High Compression Rate Research Articles

Static and dynamic diamond anvil cell (s-dDAC): A bidirectional remote controlled device for static and dynamic compression/decompression

A novel bidirectional remotely controlled device for static and dynamic compression/decompression using diamond anvil cells (DACs) has been developed that can control pressure in an accurate and consistent manner. Electromechanical piezoelectric actuators are applied to a conventional DAC, allowing applications under a variety of pressure conditions. Using this static and dynamic DAC (s-dDAC), it is possible to addresses the poorly studied experimental regime lying between purely static and purely dynamic studies. The s-dDAC, driven by three piezoelectric actuators, can be combined with a time-resolved spectral measurement system and high-speed imaging device to study the structural changes, chemical reactions, and properties of materials under extreme conditions. The maximum compression/decompression rate or pressure range highly depends on the culet size of the anvil, and a higher compression rate and wider pressure range can be realized in a DAC with smaller anvil culet. With our s-dDAC, we have been able to achieve the highest compression rate to date with a 300 μm culet anvil: 48 TPa/s. An overview of a variety of experimental measurements possible with our device is presented.

Novel Data Compression Algorithm for Transmission Line Condition Monitoring

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data.

A combination of digital design and three-dimensional printing to assist treatment of thoracolumbar compression fractures using percutaneous kyphoplasty

ObjectiveTo evaluate the clinical efficacy of the preoperative digital design combined with three-dimensional (3D) printing models to assist percutaneous kyphoplasty (PKP) treatment for thoracolumbar compression fractures. MethodsFrom January 2018 to August 2020, we obtained data of 99 patients diagnosed thoracolumbar compression fractures. These patients were divided into control group (n = 50) underwent traditional PKP surgery, and observation group (n = 49) underwent preoperative digital design combined with 3D printing model assisted PKP treatment. The clinical efficacy was evaluated with five parameters, including operation time, number of intraoperative radiographs, visual analogue scale (VAS) score, Cobb Angle change, and high compression rate of injured vertebrae. ResultsThere were statistically significant differences of operation time and number of intraoperative radiographs between the two groups (P < 0.05). For VAS score, Cobb Angle change and vertebral height compression rate, all of these three parameters were significantly improved when the patients accepted surgery treatment in two groups (P < 0.05). However, there were no significant differences between control group and observation group for these three parameters either before or after surgery (P > 0.05). ConclusionsThrough the design of preoperative surgical guide plate and the application of 3D printing model to guide the operation, the precise design of preoperative surgical puncture site and puncture Angle of the injured vertebra was realized, the number of intraoperative radiographs was reduced, the operation time was shortened and the operation efficiency was improved.

(Invited) Electrochemical Ammonia Compression

Every year a large amount of ammonia is mechanically compressed with a low efficiency (B65%) as an environmentally friendly chemical for fuel storage, transportation, food preservation and air conditioning. Electrochemical compression with a high efficiency (93%) offers an opportunity for substantial energy savings. But ammonia is never considered electrochemically compressible because of the known decomposition at high applied potentials. The concept of using a carrier gas to co-compress ammonia was proposed but never experimentally validated. Here for the first time, we realized electrochemical compression of ammonia by using a proton exchange membrane asan NH4+ conductor and hydrogen as a carrier to avoid decomposition. The ammonia transfer mechanism and kinetics in a Nafion membrane was investigated and verified using electro-analytical methods, and the continuous electrochemical ammonia compression at a constant voltage of 200 mV with a stable current density for 7 hours has been demonstrated. NH4+ transports in the Nafion membrane by hopping and vehicular mechanisms with a high NH4+ conductivity of 4 102 S cm1 at 50% RH and 70 8C, and a constant transfer ratio of 2 between NH3 and H2 ensure a stable and high compression rate

ACAE-REMIND for online continual learning with compressed feature replay

Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset.

Sparse Variational Deterministic Policy Gradient for Continuous Real-Time Control

Recent advancements in deep reinforcement learning for real control tasks have received interest from many researchers and field engineers in a variety of industrial areas. However, in most cases, optimal policies obtained by deep reinforcement learning are difficult to implement on cost-effective and lightweight platforms such as mobile devices. This can be attributed to their computational complexity and excessive memory usage. For this reason, this article proposes an off-policy deep reinforcement learning algorithm called the sparse variational deterministic policy gradient (SVDPG). SVDPG provides highly efficient policy network compression under the standard reinforcement learning framework. The proposed SVDPG integrates Bayesian pruning, which is known as a state-of-the-art neural network compression technique, with the policy update in an actor–critic architecture for reinforcement learning. It is demonstrated that SVDPG achieves a high compression rate of policy networks for continuous control benchmark tasks while preserving a competitive performance. The superiority of SVDPG in low-computing power devices is proven by comparing the level of compression in terms of the memory requirements and computation time on a commercial microcontroller unit. Finally, it is confirmed that the proposed SVDPG is also reliable in real-world scenarios since it can be applied to the swing-up control of an inverted pendulum system.

