Data Compression - Science topic

Data compression and partitioning are critical techniques for optimizing performance in large-scale data management systems such as SAP HANA. As businesses increasingly rely on real-time analytics and massive data processing, the efficiency of database operations becomes paramount. This study explores the impact of data compression and partitioning...

Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region-based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high ef...

Open artificial intelligence (A.I.) applications, including ChatGPT, are gaining recognition across diverse research domains, including healthcare, due to their effective handling of inquiries related to A.I. implementation in healthcare. Despite the growing use of blockchain technology in healthcare systems, existing research struggles with storag...

Objective. We present a prototype add-on multiplexer for retro-fitting high-channel count receive coils onto systems with analog readout chains, such as the MR-linac. This particular system is currently equipped with two coil elements, containing only four channels each. Approach. We developed a 4:1 multiplexer board based on time-division. CMOS-ba...

Low-rank matrix approximation (LoRMA) is a fundamental tool for compressing high-resolution data matrices by extracting important features while suppressing redundancy. Low-rank methods, such as global singular value decomposition (SVD), apply uniform compression across the entire data matrix, often ignoring important local variations and leading t...

Deoxyribonucleic acid (DNA) data storage is expected to become a key medium for large-scale data. Biomedical data images typically require substantial storage space over extended periods, making them ideal candidates for DNA data storage. However, existing DNA data storage models are primarily designed for generic files and lack a comprehensive ret...

This paper presents a technique to compress data without loss for an electrocardiography (ECG) signal surveillance system in portable body area networks. The goal is to reduce storage space and power consumption, ensuring longer battery life. The proposed method includes lossless compression, run-length coding and Golomb–Rice encoding, along with p...

Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing waste and operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challenges due to the limited computational power and bandwidth of edge devices. This study investigates how to perform VAD ef...

Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existing methods often produce noticeable artifacts when compressing text. To tackle this challenge, we propose versatile perceptual screen image compression with diffusion ren...

Wireless body area networks (WBAN) containing wearable sensing medical devices have enticed huge attention by providing high‐quality medical services to people without restraint in their day‐to‐day activities. WBAN monitors elderly people or patients suffering from any long‐lasting diseases from their place without being hospitalized, saving critic...

n medical imaging, skin lesion prediction and classification is highly crucial while predicting skin malignancy. Various prevailing deep learning-based CAD diagnosis approaches show poor performance. It is incredibly challenging to diagnose skin lesions with complex features like artefacts, boundary analysis, low contrast images with poor foregroun...

Due to the need to store and transfer an ever-increasing volume of geophysical data, the problem of developing effective compression algorithms is becoming increasingly important. In order to use geophysical data in practical applications, some preliminary processing is carried out (for example, filtration of anomalies and data interpolation). In t...

Remote monitoring of transmission lines plays a vital role in ensuring the stable operation of power systems, especially in regions with weak or unstable network signals, where efficient data transmission and storage are essential. However, traditional image compression methods face significant limitations in both quality and efficiency when applie...

Efficient time series forecasting is essential for smart energy systems, enabling accurate predictions of energy demand, renewable resource availability, and grid stability. However, the growing volume of high-frequency data from sensors and IoT devices poses challenges for storage and transmission. This study explores Discrete Wavelet Transform (D...

The present work aims at developing a fixed‐length representation for multivariate time series with varying lengths and apply it to the classification of aerial target activities. Numerous machine learning classification methods rely on fixed‐length sequences, which makes them not directly applicable to target feature time series of varying lengths...

Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between different compression levels. Consequently, it raises significant challenges in applying the neural image compression to edge devices that are developed for powerful server...

Efficient data compression technologies are crucial to reduce the cost of long-term storage and file transfer in whole genome sequencing studies. This study benchmarked four specialized compression tools developed for paired-end fastq.gz files DRAGEN ORA 4.3.4 (ORA), Genozip 15.0.62, repaq 0.3.0, and SPRING 1.1.1 using three subjects from the genom...

