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Dimensionality Reduction (DR) is the process of reducing the numerous features or random variables under consideration to a limited number of features by obtaining a set of principal variables. These techniques cater great values in machine learning, which come in handy to simplify a classification or a regression dataset, thereby yielding a better-performing predictive model. Techniques used for DR include Feature Selection methods, Matrix Factorization, AutoEncoder methods, and Manifold Learning. Merits of DR include data compression, reduced space of storage, and removal of redundant features. This paper attempts to review various techniques used to carry out dimensionality reduction while providing an exhaustive comparative study over the merits and demerits of each of the techniques used under the empirical experiments performed by the authors whose work is being reviewed. [ABSTRACT FROM AUTHOR]

Small area femtocell increases the energy efficiency of the cellular network as the distance between base station and user is very less. Mobile femtocell causes the spectrum reuse to be used at very large extent making spectral efficiency suffer. This paper discusses recently researched techniques that have been used to increase the spectral efficiency in mobile femtocell network. But these techniques also come with their own limitations. Considering this, data aggregation and compression technique can be consider as one of the best contender to increase the spectral efficiency in cellular network which is also been explained in this paper. [ABSTRACT FROM AUTHOR]

Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener-Granger's idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples., (© 2022. The Author(s).)

Addressing the shortcomings of existing ship trajectory compression methods that rely on the empirical setting of a fixed threshold and face challenges in controlling the spatial similarity before and after compression, this paper proposes a self-adaptive compression method for ship trajectories that does not require threshold setting. Initially, a hierarchical and sequential tree structure based on the trajectory characteristics is constructed to determine the importance of a ship's trajectory points. Subsequently, the dynamic time warping (DTW) operator is introduced to assess the spatial similarity of the trajectory before and after compression, exploring the laws governing similarity variations in the step changes during the compression process from lower to higher levels of the hierarchical and sequential sequence. Finally, the trajectory point that causes the largest step change in similarity within the hierarchical and sequential sequence is identified, and points at lower levels than this point are discarded, thus achieving the objective of self-adaptive adjustment of the level of compression. Our case study results demonstrate that, compared with existing ship trajectory compression methods based on empirically set thresholds, the proposed method has the following advantages: (1) it does not require presetting a fixed threshold and adaptively determines the degree of compression by identifying the trajectory point that leads to the largest step change in similarity, and (2) under the condition of a similar data compression rate, the DTW calculation value is reduced by approximately 25%, significantly enhancing the similarity of the trajectory before and after compression. [ABSTRACT FROM AUTHOR]

Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in 2018. Two key technologies are proposed in this paper: one is the calculation method of signal arrival time using very-low-frequency lightning electromagnetic pulse waveform, and the other is the compression transmission technology of lightning electromagnetic pulse waveform based on a signal principal component analysis. The results of a comparison and evaluation of the improved APLLN with the ADTD system show that the APLLN has a relative location efficiency of 69.1% and an average location error within the network of 4.5 km. [ABSTRACT FROM AUTHOR]

