Compression Ratio Research Articles

A hardware-efficient on-implant spike compression processor based on VQ-DAE for brain-implantable microsystems.

High-density implantable neural recording microsystems deal with a huge amount of data. Since the wireless transmission of the raw recorded data leads to excessive bandwidth requirements, spike compression approaches have become vital to such systems. The compression processor is designed to be implemented on the implant and so to avoid any tissue damage, the hardware cost of the processor is of great importance. The vector quantization (VQ) algorithm has proven to be effective in compression applications and spike compression systems as well. In this paper, benefiting from the capabilities of the denoising autoencoders (DAE), we propose a solution to enhance the compression performance of the VQ-based approach in terms of both reconstruction accuracy and hardware efficiency. Moreover, we develop a hardware-efficient multi-channel architecture for the proposed VQ-DAE processor. The processor has been implemented in a 180-nm CMOS technology and the validation and verification processes confirm that it provides satisfactory results. It achieves an average signal-to-noise-distortion (SNDR) of 14.51 at a spike compression ratio (SCR) of 30. Operated at a clock frequency of 192kHz and a supply voltage of 1.8V, the circuit consumes a power of 4.88 and a silicon area of 0.14 mm2 per channel.

Finite Element Modeling of Dynamic Response of RPC Columns and Frames Under Coupled Fire and Explosion

Reactive powder concrete (RPC) is widely used in ultra-high-rise buildings, hydropower stations, bridges, and other important infrastructures. To study the dynamic response and damage characteristics of RPC columns and frames considering coupled fire and explosions, an analytical model of RPC columns and frames with coupled fire and explosions was established by using ABAQUS (2021) finite element software. The dynamic response and damage degree of RPC columns under coupled fire and explosions were investigated to reveal the influence laws of parameters such as cross-section size, axial compression ratio, reinforcement rate, and fire duration on the dynamic response of RPC columns at high temperatures. The dynamic response of the frame structure was analyzed when the explosion load was applied to the bottom corner columns, side columns, and top beams, respectively. The results show that the fire severely weakened the blast resistance of RPC columns; the maximum mid-span deformation and residual deformation of RPC columns decreased with the increase in cross-section size and longitudinal bar reinforcement ratio and increased with the increase in fire duration and axial compression ratio. When the explosion load was applied to the corner columns of the bottom floor of the frame, the bottom corner columns were almost completely destroyed, and there was a significant risk of the structure collapsing. Based on the results of the data analysis, a method to enhance the explosion resistance of RC frame structures using RPC materials at high temperatures is proposed.

Analog Frontend for Big Data Compression and Instantaneous Failure Prediction in Power Management Systems

An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection from many wide-bandwidth signals within a system, as low-bandwidth information such as DC output voltage is of limited value for decision-making and failure prediction. Analog compression, data profiling, and anomaly detection methods enabled by the unique analog frontend are presented. The system significantly reduces the demand for high computational power, fast communication, and large storage space required for the task. A real-time compression ratio exceeding 100:1 was achieved by the experimental analog frontend, digitizing the analog signal at a rate of 135 MS/s with a 10-bit resolution. The motivation, existing solutions, performance metrics, and advantages of the analog frontend are demonstrated, along with the details of the circuit operation principle. The process of data collection, its intelligent processing using the analog frontend, and anomaly detection are simulated to validate the theoretical hypotheses. For experimental validation, a laboratory setup that includes a dedicated analog frontend prototype and step-down DC-DC converter was built and evaluated to demonstrate the robust performance in sampling and monitoring wide-bandwidth signals and smart data processing using analog frontend for quick decision-making.

A Parametric Study of GFRP Composite Beams with Encased I-Section using 3D Finite Element Modeling

Glass Fiber Reinforced Polymer (GFRP) materials play a crucial role in the construction industry due to their lightweight properties, corrosion resistance, and high strength. Furthermore, the GFRP reinforcement ratio is a significant factor in the strength design philosophy that governs the design of flexible members. This study presents a parametric investigation of the performance of concrete composite beams reinforced and encased with pultruded GFRP. This study investigates the effect of concrete compressive strength and GFRP reinforcement ratio on the structural behavior of composite beams with encased GFRP sections under static loads. To achieve this objective, five simply supported models were numerically simulated using the Abaqus software. The reference model comprised normal concrete with a 30 MPa compressive strength, 0.42% GFRP longitudinal reinforcing ratio, and transverse steel rebars, with the GFRP I-section encased in the center of the cross-section. The other models maintained similar properties and geometries but varied in reinforcement ratio (0.85% and 1.2%) and compressive strength (25 MPa and 20 MPa). The results showed that increasing the reinforcement ratio in composite beams with encased GFRP sections improved the ultimate capacity by approximately 29% and 41% for 0.85% and 1.2% ratios, respectively, compared to the reference beam. Conversely, reducing compressive strength below 30 MPa decreased maximum load by about 16% and 23% for 25 MPa and 20 MPa values, respectively, in relation to the reference beam.

