Compression Approach Research Articles

Fast-Convergent Wireless Federated Learning: A Voting-Based TopK Model Compression Approach

Fast-Convergent Wireless Federated Learning: A Voting-Based TopK Model Compression Approach

Security of Magnetic Resonance Medical Images using Region-based Lossless Image Compression in Healthcare Information Systems

Purpose The purpose of region-based medical image compression is to optimize the compression process by focusing on specific regions of interest within medical images. Unlike traditional compression methods that treat the entire image uniformly, region-based compression techniques identify and prioritize certain areas or regions within the image that are deemed more diagnostically significant or relevant. By allocating more resources to compressing these critical regions while reducing compression in less important areas, region-based compression methods aim to achieve higher compression efficiency while preserving diagnostic quality. This approach is particularly valuable in medical imaging, where accurate representation of anatomical structures or pathological findings is paramount for clinical diagnosis and decision-making. Region-based compression can help reduce storage requirements, transmission bandwidth, and processing time without compromising the diagnostic integrity of medical images, thereby facilitating more efficient healthcare delivery and telemedicine applications. Methods In this study, we utilized distortion-limiting compression techniques to optimize the compression process for specific regions within medical images. We employed lossless scalable RBC (Region-Based Compression) using Discrete Wavelet Transform (DWT) for Digital Imaging and Communication in Medicine (DICOM) images. The initial step involved medical image pre-processing, followed by segmentation to separate the image into regions of interest (ROI) and non-ROI. Compression techniques were then applied to reduce network bandwidth and storage requirements. Fractal lossy compression was employed for the non-ROI portion, while context-tree weighting lossless compression was proposed for the ROI portion, effectively compressing the image while rejecting noisy background elements. During decompression, the original medical image can be reconstructed using the reverse process. This approach optimizes storage and transmission efficiency while preserving diagnostic integrity in medical imaging applications. Results The experiment involved testing various medical images, and the proposed method outperformed previous techniques in terms of results. According to the findings, the improvement in Peak Signal-to-Noise Ratio (PSNR) over current techniques reached up to 24.23 dB compared to the Joint Photographic Experts Group (JPEG). Additionally, it achieved up to 12.22 dB improvement compared to other transform approaches. These significant enhancements prompted the development of a web and mobile platform for compressing and sending medical images, particularly microscopic ones, in real time. Conclusion This research focuses on employing wavelet transform techniques to compress the Region of Interest (ROI) within medical images. This ROI-based compression approach is particularly valuable as it retains essential diagnostic information while reducing the overall file size. Such a technique holds significant promise for telemedicine systems, especially in rural regions where network resources may be limited or constrained. By selectively compressing the most diagnostically relevant areas of medical images, this approach ensures that critical information is preserved while optimizing data transmission and storage efficiency. This can ultimately enhance access to medical imaging services and facilitate remote diagnosis and treatment in underserved areas with limited network infrastructure.

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides. The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Netherlands) cohort of patients who underwent first curative intent resection for colorectal liver metastases between January 2000 and February 2019. External validation was performed on whole-slide images of patients resected between October 2004 and December 2017 in another institution (Radboud University Medical Center, the Netherlands). The outcomes of interest were the automated classification of dichotomous hepatic growth patterns, distinguishing between desmoplastic hepatic growth pattern and non-desmoplatic growth pattern by a deep-learning model; secondary outcome was the correlation of these classifications with overall survival in the histopathology manual-assessed histopathological growth pattern and those assessed using neural image compression. Nine hundred and thirty-two patients, corresponding to 3.641 whole-slide images, were reviewed to develop the algorithm and 870 whole-slide images were used for external validation. Median follow-up for the development and the validation cohorts was 43 and 29 months respectively. The neural image compression approach achieved significant discriminatory power to classify 100% desmoplastic histopathological growth pattern with an area under the curve of 0.93 in the development cohort and 0.95 upon external validation. Both the histopathology manual-scored histopathological growth pattern and neural image compression-classified histopathological growth pattern achieved a similar multivariable hazard ratio for desmoplastic versus non-desmoplastic growth pattern in the development cohort (histopathology manual score: 0.63 versus neural image compression: 0.64) and in the validation cohort (histopathology manual score: 0.40 versus neural image compression: 0.48). The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.