3D modeling and reconstruction are critical to creating immersive XR experiences, providing realistic virtual environments, objects, and interactions that increase user engagement and enable new forms of content manipulation. Today, 3D data can be easily captured using off-the-shelf, specialized headsets; very often, these tools provide real-time, albeit low-resolution, integration of continuously captured depth maps. This approach is generally suitable for basic AR and MR applications, where users can easily direct their attention to points of interest and benefit from a fully user-centric perspective. However, it proves to be less effective in more complex scenarios such as multi-user telepresence or telerobotics, where real-time transmission of local surroundings to remote users is essential. Two primary questions emerge: (i) what strategies are available for achieving real-time 3D reconstruction in such systems? and (ii) how can the effectiveness of real-time 3D reconstruction methods be assessed? This paper explores various approaches to the challenge of live 3D reconstruction from typical point cloud data. It first introduces some common data flow patterns that characterize virtual reality applications and shows that achieving high-speed data transmission and efficient data compression is critical to maintaining visual continuity and ensuring a satisfactory user experience. The paper thus introduces the concept of saliency-driven compression/reconstruction and compares it with alternative state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Metagenomics is a rapidly expanding field that uses next-generation sequencing technology to analyze the genetic makeup of environmental samples. However, accurately identifying the organisms in a metagenomic sample can be complex, and traditional reference-based methods may need to be more effective in some instances. In this study, we present a novel approach for metagenomic identification, using data compressors as a feature for taxonomic classification. By evaluating a comprehensive set of compressors, including both general-purpose and genomic-specific, we demonstrate the effectiveness of this method in accurately identifying organisms in metagenomic samples. The results indicate that using features from multiple compressors can help identify taxonomy. An overall accuracy of 95% was achieved using this method using an imbalanced dataset with classes with limited samples. The study also showed that the correlation between compression and classification is insignificant, highlighting the need for a multi-faceted approach to metagenomic identification. This approach offers a significant advancement in the field of metagenomics, providing a reference-less method for taxonomic identification that is both effective and efficient while revealing insights into the statistical and algorithmic nature of genomic data. The code to validate this study is publicly available at https://github.com/ieeta-pt/xgTaxonomy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Network traffic prediction is an important network monitoring method, which is widely used in network resource optimization and anomaly detection. However, with the increasing scale of networks and the rapid development of 5-th generation mobile networks (5G), traditional traffic forecasting methods are no longer applicable. To solve this problem, this paper applies Long Short-Term Memory (LSTM) network, data augmentation, clustering algorithm, model compression, and other technologies, and proposes a Cluster-based Lightweight PREdiction Model (CLPREM), a method for real-time traffic prediction of 5G mobile networks. We have designed unique data processing and classification methods to make CLPREM more robust than traditional neural network models. To demonstrate the effectiveness of the method, we designed and conducted experiments in a variety of settings. Experimental results confirm that CLPREM can obtain higher accuracy than traditional prediction schemes with less time cost. To address the occasional anomaly prediction issue in CLPREM, we propose a preprocessing method that minimally impacts time overhead. This approach not only enhances the accuracy of CLPREM but also effectively resolves the real-time traffic prediction challenge in 5G mobile networks. [ABSTRACT FROM AUTHOR]

The network structure of digital watermarking algorithm based on deep learning is usually encoder‐noise layer‐decoder. Most of the existing encoders suffer from the problem of insufficient feature extraction, and the introduction of simulated differentiable joint photographic experts group (JPEG) compression in the noise layer cannot ensure the robustness under real JPEG. In this paper, a watermarking algorithm based on multi‐scale auto‐encoder is proposed, which can effectively extract the image feature information by combining with the channel attention mechanism. At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy. This paper also proposes a robust training scheme against JPEG compression, which can guide the model to store the watermark in the low‐frequency region needed for decoding. Experimental results show that the peak signal‐to‐noise ratio (PSNR) of the proposed algorithm is above 48 and the decoding rate is above 99% under JPEG compression with quality factor Q = 50. Moreover, this scheme can effectively promote the combination of noise layer in training. In addition, the proposed algorithm is also robust to other common network noises. [ABSTRACT FROM AUTHOR]

Amid the conflict between the current huge data volumes and the requirement for large-compression-ratio compression in space-borne multichannel SAR, this paper proposes a data compression method that uses the adaptive bit allocation BAQ method in the Doppler domain of space-borne multichannel SAR. Specifically, 3MBAQ, which denotes multichannel, multipulse, multiweight block-adaptive quantization, performs signal reconstruction through a Krieger filter to obtain the Doppler spectrum under azimuthal multichannel and multipulse conditions and then achieves nonuniform bit allocation by using the multiple weight contributions between subband spectra. Furthermore, large-compression-ratio compression can be realized in homogeneous scenarios by using the adaptive bit allocation BAQ method together with the improved bit error control (BEC) algorithm. In this paper, the effectiveness of the 3MBAQ method is verified on GF3 measured data. Moreover, compared to the SQNR and NMSE of the BAQ, A-BAQ, A-BAQ-BEC, MCBAQ-ASQ, and MCBAQ-BEC methods under the same conditions, it is shown that there is an optimal 1.3434 dB improvement in SQNR and a 0.0241 improvement in NMSE using the 3MBAQ algorithm, and the complexity of the algorithm for 3MBAQ is O (N 2 l o g (N)) , which is within acceptable limits, clearly proving the superiority of the 3MBAQ method. [ABSTRACT FROM AUTHOR]

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG., (© 2024. The Author(s).)