This work introduces the Schmidt quantum compressor, an innovative approach to quantum data compression that leverages the principles of Schmidt decomposition to encode quantum information efficiently. In contrast to traditional variational quantum autoencoders, which depend on stochastic optimization and face challenges such as shot noise, barren...

Healthcare systems generate vast amounts of sensitive data, and ensuring its security and privacy remains a significant challenge. Various traditional solutions, including encryption techniques, access control mechanisms, and centralized cloud‐based systems, have been developed to address these issues. However, these approaches often face challenge...

Data compression is a fundamental technology that enables efficient storage and transmission of information. However, traditional compression methods are approaching their theoretical limits after 80 years of research and development. At the same time, large artificial intelligence models have emerged, which, trained on vast amounts of data, are ab...

The advent of the sixth-generation (6G) wireless networks, enhanced by artificial intelligence, promises ubiquitous connectivity through Low Earth Orbit (LEO) satellites. These satellites are capable of collecting vast amounts of geographically diverse and real-time data, which can be immensely valuable for training intelligent models. However, lim...

An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimension, an autoencoder works to achieve data compression, extracting the essence of the high-dimensional data into the resulting latent space. We review the fundamentals of...

Universal Testing Machines integrated with Load, Strain, and Torque (UTM-LST) measurement systems are essential for evaluating mechanical properties of materials under complex stress states. External balance measurements, which capture forces and moments applied to a specimen, play a pivotal role in deriving meaningful insights from mechanical test...

We introduce a simple yet powerful invariant relation connecting four successive terms of a class of exponentially decaying alternating functions. Specifically, for the sequence defined by f(n) = ((1/2)^n + (-1)^n) / n, we prove that the combination [(n-2)f(n-2) + (n-3)f(n-3)] / [n f(n) + (n-1)f(n-1)] is universally equal to 4 for all integers n >=...

This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose Online Compressed Gradient Tracking with one-point Bandit Feedback (OCGT-BF), a novel algorithm that harness data compression and gradient-free optimization techniques in distributed...

The notion of Wheeler languages is rooted in the Burrows-Wheeler transform (BWT), one of the most central concepts in data compression and indexing. The BWT has been generalized to finite automata, the so-called Wheeler automata, by Gagie et al. [Theor. Comput. Sci. 2017]. Wheeler languages have subsequently been defined as the class of regular lan...

In the last two decades, word-based text compression has shown to be the key to efficiently handling large collections of text not only due to yielding important storage savings but, more importantly, because it allowed boosting the performance of some traditional text retrieval systems. The reason is that when the appropriate compression technique...

The underwater conditions of the coastal ecosystem require careful monitoring to anticipate potential environmental hazards. Moreover, the unique characteristics of the marine underwater environment have presented numerous challenges for the advancement of underwater sensor networks. Current studies have not extensively integrated Digital Twins wit...

Introduction: The surge in demand for modern applications has high resolution images which causes a web performance optimization problem. Most traditional client-side and server-side image optimization processes tend to overlook the complete solution that puts into account load time, response time, and bandwidth usage. This research proposes an ada...

Using the second law of information dynamics and the mass–energy–information equivalence principle, we show that gravitational attraction manifests as a requirement to reduce the information entropy of matter objects in space. This is another example of data compression and computational optimization in our universe, which supports the possibility...

The train transmission system is a critical component of railway operations, playing a pivotal role in ensuring service safety and reliability. However, existing condition monitoring approaches face two major challenges: (1) the coupling of rich multimodal signals, such as vibration, acoustics, current, and rotational speed, is often overlooked, li...

The emerging generation of radio-interferometric (RI) arrays are set to form images of the sky with a new regime of sensitivity and resolution. This implies a significant increase in visibility data volumes, scaling as $\mathcal{O}(Q^{2}B)$ for $Q$ antennas and $B$ short-time integration intervals (or batches), calling for efficient data dimensiona...

