount of data to be stored and it can be applied in several data collecting processes, being generated by lossy or lossless compression algorithms. Due to its large amount of data, the use of compression is desirable in ECG signals. In this work, we present the accepted nonlinear iterative partial least squares (NIPALS) method as an option to ECG compression method, as recommended by Nicolosi (1999). In addition, we compare the results based in an adaptive and non-adaptive version of this method, by using the MIT arrhythmia database. As a help to obtain a better comparison, we have developed an abnormality indicator related to possible abnormalities in the waveform and a decision method that helps to choose between adaptive or non-adaptive approach. Results showed that the adaptive approach is better than the non-adaptive approach, for the NIPALS compression algorithm.

关键词： component analysis adaptive comparison principal component analysis PCA NIPALS electrocardiogram ECG compression algorithms data compression

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Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling

Knowledge-Aided Semantic Communication Leveraging Probabilis...

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IEEE Conference on Vehicular Technology (VTC)

作者： Haowen Wan Qianqian Yang Jiancheng Tang Zhiguo Shi Polytechnic Institute Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Jinhua Institute of Zhejiang University Jinhua China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset, which is then shared as common knowledge between the transmitter and receiver. We evaluate the importance of various semantic features and present a PGM-based compression algorithm designed to eliminate predictable portions of semantic information. Furthermore, we introduce a technique to reconstruct the discarded semantic information at the receiver end, generating approximate results based on the PGM. Simulation results indicate a significant improvement in transmission efficiency over existing methods, while maintaining the quality of the transmitted images.

关键词： Training Vehicular and wireless technologies Graphical models Transmitters Simulation Redundancy Receivers Probabilistic logic Robustness compression algorithms

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MusicGen-Stem: Multi-stem music generation and edition through autoregressive modeling

MusicGen-Stem: Multi-stem music generation and edition throu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Simon Rouard Robin San Roman Yossi Adi Axel Roebel Meta & UMR STMS IRCAM-CNRS-Sorbonne Univ Meta FAIR Univ. de Lorraine CNRS Inria Loria Meta & Hebrew Univ. of Jerusalem UMR STMS IRCAM-CNRS Sorbonne Univ

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While most music generation models generate a mixture of stems (in mono or stereo), we propose to train a multi-stem generative model with 3 stems (bass, drums and other) that learn the musical dependencies between them. To do so, we train one specialized compression algorithm per stem to tokenize the music into parallel streams of tokens. Then, we leverage recent improvements in the task of music source separation to train a multi-stream text-to-music language model on a large dataset. Finally, thanks to a particular conditioning method, our model is able to edit bass, drums or other stems on existing or generated songs as well as doing iterative composition (e.g. generating bass on top of existing drums). This gives more flexibility in music generation algorithms and it is to the best of our knowledge the first open-source multi-stem autoregressive music generation model that can perform good quality generation and coherent source editing. Code and model weights will be released and samples are available on ***/musicgenstem.

关键词： Source separation Codes Instruments Signal processing algorithms Acoustics Multiple signal classification Iterative methods Speech processing Streams compression algorithms

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Whisper-GPT: A Hybrid Generative LLM For Speech And Music

Whisper-GPT: A Hybrid Generative LLM For Speech And Music

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Prateek Verma Stanford University Stanford CA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representations and discrete tokens simultaneously as part of a single architecture. There has been a huge surge in generative audio, speech, and music models that utilize discrete audio tokens derived from neural compression algorithms, e.g. ENCODEC. However, one of the major drawbacks of this approach is handling the context length. It blows up for high-fidelity generative architecture if one has to account for all the audio contents at various frequencies for the next token prediction. By combining continuous audio representation like the spectrogram and discrete acoustic tokens, we retain the best of both worlds: Have all the information needed from the audio at a specific time instance in a single token, yet allow LLM to predict the future token to allow for sampling and other benefits discrete space provides. We show how our architecture improves the perplexity and negative log-likelihood scores for the next token prediction compared to a token-based LLM for speech and music. 1

关键词： Large language models Hybrid power systems Acoustics Multiple signal classification Speech processing Surges Spectrogram compression algorithms

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