Fast Compression Research Articles

Wave propagation in an unconsolidated granular material: A micro-mechanical approach

We provide a theoretical analysis to support the presence of both slow and fast compression waves in an unconsolidated, fully saturated, granular material. We derive the constitutive relation for such an aggregate based upon a micro-mechanics analysis. In doing this, we take in account the coupling between the solid particles and fluid. As a consequence of this coupling, the lubrication layer provides a connection between particles, both when they are separating and when they are compressing. The predictions of the speed and attenuation of the fast compression waves compare well with experimental data over the range of frequencies for which the nonlinear dissipation associated with the relative velocities between solid and fluid is negligible. Slow waves are also predicted without comparison, because of the absence of clear experimental data. Predictions of the speed and attenuation for the shear wave are also provided and show a good agreement with experimental data when surface roughness is taken into account.

A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.

A resistively-heated dynamic diamond anvil cell (RHdDAC) for fast compression x-ray diffraction experiments at high temperatures.

A resistively-heated dynamic diamond anvil cell (RHdDAC) setup is presented. The setup enables the dynamic compression of samples at high temperatures by employing a piezoelectric actuator for pressure control and internal heaters for high temperature. The RHdDAC facilitates the precise control of compression rates and was tested in compression experiments at temperatures up to 1400 K and pressures of ∼130 GPa. The mechanical stability of metallic glass gaskets composed of a FeSiB alloy was examined under simultaneous high-pressure/high-temperature conditions. High-temperature dynamic compression experiments on H2O ice and (Mg, Fe)O ferropericlase were performed in combination with time-resolved x-ray diffraction measurements to characterize crystal structures and compression behaviors. The employment of high brilliance synchrotron radiation combined with two fast GaAs LAMBDA detectors available at the Extreme Conditions Beamline (P02.2) at PETRA III (DESY) facilitates the collection of data with excellent pressure resolution. The pressure-temperature conditions achievable with the RHdDAC combined with its ability to cover a wide range of compression rates and perform tailored compression paths offers perspectives for a variety of future experiments under extreme conditions.

Shortcuts to Adiabaticity for Optical Beam Propagation in Nonlinear Gradient Refractive-Index Media.

In recent years, the concept of “shortcuts to adiabaticity" has been originally proposed to speed up sufficiently slow adiabatic process in various quantum systems without final excitation. Based on the analogy between classical optics and quantum mechanics, we present a study on fast non-adiabatic compression of optical beam propagation in nonlinear gradient refractive-index media by using shortcuts to adiabaticity. We first apply the variational approximation method in nonlinear optics to derive the auxiliary equation for connecting the beam width with the refractive index of the medium. Then, the gradient refractive index is inversely designed through the perfect compression of beam width with the appropriate boundary conditions. Finally, the comparison with conventional adiabatic compression is made, showing the advantage of our shortcuts.

Effect of Image Compression using Fast Fourier Transformation and Discrete Wavelet Transformation on Transfer Learning Wafer Defect Image Classification

Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features whilst maintaining an acceptable classification accuracy. This paper reports on the effect of image compression using Fast Fourier Transformation and Discrete Wavelet Transformation on transfer learning wafer defect image classification. A total of 500 images with 5 classes with 4 defect classes and 1 non-defect class were split to 60:20:20 ratio for training, validating and testing using InceptionV3 and Logistic Regression classifier. However, the input images were compressed using Fast Fourier Transformation and Discrete Wavelet Transformation using 4 level decomposition and Debauchies 4 wavelet family. The images were compressed by 50%, 75%, 90%, 95%, and 99%. As a result, the Fast Fourier Transformation compression show an increase from 89% to 94% in classification accuracy up to 95% compression, while Discrete Wavelet Transformation shows consistent classification accuracy throughout albeit diminishing image quality. From the experiment, it can be concluded that FFT and DWT image compression can be a reliable method for image compression for grayscale image classification as the image memory space drop 56.1% while classification accuracy increased by 5.6% with 95% FFT compression and memory space drop 55.6% while classification accuracy increased 2.2% with 50% DWT compression.