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder., (© 2024. The Author(s).)

Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compression method for measurements in electric vehicles. The method is grounded in polynomial regression and enables the use of the adaptive method without the need for parameters or training of the model which, representing an improvement over existing techniques. At a compression rate of 99.75% in an ambient temperature of 25 °C on average across all drive cycles compared, the root mean square error (RMSE) was 5.62 mV. Impressively, at a compression rate of 99%, the RMSE decreased to 3.12 mV. Furthermore, an implementation on a low-power STM32 microcontroller can compress 600 data points in just 35 milliseconds, demonstrating its suitability for real-time applications. These results highlight the potential of our approach to significantly improve the efficiency and accuracy of voltage measurement compression in electric vehicles, paving the way for advancements in electric vehicle technology. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a variety of applications, including entertainment, surveillance, and delivery. However, the real-time Motion Estimation (ME) of UAVs is challenging due to the high speed and unpredictable movements of these vehicles. This paper presents a novel algorithm for optimizing ME for Remotely Controlled Vehicles (RCVs), with a particular focus on UAVs. The proposed algorithm, called Motion Dynamics Input Search (MDIS), incorporates information from vehicle motion dynamics estimation to enhance the accuracy and efficiency of ME. The MDIS algorithm addresses the challenges associated with real-time ME in RCVs by leveraging user input to guide the search for the most similar blocks in the previous video frame. Through extensive experimentation and evaluation, this study demonstrates the effectiveness of the proposed algorithm in improving ME performance for RCVs. The findings highlight the potential impact of user interaction and motion dynamics estimation in shaping the future of ME algorithms for RCVs and similar applications. [ABSTRACT FROM AUTHOR]

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods. [ABSTRACT FROM AUTHOR]

Geometry processing is a software technology that facilitates the appropriate and efficient conversion and processing of digital geometric data, including sampled points, curves, surfaces, images, and volumetric data, based on the intended application. It is a versatile technology and is increasingly adopted in diverse fields that rely on digital technologies and for the popularization of digital transition. Geometry processing is based on a robust and extensive mathematical framework that has been extensively investigated. As the scope of potential applications expands, the practical applications of these theories have increased accordingly. Geometry processing is currently employed for shape acquisition, analysis, design, and inspection in various fields, such as manufacturing, social infrastructure, medicine, natural science, and art. As the range of applications expands, several issues have emerged that require further investigation, such as increasing the accuracy and managing large-scale data. The objective of this special issue is to provide new insights into geometric-processing capabilities. This issue comprises five papers that present novel aspects of this technology and two comprehensive reviews pertaining to aesthetic curves and related topics, as well as the applications of X-ray computed tomography (CT) in manufacturing. The papers included in this issue are as follows: - Aesthetic curves and related geometries - Object recognition from laser-scanned point cloud - CAD data compression via wavelet transform - Application of structure-from-motion - Image analysis using Delaunay graphs - Surface extraction from X-ray CT scan data - Automation of manufacturing using X-ray CT scanning The data types and methods used for geometry processing are diverse and depend on the scenarios. In this issue, we present a diverse range of studies pertaining to geometry processing that encompasses both fundamental concepts and applied studies. We are grateful to all the contributors, reviewers, and editorial staff for their dedication and support in realizing this issue. [ABSTRACT FROM AUTHOR]