Reconstructing Human Pose From Inertial Measurements: A Generative Model-Based Compressive Sensing Approach

Reconstructing Human Pose From Inertial Measurements: A Generative Model-Based Compressive Sensing Approach

Combinative model compression approach for enhancing 1D CNN efficiency for EIT-based Hand Gesture Recognition on IoT edge devices

Tiny Machine Learning is rapidly evolving in edge computing and intelligent Internet of Things (IoT) devices. This paper investigates model compression techniques with the aim of determining their compatibility when combined, and identifying an effective approach to improve inference speed and energy efficiency within IoT edge devices. The study is carried out on the application scenario of Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which involves complex signal processing and needs real-time processing and energy efficiency. Therefore, a customized 1-Dimensional Convolutional Neural Network (1D CNN) HGR classification model has been designed. An approach based on strategically combining model compression techniques was then implemented resulting in a model customized for faster inference and improved energy efficiency for IoT embedded devices. The model size became compact at 10.42 kB, resulting in a substantial size reduction of 98.8%, and an inference gain of 94.73% on a personal computer with approximately 8.56% decrease in accuracy. The approach of combinative model compression techniques was applied to a wide range of edge-computing IoT devices with limited processing power, resulting in a significant improvement in model execution speed and energy efficiency for these devices. Specifically, there was an average power consumption gain of 52% for Arduino Nano BLE and 34.05% for Raspberry Pi 4. Inference time was halved for Arduino Nano BLE Sense, Nicla Sense, and Raspberry Pi 4, with a remarkable gain of 94% for ESP32.

The impacts of roller compaction on the quality attributes of simultaneously compressed micro and minitablets

The challenges of developing good quality low dose minitablets was assessed by systematically studying the effects of ibuprofen (IBU, a model compound) particle sizes (6–58 µm D50) and concentrations (0.1–3 %w/w), roller compaction forces (3–7 kN/cm), and the minitablet sizes (1.2, 1.5 and 2 mm diameter). A novel compression approach, where all three minitablet sizes were simultaneously produced in a single compression run was used. Roller compacted ribbons, granules, minitablets were characterized for physico-mechanical properties and minitablets were also characterized for stratified content uniformity and weight uniformity. The results showed that roll force was the more dominant factor to ribbon solid fraction or tensile strength and granule size enlargement. Minitablets obtained from the granules had good weight uniformity; all but one batch met the <Ph. Eur. 2.9.5 > criteria. The precise control of tooling lengths across the various sizes was found profoundly important for achieving expected weights, solid fraction, and tensile strength of the simultaneously produced minitablets. The roller compaction process considerably improved the CU variability of the minitablets as compared to the direct compression process. Smaller particle size and higher concentration of IBU, increased roller compaction force, and larger minitablet size improved the potency and content uniformity; however, only the minitablet size was a statistically significant factor in this study. As a product strategic design criterion, a threshold of 25 minitablets in a dosage unit would ensure robust downstream filling and weight verification operations as well as dose accuracy and uniformity (would pass <USP 905> stage 1 criteria). This study results demonstrated feasibility of the novel simultaneous compression approach and the roller compaction process in developing good quality minitablets.

Spinal laser interstitial thermal therapy and radiotherapy for thoracic metastatic epidural spinal cord compression.