Conventional and unconventional gas emissions and particle matter emissions of methanol CI engine with different EHN addition and compression ratios

Conventional and unconventional gas emissions and particle matter emissions of methanol CI engine with different EHN addition and compression ratios

Establishing Normative Values for Entire Spinal Cord Morphometrics in East Asian Young Adults.

The quantitative assessment of spinal cord volume is still in the early stages of development. Recently, normative morphometric values of the cervical spinal cord have been reported. This study aimed to establish normative values for spinal cord morphometry, extending beyond the cervical region to include the thoracic and lumbar spinal cord, and to examine the influence of sex and ethnicity on these measurements. This prospective study included 28 young, healthy, East Asian volunteers (14 males and 14 females; mean age, 30.14 ± 4.07 years) who underwent spinal cord MRI using a 3T scanner. The cross-sectional areas (CSAs), anteroposterior (AP) and transverse diameters, and compression ratios of the entire spinal cord were calculated. Additionally, the effects of sex and ethnicity on spinal cord volumetry were evaluated, with the influence of ethnicity assessed by comparing the findings with a Caucasian dataset from the PAM50 study. The CSAs demonstrated two enlargements at the cervical and lumbar levels. The cervical enlargement at C4-5 exhibited an elliptical shape, while the lumbar enlargement at T12 appeared more circular. The CSAs and AP and transverse diameters of the spinal cords in males were significantly larger than that of females (P < 0.001). The spinal cord compression ratios in East Asians were significantly lower than those in Caucasians (P < 0.001). This study revealed that the two spinal cord enlargements exhibit different patterns and suggest significant differences in spinal cord morphometric values according to sex and ethnicity.

Effect of high compression ratio on thermal efficiency and unburned ammonia emissions of a dual-fuel high-pressure direct injection marine ammonia engine

Effect of high compression ratio on thermal efficiency and unburned ammonia emissions of a dual-fuel high-pressure direct injection marine ammonia engine

A new criterion and method for classifying combustion modes in a high compression ratio spark ignition engine

A new criterion and method for classifying combustion modes in a high compression ratio spark ignition engine

Design and analyses of an AB5 - metal hydride based canister for hydrogen compressor application

The reversible hydrogen storage nature of metal hydride (MH) is widely accepted for the development of MH based energy devices like energy storage, hydrogen compressors, etc. For efficient working of such devices, the appropriate thermal management of canister is very important i.e. extraction and supply of heat during hydrogenation and dehydrogenation, respectively. The accumulation of heat reduces the hydrogen storage capacity as well as detoriarates the reaction rates because of increase in MH temperature. Therefore, to optimise the MH heat transfer, in the present work, a helical coil is introduced within the MH Canister (MHC) to circulate the heat transfer fluid in addition to an external cooling jacket. The purpose of using helical coil is to get more surface contact with MH because of the poor thermal conductivity of MH. The performance analyses have been carried out through a 3-D Finite Volume Approach employed with La0.9Ce0.1Ni5. The analyses include the influence of coefficient of heat transfer, pressure, and temperature on the designed canister as well as the compressor performance. Initially, the numerical model is validated with experimental results available in the literature and observed in good match. Later, the performance of MHC is predicted for different operating conditions which results in the estimated storage capacity of hydrogen is about 1.48wt% at 25bar and 298K. In addition, it is observed that at an elevated supply pressure, the reaction rates increased as well as the rise in MHC temperature increased due to exothermic heat. Later, the MHC is tested for the application as hydrogen compressor which results in the compression ratio of 10.8 with a discharge pressure of 270bar at 140°C providing ~20% efficiency.

Molecular Dynamics Simulation of the Hydration Lubrication Mechanism of CS-MPC/CS-ChS NPs.