Image encryption using dynamic DNA encoding and pixel scrambling using composite chaotic maps

In this digital world, encryption plays an important role in various domains. Securing the information is the main goal when transfer of information takes place. Image encryption is a very important part of this as it applies to various domains like medical, multimedia, defence etc. A new method for image encryption is proposed here taking into account the “confusion-diffusion” structure. While working with secure data, requirements like fast computation, compression and processing are important issues. Deoxyribonucleic Acid (DNA) encoding has the capability to cope up with this requirement. In this paper a new algorithm is proposed based on composite chaos map for pixel scrambling and hence image encryption. First the algorithm takes the image and XORs with a composite chaotic map which further goes into dynamic DNA encoding followed by pixel scrambling which results in the image being very random thus it becomes less prone to attacks. The method discussed performs efficiently as shown in the experimental results.

Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks

Accurate classification of electrocardiogram (ECG) signals is of significant importance for automatic diagnosis of heart diseases. In order to enable intelligent classification of arrhythmias with high accuracy, an accurate classification method based intelligent ECG classifier using the fast compression residual convolutional neural networks (FCResNet) is proposed. In the proposed method, the maximal overlap wavelet packet transform (MOWPT), which provides a comprehensive time-scale paving pattern and possesses the time-invariance property, was utilized for decomposing the original ECG signals into sub-signal samples of different scales. Subsequently, the samples of the five arrhythmia types were utilized as input to the FCResNet such that the ECG arrhythmia types were identified and classified. In the proposed FCResNet model, a fast down-sampling module and several residual block structural units were incorporated. The proposed deep learning classifier can substantially alleviate the problems of low computational efficiency, difficult convergence and model degradation. Parameter optimizations of the FCResNet were investigated via single-factor experiments. The datasets from MIT-BIH arrhythmia database were employed to test the performance of the proposed deep learning classifier. An averaged accuracy of 98.79% was achieved when the number of the wide-stride convolution in fast down-sampling module was set as 2, the batch size parameter was set as 20 and wavelet subspaces of low frequency bands in MOWPT were selected as input of the classifier. These analysis results were compared with those generated by some comparison methods to validate the superiorities and enhancements of the proposed method.

A Fast and Efficient Lossless Compression Technique for Greyscale Images

The growth of cloud based remote healthcare and diagnosis services has resulted, Medical Service Providers (MSP) to share diagnositic data across diverse environement. This medical data are accessed across diverse platforms, such as, mobile and web services which needs huge memory for storage. Compression technique helps to address and solve storage requirements and provides for sharing medical data over transmission medium. Loss of data is not acceptable for medical image processing. As a result, this work considers lossless compression for medical in particular and in general any greyscale images. Modified Huffman encoding (MH) is one of the widely used technique for achieving lossless compression. However, due to longer bit length of codewords the existing Modified Huffman (MH) encoding technique is not efficient for medical imaging processing. Firstly, this work presents Modified Refined Huffman (MRH) for performing compression of greyscale and binary images by using diagonal scanning method. Secondly, to minimize the computing time parallel encoding method is used. Experiments are conducted for wide variety of images and performance is evaluated in terms of Compression Ratio, Computation Time and Memory Utilization. The proposed MRH achieves significant performance improvement in terms of Compression Ratio, Computation Time and Memory Usage over its state-of-the-art techniques, such as, LZW, CCITT G4, JBIG2 and Levenberg–Marquardt (LM) Neural Network algorithm. The overall results achieved show the applicability of MRH for different application services.

LCQS: an efficient lossless compression tool of quality scores with random access functionality

BackgroundAdvanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.ResultsIn this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.ConclusionThe ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.

Density diagnosis based on ps-duration-pulse X-ray backlighting for fast ignition compression

To provide significant parameters for fast ignition coupling efficiency and density diagnosis for higher compression, a ps-duration X-ray backlighter has been produced with ps-duration laser on Shenguang-Ⅱ updated facility. The radiation properties such as backlighting image resolution and the photons arriving at the image plate have been successfully measured. Based on the conformed conditions, density diagnosis of an indirectly-driven fast ignition target using ps-duration backlighting has been carried out. The obtained short-pulse backlighting radiography shows that the imploded shell shape agrees well with that of simulation and the areal density exceeds 50 mg/cm2. The short-pulse backlighting radiography also shows the hydrodynamic instability which might be caused by the asymmetric compression. Further investigations and attempts to improve implosion performance to a higher density are in progress.