The main issue when dealing with the non-adaptive scalar quantizers is their sensitivity to variance-mismatch, the effect that occurs when the data variance differs from the one used for the quantizer design. In this paper, we consider the influence of that effect in low-rate (2-bit) uniform scalar quantization (USQ) of Laplacian source and also we propose adequate measure to suppress it. Particularly, the approach we propose represents the upgraded version of the previous approaches used to improve performance of the single quantizer. It is based on dual-mode quantization that combines two 2-bit USQs (with adequately chosen parameters) to process input data, selected by applying the special rule. Analysis conducted in theoretical domain has shown that the proposed approach is less sensitive to variance-mismatch, making the dual-mode USQ more efficient in terms of robustness than the single USQ. Also, a gain is achieved compared to other 2-bit quantizer solutions. Experimental results are also provided for quantization of weights of the multi-layer perceptron (MLP) neural network, where good matching with the theoretical results is observed. Due to these achievements, we believe that the solution we propose can be a good choice for compression of non-stationary data modeled by Laplacian distribution, such as neural network parameters. [ABSTRACT FROM AUTHOR]

The Argo buoy detects marine environmental data by making profile movements in the ocean and transmits the profile detection data to the shore base through the communication terminal. However, due to the large volume of data collected from profile detections and the continuous operation of the terminal, the remote communication of buoys is characterized by lengthy communication times and significant power consumption. A low-power Beidou transparent transmission terminal is designed to solve these problems in this paper. The terminal performs low-power operation management and power management for terminal remote communication. After the end of a communication process, the microcontroller turns the Beidou module power off and enters STOP mode. Before the next communication process begins, the serial port wakes up the microcontroller, which powers up the Beidou module. Before the remote communication, the microcontroller compacts the profile detection data collected by the buoy to reduce the quantity of remote communication data. In this paper, a variety of data compression methods are used to compare the compression rate, and the best compression method is selected according to the format characteristics of the data. The results show that the Beidou transparent transmission terminal of the Argo buoy can realize low power consumption for remote communication in ocean exploration. The terminal reduces power consumption by 77.282% per communication, and the average number of remote communications for each profile detection data are reduced by 55 times. The low-power Beidou transparent transmission terminal improves the battery life and is conducive to the long-term operation of the buoy. [ABSTRACT FROM AUTHOR]

Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper's novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods. [ABSTRACT FROM AUTHOR]

Simplifying 3D building models, effectively reducing model complexity and improving mapping efficiency, is an important part of 3D GIS. In order to address the problem that the simplification basis considered by most 3D building model data compression algorithms is mainly based on geometric features and fails to retain the visual features of the model, this paper proposed a half-folding simplification algorithm for 3D building models that took the visual features into account. The algorithm incorporated regional boundary constraints in the pre-processing stage to focus on preserving the boundary features of the model, and incorporated visual constraints, such as the vertex importance, edge length, and texture, in the calculation of the edge folding cost to construct a new error metric model. The simplification order was changed by redefining the edge folding cost, and a trade-off between compression rate and retention rate was made by using half-edge folding to maximise the retention of the visual features of the building. The experimental results showed that the algorithm could effectively reduce the number of elements drawn by the system and achieve a high level of detail information retention in the visual feature region of the model with a good visual representation. [ABSTRACT FROM AUTHOR]

This paper proposes a reinforced concrete (RC) boundary beam-wall system that requires less construction material and a smaller floor height compared to the conventional RC transfer girder system. The structural performance of this system subjected to axial compression was evaluated by performing a structural test on four specimens of 1/2 scale. In addition, three-dimensional nonlinear finite element analysis was also performed to verify the effectiveness of the boundary beam-wall system. Three test parameters such as the lower wall length-to-upper wall length ratio, lower wall thickness, and stirrup details of the lower wall were considered. The load-displacement curve was plotted for each specimen and its failure mode was identified. The test results showed that decrease in the lower wall length-to-upper wall length ratio significantly reduced the peak strength of the boundary beam-wall system and difference in upper and lower wall thicknesses resulted in lateral bending caused by eccentricity in the out-of-plane direction. Additionally, incorporating cross-ties and reducing stirrup spacing in the lower wall significantly improved initial stiffness and peak strength, effectively minimizing stress concentration., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations to rare diseases. In this paper, we propose FoPro-KD, a novel framework that leverages the power of frequency patterns learned from frozen pre-trained models to enhance their transferability and compression, presenting a few unique insights: 1) We demonstrate that leveraging representations from publicly available pre-trained models can substantially improve performance, specifically for rare classes, even when utilizing representations from a smaller pre-trained model. 2) We observe that pre-trained models exhibit frequency preferences, which we explore using our proposed Fourier Prompt Generator (FPG), allowing us to manipulate specific frequencies in the input image, enhancing the discriminative representational transfer. 3) By amplifying or diminishing these frequencies in the input image, we enable Effective Knowledge Distillation (EKD). EKD facilitates the transfer of knowledge from pre-trained models to smaller models. Through extensive experiments in long-tailed gastrointestinal image recognition and skin lesion classification, where rare diseases are prevalent, our FoPro-KD framework outperforms existing methods, enabling more accessible medical models for rare disease classification. Code is available at https://github.com/xmed-lab/FoPro-KD.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals., (© 2024. The Author(s).)

