Compression Capability Research Articles

High‑Temperature Image Pre‐Processing Based on ε‐Ga2O3 Photo‐Synapses

AbstractThe era of big data has brought about substantial challenges in terms of power consumption and communication bandwidth. Edge‐based neuromorphic computing with data pre‐processing can help alleviate these burdens and enhance overall system efficiency. However, existing neuromorphic devices are typically limited to operation at room temperature, restricting their applications in extreme environments. Here, the use of wide‐bandgap ε‐Ga2O3 photo‐synapses as image pre‐processors is demonstrated, even at temperatures as high as 650 K. This is possible because of the high‐quality ε‐Ga2O3 epitaxial films, which exhibit angstrom‐level surface roughness, as evidenced by the highly uniform 152 photodetector devices. These devices show enhanced photo‐synaptic behavior at elevated temperatures, enabling high‐temperature image compression capability. The 4 × 4‐pixel images are compresed into 4 × 1 vectors, achieving an image recognition accuracy of 100% at 650 K. This work demonstrates the potential of ε‐Ga2O3 artificial synapses for extremely high‐temperature computing.

Implementation of multi-walled carbon nanotube incorporated GFRP as an alternative for CFRP in strengthening of concrete cylinders

Carbon fibre is the most widely utilized fibre for strengthening and retrofitting applications in the construction sector. The high cost of carbon fibre necessitates the identification of an alternative system for the retrofitting and repairing of concrete structures. In the current study, the characteristics of Glass Fibre Reinforced Polymer (GFRP), Carbon Fibre Reinforced Polymer (CFRP), and Multi-Walled Carbon Nanotube (MWCNT) incorporated GRFP are assessed with epoxy as a matrix for concrete cylinder confinement. The evaluation of one and two-layer CFRP confinement on concrete cylinders is carried out and outcomes are compared with the three-layer MWCNT incorporated GFRP confinement. A significant increase was observed in the axial compressive load-bearing capability of specimens with 1 wt percentage (wt%) MWCNT incorporated GFRP confined concrete cylinders. The axial load-carrying capability of concrete specimens with one layer of CFRP confinement was equivalent to that of specimens with 1 wt% MWCNT incorporated three-layer GFRP confinement. The axial strain of MWCNT incorporated three-layer GFRP confined specimens was 75 % more than one-layer and 12 % higher than two-layer CFRP confined specimens. According to observations, MWCNT-incorporated FRP confinement improves the energy absorption and ductility index. Ultrasonic pulse velocity test results confirmed the potency of three-layer GFRP confinement as a replacement for one and two layers of CFRP. A cost analysis is also carried out to validate the economic viability of the MWCNT-based GFRP compared to CFRP confinement.

Towards the characterization of representations learned via capsule-based network architectures

Capsule Neural Networks (CapsNets) have been re-introduced as a more compact and interpretable alternative to standard deep neural networks. While recent efforts have proved their compression capabilities, to date, their interpretability properties have not been fully assessed. Here, we conduct a systematic and principled study towards assessing the interpretability of these types of networks. We pay special attention towards analyzing the level to which part-whole relationships are encoded within the learned representation. Our analysis in the MNIST, SVHN, CIFAR-10, and CelebA datasets on several capsule-based architectures suggest that the representations encoded in CapsNets might not be as disentangled nor strictly related to parts-whole relationships as is commonly stated in the literature.

