Fast Compression Research Articles

Role of tympanocentesis in the prevention of middle ear barotrauma induced by fast buoyant ascent escape from 200 m underwater.

We aimed to study middle ear barotrauma caused by fast compression followed by buoyant ascent escape from 200 m underwater and its effect on the auditory system, and to validate the preventive effect of tympanocentesis on middle ear barotrauma. Twenty Sprague Dawley rats were divided into two groups: rats in group A underwent a simulated fast buoyant ascent escape from a depth of 200 m, while those in group B underwent tympanocentesis before the procedure described for group A. Ear endoscopy, acoustic conductance, and auditory brainstem response (ABR) tests were conducted before and after the procedure to evaluate the severity of middle ear barotrauma and auditory function in both groups. Additionally, histopathological examination of the middle ear in both groups was conducted to evaluate the severity of middle ear barotrauma by observing submucosal haemorrhage. None of the ears in either group showed any abnormalities before the experiment. In group A, middle ear barotrauma was universally observed after the simulation procedure. The tympanograms of all ears were initially type A and became type B after the procedure. Further, after the simulation, the hearing thresholds at different frequencies (4, 8, 16, 24, and 32 kHz) assessed by ABR significantly increased compared to those before the procedure. In group B, no middle ear barotrauma was observed, and the hearing threshold at each frequency did not change significantly compared with post-puncturing. After dissecting the middle ear, gross pathological observations were consistent with the above results. Microscopically, blood accumulation and submucosal haemorrhage in the middle ear cavity were observed in group A but not in group B. Fast buoyant ascent from 200 m underwater can cause middle ear barotrauma, resulting in hearing loss. Tympanic membrane puncture can effectively prevent middle ear barotrauma caused by the rapid buoyant ascent escape procedure.

A Comparative Study of Text-Based Lossless Compression

Lossless data compression is a critical technique used to reduce file sizes without any loss of information during the encoding and decoding processes. This study presents a comparative analysis of two widely-used lossless compression algorithms: Huffman Encoding and Lempel-Ziv-Welch (LZW). The primary objective is to evaluate the performance of these algorithms in terms of compression ratio, compression time, decompression time, and space savings. The analysis was conducted on 100 files of varying sizes. The results demonstrate that the LZW algorithm outperforms Huffman Encoding, offering superior compression ratios, faster compression and decompression times, and greater disk space savings. These findings highlight the effectiveness of LZW for efficient data compression in practical applications.

Fast lossless images compression for synchrotron radiation facility using deep learning and hybrid architecture

Fast lossless images compression for synchrotron radiation facility using deep learning and hybrid architecture

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRAIII.

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia-water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

Memory‐efficient compression of 𝒟ℋ2‐matrices for high‐frequency Helmholtz problems

AbstractDirectional interpolation is a fast and efficient compression technique for high‐frequency Helmholtz boundary integral equations, but requires a very large amount of storage in its original form. Algebraic recompression can significantly reduce the storage requirements and speed up the solution process accordingly. During the recompression process, weight matrices are required to correctly measure the influence of different basis vectors on the final result, and for highly accurate approximations, these weight matrices require more storage than the final compressed matrix. We present a compression method for the weight matrices and demonstrate that it introduces only a controllable error to the overall approximation. Numerical experiments show that the new method leads to a significant reduction in storage requirements.

Effects of Y2O3 on the hot deformation behavior and microstructure evolution of Al2O3-cu/35Cr3TiB2 electrical contact composites

The Al2O3-Cu/35Cr3TiB2 and 0.5Y2O3/Al2O3-Cu/35Cr3TiB2 composites were prepared by a combination of fast hot compression sintering and internal oxidization. The effects of Y2O3 on the hot deformation behavior and microstructure evolution of the composites were investigated. According to the hot deformation data, the constitutive equations, hot processing maps, and critical dynamic recrystallization models were constructed for the two composites. The Y2O3 addition enhanced the flow stress, activation energy, and hot processing properties of the composites. This led to a reduction in the percentage of dynamic recrystallization structure from 25.3% to 19.4%, and decreased the amount of high-angle grain boundaries from 77.8% to 71.4%. Y2O3 was distributed at grain boundaries and phase interfaces, effectively impeding dislocation movement and grain boundary migration. Notably, the strain distribution within Y2O3 exhibited greater homogeneity compared to the Cu matrix. More twins and stacking faults were observed inside the 0.5Y2O3/Al2O3-Cu/35Cr3TiB2 composites, which synergistically strengthened the matrix.

Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments.

Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s-1, which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

Fast Video-Based Point Cloud Compression Based on Early Termination and Transformer Model

Fast Video-Based Point Cloud Compression Based on Early Termination and Transformer Model

CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models

Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design requirements: memory efficiency, low latency, and adaptability to dynamic data distribution. This paper presents CAFE, a Compact, Adaptive, and Fast Embedding compression framework that addresses the above requirements. The design philosophy of CAFE is to dynamically allocate more memory resources to important features (called hot features), and allocate less memory to unimportant ones. In CAFE, we propose a fast and lightweight sketch data structure, named HotSketch, to capture feature importance and report hot features in real time. For each reported hot feature, we assign it a unique embedding. For the non-hot features, we allow multiple features to share one embedding by using hash embedding technique. Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features. We theoretically analyze the accuracy of HotSketch, and analyze the model convergence against deviation. Extensive experiments show that CAFE significantly outperforms existing embedding compression methods, yielding 3.92% and 3.68% superior testing AUC on Criteo Kaggle dataset and CriteoTB dataset at a compression ratio of 10000x. The source codes of CAFE are available at GitHub.

Poroelastic model of the lungs at low frequencies predicted by Biot's theory

Biot's theory of poroelastic wave propagation inherently lends itself to elucidate the characteristics of a biphasic medium comprising solid and fluid components, such as biological tissues. One of the intricately complex biological tissues that remains poorly understood is the lungs since their properties diversify significantly through their pore geometries affected by inspiratory positive airway pressure (IPAP) and applied frequency range. One hypothesizes that the vibroacoustic behaviour of the lungs can be predicted by Biot's theory, as the nature of the lungs aligns with the principles of the theory at low frequencies. This study aims to analytically investigate the vibroacoustic behaviour of the lungs, considering 10 and 20-cm H;2O IPAP. Utilizing a fractional derivative formulation, one predicts the complex-valued shear wave speed, as well as the fast and slow compression wave speeds, for frequencies ranging from 5 to 100 Hz. A 3D digital thorax twin study using these predicted wave speeds, particularly at 28 Hz and 20 cm H;2O IPAP, is validated against experimental data from the literature. Consequently, applying Biot's theory provides a valuable framework for understanding the dynamic vibroacoustic behaviour of the lung tissues in response to varying IPAP and low frequencies.

LFC-UNet: learned lossless medical image fast compression with U-Net.

In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle the imbalance in residual categories. In terms of network architecture, we introduce skip connections from U-Net to better capture image features, thereby obtaining accurate probability estimates. Furthermore, our framework boasts excellent encoding speed, as the model is able to acquire all residuals and residual probabilities in a single inference pass. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on medical datasets while also offering the fastest processing speed. As illustrated by an instance using head CT data, our approach achieves a compression efficiency of 2.30 bits per pixel, with a processing time of only 0.320 seconds per image.

Waste Cotton-Derived Fiber-Based Thermoelectric Aerogel for Wearable and Self-Powered Temperature–Compression Strain Dual-Parameter Sensing

The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles. This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal. The rational recycling of wasted textiles and their transformation into high-value-added emerging products, such as smart wearable devices, is fascinating. Here, we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature–compression strain dual-parameter sensing properties. The thermoelectric aerogel exhibits a fast compression response time of 0.2 s, a relatively high Seebeck coefficient of 43 μV·K−1, and an ultralow thermal conductivity of less than 0.04 W·m−1·K−1. The cross-linking of trimethoxy(methyl)silane (MTMS) and cellulose endowed the aerogel with excellent elasticity, allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition. In addition, based on the thermoelectric effect, the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal. Furthermore, the wearable system, prepared by connecting the aerogel-prepared array device with a wireless transmission module, allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping. Hence, our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

The Effects of Signal to Noise Ratio, T60 , Wide-Dynamic Range Compression Speed, and Digital Noise Reduction in a Virtual Restaurant Setting.