The mechanical properties of deep rock masses are significantly influenced by temperature and other factors. The effect of temperature on the strength of deep rock masses will pose a serious challenge to deep resource exploitation and engineering construction. In this paper, the thermal-mechanical coupling calculation model is established by particle flow code (PFC2D) to study the uniaxial compression response of rock masses with microcracks after temperature load. The strength of failure, microcracks, and strain was analyzed. The results show that: (i) When the soft rock thickness ratio Hs/H < 0.5, the displacement caused by the applied temperature is concentrated at the structural plane, and the contact force is concentrated at the end of the initial microcrack. When Hs/H ≥ 0.5, the displacement caused by the applied temperature is concentrated on both sides of the initial microcrack, and the contact force is concentrated in the hard rock area. (ii) The number of microcracks decreases with the increase of soft rock thickness under different working conditions. When the soft rock thickness ratio Hs/H < 0.5, the relationship curve between the number of microcracks and the vertical strain shows two stages of change. When Hs/H ≥ 0.5, the relationship curve between the number of cracks and the vertical strain changes shows three stages of change. (iii) When the soft rock thickness ratio Hs/H < 0.5, the failure strength decreases with the increase of soft rock thickness ratio at T = 100°C and 200°C. When T = 300°C and 400°C, the failure strength decreased first and then increased. When Hs/H ≥ 0.5, the failure strength increases with the increase of soft rock thickness at T = 200°C, 300°C, and 400°C. At T = 100°C, the failure strength decreases with the increase of soft rock thickness., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Qiu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

End-to-end disparity estimation algorithms based on cost volume deployed in edge-end neural network accelerators have the problem of structural adaptation and need to ensure accuracy under the condition of adaptation operator. Therefore, this paper proposes a novel disparity calculation algorithm that uses low-rank approximation to approximately replace 3D convolution and transposed 3D convolution, WReLU to reduce data compression caused by the activation function, and unimodal cost volume filtering and a confidence estimation network to regularize cost volume. It alleviates the problem of disparity-matching cost distribution being far away from the true distribution and greatly reduces the computational complexity and number of parameters of the algorithm while improving accuracy. Experimental results show that compared with a typical disparity estimation network, the absolute error of the proposed algorithm is reduced by 38.3%, the three-pixel error is reduced to 1.41%, and the number of parameters is reduced by 67.3%. The calculation accuracy is better than that of other algorithms, it is easier to deploy, and it has strong structural adaptability and better practicability. [ABSTRACT FROM AUTHOR]

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The short message function of the BeiDou satellite system, due to its strong concurrent processing capabilities, can quickly and accurately send information to the target location in emergency situations. However, because of data redundancy and limitations on message length, a single piece of information often requires multiple transmissions through BeiDou short messages to be completed, resulting in limited transmission capacity. To improve the transmission capacity of information, it is necessary to compress the information during transmission using BeiDou's short message communication function for maritime safety information. This paper proposes a Byte Encoding-enhanced Prediction by Partial Matching, variant D (BPPMd) algorithm that is particularly suitable for transmitting maritime safety information. Combined with a maritime safety information encoding algorithm (ME), it further improves compression efficiency, optimizes byte space, reduces information redundancy, and ensures the accuracy of the information. In this study, we constructed a maritime safety information dataset that includes three categories of information: meteorological warnings, navigation warnings, and disaster warnings. Experimental results show that the proposed algorithm is particularly suitable for compressing the maritime safety information dataset and outperforms other benchmark algorithms. Therefore, this study indicates that the proposed lossless compression method can be a feasible and effective solution for BeiDou short message communication. [ABSTRACT FROM AUTHOR]