Data should be made as simple as possible but not simpler: The method chosen for dimensionality reduction and its parameters can affect the clustering of runners based on their kinematics

Dimensionality reduction is a critical step for the efficacy and efficiency of clustering analysis. Despite the multiple available methods, biomechanists have often defaulted to Principal Component Analysis (PCA). We evaluated two PCA- and one autoencoder-based dimensionality reduction methods for their data compression and reconstruction capability, assessed their effect on the output of clustering runners’ based on kinematics, and discussed their implications for the biomechanical assessment of running technique. Eighty-four participants completed a 4-minute run at 12 km/h while trunk and lower-limb kinematics were collected. Data reconstruction quality was assessed for Direct PCA (PCA directly on original variables) and Fourier PCA (modelling time series as Fourier series and then applying PCA) using popular variance explained criteria; and a feedforward autoencoder (AE). Agglomerative hierarchical clustering was then applied and the agreement between the resulting partitions was assessed. Meaningful errors in the reconstructed signals were found when applying popular variance explained criteria, suggesting reconstruction error should be assessed to make a more informed decision about how many components to retain for further analysis. Direct PCA, Fourier PCA and AE yielded different clusters, warranting caution when comparing outcomes from studies that use different dimensionality reduction techniques: each method may be sensitive to different data features. Direct PCA retaining 99 % of the original variance emerged as the best compromise of data compression, reconstruction quality and cluster separability in our dataset. We encourage biomechanists to experiment with diverse dimensionality reduction methods to optimise clustering outcomes and enhance the real-world applicability of their findings.

High Compression in Palladium Alloy Metal Foil Pumps

Metal foil pumps (MFPs) are key components in the direct internal recycling inner fuel cycle loop for the recovery of hydrogen isotopes from deuterium-tritium fusion exhaust. Operating under vacuum conditions, they utilize superthermal hydrogen as the feed gas in a process called superpermeation. A notable feature of MFPs is their ability to pump against a pressure gradient. This study examines the compression capabilities of PdAg and PdCu MFPs at low temperatures with a constant feed pressure of 10 Pa. At 75°C, compression ratios exceeding 200 were readily achieved, with downstream pressures exceeding 4500 Pa using PdCu. For both alloys, net fluxes decreased by only ~15% at downstream pressures of 1000 Pa, which offers potential simplifications for the downstream pump train. Performance declined markedly when the temperature was elevated to 200°C. Pump curves were constructed and advocated as the most appropriate manner to assess MFP performance. Separate pressure-driven-permeation experiments at relevant conditions were conducted, providing a direct measurement of the hydrogen dissociation constant k d , which was found to be in good agreement with the previous literature. These measurements were used to predict pump curves and maximum compression ratios by balancing superpermeation with pressure-driven permeation, achieving excellent agreement with experiment. Last, experiments using asymmetric MFPs revealed the detrimental impact that surface impurities have on performance in this system.

TEC-CNN: Towards Efficient Compressing Convolutional Neural Nets with Low-rank Tensor Decomposition

Most state-of-the-art convolutional neural networks (CNNs) are characterised by excessive parameterisation, leading to a high computational burden. Tensor decomposition has emerged as a model reduction technique for compressing deep neural networks. Previous approaches have predominantly relied on either Tucker decomposition or Canonical Polyadic (CP) decomposition for CNNs. However, CP decomposition exhibits exceptional compression capabilities in comparison to Tucker decomposition, which results in a more pronounced accuracy loss. This paper introduces an efficient model compression method, termed TEC-CNN, designed to achieve significant compression while preserving accuracy levels comparable to those of the original models. In TEC-CNN, convolutional layers are identified to obtain convolutional kernels by analysing given models under the principles of low-rank tensor decomposition, and then, calculating the ranks of convolutional kernels. Furthermore, an efficient decomposition schema for the convolutional kernel is proposed with approximate kernel tensor for reducing parameters. Additionally, a novel format of a convolutional sequence is presented and constructed with a reduced number of parameters to replace the original convolutional layers. Finally, the effectiveness of TEC-CNN is assessed across a range of computer vision tasks. For instance, in CIFAR-100 classification, ResNet18 is compressed to 4.1 MB, while Unext, when applied to image segmentation using the International Skin Imaging Collaboration (ISIC) dataset, is reduced to 3.419 MB. When employed for fire object detection with Yolov7, TEC-CNN achieves a model size reduction of 71.6 MB. Comprehensive experimental results underscore that our approach achieves significant model compression while preserving model performance.