KT and azimuth sub‐region deramp‐based high‐squint SAR imaging algorithm mounted on manoeuvring platforms

Owing to the existence of high-order motion parameters and vertical velocity, severe range–azimuth coupling and two-dimensional spatial variability of the imaging parameters degrade the image quality of high-squint synthetic aperture radar (SAR) mounted on manoeuvring platforms. To accommodate these issues, a novel sub-aperture SAR imaging algorithm based on keystone transform (KT) and azimuth sub-region deramp is proposed. In the range dimension processing, a correction function is constructed based on the scene centre to realise non-spatial-variant range cell migration (RCM) correction and range–azimuth decoupling, and then the residual spatial-variant RCM is removed by the KT. In the azimuth dimension processing, considering the impact of the range distortion on the azimuth modulated phase, accurate analytical formula of the signal in the azimuth time domain is deduced. On this basis, a fast azimuth compression method based on the azimuth sub-region deramp is proposed, and the theoretical effective imaging area is analysed. The theoretical analysis and the simulations show that the proposed imaging algorithm has high computational efficiency and can effectively expand the imaging area of high-squint SAR mounted on manoeuvring platforms.

The development of modern automated image processing and transfer systems for agriculture unmanned aerial vehicles

The paper contains results of analytic research of unmanned aerial vehicles using in agriculture. The main problems arising in the creation and subsequent large volumes of high-resolution images real time transfer in unmanned aerial vehicles are highlighted. The Automated image processing and transfer system using new methods of information compression on unmanned aerial vehicles board is proposed. The paper considers the issues of consider the problems of constructing new orderings of Walsh functions and constructing fast compression algorithms in synthesized systems of discrete Walsh functions. For processing and subsequent transmission of information from UAVs recommended to use the fast DWT procedure, it allows for a hardware implementation capable of the real-time conversion performing due to its simplicity. The introduction of the proposed solutions for UAVs in agriculture allows to increase accurasy of electronic cartographic material, to keep electronic records of agricultural operations, to carry out operational monitoring of the crops state and to respond quickly for violations and deviations, to predict crop yields and plan their activities for short-term and long-term prospects.

Improving Elasticity of Conductive Silicone Rubber by Hollow Carbon Black

Carbon black-based conductive rubber composites have important impacts on electromagnetic interference(EMI) shielding applications. However, an excessive amount of carbon black in the recipes of these conductive rubbers has caused their weak elasticity. Herein, hollow carbon black(HCB) particles were used to tune the elasticity of conductive rubber composites. Unique hollow morphology produced a better compression recovery of HCB than other solid carbon black, such as acetylene black. When the coupling agent was bonded to HCB, their conductive silicone rubber composites were featured by high stretching resilience, a fast compression recovery and excellent conductivity to satisfy the electromagnetic interference shielding requirements. Importantly, the rubber composites with coupling HCB had extremely low variations of mechanical property, conductivity and EMI shielding effectiveness after thermal accelerated aging tests. It is therefore revealed that the elasticity of HCB and its interfacial chemical coupling with rubber chains both play crucial roles in adjusting the elasticity of conductive rubber to sever long-term EMI protection.

Actomyosin controls planarity and folding of epithelia in response to compression.

Throughout embryonic development and adult life, epithelia are subjected to compressive deformations. While these have been shown to trigger mechanosensitive responses such as cell extrusion and differentiation, which span tens of minutes, little is known about how epithelia adapt to compression over shorter timescales. Here, using suspended epithelia, we uncover the immediate response of epithelial tissues to the application of in-plane compressive strains (5-80%). We show that fast compression induces tissue buckling followed by actomyosin-dependent tissue flattening that erases the buckle within tens of seconds, in both mono- and multi-layered epithelia. Strikingly, we identify a well-defined limit to this response, so that stable folds form in the tissue when compressive strains exceed a 'buckling threshold' of ~35%. A combination of experiment and modelling shows that this behaviour is orchestrated by adaptation of the actomyosin cytoskeleton as it re-establishes tissue tension following compression. Thus, tissue pre-tension allows epithelia to both buffer against deformation and sets their ability to form and retain folds during morphogenesis.