An efficient field-programmable gate array-based hardware oriented block motion estimation algorithm based on diamond adaptive rood pattern search algorithm for multi-standard video codec

Motion estimation and motion compensation techniques are effectively used for removing temporal redundancy between adjacent frames during video compression, but video quality will be degraded with the traditional pixel-based matching algorithms. To overcome this issue, diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithms are proposed in this manuscript for multi-stranded codec hardware design, which provide a high compression rate with less computational complexity. In this work, a diamond adaptive rood pattern search algorithm is used, which is a combination of a diamond-search algorithm and adaptive rood pattern search. In addition, the proposed algorithm applies multiple search strategies and terminates unnecessary searching while reserving high data regularity. The proposed diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithm for multi-stranded codec hardware design provides good performance in terms of complex calculations reduction and reaches a high speed in comparison to traditional search algorithms. The proposed architecture is implemented in Xilinx ISE design 14.5 suite and the implemented results are compared to the existing motion estimation techniques on field-programmable gate array devices. Simulation results shows that the diamond adaptive rood pattern search algorithm for multi-stranded codec hardware design provides a very good improvement in terms of computations overhead reduction, with 96.4%, 97.3% latency and 98.7% throughput and achieves a high compression rate without video quality degradation.

Previous Intensive Running or Swimming Negatively Affects CPR Effectiveness.

Survival outcomes increase significantly when cardiopulmonary resuscitation (CPR) is provided correctly, but rescuers’ fatigue can compromise its delivery. We investigated the effect of two exercise modes on CPR effectiveness and physiological outputs. After 4 min baseline conditions, 30 lifeguards randomly performed a 100 m run and a combined water rescue before 4 min CPR (using an adult manikin and a 30:2 compression–ventilation ratio). Physiological variables were continuously measured during baseline and CPR using a portable gas analyzer (K4b2, Cosmed, Rome, Italy) and CPR effectiveness was analyzed using two HD video cameras. Higher oxygen uptake (23.0 ± 9.9 and 20.6 ± 9.1 vs. 13.5 ± 6.2 mL·kg·min−1) and heart rate (137 ± 19 and 133 ± 15 vs. 114 ± 15 bpm), and lower compression efficacy (63.3 ± 29.5 and 62.2 ± 28.3 vs. 69.2 ± 28.0%), were found for CPRrun and CPRswim compared to CPRbase. In addition, ventilation efficacy was higher in the rescues preceded by intense exercise than in CPRbase (49.5 ± 42.3 and 51.9 ± 41.0 vs. 33.5 ± 38.3%), but no differences were detected between CPRrun and CPRswim. In conclusion, CPRrun and CPRswim protocols induced a relevant physiological stress over each min and in the overall CPR compared with CPRbase. The CPRun protocol reduces the compression rate but has a higher effectiveness percentage than the CPRswim protocol, in which there is a considerably higher compression rate but with less efficacy.

Effects of graded porous structures on the mechanical and electrical properties of Ketjenblack/silicone-rubber composites

In this study, Ketjenblack/silicone-rubber composites with gradually changing pore sizes are produced to develop strain sensor materials whose electrical resistivities change with an increase in strain. The pore sizes and distributions are evaluated by X-ray computed tomography and compared to uniform porous composites containing the same ingredients. Results showed that the resistivities of the graded porous composites decreased more significantly than those of the uniform porous composites at high compression rates of greater than 20%. Analytical results of strain distribution based on the digital image correlation method indicated that the resistivity changes at high compression rates were caused by a gradual increase in size of the area with low resistivity. The changes in resistivity demonstrated that graded porous composites are promising strain sensor materials that can be used under high compression rates.

A hybrid compression method for the NMR data based on window averaging and Discrete Cosine Transform

A large amount of raw echo data is collected by NMR logging, especially in multi-dimensional NMR logging, and a direct inversion for the raw echo data is time-consuming, so the echo data collected need to be compressed before inversion to improve inversion speed. This paper proposes a multi-dimensional NMR data compression method based on Discrete Cosine Transform (DCT). Utilizing the energy concentration characteristics of DCT, the low-frequency information of the echo data is retained to realize the compression of raw echo data. On this basis, a hybrid compression (WAC-DCT) method is proposed, which uses the window averaging method (WAC) to pre-compress the raw echo data, and then uses the DCT compression method to compress the pre-compressed echo data again to achieve a high compression rate. The compression effects of the WAC method, the FastSVD method, the WAC-SVD method, the DCT method and the WAC-DCT method are compared by numerical simulation. The results show that some fluid information is lost in the DCT compression process with a low compression rate. The FastSVD method, the WAC-SVD method and the WAC-DCT method all have the characteristic of a high compression rate. When the number of compressed echoes is very small, the WAC-DCT method has obvious advantage in T1 distribution. Compared with the case of without compression, the WAC-DCT method not only ensures a high compression rate, but also effectively reduces the inversion time, which has a good application effect in multi-dimensional NMR data compression.

