Huffman Coding Research Articles

47 Gbps 100 m ultra-high-speed free-space visible light tricolor laser communication system utilizing time domain hybrid Huffman coding

The escalating demand for greater transmission capacities in the forthcoming 6 G communication landscapes necessitates the investigation of upper segments of the electromagnetic spectrum by both academic institutions and the industrial sector. This effort aims to circumvent the impending spectrum resource limitations. Against this backdrop, laser diodes (LDs) emerge as a critical technology for high-speed visible light communication (VLC), owing to their significant modulation bandwidth potential. This paper details what we believe to be a novel visible light laser communication (VLLC) system powered by red/green/blue (RGB) tricolor laser diodes. It highlights a successful 100-meter free-space transmission achieved through a time domain hybrid huffman coding (TDHHC) technique. The system's performance review unveiled impressive data transmission rates for the red, green, and blue laser diodes channels at 16.852 Gbps, 14.442 Gbps, and 15.755 Gbps, respectively, culminating in a cumulative transmission rate of 47.049 Gbps while maintaining a bit error rate (BER) beneath the stringent threshold of 3.8E-3, mandated by 7% hard decision forward error correction (HD-FEC) standards. As far as we known, this marks the highest data rate documented in a long-distance tricolor VLLC system.

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

Adaptive region algorithm based on a ternary optical computer

The adaptive region algorithm is an improved compression algorithm based on Huffman coding. Because of the large number of rules for dividing regions in the algorithm, there are problems such as high computing costs, slow speed, and low compression efficiency. To address these problems, this paper investigates the adaptive region algorithm based on a ternary optical computer (TOC) combined with the characteristics of a ternary optical computer, such as many data bits, high parallelism, and three-valued coding. According to the characteristics of TOC three-valued coding, this paper designs a three-valued character coding scheme that can effectively shorten the coding length of characters by changing the original coding rules and further improve the compression efficiency of the adaptive region algorithm. Furthermore, in conjunction with the advantages of TOC enabling parallel computation, this paper presents an efficient computational scheme capable of effectively improving computational efficiency during the process of region partitioning. Through case studies, the compression efficiency and computational efficiency of the adaptive region algorithm implemented on TOC and an electronic computer were analyzed, respectively. It was found that the compression efficiency of the TOC-based algorithm is 50.4%, while that of the electronic-computer-based algorithm is only 36%. In the comparison of computational efficiency, the computational time complexity of TOC is O(n), whereas that of the electronic computer (EC) is O(n2). Finally, it is concluded through experimental validation that the TOC-based adaptive region compression algorithm performs well in terms of computational performance and compression efficiency, giving full play to the three-valued coding characteristics of TOC-based as well as the advantages of being able to realize parallel computation.

Numerical simulation of 3D vorticity dynamics with the Diffused Vortex Hydrodynamics method

In this paper the three-dimensional extension of the Diffused Vortex Hydrodynamics (DVH) is discussed along with free vorticity dynamics simulations. DVH is a vortex particle method developed in-house and widely validated in a 2D framework. The DVH approach has been embedded in a new frontend to the open-source code PEPC, the Pretty Efficient Parallel Coulomb solver. Within this parallel Barnes–Hut tree code, a superposition of elementary heat equation solutions in a cubic support is performed during the diffusion step. This redistribution avoids excessive clustering or rarefaction of the vortex particles, providing robustness and high accuracy of the method. An ascending vortex dipole at various resolutions is selected as a test-case and heuristic convergence measurements are performed, taking into account the conservation of prime integrals and the energy–enstrophy balance.

Selected Aspects of Research on Railway Transport Safety

This article showcases the findings of research conducted at the Faculty of Transport, Electrotechnics, and Informatics of the Technological-Humanistic University in Radom. Initially, it delves into a statistical analysis of measurements procured during automatic ultrasonic testing. By integrating these measurements with distinct features of the diagnostic method and fusing differential coding with traditional lossless compression techniques (like Huffman and dictionary coding), a potent measurement compression algorithm was developed. This algorithm subsequently found application in railway track diagnostics. The primary objective of this article is to illustrate this algorithm's efficiency and benchmark its advantages against other compression techniques. Subsequently, the paper delineates the design of a system tailored for collating operational data and evaluating the reliability and safety of railway automation systems. This concept was actualized under the auspices of a project sponsored by the National Center for Research and Development, PBS3/A6/29/2015. A spotlight will be thrown on the software that acts as an integrative tool, assimilating data from various systems. In its final segments, the article discusses the initiative to harness open systems, premised on the PN-EN 50159:2011 standard, to bolster the safety standards of railway transportation. The first one resulted from the maximum train speed, the second one is the variety of rolling stock used on this line (compartmental and non-compartmental wagons). The part of research concerning collection of measurement material was performed within the framework of completed research work PBS3/A6/29/2015. As a result of conducted in-depth literature analysis as well as performed measurements and calculations, it allowed to develop a model and software for simulating system operation in real conditions. A prototype of this system, accompanied by select research outcomes from railway routes, will be laid out. Ultimately, the article seeks to emphasize the significance of diverse research endeavors that hold the potential to directly enhance the safety and operational efficiency of railway transport.

Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis.

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency.

Comparative Analysis of Channel Coding in Communication Technology

The performance analysis of Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) employing error-correcting code is highlighted in this work. Several error-correcting code types were employed via an Additive White Gaussian Noise (AWGN) channel to determine the bit error rate. Two techniques were utilized as encoders and decoders: hamming code and cyclic code. Source coding used Huffman coding. Essentially, signal energy to noise power density ratio (Eb/No) and the bit rate error (BER) were used to measure performance. Comparisons were also made between BPSK and QPSK in terms of symbol error capability. MATLAB R2022b was the program used for all the simulations. The conclusion is that different coding methods have a certain error correction range, and the signal-to-noise ratio (SNR) can only reduce the BER in the error correction range, and if it is not within the error correction range, it will increase the bit error rate.

Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm.

Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications.

Reversible Data hiding based on Three-Level Hierarchical Embedding with Histogram Mapping in Encrypted Images

This paper proposes a reversible data-hiding (RDH) approach based on a three-level hierarchical embedding with histogram mapping in encrypted images for a cloud environment. The proposed approach uses two-level encryption followed by the three-level hierarchical embedding process. The two-level encryption uses an intra-inter block permutation with an exclusive OR operation. In the three-level hierarchical embedding two predictors namely strong predictor and weak predictor are estimated. The strong predictor is used in level 1 embedding, while the weak predictor is used in level 2 embedding. A histogram mapping is then performed on the location map obtained during the level 1 and level 2 embedding process and is then compressed using the Huffman coding. The compressed location map information is then further embedded as the level 3 embedding process. In the extraction phase, the compressed location map information is first extracted, then the level 2 data followed by the level 1 data. The evaluation of the proposed data embedding approach is done using the Bossbase and Bows-2 datasets that result in an embedding rate of 3.806 bpp and 3.513 bpp respectively.

Study of Air Ordering Procedure for Information Broadcasting

Wireless data broadcasting is a new way to share public information with many mobile users. To measure how well these systems work, we look at two main things: how long it takes to access the information (access latency) and how long the device has to stay active (tuning time). Indexing technology helps reduce tuning time by letting devices find information quickly and then switch to a low-power mode while waiting. Different indexing methods work better or worse, so comparing them can be hard. In this paper, I look at and compare some popular indexing methods for wireless data broadcasting, such as distributed index, exponential index, hash table, and Huffman tree index. I use probability theory to analyze how each method performs. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6393

Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

An image steganography algorithm via a compression and chaotic maps

Steganography is widely recognized as an effective method for protecting information via digital media. This paper presents an innovative image steganography algorithm incorporating image compression, chaotic maps, and the least significant bit. The process begins with the compression of a confidential medical image using Huffman encoding. The compressed image then undergoes shuffling, facilitated by the chaotic logistic map. The bits from the shuffled image are discreetly embedded into randomly selected pixels of the cover image, guided by the chaotic piecewise smooth map. The resulting stego image is generated. Statistical analyses are applied to both the cover and stego images for evaluation. The proposed algorithm is compared against state-of-the-art algorithms, and the results demonstrate its superiority over existing methods.

High Speed Coding Unit Depth Identification Based on Texture Image Information Using SVM

High-Efficiency Video Coding (HEVC/H.265) is a new video coding standard with half the bit rate of its predecessor, Advanced Video Coding (AVC/H.264). AVC/H.264 uses macroblocks, processing units between 4×4 and 16×16 pixels in size. H.265 uses Coding Tree Units (CTUs), a more complicated block structure that lets images be as large as 64×64 pixels. However, changing from macroblocks to coding tree units is essential for H.265 to become more efficient. Using the quadtree structure to divide the Coding Unit (CU) makes it harder for HEVC to find the optimal rate distortion. This paper presents a Support Vector Machine (SVM)-based method for finding the fastest coding unit division in intra-prediction HEVC without compromising compression efficiency. All partitions of CTU are assessed using five characteristics: Standard Deviation (SD), Root Mean Square Error (RMSE), Sub CU Complexity Difference (SCCD), Directional Complexity (DC), and Quantization Parameter (QP) to optimize the intra-prediction of HEVC in all intra-configurations. Simulations have been carried out to estimate the performance of the proposed machine learning-based algorithm using test sequences with different resolutions. Simulation results have shown that combining directional complexity and standard deviation gives a more accurate classification. SVM has been used to separate split-unsplit samples, and the standard rate-distortion optimization technique has been used to separate samples that are hard to separate. The results have shown a reduction of 67.44% in encoding time with a slight increase in bit rate.