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks. [ABSTRACT FROM AUTHOR]

With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the increasing throughput of modern sequencing instruments, the cost of storing and transmitting sequencing data has also increased dramatically. Although many tools have been developed to compress sequencing data, there is still a need to develop a compressor with a higher compression ratio. We present a two-step framework for compressing sequencing data in this paper. The first step is to repack original data into a binary stream, while the second step is to compress the stream with a LZMA encoder. We develop a new strategy to encode the original file into a LZMA highly compressed stream. In addition an FPGA-accelerated of LZMA was implemented to speedup the second step. As a demonstration, we present repaq as a lossless non-reference compressor of FASTQ format files. We introduced a multifile redundancy elimination method, which is very useful for compressing paired-end sequencing data. According to our test results, the compression ratio of repaq is much higher than other FASTQ compressors. For some deep sequencing data, the compression ratio of repaq can be higher than 25, almost four times of Gzip. The framework presented in this paper can also be applied to develop new tools for compressing other sequencing data. The open-source code of repaq is available at: https://github.com/ OpenGene/repaq. [ABSTRACT FROM AUTHOR]

In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms. [ABSTRACT FROM AUTHOR]

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a highquality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning. [ABSTRACT FROM AUTHOR]

With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and storage due to their complex structure, high volume of data points, strong time-series characteristics, and large amounts of data. To address the shortcomings of existing compression algorithms in quantization methods for large amounts of data in the instruction-domain, this paper proposes a quantization method based on distortion rate evaluation and linear fitting entropy reduction transformation, which aims to compress state signals such as the load power and load current while ensuring the availability of the data. This approach provides technical support for the transmission of high-frequency big data and meets the lightweight data acquisition requirements of digital twins for CNC machine tools. Compared to the empirical approach, this approach was more accurate and more computationally efficient. [ABSTRACT FROM AUTHOR]

Cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing. [ABSTRACT FROM AUTHOR]

The identification and analysis of harmonics, frequency, and transient events are essential today. It is necessary to have available data relating to harmonics, frequency, and transient events to understand power systems and their proper control and analysis. Power quality monitoring is the first step in identifying power quality disturbances and reducing them and, as a result, improving the performance of the power system. In this paper, while presenting different methods for measuring these quantities, we have made some corrections to them. These reforms have been obtained through the analysis of power network signals. Finally, we introduce a new monitoring system capable of measuring harmonics, frequency, and transient events in the network. In addition, these values are provided for online and offline calculations of harmonics, frequency, and transient events. In this paper, two new and practical methods of the "algebraic method" are used to calculate network harmonics and wavelet transform to calculate transient modes in the network. Furthermore, the proposed monitoring system is able to reduce the amount of data-storage memory. The results of the simulations performed in this article show the superiority of using the new method presented for online and offline monitoring of power quality in electric power systems. [ABSTRACT FROM AUTHOR]

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet. [ABSTRACT FROM AUTHOR]

With the wide application of Internet mobile devices in many industries, the hybrid teaching mode of online and offline has also become a research hotspot in the field of education. In the process of constructing the music online teaching platform based on mobile platform MOOC, the distribution of samples will affect the data recognition results of the system. Therefore, this paper uses convolutional neural network as the backbone network to extract data features and improve the resolution. At the same time, in the process of data compression, this paper realizes the global average pool and unified parameters by improving the attention mechanism, so that all parameters interact with their K adjacent parameter characteristics, so as to reduce the overdependence between channels and reduce the complexity of the overall calculation. For the analysis of complexity, this paper detects the time required for serial training and parallel training, intercepts the average value of the parameters of all nodes, and obtains the optimal number of nodes of this model. Finally, combined with the characteristics of music teaching, this paper designs a mobile MOOC music teaching platform based on convolutional neural network. The platform includes modules such as basic information management and music course resource construction and applies it to the actual music course teaching process. The performance of the algorithm and the feasibility of the system are verified by classroom activity test, hoping to provide some reference for the research in the field of music mixed education. [ABSTRACT FROM AUTHOR]