Impact of video quality when evaluating video-assisted cardiopulmonary resuscitation: a randomized, controlled simulation trial

BackgroundAlthough not routinely established during cardiopulmonary resuscitation (CPR), video-assisted CPR has been described as beneficial in the communication with emergency medical service (EMS) authorities in out-of-hospital cardiac arrest scenarios. Since the influence of video quality has not been investigated systematically and due to variation of quality of a live-stream video during video-assisted CPR, we investigated the influence of different video quality levels during the evaluation of CPR performance in video sequences.MethodsSeven video sequences of CPR performance were recorded in high quality and artificially reduced to medium and low quality afterwards. Video sequences showed either correct CPR performance or one of six typical errors: too low and too high compression rate, superficial and increased compression depth, wrong hand position and incomplete release. Video sequences were randomly assigned to the different quality levels. During the randomised and double-blinded evaluation process, 46 paramedics and 47 emergency physicians evaluated seven video sequences of CPR performance in different quality levels (high, medium and low resolution).ResultsOf 650 video sequences, CPR performance was evaluable in 98.2%. CPR performance was correctly evaluated in 71.5% at low quality, in 76.8% at medium quality, and in 77.3% at high quality level, showing no significant differences depending on video quality (p = 0.306). In the subgroup analysis, correct classification of increased compression depth showed significant differences depending on video quality (p = 0.006). Further, there were significant differences in correct CPR classification depending on the presented error (p < 0.001). Allegedly errors, that were not shown in the video sequence, were classified in 28.3%, insignificantly depending on video quality. Correct evaluation did not show significant interprofessional differences (p = 0.468).ConclusionVideo quality has no significant impact on the evaluation of CPR in a video sequence. Even low video quality leads to an acceptable rate of correct evaluation of CPR performance. There is a significant difference in evaluation of CPR performance depending on the presented error in a video sequence.Trial registrationGerman Clinical Trial Register (Registration number DRKS00015297) Registered on 2018-08-21.

A Data Compress Algorithm for Daisy Chain Communication System

In the era of big data, traditional data compression methods cannot complete data compression with features of large volume, rapid growth and complex structure, especially in the processing of real-time data compression. The compression algorithm proposed in this paper is improved on the basis of a lossless bit-swap compression coding algorithm. The coding algorithm is based on VAE model and uses classical ANS compression technology to code multiple potential variables, so as to realize the rapid compression and decompression of data. The experimental results show that the algorithm proposed in this paper can compress data with a high compression rate(47.65%), which is higher than LZAM algorithm and Bit-Swap algorithm.

Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update Compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.

Self-similar pulse compression by suspended core photonic crystal fiber with cubic-quintic nonlinearities

An enhanced self-similar pulse compression with suspended core photonic crystal fiber has been theoretically studied. The proposed model well support the essential criteria of having very low dispersion and increased nonlinearity with increasing suspension factor resulting high compression rate within short length of the fiber. Unlike the conventional tapering methods, the suspended core photonic crystal fiber can achieve the self-similar conditions more easily and reduces the practical difficulties to achieve the desired fiber parametric values. We have also studied the possible deformations of the fiber and temperature variations that can effect the pulse compression process and compression factor.

A General Study of Light Gauge Steel Building – Case Study

In general, light gauge steel structural Cold-Formed Steel (CFS) members can lead to added economical design than Hot rolled Steel (HRS) members due to their high strength to weight ratio, ease ofbuilding, and fabrication. It is increased post-buckling strength and yield strength. Such members are vulnerable to local buckling at comparatively low compressive, shear, bearing stress, and bending. The utilization of light gauge steel structures has gotten progressively famous in various fields of building development. The Cold forms of hollow steel sections have a high compressive loading rate, and using infill material like Geopolymer concrete brings the best result in beams and columns joints. Sound Insulation and Head are done correctly according to the necessities of external and internal walls. Economical construction is source income for poor people.

FPGA implementation of Lempel-Ziv data compression

When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.