Efficiently capturing multidimensional signals containing spectral and temporal information is crucial for intelligent machine vision. Although in-sensor computing shows promise for efficient visual processing by reducing data transfer, its capability to compress temporal/spectral data is rarely reported. Here we demonstrate a programmable two-dime...

In the contemporary field of wireless sensing, passive sensing leveraging channel state information (CSI) has found widespread applications across diverse scenarios, including behavior recognition, keystroke recognition, breath detection, and indoor localization. To ensure optimal sensing performance, wireless devices often collect a substantial nu...

In medical imaging, skin lesion prediction and classification is highly crucial while predicting skin malignancy. Various prevailing deep learning-based CAD diagnosis approaches show poor performance. It is incredibly challenging to diagnose skin lesions with complex features like artefacts, boundary analysis, low contrast images with poor foregrou...

In contrast to two-dimensional skeletal data, which exhibits inherent susceptibility to environmental interference such as complex backgrounds and illumination variations, three-dimensional skeletal data demonstrates superior robustness against these confounding factors. This enhanced spatial representation capability enables more accurate feature...

Low-rank representations such as the Tucker decomposition underlie many frequentist methods for tensor analysis. Bayesian analogues, in contrast, have received less attention. Notably missing in the literature is a Bayesian Tucker decomposition with orthogonal factor matrices—a standard interpretability restriction in frequentist settings. We propo...

Semantic communication is emerging as a promising communication paradigm, where semantic coding plays an essential role by explicitly extracting task-critical information. Prior efforts toward semantic coding often rely on learning-based feature extraction methods but tend to overlook data compression and lack a rigorous theoretical foundation. To...

Data compression is a critical technology for large-scale plasma simulations. Storing complete particle information requires Terabyte-scale data storage, and analysis requires ad-hoc scalable post-processing tools. We propose a physics-aware in-situ compression method using Gaussian Mixture Models (GMMs) to approximate electron and ion velocity dis...

This paper applies the Oscillatory Dynamics Transductive-Bridging Theorem (ODTBT) to the historiographical reconstruction presented in A Study of al-Muqtafi on Kitab al-Rawdatayn by Ibn al-Barzali. Al-Barzali's text, revisited methodologically by Al-Bahadli and Flayeh, is shown to reflect recursive epistemological structures that mirror the oscilla...

We propose an efficient image compression scheme leveraging Vision Mamba and dynamic convolution, addressing the limitations of existing methods, such as failure to capture long‐range pixel dependencies and high computational complexity. Our approach improves both global and local information learning with reduced computational cost. Experimental r...

3D Gaussian Splatting (3DGS) has emerged as a cutting‐edge technique for real‐time radiance field rendering, offering state‐of‐the‐art performance in terms of both quality and speed. 3DGS models a scene as a collection of three‐dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Des...

In the pursuit of effective real-time video transmission for First-Person View (FPV) drone systems, optimizing the encoding process is paramount. Traditional encoding methods, reliant on pre-encoding demosaicking, often fall short in balancing the trade-off between video quality and latency, essential for seamless real-time feedback. This work prop...

The proliferation of healthcare data and the increasing reliance on cloud infrastructures have intensified concerns about security, data privacy, and accessibility. Addressing the critical challenge of safeguarding sensitive patient information, this study proposes a new hybrid encryption architecture that integrates Principal Component Analysis (P...

Real-time monitoring requirements of structural integrity in metallic components across aerospace and automotive and civil engineering sectors drive the need to create reliable image compression methods. The detection of small defects including cracks and corrosion as well as deformations depends greatly on high-resolution imaging systems. The mass...

Context. A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, among which human-made Earth-orbiting satellites and debris remain major contaminants. Existing pipelines can effectively identify satellite trails, but they often miss more complex signatures, such as collections of satellite g...

Maize breeding and genetic studies are highly dependent on linking genetic markers such as singlenucleotide polymorphisms (SNPs) to phenotypes of interest, with Genome-Wide Association Studies (GWAS)serving as a crucial tool in this process. However, traditional statistical methods for analyzing these phenotypes in GWAS can be computationally inten...