Generation of ultrashort cylindrical vector beams from a Mamyshev oscillator.

Cylindrical vector beams (CVBs), characterized by their spatially non-uniform but rotationally symmetric polarization distributions, have garnered considerable attention due to their unique properties. In this Letter, we demonstrate the generation of cylindrical vector beams directly from a Yb-doped Mamyshev oscillator (MO). Both the radially and azimuthally polarized vector beams are achieved with a pulse duration of 3.6 ps and a pulse energy of 8.2 nJ at the repetition rate of 15.25 MHz. We also find that the filtered linearly polarized beam from the output CVBs can be homogeneously dechirped to 114 fs using the external grating pairs, indicating the potential intracavity pulse compression capability for the output CVBs. Simultaneously, ultrafast scalar Gaussian beams can also be obtained from another arm. These results can give insights into the experimental design and realization of high-energy and high-power structured (CVB or vortex) ultrashort lasers.

Skin‐Inspired High‐Performance E‐Skin With Interlocked Microridges for Intelligent Perception

AbstractElectronic skin is increasingly receiving tremendous attention for its potential applications in medical rehabilitation and human‐machine interaction. However, the trade‐off between detection range and sensitivity of e‐skin has not been well addressed, although various strategies have been proposed. Interlocked microridges between the epidermis and dermis can effectively transfer stress to mechanoreceptors, allowing human skin to exhibit excellent sensitivity even upon both subtle and large external stimuli. Herein, inspired by human skin, a novel bionic e‐skin is developed in which interlocked microridges are introduced between the sensitive layer and interdigitated electrode. Thanks to the interlocked microridges, excellent compression capability and remarkable change of contact area between sensitive layer and interdigitated electrode can be achieved and the e‐skin exhibits an ultrahigh sensitivity (≈1502.5 kPa−1), excellent durability (10 000 cycles), a short response time (10 ms) as well as a wide detection range (≈160 kPa). Moreover, due to the effective transmission of external stress from a sensitive layer to an interdigitated electrode, such bionic e‐skin has ability to detect a wide range of human vital signs and vibrations caused by sound waves. Such facile preparation of bionic interlocked microridges opens a new pathway to achieve high‐performance e‐skins and extend their application prospects in future wearable intelligent systems.

Strengthening and embrittlement effect of cryogenic temperature on fiber reinforced geopolymer composite

Understanding the cryogenic behaviour of geopolymer composite (GPC) is essential to implement them in real structural elements at cryogenic temperatures. The paper presents a comprehensive investigation on the compressive strength, flexural behaviours, and energy absorption capability of both plain GPC and fiber-reinforced geopolymer composite (FRGPC) containing different fiber types and contents subjected to in-situ −170 °C. The results demonstrate that the compressive strength of plain GPC and FRGPC increases by 104.9 %–213.6 %, and the flexural strength increases by 73.4 %–212.5 %, exhibiting a significant cryogenic strengthening effect. Moreover, compared to flexural strength, compressive strength of GPCs displays a higher sensitivity to the cryogenic strengthening effect. The failure mode of FRGPCs changed from ductile failure to ductile-brittle failure at −170 °C, which is due to the decreased ductility induced by the cryogenic embrittlement effect. Additionally, cryogenic exposure has a greater impact on reducing the ductility of FRGPCs containing PP fibers than those with steel fibers. However, the negative effects of cryogenic embrittlement effect on ductility can be mitigated by incorporating steel fibers and increasing fiber content. The further analysis reveals that strengthening and embrittlement effect are associated with the increased cumulative energy and the sudden release of local energy, respectively. These findings contribute to the understanding of GPC performance at cryogenic temperature, which can guide the development of more resilient and durable GPC for cryogenic applications.