Adaptive quadtree splitting parallelization (AQSP) algorithm for the VVC standard

The Versatile Video Coding (VVC) standard, also known as H.266, was released in 2020 as the natural successor to the High Efficiency Video Coding (HEVC) standard. Among its innovative coding tools, VVC extended the concept of quadtree (QT) splitting to the multi-type tree (MTT) structure, introducing binary and ternary partitions to enhance HEVC’s coding efficiency. While this brought significant compression improvements, it also resulted in a substantial increase in encoding time, primarily due to VVC’s larger Coding Tree Unit (CTU) size of 128×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}128 pixels. To mitigate this, this work introduces a flexible parallel approach for the QT traversal and splitting scheme of the VVC encoder, called adaptive quadtree splitting parallelization (AQSP) algorithm. This approach is based on the distribution of coding units (CUs) among different threads using the current depth level of the QT as a basis to minimize the number of broken dependencies. In this way, the algorithm achieves a good trade-off between time savings and coding efficiency. Experimental results show that, when compared with the original VVC encoder, AQSP achieves an acceleration factor of 2.04×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} with 4 threads at the expense of a low impact in terms of BD rate. These outcomes position AQSP competitively in comparison with other state-of-the-art approaches.

Arithmetic N-gram: an efficient data compression technique

Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Optimizing Medical IoT Disaster Management with Data Compression

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.

Efficient VVC Encoding Using Hierarchical Parallelization: A Comprehensive Analysis

This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to × 86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.

Barriers and facilitators for implementation of automated home medication dispensers in home care from Dutch professionals' perspective: a qualitative study.

Present-day home care needs to be more efficiently organized in view of the ageing of the population and the current nursing shortages. Ensuring safe medication use is part of the challenge. The number of required visits could be reduced if automated home medication dispensers (AHMD) are adequately implemented. However, the barriers and facilitators for implementation are unknown. We aimed to explore determinants (barriers, facilitators or both) for implementing AHMD in home care, from home care nurses' perspective. Semistructured interviews were conducted with home care nurses. Determinants were identified through thematic content analysis. The first four transcripts were coded inductively. Then, a code tree was developed based on the Tailored Implementation for Chronic Diseases checklist, consisting of seven domains. Each code/determinant was then labelled as a barrier, facilitator or both. Fifteen home care nurses were interviewed. The most relevant domains were innovation factors, individual health professional factors and patient factors. The most mentioned barrier was the required unplanned visits when patients do not withdraw medication within the scheduled time limit (alarm). According to our respondents, carefully assessing patients' eligibility (e.g. learnability) and properly instructing and guiding them will help prevent these alarms from happening. Next to these determinants, motivating patients to start using an AHMD and professionals having sufficient knowledge and confidence were the most mentioned facilitators. An overview of 78 determinants from nurses' perspective for implementation of AHMD in home care was established. This should form the basis for developing strategies for implementing AHMD in home care. Further research is recommended to investigate the perceived determinants from the patients', relatives' and informal caregivers' perspectives, and to prioritize the determinants from all perspectives.

An efficient versatile video coding motion estimation hardware

Versatile Video Coding (VVC) is the latest video coding standard. It provides higher compression efficiency than the previous video coding standards at the cost of significant increase in computational complexity. Motion estimation (ME) is the most time consuming and memory intensive module in VVC encoder. Therefore, in this paper, we propose an efficient VVC ME hardware. It is the first VVC ME hardware in the literature. It has real time performance with small hardware area. This efficiency is achieved by using a 64 × 64 systolic processing element array to support maximum coding tree unit (CTU) size of 128 × 128 and by using a novel memory-based sum of absolute differences (SAD) adder tree to calculate SADs of 128 × 128 CTUs. The proposed VVC ME hardware reduces memory accesses significantly by using an efficient data reuse method. It can process up to 30 4 K (3840 × 2160) video frames per second.