The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven effective, they often lack robust representation learning and depend heavily on expert-crafted features. Although deep learning offer...

A bit catastrophe, loosely defined, is when a change in just one character of a string causes a significant change in the size of the compressed string. We study this phenomenon for the Burrows-Wheeler Transform (BWT), a string transform at the heart of several of the most popular compressors and aligners today. The parameter determining the size o...

We propose SUMART, a method for summarizing and compressing the volume of verbose subtitle translations. SUMART is designed for understanding translated captions (e.g., interlingual conversations via subtitle translation or when watching movies in foreign language audio and translated captions). SUMART is intended for users who want a big-picture a...

Distributed learning and Edge AI necessitate efficient data processing, low-latency communication, decentralized model training, and stringent data privacy to facilitate real-time intelligence on edge devices while reducing dependency on centralized infrastructure and ensuring high model performance. In the context of global navigation satellite sy...

Research Aim: Study the possibility of optimizing the computational offloading of deep neural networks by reducing the volume of data sent to the cloud with a focus on the application of human activity recognition with deep learning. Research method: In this research, three proposed methods of reducing the number of data samples, reducing the preci...

Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet principled explanations for their underlying mechanisms and several phenomena, such as scaling laws, hallucinations, and related behaviors, remain elusive. In this work, we revisit the classical relationship between compression and prediction, grounded...

The Transformer-based target detection model, DETR, has powerful feature extraction and recognition capabilities, but its high computational and storage requirements limit its deployment on resource-constrained devices. To solve this problem, we first replace the ResNet-50 backbone network in DETR with Swin-T, which realizes the unification of the...

Previous work has demonstrated that isomorphisms on dyadic structures (e.g., graphs and multidimensional networks) do not, in general, preserve their formal knowledge. In other words, even if two graph-like structures are isomorphic, the process of transforming one into the other can introduce a spurious loss or gain of irreducible information, an...

Johnson and Lindenstrauss (Contemporary Mathematics, 1984) showed that for $n > m$, a scaled random projection $\mathbf{A}$ from $\mathbb{R}^n$ to $\mathbb{R}^m$ is an approximate isometry on any set $S$ of size at most exponential in $m$. If $S$ is larger, however, its points can contract arbitrarily under $\mathbf{A}$. In particular, the hypergri...

Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neur...

Millions of people across the globe are affected by the life-threatening disease of Malaria. To achieve the remote screening and diagnosis of the disease, the rapid transmission of large-size microscopic images is necessary, thereby demanding efficient data compression techniques. In this paper, we argued that well-classified images might lead to h...

Data compression plays a vital role in data management and information theory by reducing redundancy. However, it lacks built-in security features such as secret keys or password-based access control, leaving sensitive data vulnerable to unauthorized access and misuse. With the exponential growth of digital data, robust security measures are essent...

In this research, a new hybrid compression method is introduced, which is based on Beta function and singular vector sparse reconstruction (SVSR). The proposed technique incorporates wavelet filters whose coefficients are obtained from the beta function and its derivatives. These filters are employed to decompose the original crop image, and SVSR i...

In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (CS), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compressio...

We propose an online data compression approach for efficiently solving distributionally robust optimization (DRO) problems with streaming data while maintaining out-of-sample performance guarantees. Our method dynamically constructs ambiguity sets using online clustering, allowing the clustered configuration to evolve over time for an accurate repr...

Topicality. Nowadays, binary search trees are widely used to speed up searching, sorting, and selecting array elements. But the computational complexity of searching using a binary tree is proportional to its height, which depends on the sequence of processing the elements of the array. In order to reduce the height of a tree, its balancing is peri...

With the growing demand for large-scale urban observation, multi-channel technology has become a cornerstone of high-resolution wide-swath SAR systems. The challenge of storing and transmitting the large data volumes generated by multi-channel systems has driven the development of advanced data compression techniques. However, in onboard implementa...