Osteoarthritis (OA) remains a significant clinical challenge, with current treatments like sodium hyaluronate injections offering limited efficacy due to suboptimal lubrication and rapid degradation. In this study, we explored an advanced solution to these issues by investigating the colubrication mechanism of a composite biolubricant consisting of 2-methacryloyloxyethyl phosphorylcholine-modified chitosan (CS-MPC) and chondroitin sulfate-modified chitosan nanoparticles (CS-ChS NPs) using molecular dynamics (MD) simulations. Results show that the composite lubricant outperforms individual CS-MPC or CS-ChS NPs, exhibiting a lower coefficient of friction (COF) and a superior load-bearing capacity. The CS-MPC/CS-ChS NP system maintains a consistent compression ratio of -0.5% under different external pressures and exhibited a low coefficient of friction (COF) of 0.041 across varying shear velocities. The sulfate groups on CS-ChS NPs interact with CS-MPC chains, stabilizing the system through electrostatic interactions and enabling the effective dispersion of normal stress, thereby protecting the substrate surface from wear. This enhanced performance is attributed to the formation of a multilayered hydration shell around the lubricant molecules. The hydration shells provide superior lubrication, contributing to the system's robustness under varying loads. These findings offer critical insights into designing high-performance biolubricants for joint protection, presenting a promising avenue for future OA treatments.

Lossless compression of wireless capsule image using the Tetrolet transform coupled with modified capsule SPIHT

ABSTRACT The diagnostic technique known as wireless capsule endoscopy (WCE) is helpful in determining gastrointestinal issues. The capsule’s passive motion produces redundant data, and sending large amounts of data uncompressed costs more money and energy. Due to the intrinsic properties of the capsule, it is particularly desirable to have a low-complexity, low-power design that can compress the lossless compression module while still complying with capsule endoscopy regulations. This work’s primary goal is to propose a lossless compression with minimal complexity and power technique for WCE image compression. WCE images are first transformed to YUV colour space in order to remove unnecessary information from chroma components. The Tetrolet transform is then used to break down the YUV image. A Tetrolet transform is a shape made up of four tetrominoes, or squares of equal size. In order to reduce latency and raise threshold, the decomposed picture is then compressed using a Modified Capsule SPIHT (MCSPIHT) encoder. The resulting coefficients are encoded using the low complexity MCSPIHT encoder. Our proposed method results in an average compression ratio of 90.447.

Effect of a Compression Ratio Increase and High-Flow-Rate Injection on the Combustion Characteristics of an Ammonia Direct Injection Spark-Ignited Engine

Effect of a Compression Ratio Increase and High-Flow-Rate Injection on the Combustion Characteristics of an Ammonia Direct Injection Spark-Ignited Engine

The possibility of reducing the anthropogenic load of diesel engines on the environment by changing the compression ratio

The article is devoted to solving environmental problems arising from the operation of mobile energy vehicles with diesel engines in the agro-industrial sector and installed on cars, tractors, combines. Contrary to all forecasts, the use of environmentally friendly engines capable of competing with piston engines is limited in agricultural production, therefore, solving the problem of reducing harmful emissions from exhaust gases of diesel engines used in agriculture is very important and relevant. The research is aimed at solving one of the key tasks of our time – reducing the anthropogenic load from harmful emissions of diesel engines on the environment by changing the compression ratio. As a result of the test, it was found that an increase in the compression ratio from 13.5 to 15.5 leads to a decrease in emissions from exhaust gases of solid particles (PM), hydrocarbons (CxHy) and carbon monoxide (CO), however, over the entire load characteristic from Pe =0 to Pe=1.24 MPa at 1900 min-1 emissions of nitrogen oxides (NOx) increases. Emissions of particulate matter from exhaust gases are reduced from 0.122 to 0.063. When the compression ratio changes from 13.5 to 15.5, the estimated values change: for nitrogen oxides – by 1.88 times; for carbon monoxide – by 1.22 times; for hydrocarbons – by 1.35 times; for solid particles – by 1.15 times. The evaluation results showed that the values of the anthropogenic load were Htn = 78.31 ut/g with serial configuration at = 13.5 and Htn = 90.4 ut/g at = 14.5. This indicates a decrease of 1.15 times with a decrease to 13.5. In order to obtain the best results in reducing the anthropogenic load on the environment, it is recommended to use complex methods, for example, simultaneous changes in the degree of compression and catalytic neutralization.