Effects of High Strain Rate and Self-heating on Plastic Deformation of Metal Materials Under Fast Compression Loading

The results of dynamic tests for compression of four metals after annealing (aluminum alloy, copper, brass, stainless steel) in the range of strain rates 100–10,000 s–1 are presented. To obtain dynamic true stress-true plastic strain curves a specific dynamic upsetting computation method was used starting from time variation of force pulse. The high-speed loading of specimens with a height to diameter ratio equal to 1.5 was carried out using three experimental devices: a drop-hammer with a free-falling mass, a ram impact machine with rubber accelerators and an impact machine with powder acceleration. A technique for direct measuring the specimen temperature during plastic deformation using an “artificial specimen” thermocouple (specimen-thermocouple) was proposed, in which the deformable metal itself is the “hot” junction, while the “cold” junction is connected to the measuring instrument. A thermoelectrode made of an alloy of noble metals tungsten and rhenium with a diameter of 0.1 mm was welded to the specimen by radiation from a pulsed YAG:Nd3+ laser. As a result of the tests, true dynamic stress–strain diagrams and flow stress versus strain rate were obtained. Experimental estimation concerning self-heating temperature variation corresponding to high-speed plastic deformation at room temperature is also developed. Experiments for determining the temperature effect of plastic deformation of the samples after annealing during cold settling showed that at the high speed of ~ 50 m s−1 and high strain-rates of ~ 10,000 s−1 the increase in temperature of the specimens compressed to a degree of deformation of 0.6 reached 55 °C for an aluminum alloy, 60 °C for copper, 80 °C for brass and 140 °C for stainless steel.

Fast Compression For Brain Mr Images With Proposed Algorithms

Growth in information storage and retrieval significantly depend on images in various domains as the information representation and understand ability is significantly higher. The challenges in processing the complete information in image formats are obtained during storage and transmission. Also, the information extractions from images are significantly difficult compared to information extractions from text. Nonetheless, the incorporation of image analysis for disease detection involves gigantic amount of image data storage, which is a concern of financial drawbacks. Hence, the images used for the analysis must be compressed for storage. However, the complexity of image compression is critical as the information loss can cause significant difference in disease detections. Thus the traditional lossy image compression methods cannot be applied to this problem. Hence, this work addresses the optimal compression of the medical images without vital information loss and with ominously high compression ratio as the second objective of this work.

Fast Video Compression Method based on Wavelet Transform and Simple Efficient Search Algorithm

Fast Video Compression Method based on Wavelet Transform and Simple Efficient Search Algorithm

Detecting shocked intergalactic gas with X-ray and radio observations

Detecting the thermal and non-thermal emission from the shocked cosmic gas surrounding large-scale structures represents a challenge for observations, as well as a unique window into the physics of the warm-hot intergalactic medium. In this work, we present synthetic radio and X-ray surveys of large cosmological simulations in order to assess the chances of jointly detecting the cosmic web in both frequency ranges. We then propose best observing strategies tailored for existing (LOFAR, MWA, and XMM) or future instruments (SKA-LOW and SKA-MID, Athena, and eROSITA). We find that the most promising targets are the extreme peripheries of galaxy clusters in an early merging stage, where the merger causes the fast compression of warm-hot gas onto the virial region. By taking advantage of a detection in the radio band, future deep X-ray observations will probe this gas in emission, and help us to study plasma conditions in the dynamic warm-hot intergalactic medium with unprecedented detail.

Optimized manufacturing of thermoplastic biocomposites by fast induction-heated compression moulding: Influence of processing parameters on microstructure development and mechanical behaviour

The optimization of processing tools and manufacturing procedures are key issues for the development of high-performance biocomposite materials. In this study, we investigated the manufacturing of commingled flax/polypropylene fabrics by fast induction-heated compression moulding to produce lightweight thermoplastic biocomposites. The processing/microstructure/mechanical behaviour relationships of these biocomposites were established based on a careful characterisation of material’s microstructure through SEM and X-ray tomography. Elastic properties of the biocomposites could be maintained even at high processing temperature, but their strength was drastically decreased due to extensive fibre degradation and formation of macro-pores. An optimised set of processing parameters was found for an improved microstructure with limited porosity and fibre degradation. The use of fast induction-heated systems can thus be an interesting solution to overcome the thermal degradation processes occurring with natural fibres reinforced biocomposites, hence opening the doors for the use of higher melting point polymer matrices.

A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

PurposeThis paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.Design/methodology/approachThis fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.FindingsExperiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.Originality/valueThis paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.