Lightweight and compressible PANI/Ti3C2Tx/EVA composite foam for tunable microwave absorption

Dynamic deformation and absorbing regulation are still challenging for designing lightweight and high-efficiency microwave absorbing materials, which are of great indispensability to satisfy changeable and complex electromagnetic environments. Herein, a compressible composite foam with sea-island structure was prepared by loading porous polyaniline (PANI)/Ti3C2Tx composites on ethylene/vinyl acetate (EVA) skeleton via a thermally induced phase separation strategy. The composite foam exhibits a minimum reflection loss (RLmin) of −57.84 dB and an effective absorption bandwidth (EAB, RL ≤ −10 dB) of 1.86 GHz (8.2–10.05 GHz) at 3.4 mm. Notably, the microwave absorption (MA) performance can be precisely regulated by simply adjusting the compression ratio of composite foam. The RL peaks shift towards higher frequencies with increasing compression ratios, originating from changes in the internal conductive network. Even under 30 % compression strain, the composite foam still maintains RLmin of −21.53 dB and broadens EAB up to 2.71 GHz at 2.0 mm. This combination of compression capabilities and dynamically tunable MA performance may expand a novel approach to designing multifunctional polymer-based foam.

Information compression via hidden subgroup quantum autoencoders

We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown symmetries of the hidden subgroup type. We prove that data with a given group structure can be compressed with the same query complexity as the hidden subgroup problem, which is exponentially faster than the best-known classical algorithms. We moreover design a quantum algorithm that variationally finds the group structure and uses it to compress the data. There is an encoder and a decoder, along the paradigm of quantum autoencoders. After the training, the encoder outputs a compressed data string and a description of the hidden subgroup symmetry, from which the input data can be recovered by the decoder. In illustrative examples, our algorithm outperforms the classical autoencoder on the mean squared value of test data. This classical-quantum separation in information compression capability has thermodynamical significance: the free energy assigned by a quantum agent to a system can be much higher than that of a classical agent. Taken together, our results show that a possible application of quantum computers is to efficiently compress certain types of data that cannot be efficiently compressed by current methods using classical computers.

Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization

Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.

Extremely-Compressed SSDs with I/O Behavior Prediction

As the data volume continues to grow exponentially, there is an increasing demand for large storage system capacity. Data compression techniques effectively reduce the volume of written data, enhancing space efficiency. As a result, many modern SSDs have already incorporated data compression capabilities. However, data compression introduces additional processing overhead in critical I/O paths, potentially affecting system performance. Currently, most compression solutions in flash-based storage systems employ fixed compression algorithms for all incoming data without leveraging differences among various data access patterns. This leads to sub-optimal compression efficiency. This article proposes a data-type-aware Flash Translation Layer (DAFTL) scheme to maximize space efficiency without compromising system performance. First, we propose an I/O behavior prediction method to forecast future access on specific data. Then, DAFTL matches data types with distinct I/O behaviors to compression algorithms of varying intensities, achieving an optimal balance between performance and space efficiency. Specifically, it employs higher-intensity compression algorithms for less frequently accessed data to maximize space efficiency. For frequently accessed data, it utilizes lower-intensity but faster compression algorithms to maintain system performance. Finally, an improved compact compression method is proposed to effectively eliminate page fragmentation and further enhance space efficiency. Extensive evaluations using a variety of real-world workloads, as well as the workloads with real data we collected on our platforms, demonstrate that DAFTL achieves more data reductions than other approaches. When compared to the state-of-the-art compression schemes, DAFTL reduces the total number of pages written to the SSD by an average of 8%, 21.3%, and 25.6% for data with high, medium, and low compressibility, respectively. In the case of workloads with real data, DAFTL achieves an average reduction of 10.4% in the total number of pages written to SSD. Furthermore, DAFTL exhibits comparable or even improved read and write performance compared to other solutions.

Efficient Visual Computing With Camera RAW Snapshots.

Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel ρ-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed ρ-Vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the ρ-Vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.