This paper studies mechanical properties and energy damage evolution of fiber-reinforced cemented sulfur tailings (CSTB) backfill. The effects of fiber length and fiber content on the stress, toughness and failure properties of the CSTB were systematically revealed. In addition, the energy index evolution law was studied, and the energy damage evolution mechanism of CSTB was revealed. The results show that the deformation failure of fiber-reinforced CSTB mainly goes through four stages: initial crack compaction, linear elastic deformation, yield failure and post-peak failure. The peak stress and residual stress of the CSTB firstly increase and then decrease with the increase of fiber content and the addition of fiber can promote the change from brittle failure to ductile failure of the CSTB. Adding appropriate amount of fiber can improve the toughness of CSTB, and the influence degree of fiber length on the toughness index of CSTB is 6mm>12mm>3mm. The total strain energy increases linearly along the variation of fiber content, while the elastic strain energy and dissipated energy increase exponentially at the peak stress point. In the process of CSTB deformation and failure, "gentle-linear growth-slow growth-rapid decline" is for elastic strain energy, while "gentle-slow growth-rapid growth-linear growth" is for dissipation energy. The damage and failure of CSTB mainly experienced four stages: initial damage, slow growth of damage, accelerated damage and damage failure, and the damage evolution curve also showed the changing characteristics of "gentle-slow growth-rapid growth-linear growth". The CSTB without added fiber showed obvious "Y-type" and "linear-type" shear failure characteristics and the phenomenon of shear cracks penetrating the backfill appeared. No big shear crack occur when it is damaged, showing that the fiber addition restrain the crack growth and improve the overall crack resistance of the CSTB. Hydration products are obviously distributed on the surface of the fiber, which indicates that the fiber will be evenly dispersed in the CSTB and form a certain bonding force with the cement-tailings matrix, thus improving the overall mechanical properties of the CSTB., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, the uniaxial compression damage characteristics of specimens are analyzed containing holes using PFC2D. In addition, the crack propagation, stress distribution and energy development characteristics of the specimens were systematically discussed. The findings indicate that the strength parameters of various specimens drop initially and then increase with increasing center point connecting angle, in comparison to intact rock. The most significant reduction in strength parameters is observed at a center point connecting angle of 45°. The stress concentration around the holes occurs prior to crack initiation and vanishes upon specimen failure. The number of cracks in the specimens is small and the propagation length is short before touching the peak value, while the cracks expand rapidly in a short period of time after the stress touches the peak value, and the crack development rules in the two stages are quite different. With increasing center point connecting angle, the pre-peak energy and total energy drop first and then increase. After touching the peak value, the specimen is dominated by energy release and the ability to absorb energy is weakened. In the case of center point connecting angle of 45°, the specimen has the largest energy difference coefficient and the worst ability to resist damage. The damage behavior can be composed of no damage, initial damage and accelerated damage take into account the damage change rules of the specimens., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2023 Xuan, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Incorporating T-shaped steel and square steel tubes into a cross shaped concrete column can significantly improve the seismic performance of the cross shaped column. However, the experimental samples are limited, so ABAQUS finite element (FE) analysis method was adopted in this paper to study the seismic performance of this cross shaped column, calculate and verify three specimens in the existing reference. Based on the reliable model, parameter analysis was carried out (25 specimens in total). The results show that the established model has a high degree of coincidence in the hysteretic curve, skeleton curve and failure mode, and the error of ultimate bearing capacity and ductility is within 10%. The configuration of T-shaped steel and square steel tubes inside the cross column can meet the ductility requirements specified in the standard under high axial compression ratio. The ultimate bearing capacity of the cross shaped column increases with the increase of the thickness of the square steel tube, but the ductility deteriorates. The increase in steel tube size increases the strength of the concrete in the core area, and the seismic performance of the cross shaped column was improved. Increasing the thickness of the T-shaped steel flange can better improve the seismic performance of the cross shaped column compared to increasing the thickness of the T-shaped steel web plate. Increasing the height of the specimen will significantly reduce its seismic performance. When the shear span ratio is not greater than 4.1, the ductility can meet the standard requirements. The error of the formula for calculating the compression-bending bearing capacity proposed based on existing calculation methods is less than 5%., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, based on the parametric model of the matrix of discrete cosine transform (DCT), and using an exhaustive search of the parameters' space, we seek for the best approximations of 8-point DCT at the given computational complexities by taking into account three different scenarios of practical usage. The possible parameter values are selected in such a way that the resulting transforms are only multiplierless approximations, i.e., only additions and bit-shift operations are required. The considered usage scenarios include such cases where approximation of DCT is used: (i) at the data compression stage, (ii) at the decompression stage, and (iii) both at the compression and decompression stages. The obtained results in effectiveness of generated approximations are compared with results of popular known approximations of 8-point DCT of the same class (i.e., multiplierless approximations). In addition, we perform a series of experiments in lossy compression of natural images using popular JPEG standard. The obtained results are presented and discussed. It should be noted, that in the overwhelming number of cases the generated approximations are better than the known ones, e.g., in asymmetric scenarios even by more than 3 dB starting from entropy of 2 bits per pixel. In the last part of the paper, we investigate the possibility of hardware implementation of generated approximations in Field-Programmable Gate Array (FPGA) circuits. The results in the form of resource and energy consumption are presented and commented. The experiment outcomes confirm the assumption that the considered class of transformations is characterized by low resource utilization. [ABSTRACT FROM AUTHOR]