Hearing aid processing in realistic listening environments is difficult to study effectively. Often the environment is unpredictable or unknown, such as in wearable aid trials with subjective report by the wearer. Some laboratory experiments create listening environments to exert tight experimental control, but those environments are often limited by physical space, a small number of sound sources, or room absorptive properties. Simulation techniques bridge this gap by providing greater experimental control over listening environments, effectively bringing aspects of the real-world into the laboratory. This project used simulation to study the effects of wide-dynamic range compression (WDRC) and digital noise reduction (DNR) on speech intelligibility in a reverberant environment with six spatialized competing talkers. The primary objective of this study was to determine the efficacy of WDRC and DNR in a complex listening environment using virtual auditory space techniques. Participants of greatest interest were listeners with hearing impairment. A group of listeners with clinically normal hearing was included to assess the effects of the simulation absent the complex effects of hearing loss. Virtual auditory space techniques were used to simulate a small restaurant listening environment with two different reverberation times (0.8 and 1.8 sec) in a range of signal to noise ratios (SNRs) (-8.5 to 11.5 dB SNR). Six spatialized competing talkers were included to further enhance realism. A hearing aid simulation was used to examine the degree to which speech intelligibility was affected by slow and fast WDRC in conjunction with the presence or absence of DNR. The WDRC and DNR settings were chosen to be reasonable estimates of hearing aids currently available to consumers. A WDRC × DNR × Hearing Status interaction was observed, such that DNR was beneficial for speech intelligibility when combined with fast WDRC speeds, but DNR was detrimental to speech intelligibility when WDRC speeds were slow. The pattern of the WDRC × DNR interaction was observed for both listener groups. Significant main effects of reverberation time and SNR were observed, indicating better performance with lower reverberation times and more positive SNR. DNR reduced low-amplitude noise before WDRC-amplified the low-intensity portions of the signal, negating one potential downside of fast WDRC and leading to an improvement in speech intelligibility in this simulation. These data suggest that, in some real-world environments that include both reverberation and noise, older listeners with hearing impairment may find speech to be more intelligible if DNR is activated when the hearing aid has fast compression time constants. Additional research is needed to determine the appropriate DNR strength and to confirm results in wearable hearing aids and a wider range of listening environments.

Dynamic magnetic field magnetorheological finishing with constant load and variable gap

Magnetorheological finishing is limited by weak polishing forces and small polishing areas, resulting in a low polishing efficiency. This study formulates a method for dynamic magnetic field magnetorheological finishing in compression-shear mode with a constant load and variable gap, which can remodel the magnetic particle string using a dynamic magnetic field and enhance the polishing force by a constant load and variable gap. To investigate the impact of process parameters on macro and micro machining processes, as well as their effects, a series of experiments were conducted to investigate the polishing force, machining clearance, and machining effect of single-crystal silicon wafers under four different conditions: static magnetic field magnetorheological finishing (T1), dynamic magnetic field magnetorheological finishing (T2) at a constant gap, static magnetic field magnetorheological finishing (T3), and dynamic magnetic field magnetorheological finishing (T4) with a constant load and variable gap. The results indicated that the shearing force and processing quality significantly improved in the constant-load and variable-gap modes, and the process of machining clearance reduction involved two stages: fast and slow compression. The shearing force of T3 increased by 35 % compared to T1, and T4 increased by 20 % compared to T2. The material removal rate of T3 and T4 increased over time, in contrast to those of T1 and T2. The key parameters of the T4 single-factor shear and machining gap testing experiments show that the constant load variable gap compression process adaptively adjusts the machining gap such that the chain string structure is in a body-centered cubic structure (BCT), increasing the number of abrasive contacts and the depth of pressure into the workpiece to achieve a significant increase in the polishing shear constant load.

Dynamic high-order buckling and spontaneous recovery of active epithelial tissues

Active epithelial tissues can adapt to quasi-static compressive forces through buckling instability, but their responses to dynamic forces at shorter timescales remain elusive. We firstly establish a cytoarchitectural model that can accurately capture the experimentally observed high-order buckling and postbuckling (e.g., spontaneous flattening and stress recovery) behaviors of epithelia under fast compression. It is found that the stress evolution of epithelia can be divided into three stages: loading, phase transition, and stress recovery. In the loading stage, we observe the high-order instability with a buckling mode highly correlated with the strain rate, and derive its analytical relation, showing that the rate-dependent buckling mode is quantitatively determined by the viscoelastic and geometrical characteristics of epithelia. In the phase transition and stress recovery stages, we demonstrate that the postbuckling process is governed by the active tension generated by the actomyosin network. Furthermore, by proposing a minimal model, we obtain the explicit solutions of the flattening time and stress recovery extent as functions of the applied strain or strain rate, which are in quantitative agreement with our simulations and relevant experiments. In addition, depending on the stress evolution route, we construct a universal phase diagram for the morphology evolution of the epithelia in a wide range of strain and strain rate. This study elucidates the dominative roles of the activity and rheological characteristics of active soft materials in their dynamic mechanical behaviors, offering an approach for studying the complex morphology evolution.