Compressing spectral kernels in Gaussian Process: Enhanced generalization and interpretability

The modeling capabilities of a Gaussian Process (GP), such as generalization, nonlinearity, and smoothness, are largely determined by the choice of its kernel. A popular family of kernels for GPs, the spectral mixture (SM) kernels, have the desirable property that with a large number of spectral components they can approximate any stationary kernel. However, using a large number of SM components increases the risk of overfitting and hinders interpretability. To overcome these challenges, we propose a compression algorithm incorporating component pruning and component merging for GPs. Here SM components with small signal variance are removed, and a moment-matching merge method is proposed to further reduce the number of SM components. The main novelty of the proposed method is a similarity measure between SM components based on their normalized cross-correlation, which is related to the Bhattacharyya coefficient. We derive a greedy GP compression algorithm and perform a comparative evaluation over various learning tasks in terms of forecasting performance and compression capability. Results substantiate the beneficial effect of the method, both in terms of generalization and interpretability.11Source code:https://github.com/ck2019ML/Gaussian-Process-Model-Compression.

Enhancing Mechanical Properties of Clay Brick by Using Stone Powder

Abstract Hundreds of studies have been written in the last several decades on the advantages of using stone powder as a raw material in the production of fired clay bricks. The durability and long-term behavior of the finished product, however, have received very little attention in the literature. Clay bricks are generally fired at high temperatures in developing countries, which reduces the mechanical performance of the bricks. This is especially evident in extreme environmental settings where weathering leads to significant damage. The evaluation of concrete waste (stone powder) used to make fired clay bricks is the main topic of this study. There are two sections: the first evaluates how adding stone powder to clay bricks improves their physical characteristics such absorption, efflorescence, density, and firing shrinkage. The impact of stone powder on the mechanical characteristics of specimens of burned clay bricks, such as compressive and flexural strengths, is covered in the second section. The percentages of stone powder in the clay bricks are 0 %, 5 %, 10 %, 15 %, and 20%. While the ratio of dry soil to water content remains is 0.3. In this work three fire phases are used untel to the maximum temperature is reached. The first one is 300 °C, the second phase is 600 °C, and 900 °C for the third phase. The water absorption of specimens decreased as the quantity of stone powder increased, and efflorescence also decreased, according to the results for the physical attributes. The density does, however, somewhat rise with the amount of stone powder. Additionally, when the amount of stone powder was increased, the experimental results indicated that firing shrinkage decreased. Mechanically considered, clay brick specimens with 20% more stone powder showed stronger compressive flexural capabilities.

Synthesis and characterization of TiNbZrMo medium-entropy bio-composites: Microstructure, mechanical properties, and in vitro degradation.

This study reports the synthesis and characterization of hydroxyapatite (HA)-based bio-composites reinforced with varying amounts (by weight, 1-15 wt.%) of bio-medium entropy alloy (BioMEA) for load-bearing implant applications. BioMEA powders consisting of Ti, Nb, Zr, and Mo were mechanically alloyed for 100 h and subsequently added to HA using powder metallurgy techniques. To show the effect of BioMEA, the microstructure, density, and mechanical tests have been conducted and the synthesized BioMEA was characterized by scanning electron microscope (SEM), x-ray diffractometer (XRD), and Fourier-transform infrared spectroscopy (FTIR) analysis. In addition, in vitro degradation behavior and bioactivity analyses of bio-composites have been conducted. XRD analysis revealed the formation of BioMEA after 20 h of mechanical alloying. The highest density value of 2.47 g/cm3 was found in 15 wt.% BioMEA-reinforced bio-composite. The addition of BioMEA reinforcement led to a significant increase in hardness and tensile strength values, with the highest values observed at 15 wt.% reinforcement. Compression tests demonstrated a significant increase in compressive strength and deformation capability of the bio-composites with the highest values observed at 15 wt.% BioMEA addition. The highest toughness of 7.68 kJ/m2 was measured in 10 wt.% MEA-reinforced bio-composites. The produced bio-composite materials have an elastic modulus between 3.5-5.5 GPa, which may provide a solution to the stress shielding problems caused by the high elastic modulus of metallic implant materials. The most severe degradation occurred in 15 wt.% MEA-reinforced bio-composites, and the effect of degradation caused a decrease in Ca and an increase in Ti-Ni-Zr-Mo in all bio-composites. These findings suggest that HA/BioMEA bio-composites have the potential to be developed as advanced biomaterials with moderate mechanical and biological properties for load-bearing implant applications.