Performance-Based Multi-Objective Optimization of Four-Limb CFST Lattice Columns

In this paper, the low-cycle reciprocating load test was carried out on four-limb concrete-filled steel tubular (CFST) lattice columns with different slenderness ratios and axial compression ratios, and the seismic performance was studied. Two performance indicators, namely damage and hysteretic energy dissipation, were defined as the objective functions, and the axial compression ratio was used as an optimization variable to perform the multi-objective optimization analysis of four-limb CFST lattice columns. Optimization using the max–min problem approach aims to optimize the axial compression ratio to minimize damage and maximize the dissipation of hysteresis energy. The seismic performances before and after optimization were determined using a restoring force model and were evaluated by the finite element method under different axial compression ratios. The results show that, under low-cycle reciprocating loads, the load–displacement hysteresis curve is a bow shape (Members 1 and 2), inverse S-shape (Member 3), and approximate shuttle shape (Member 4). Through multi-objective optimization, the optimized axial compression ratio is 0.25 and the finite element analysis indicates that the optimal seismic performance is at an axial compression ratio of 0.25. Through the optimized design, the maximum horizontal load of lattice columns, the elastic stiffness, the dissipation capacity, and the seismic performance are all improved, under the premise of satisfying the structural safety.

An autoencoder for compressing angle-resolved photoemission spectroscopy data

Abstract Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in k. To test the data representation capacity of ARPESNet, we compare k-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

Experimental and 1D Modeling Analysis of Thermoelectric Generation to Improve the Performance of Compressed Natural Gas Heavy-Duty Engines Used in Commercial 22-Ton Trucks

&lt;div&gt;A significant amount of chemical fuel energy in internal combustion engines is wasted through exhaust heat. Waste heat recovery (WHR) systems can transform the heat into electrical energy using thermoelectric generators (TEG). This work utilizes a 1D CFD model to demonstrate the potential of TEG-WHR in improving the thermal efficiency of mass-production, compressed natural gas (CNG) engines used in commercial 22-ton heavy-duty trucks. First, the TEG with heat exchanger experiments are performed to measure thermal and electrical performance data under different fin pitches and inlet gas conditions (Re number, temperature, gas flow rate). These data are used to develop and validate a TEG model, which considers user-defined functions of heat transfer and flow friction coefficients to reproduce measured thermal/electrical characteristics of the integrated TEG with its heat exchanger. The engine experiments are conducted based on the speed–torque map (51 test conditions) of the JE05 heavy-duty cycle using the turbocharged engine equipped with a multi-port injection system. The engine model is calibrated and validated against test data under base conditions (using production valve timings), optimal variable valve actuation (VVA), and variable compression ratio (VCR). Finally, the high-fidelity engine and TEG models are integrated to predict the electrical power generated by a compact TEG-WHR (three-layer, size: 1.3 × A4-paper). The integrated model considers a tradeoff between TEG-generated power and engine pumping loss. Simulation results show that the compact TEG can generate effective 20–701 W electrical powers, translating to about 0.03–1.07% brake thermal efficiency improvement.&lt;/div&gt;

A novel approach to analyzing the evolution of SARS-CoV-2 based on visualization and clustering of large genetic data compactly represented in operative memory

SARS-CoV-2 is a virus for which an outstanding number of genome variants were collected, sequenced and stored from sources all around the world. Raw data in FASTA format include 16.8 million genomes, each ≈29,900 nt (nu­cleotides), with a total size of ≈500 ∙ 109 nt, or 465 Gb. We suggest an approach to data representation and organization, with which all this can be stored losslessly in the operative memory (RAM) of a common PC. Moreover, just ≈330 Mb will be enough. Aligning all genomes versus the initial Wuhan-Hu-1 reference sequence allows each to be represented as a data structure containing lists of point mutations, deletions and insertions. Our implementation of such data represen­tation resulted in a 1:1500 compression ratio (for comparison, compression of the same data with the popular WinRAR archiver gives only 1:62) and fast access to genomes (and their metadata) and comparisons between different genome variants. With this approach implemented as a C++ program, we performed an analysis of various properties of the set of SARS-CoV-2 genomes available in NCBI Genbank (within a period from 24.12.2019 to 24.06.2024). We calculated the distribution of the number of genomes with undetermined nucleotides, ‘N’s, vs the number of such nucleotides in them, the number of unique genomes and clusters of identical genomes, and the distribution of clusters by size (the number of identical genomes) and duration (the time interval between each cluster’s first and last genome). Finally, the evolution of distributions of the number of changes (editing distance between each genome and reference sequence) caused by substitutions, deletions and insertions was visualized as 3D surfaces, which clearly show the process of viral evolution over 4.5 years, with a time step = 1 week. It is in good correspondence with phylogenetic trees (usually based on 3–4 thousand of genome variant representatives), but is built over millions of genomes, shows more details and is independent of the type of lineage/clade classification.

Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface

Abstract This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless implantable brain machine interface (iBMI). The architectural trade-offs and implications of the proposed method are quantitatively analyzed in terms of compression ratio and spike information preservation. For the latter, we used metrics such as root-mean-square error and correlation coefficient between the original and recovered signals to assess the effect of neuromorphic compression on the spike shape. Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection. We demonstrate that a data compression ratio of 15-265 per channel can be achieved by transmitting address-event pulses for two different biological datasets. The compression ratio increases to 200-50K per channel, 50× more than in prior works, by selective transmission of event pulses corresponding to neural spikes. A correlation coefficient of ∼0.9 and spike detection accuracy of over 90% were obtained for the worst-case analysis involving 10K-channel simulated recording and typical analysis using 100 or 384-channel real neural recordings. We also analyzed the collision handling capability for up to 10K channels and observed no significant error, indicating the scalability of the proposed pipeline. We also present initial results to show the ability of intention decoders to work directly on the events generated by the neuromorphic front-end.

Investigating The Combustion Performance of Dual Fuel Combustion With Diesel and Port Injected Hydrogen In A Large Bore Locomotive Engine

Abstract The heavy-duty transportation sector has primarily relied on conventional diesel combustion engines given their reliability and high thermal efficiency relative to spark ignition engines, but increased focus on reducing greenhouse gas emissions has led to investigation into alternative fuels. Gaseous hydrogen fuel has garnered a great deal of recent interest in the engine community given it has zero carbon, but hydrogen is not available at the scale and cost that petroleum fuels are currently available, and this is a barrier to adoption for industries that are looking to decarbonize their operations. Because of the fuel flexibility provided, dual fuel technology offers a pathway for some industries to adopt hydrogen as a fuel source while maintaining sufficient flexibility in times and locations where the new fuel is not yet available. This computational study investigates dual fuel combustion in a large bore locomotive engine architecture using direct injected diesel and port injected gaseous hydrogen fuel. With an optimal port fuel injection configuration from previous work [18], simulations of varying substitution ratio, compression ratio, intake manifold air temperature, diesel injection timing, and diesel injection pressure were performed to understand their effect on combustion performance. Results indicated that both increased substitution ratio and higher intake air temperature accelerates hydrogen flame propagation and can result in high peak cylinder pressures. Additionally, diesel injection timing and injection pressure were demonstrated as effective methods for controlling dual fuel combustion heat release rates.

Does amplitude compression help or hinder attentional neural speech tracking?

Amplitude compression is an indispensable feature of contemporary audio production and especially relevant in modern hearing aids. The cortical fate of amplitude-compressed speech signals is not well-studied, however, and may yield undesired side effects: We hypothesize that compressing the amplitude envelope of continuous speech reduces neural tracking. Yet, leveraging such a 'compression side effect' on unwanted, distracting sounds could potentially support attentive listening if effectively reducing their neural tracking. In this study, we examined 24 young normal-hearing (NH) individuals, 19 older hearing-impaired (HI) individuals, and 12 older normal-hearing individuals. Participants were instructed to focus on one of two competing talkers while ignoring the other. Envelope compression (1:8 ratio, loudness-matched) was applied to one or both streams containing short speech repeats. Electroencephalography (EEG) allowed us to quantify the cortical response function and degree of speech tracking. With compression applied to the attended target stream, HI participants showed reduced behavioural accuracy, and compressed speech yielded generally lowered metrics of neural tracking. Importantly, we found that compressing the ignored stream resulted in a stronger neural representation of the uncompressed target speech. Our results imply that intelligent compression algorithms, with variable compression ratios applied to separated sources, could help individuals with hearing loss suppress distraction in complex multi-talker environments.Significant statement Amplitude compression, integral in contemporary audio production and hearing aids, poses an underexplored cortical challenge. Compressing the amplitude envelope of continuous speech is hypothesized to diminish neural tracking. Yet, capitalizing on this 'compression side effect' for distracting sounds might enhance attentive listening. Studying normal-hearing (NH), older hearing-impaired (HI), and older normal hearing individuals in dual-talker scenarios, we applied envelope compression to speech streams. Both NH and HI participants showed diminished neural tracking with compression on the speech streams. Despite weaker tracking of a compressed distractor, HI individuals exhibited stronger neural representation of the concurrent target. This suggests that adaptive compression algorithms, employing variable ratios for distinct sources, could aid individuals with hearing loss in suppressing distractions in complex multi-talker environments.