The exponential growth in data generation has become a ubiquitous phenomenon in today's rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

Effective compression of point clouds is essential for implementing virtual and mixed reality applications, which require encoding millions or even tens of millions of points. This paper offers a new geometric compression for point clouds based on sparse cascaded residuals and sparse attention. A sparse cascaded residual module is posited to connect multiple residual modules through shortcuts, thereby augmenting the network's learning capacity and compression efficacy. The authors developed a sparse attention module to acquire global features by computing interdependencies among points, enhancing compression performance to a greater extent. Trade‐off parameters are employed to optimize the rate and distortion. The authors' method outperforms the state‐of‐the‐art open‐source method regarding rate‐distortion on the ShapeNet, ModelNet, and Microsoft Voxelized Upper Bodies datasets, with average bjøntegaard‐delta (BD)‐rate gains of −14.44% and −15.38%. [ABSTRACT FROM AUTHOR]

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China's first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar. [ABSTRACT FROM AUTHOR]

To authenticate a controller area network (CAN) data frame, a message authentication code (MAC) must be sent along with the CAN frame, but there is no space reserved for the MAC in the CAN frame. Recently, difference-based compression (DBC) algorithms have been used to create a space inside the frame. DBC has the advantage of being very efficient, but its drawback is that, if an error occurs in one frame, the effects of that error propagate to subsequent frames. In this paper, a CAN data compression algorithm is proposed that compresses the current frame without relying on previous frames. Therefore, an error generated in one frame cannot be propagated to subsequent frames. In addition, a CAN signal grouping technique is proposed based on entropy analysis. To efficiently authenticate CAN frames, the length of the compressed data must be 4 bytes or less (4BL). Simulation shows that the 4BL-compression ratio of a Kia Sorento vehicle is 99.36% in the DBC method, but 100% in the proposed method. In an LS Mtron tractor, the 4BL-compression ratio is 98.58% in the DBC method, but 100% in the proposed method. In addition, the execution time of the proposed compression algorithm is only 27.39% of that of the DBC algorithm. The results show that the proposed algorithm has better compression characteristics for CAN security than the DBC algorithms. [ABSTRACT FROM AUTHOR]

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems. [ABSTRACT FROM AUTHOR]