Video-assisted cardiopulmonary resuscitation: Does the camera perspective matter? A randomized, controlled simulation trial

Background Dispatcher assistance can help to save lives during layperson cardiopulmonary resuscitation during cardiac arrest. The aim of this study was to investigate the influence of different camera positions on the evaluation of cardiopulmonary resuscitation performance during video-assisted cardiopulmonary resuscitation. Methods For this randomized, controlled simulation trial, seven video sequences of cardiopulmonary resuscitation performance were recorded from three different camera positions: side, foot and head position. Video sequences showed either correct cardiopulmonary resuscitation performance or one of the six typical errors: low and high compression rate, superficial and increased compression depth, wrong hand position or incomplete release. Video sequences with different cardiopulmonary resuscitation performances and camera positions were randomly combined such that each evaluator was presented seven individual combinations of cardiopulmonary resuscitation and camera position and evaluated each cardiopulmonary resuscitation performance once. A total of 46 paramedics and 47 emergency physicians evaluated seven video sequences of cardiopulmonary resuscitation performance from different camera positions. The primary hypothesis was that there are differences in accuracy of correct assessment/error recognition depending on camera perspective. Generalized linear multi-level analyses assuming a binomial distribution and a logit link were employed to account for the dependency between each evaluator's seven ratings. Results Of 651 video sequences, cardiopulmonary resuscitation performance was evaluable in 96.8% and correctly evaluated in 74.5% over all camera positions. Cardiopulmonary resuscitation performance was classified correctly from a side perspective in 81.3%, from a foot perspective in 68.8% and from a head perspective in 73.6%, revealing a significant difference in error recognition depending on the camera perspective (p = .01). Correct cardiopulmonary resuscitation was mistakenly evaluated to be false in 46.2% over all perspectives. Conclusions Participants were able to recognize significantly more mistakes when the camera was located on the opposite side of the cardiopulmonary resuscitation provider. Foot position should be avoided in order to enable the dispatcher the best possible view to evaluating cardiopulmonary resuscitation quality.

Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs may introduce a variety of impairments in the audio signal. Many works have tackled the problem of audio enhancement and compression artifact removal using deep-learning techniques. However, only a few works tackle the restoration of heavily compressed audio signals in the musical domain. In such a scenario, there is no unique solution for the restoration of the original signal. Therefore, in this study, we test a stochastic generator of a Generative Adversarial Network (GAN) architecture for this task. Such a stochastic generator, conditioned on highly compressed musical audio signals, could one day generate outputs indistinguishable from high-quality releases. Therefore, the present study may yield insights into more efficient musical data storage and transmission. We train stochastic and deterministic generators on MP3-compressed audio signals with 16, 32, and 64 kbit/s. We perform an extensive evaluation of the different experiments utilizing objective metrics and listening tests. We find that the models can improve the quality of the audio signals over the MP3 versions for 16 and 32 kbit/s and that the stochastic generators are capable of generating outputs that are closer to the original signals than those of the deterministic generators.

Image Compression and Reconstruction Based on PCA

In view of the disadvantages of large image space, high dimension of feature representation and large storage, this paper uses principal component analysis to compress the data, which can effectively reduce the loss of information, reduce the dimension of data and extract features in all aspects of image compression. In this paper, based on the analysis of PCA algorithm dimension reduction, reconstruction, feature extraction principle, the PCA algorithm is applied to image compression and reconstruction. Through the principal component analysis of sample variables, the feature vector of sample variance is calculated, and its principal component is extracted, and the contribution rate is calculated to realize image PCA compression. By extracting different eigenvalues from the original image and calculating its compression ratio and contribution rate, we can see that the clarity of the image is positively correlated with the value of the eigenvalue, and the larger the value of the eigenvalue, the higher the contribution rate. PCA based image processing technology is simple to use, high compression rate and high quality of image reconstruction, which is unmatched by many other methods.

Oligomodal metamaterials with multifunctional mechanics

Mechanical metamaterials are artificial composites that exhibit a wide range of advanced functionalities such as negative Poisson's ratio, shape shifting, topological protection, multistability, extreme strength-to-density ratio, and enhanced energy dissipation. In particular, flexible metamaterials often harness zero-energy deformation modes. To date, such flexible metamaterials have a single property, for example, a single shape change, or are pluripotent, that is, they can have many different responses, but typically require complex actuation protocols. Here, we introduce a class of oligomodal metamaterials that encode a few distinct properties that can be selectively controlled under uniaxial compression. To demonstrate this concept, we introduce a combinatorial design space containing various families of metamaterials. These families include monomodal (i.e., with a single zero-energy deformation mode); oligomodal (i.e., with a constant number of zero-energy deformation modes); and plurimodal (i.e., with many zero-energy deformation modes), whose number increases with system size. We then confirm the multifunctional nature of oligomodal metamaterials using both boundary textures and viscoelasticity. In particular, we realize a metamaterial that has a negative (positive) Poisson's ratio for low (high) compression rate over a finite range of strains. The ability of our oligomodal metamaterials to host multiple mechanical responses within a single structure paves the way toward multifunctional materials and devices.