Spinal laser interstitial thermal therapy (sLITT) is a less invasive alternative to surgery for metastatic epidural spinal cord compression. Here, we analyze outcomes of patients treated with sLITT either in conjunction with radiotherapy or as a standalone salvage therapy. We included patients with thoracic vertebral metastatic cord compression treated with sLITT. Outcomes included freedom from local failure (FFLF) and overall survival (OS). Factors associated with FFLF were identified with univariable and multivariable analyses via a Cox proportional hazards model. Between 2013-2022, 129 patients received sLITT to 144 vertebral segments; 69% were radiotherapy naïve, 81% were radioresistant histologies, and 74% were centered in the vertebral body. Median age was 61 years. Pre-sLITT Bilsky score was 3 in 28%, 2 in 33%, and 1c in 37%. Radiotherapy was delivered in conjunction with sLITT for 80% of cases, including 68% that received stereotactic radiotherapy, at a median of 5 days after sLITT. Median follow-up was 9.1 months. One-year FFLF and OS was 80% and 78%, respectively. On multivariable analysis, variables independently associated with adverse FFLF included paraspinal/foraminal disease location (p = 0.001), and post-sLITT imaging Bilsky score of 2 (p = 0.073) or 3 (p = 0.011). Prior radiotherapy, technique of radiotherapy, and time between radiotherapy and sLITT were not associated with FFLF. sLITT with radiotherapy is an effective minimally invasive treatment approach for thoracic metastatic epidural spinal cord compression. Early treatment response may serve as a prognostic imaging biomarker.

An Additively Optimal Interpreter for Approximating Kolmogorov Prefix Complexity.

We study practical approximations of Kolmogorov prefix complexity (K) using IMP2, a high-level programming language. Our focus is on investigating the optimality of the interpreter for this language as the reference machine for the Coding Theorem Method (CTM). This method is designed to address applications of algorithmic complexity that differ from the popular traditional lossless compression approach based on the principles of algorithmic probability. The chosen model of computation is proven to be suitable for this task, and a comparison to other models and methods is conducted. Our findings show that CTM approximations using our model do not always correlate with the results from lower-level models of computation. This suggests that some models may require a larger program space to converge to Levin's universal distribution. Furthermore, we compare the CTM with an upper bound on Kolmogorov complexity and find a strong correlation, supporting the CTM's validity as an approximation method with finer-grade resolution of K.

AmLuCEP: Amalgamating LUT-based Compression and Adaptive Encoding Assisted Block Placement To Improve Lifetime of PCM-based Main Memories

With the rising demands for high capacity memory and poor scalability of the existing DRAM-based main memories, the emerging Non-volatile memories captures higher attention due to their high density and low leakage power consumption. However, the possible consideration of such memories as alternatives of DRAM is largely hindered by their intrinsic drawbacks like high write latency, high write energy and low write endurance. In this article, we propose an integrated solution by combining the effect of compression and encoding assisted block placement to improve lifetime of NVMs. We have developed a compression technique called LUT_Comp by exploiting the word-level redundancy present in the words of the incoming cache blocks to NVM. LUT_Comp remains effective in reducing bit-flips in NVMs by offering a balance in compression ratio and coverage (Cov). Additionally, we also propose an encoding based block placement policy that places the compressed blocks in the appropriate half within the memory. The integrated approach of compression and block placement termed AmLuCEP offers a uniform bit-flips distribution while further reducing bit-flips in NVM. Experimental results show that AmLuCEP reduces bit-flips by 54%, 42%, 37%, 21%; energy consumption by 41%, 28%, 24%, 13%; and improves lifetime by 57%, 40%, 38%, 21% over baseline and the existing techniques READ [ 38 ], COEF [ 39 ] and SELEC [ 13 ], respectively.

Non-homogeneous anisotropic bulk viscosity for acoustic wave attenuation in weakly compressible methods

A major limitation of the weakly compressible approaches to simulate incompressible flows is the appearance of artificial acoustic waves that introduce mass conservation errors and lead to spurious oscillations in the force coefficients. In this work, we propose a non-homogeneous anisotropic bulk viscosity term to effectively damp the acoustic waves. By implementing this term in a computational framework based on the recently proposed general pressure equation, we demonstrate that the non-homogeneous and anisotropic nature of the term makes it significantly more effective than the isotropic homogeneous version widely used in the literature. Moreover, it is computationally more efficient than the pressure (or mass) diffusion term, which is an alternative mechanism used to suppress acoustic waves. We simulate a range of benchmark problems to comprehensively investigate the performance of the bulk viscosity on the effective suppression of acoustic waves, mass conservation error, order of convergence of the solver, and computational efficiency. The proposed form of the bulk viscosity enables fairly accurate modelling of the initial transients of unsteady simulations, which is highly challenging for weakly compressible approaches, and to the best of our knowledge, existing approaches can't provide an accurate prediction of such transients.