Image retrieval (IR) has become a crucial challenge in computer vision with the exponential growth of digital imagery. The existing methods employ a single hash source, which may overlook deep details in the image, and they struggle to handle the complexity and diversity of modern visual data. This study addresses this limitation by proposing a nov...

The combination of Unmanned Aerial Vehicles (UAVs) and Mobile Edge Computing (MEC) effectively meets the demands of user equipments (UEs) for high-quality computing services, low energy consumption, and low latency. However, in complex environments such as disaster rescue scenarios, a single UAV is still constrained by limited transmission power an...

The growing complexity and volume of genomic and omics data present critical challenges for storage, transfer, and analysis in edge–cloud platforms. Existing compression techniques often involve trade-offs between efficiency and speed, requiring innovative approaches that ensure scalability and cost-effectiveness. This paper introduces a lossless c...

This work presents an optical encryption process for various types of information related to 3D worlds (scenes) or 2D images, utilizing Computer-Generated Holograms (CGHs). It also introduces a modification to the Dual Random Phase Encoding (DRPE) encryption algorithm by incorporating pixel shuffling. This proposal enables the use of either a singl...

Comprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections has made it effectively impossible to search these data using tools such as the Basic Local Alignment Search Tool (BLAST) and its successors. Here, we present a technique ca...

This comprehensive survey examines the field of alphabetic codes, tracing their development from the 1960s to the present day. We explore classical alphabetic codes and their variants, analyzing their properties and the underlying mathematical and algorithmic principles. The paper covers the fundamental relationship between alphabetic codes and com...

The inspiral, merger, and ringdown of Massive Black Hole Binaries (MBHBs) is one the main sources of Gravitational Waves (GWs) for the future Laser Interferometer Space Antenna (LISA), an ESA-led mission in the implementation phase. It is expected that LISA will detect these systems throughout the entire observable universe. Robust and efficient da...

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their abili...

For applications such as smart cities, environmental monitoring, and industrial automation in critical infra, sensor networks are important, but they are confronted with serious issues regarding scalability, energy consumption, and reliable communication. To overcome these challenges, an adaptive smart sensor node communication framework (AS-SNCF)...

The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limite...

In an era of rapid technological advancements and unprecedented data inundation, sparsity has emerged as a key property with profound implications in various fields. One important application of sparsity is sparse signal recovery, which involves reconstructing signals from limited observations and is of great importance in medical imaging, communic...

In Parkinson's Disease (PD), deep brain stimulation of the subthalamic nucleus (STN‐DBS) reliably improves motor symptoms, and the circuits mediating these effects have largely been identified. However, non‐motor outcomes are more variable, and it remains unclear which specific brain circuits need to be modulated or avoided to improve them. Since n...

This paper is devoted to studying the application of the block Krylov subspace method for approximation of the truncated tensor SVD (T-SVD). The theoretical results of the proposed randomized approach are presented. Several experimental experiments using synthetics and real-world data are conducted to verify the efficiency and feasibility of the pr...

Learning-based image compression methods have recently emerged as promising alternatives to traditional codecs, offering improved rate-distortion performance and perceptual quality. JPEG AI represents the latest standardized framework in this domain, leveraging deep neural networks for high-fidelity image reconstruction. In this study, we present a...

Image compression plays a crucial role in optimising storage and transmission efficiency. This paper evaluates the performance of Run-Length Encoding (RLE), Huffman Coding, and Lempel-Ziv-Welch (LZW) algorithms for compressing grayscale PNG and JPG images. The study analyses their effectiveness using compression ratio, bits per pixel, and compressi...

Phasor measurement units (PMUs) are increasingly being deployed in power systems due to their high sampling rates and diverse data sampling types. However, this undoubtedly poses significant challenges to data centers in terms of data storage and transmission. This article proposes an adaptive rank-based tensor ring (TR) method for PMU data compres...