Simultaneous measurements of volume, pressure, optical images, and crystal structure with a dynamic diamond anvil cell: A real-time event monitoring system.

The dynamic diamond anvil cell (dDAC) technique has attracted great interest because it possibly provides a bridge between static and dynamic compression studies with fast, repeatable, and controllable compression rates. The dDAC can be a particularly useful tool to study the pathways and kinetics of phase transitions under dynamic pressurization if simultaneous measurements of physical quantities are possible as a function of time. We here report the development of a real-time event monitoring (RTEM) system with dDAC, which can simultaneously record the volume, pressure, optical image, and structure of materials during dynamic compression runs. In particular, the volume measurement using both Fabry-Pérot interferogram and optical images facilitates the construction of an equation of state (EoS) using the dDAC in a home-laboratory. We also developed an in-line ruby pressure measurement (IRPM) system to be deployed at a synchrotron x-ray facility. This system provides simultaneous measurements of pressure and x-ray diffraction in low and narrow pressure ranges. The EoSs of ice VI obtained from the RTEM and the x-ray diffraction data with the IRPM are consistent with each other. The complementarity of both RTEM and IRPM systems will provide a great opportunity to scrutinize the detailed kinetic pathways of phase transitions using dDAC.

Efficient in-situ image and video compression through probabilistic image representation

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi-dimensional image and video compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. Furthermore, it adopts a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. The properties of our proposed method include high reconstruction quality at a wide range of compression rates while preserving key local details, applicability to a variety of different image/video types and of different dimensions, computational scalability, progressive transmission and ease of tuning. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP compares favorably to—and often uniformly outperforms—a wide range of popular image/video compression approaches, including JPEG, JPEG2000, AVI, BPG, MPEG4, HEVC, AV1, and three neural network-based methods.

TERSE/PROLIX (TRPX) - a new algorithm for fast and lossless compression and decompression of diffraction and cryo-EM data.

High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, TERSE/PROLIX (or TRPX for short) is presented, a novel lossless compression algorithm specifically designed for diffraction data. The algorithm is compared with established lossless compression algorithms implemented in gzip, bzip2, CBF (crystallographic binary file), Zstandard(zstd), LZ4 and HDF5 with gzip, LZF and bitshuffle+LZ4 filters, in terms of compression efficiency and speed, using continuous-rotation electron diffraction data of an inorganic compound and raw cryo-EM data. The results show that TRPX significantly outperforms all these algorithms in terms of speed and compression rate. It was 60 times faster than bzip2 (which achieved a similar compression rate), and more than 3 times faster than LZ4, which was the runner-up in terms of speed, but had a much worse compression rate. TRPX files are byte-order independent and upon compilation the algorithm occupies very little memory. It can therefore be readily implemented in hardware. By providing a tailored solution for diffraction and raw cryo-EM data, TRPX facilitates more efficient data analysis and interpretation while mitigating storage and transmission concerns. The C++20 compression/decompression code, custom TIFF library and an ImageJ/Fiji Java plugin for reading TRPX files are open-sourced on GitHub under the permissive MIT license.

Efficient Lossless Compression of Integer Astronomical Data

Each new generation of telescope produces increasingly larger astronomical data volumes, which are expected to reach the order of exabytes in the next decade. Effective and fast data compression methods are paramount to help the scientific community contain storage costs and improve transmission times. Astronomical data differs significantly from natural and Earth-observation images, asking for specifically tailored compression approaches. This paper presents a novel lossless compression technique that employs the discrete Haar wavelet transform within the JPEG 2000 standard. Its performance is compared to that of a comprehensive selection of compressors, including fpack, the most common technique in astronomical observatories, as well as other algorithms highly competitive for other types of data. Experiments are performed on a large data set of 16 bit integer images, produced by telescopes around the world and representative of a wide variety of astronomical scenarios. The proposed technique has two modes. The first mode outperforms all the other tested techniques in terms of compression performance. It surpasses the most competitive configuration of fpack by, respectively, 5.3% (about 0.3 bits per sample), having also 4.5% lower compression and decompression times. The second mode is the fastest among all tested techniques. Its compression and decompression times are 2.5 and 3.5 times faster than the fastest configuration of fpack, while also yielding a 2.4% better compression performance (0.15 bits per sample).