HiCMC: High-Efficiency Contact Matrix Compressor

BackgroundChromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data.ResultsBy modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms the state-of-the-art method CMC by approximately 8% and the other state-of-the-art methods cooler, LZMA, and bzip2 by over 50% across multiple cell lines and contact matrix resolutions. In addition, HiCMC integrates domain-specific information into the compressed bitstreams that it generates, and this information can be used to speed up downstream analyses.ConclusionHiCMC is a novel compression approach that utilizes intrinsic properties of contact matrix, such as compartments and domains. It allows for a better compression in comparison to the state-of-the-art methods. HiCMC is available at https://github.com/sXperfect/hicmc.

Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data

The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.

Multidimensional Pruning and Its Extension: A Unified Framework for Model Compression.

Observing that the existing model compression approaches only focus on reducing the redundancies in convolutional neural networks (CNNs) along one particular dimension (e.g., the channel or spatial or temporal dimension), in this work, we propose our multidimensional pruning (MDP) framework, which can compress both 2-D CNNs and 3-D CNNs along multiple dimensions in an end-to-end fashion. Specifically, MDP indicates the simultaneous reduction of channels and more redundancy on other additional dimensions. The redundancy of additional dimensions depends on the input data, i.e., spatial dimension for 2-D CNNs when using images as the input data, and spatial and temporal dimensions for 3-D CNNs when using videos as the input data. We further extend our MDP framework to the MDP-Point approach for compressing point cloud neural networks (PCNNs) whose inputs are irregular point clouds (e.g., PointNet). In this case, the redundancy along the additional dimension indicates the point dimension (i.e., the number of points). Comprehensive experiments on six benchmark datasets demonstrate the effectiveness of our MDP framework and its extended version MDP-Point for compressing CNNs and PCNNs, respectively.

A Resource-Constrained Polynomial Regression Approach for Voltage Measurement Compression in Electric Vehicle Battery Packs

A Resource-Constrained Polynomial Regression Approach for Voltage Measurement Compression in Electric Vehicle Battery Packs

Software Optimization and Design Methodology for Low Power Computer Vision Systems

This tutorial paper addresses a low power computer vision system as an example of a growing application domain of neural networks, exploring various technologies developed to enhance accuracy within the resource and performance constraints imposed by the hardware platform. Focused on a given hardware platform and network model, software optimization techniques, including pruning, quantization, low-rank approximation, and parallelization, aim to satisfy resource and performance constraints while minimizing accuracy loss. Due to the interdependence of model compression approaches, their systematic application is crucial, as evidenced by winning solutions in the Lower Power Image Recognition Challenge (LPIRC) of 2017 and 2018. Recognizing the typical heterogeneity of processing elements in contemporary hardware platforms, the effective utilization through parallelizing neural networks emerges as increasingly vital for performance enhancement. The paper advocates for a more impactful strategy—designing a network architecture tailored to a specific hardware platform. For detailed information on each technique, the paper provides corresponding references.

Molecular interactional study of SDS and CTAB in aqueous medium of anti-HIV drugs (Emtricitabine and Lamivudine) at different temperatures: A physicochemical investigation

The present study deals with the interactions of Emtricitabine and Lamivudine (anti-HIV drugs) with anionic surfactant sodium dodecylsulphate (SDS) and cationic surfactant cetyltrimethylammonium bromide (CTAB) at (298.15–313.15) K temperature range by volumetry, compressibility and viscometric approach. The experimental values of sound velocity and density are used to compute various parameters such as apparent molar volume (φv), isentropic compressibility (κs), and apparent molar adiabatic compression (φκ) by employing cosphere overlap model. Relative viscosity parameter (ηr) evaluated from viscosity data also helps to understand various interactions prevalent in surfactant-drug-water ternary system. This attempt of extracting information about various physicochemical interactions of anti-HIV drugs with surfactants can prove beneficial to develop novel anti-HIV agents with better viral resistance activity.