Self-restoring cryogels used for the repair of hemorrhagic bone defects by modulating blood clots

Bleeding from severe traumatic bone defects poses a significant threat to patient survival, and conventional hemostatic agents prove inadequate for addressing bone defects when pressure application is not feasible. Therefore, the development of a bio-multifunctional material with outstanding hemostatic and osteogenic performances is highly desirable. In this study, chitosan/hydroxyapatite nanowires cryogels (CSHAn) were fabricated via schiff base reaction and electrostatic interaction. These CSHAn cryogels demonstrate an impressive absorption capacity, reaching up to 5387 %, and possess considerable mechanical strength, quantified at 167.92 kPa, along with significant compressive recovery capabilities. CSHAn cryogels demonstrate superior blood-clotting ability, enhanced blood cell and platelet adhesion, and activation compared to gelatin sponges. Moreover, in vitro cellular assessments indicate that the developed CSHAn2 exhibits favorable cytocompatibility and significantly promote osteogenesis by upregulating the formation of alkaline phosphatase and calcium nodules. Taken together, this multi-functional cryogels is potentially valuable for clinical applications towards hemorrhagic bone defect repair.

A Survey of Data-Driven 2D Diffusion Models for Generating Images from Text

This paper explores recent advances in generative modeling, focusing on DDPMs, HighLDM, and Imagen. DDPMs utilize denoising score matching and iterative refinement to reverse diffusion processes, enhancing likelihood estimation and lossless compression capabilities. HighLDM breaks new ground with high-res image synthesis by conditioning latent diffusion on efficient autoencoders, excelling in tasks through latent space denoising with cross-attention for adaptability to diverse conditions. Imagen combines transformer-based language models with HD diffusion for cutting-edge text-to-image generation. It uses pre-trained language encoders to generate highly realistic and semantically coherent images, surpassing competitors based on FID scores and human evaluations in DrawBench and similar benchmarks. The review critically examines each model's methods, contributions, performance, and limitations, providing a comprehensive comparison of their theoretical underpinnings and practical implications. The aim is to inform future generative modeling research across various applications.

Rectangular-to-elliptical shape transition intakes with various leading-edge for performance enhancement

This study explores the efficiency of streamline-tracing intake in scramjets within the hypersonic domain, where the Mach number (Ma) exceeds five. Focusing on a rectangular-to-elliptical shape transition intake following Busemann streamlines with a contraction ratio of 4.67, tailored for Ma 7.1, this study innovates beyond traditional design method, employing numerical simulations to interpolate iso-Ma points and shape the leading edge of the intake to enhance the performance. Three distinct leading-edge designs—using a sinusoidal function to closely track all iso-Ma points, a convex second-order polynomial to bolster compression and mass flow, and a concave polynomial to widen the starting envelope—were conceptualized. The research included experiments with 0.06 scaled models for validation and performance assessments of full-scale intakes using three-dimensional viscous simulations. Key performance metrics, such as the pressure ratio, total pressure recovery, mass flow rate, and exit Ma, were evaluated to reveal the capability of all three intake designs to operate effectively over a broad Ma range (3–7). A key finding of this study is the ability to selectively enhance either the compression capability or the total pressure recovery by strategically designing the leading edge of the intake through interpolation methods. This study underscores the importance of using numerical simulation results in the notching process as an effective design strategy to handle a wide range of off-design conditions with fixed geometry and achieve performance improvements in scramjet intakes.