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

Image privacy protection scheme based on high-quality reconstruction DCT compression and nonlinear dynamics

The protection of digital image privacy in Big Data environment is a hot issue of increasing concern. To address the contradiction between efficiency and security for privacy protection, this paper proposes a high-quality restored image privacy protection scheme based on Discrete Cosine Transform (DCT) frequency domain compression and nonlinear dynamics. First, the RGB space of the color image is converted to YCbCr domain with 4:2:0 sampling. Then, the image is divided into sub-blocks in the spatial domain, followed by block-wise DCT and quantization into frequency domain information. Next, both Alternating Current (AC) and Direct Current (DC) coefficients of all sub-blocks are individually extracted and subjected to compression encoding. Finally, a Rubik’s Cube permutation and filtering diffusion encryption algorithm based on a nonlinear dynamical chaotic system is designed. Moreover, a mechanism for generating dynamic chaotic sequences associated with plaintext is introduced to effectively counter cryptographic attacks. The combined approach of compression and encryption significantly reduces computational complexity while improving encryption efficiency. Through experimental simulation results, the compression encryption scheme demonstrates high compression ratios as well as excellent image recovery quality while providing enhanced security against common cryptographic attacks. The work in this paper provides a preferred joint compression and encryption technical solution for digital image privacy protection in Big Data network.

Numerical modeling of the crushing characteristics of single ore particle based on breakable Voronoi block model

The study of crushing characteristics of irregular single ore particles is considered crucial for understanding the secondary fragmentation mechanism, predicting particle gradation, and optimizing the gravity flow of ore particles. This study introduces a numerical simulation approach for single particle compression crushing utilizing the Breakable Voronoi block model (BVBM). The paper examines the influence of various parameters including ball diameter, loading speed, softening tensile strength factor, softening factor, and BVBM size on the crushing behavior of ore particles. It provides a suggested value range for each meso-parameter suitable for single particle modeling and crushing characteristics research. Additionally, the paper describes the evolution of BVBM crushing mode, the total number of cracks, and the number of contact bonds under the influence of different softening factors. Soft rock particles with high softening factors exhibit more significant softening behavior and ductility compared to hard rock particles with low softening factors.

Comparative analysis of optional hunting behavior in Cricetinae hamsters using the data compression approach

Research into the hunting behavior in members of the Cricetidae family offers an opportunity to reveal what changes in the predatory behavioral sequences occur when a rodent species shifts from an omnivorous to a predatory lifestyle. The study tests the following hypotheses: are there phylogenetic differences in the divergence of species’ predatory lifestyles in hamsters or do ecological factors lead to shaping their hunting behavior? We applied the data compression approach for performing comparative analysis of hunting patterns as biological “texts.” The study presents a comparative analysis of hunting behaviors in five Cricetinae species, focusing on the new data obtained for the desert hamster Phodopus roborovskii whose behavior has never been studied before. The hunting behavior of P. roborovskii appeared to be the most variable one. In contrast, behavioral sequences in P. campbelli and Allocricetulus curtatus display more significant order and predictability of behavior during hunting. Optional hunting behavior in the most ancient species P. roborovskii displayed similarities with obligate patterns in “young” Allocricetulus species. It thus turned out to be the most advanced hunter among members of the Phodopus genus. Differences in hunting sequences among Phodopus representatives suggest that the hunting behavior of these species, despite its optional mode, was subject to selection during species splitting within the genus. These results did not reveal the role played by phylogenetic differences in the divergence of species’ predatory lifestyles. They suggested that ecological conditions are the main factors in speciation of the hunting behavior in hamsters.