Compression Function Research Articles

Algebraic Fault Analysis of SHA-256 Compression Function and Its Application

Cryptographic hash functions play an essential role in various aspects of cryptography, such as message authentication codes, pseudorandom number generation, digital signatures, and so on. Thus, the security of their hardware implementations is an important research topic. Hao et al. proposed an algebraic fault analysis (AFA) for the SHA-256 compression function in 2014. They showed that one could recover the whole of an unknown input of the SHA-256 compression function by injecting 65 faults and analyzing the outputs under normal and fault injection conditions. They also presented an almost universal forgery attack on HMAC-SHA-256 using this result. In our work, we conducted computer experiments for various fault-injection conditions in the AFA for the SHA-256 compression function. As a result, we found that one can recover the whole of an unknown input of the SHA-256 compression function by injecting an average of only 18 faults on average. We also conducted an AFA for the SHACAL-2 block cipher and an AFA for the SHA-256 compression function, enabling almost universal forgery of the chopMD-MAC function.

Photopolymerization of Zeolite Filler‐Based Composites for Potential 3D Printing Application and Gas Adsorption Applications

AbstractIn recent years, photopolymerization has been found wide applications in industry and daily life, while many defective properties of polymers limit their development, including compressive strength, mechanical properties, and functions. Furthermore, zeolites are microporous solids with many industrial applications. As fillers they can improve mechanical properties of zeolite/polymer composite and transfer their properties to composites. Here the fabrication is reported of composites with very high filler contents (80–95 wt%) under mild photopolymerization conditions (visible LED light irradiation, room temperature, under air). Compared to pure poly(ethylene glycol) diacrylate (PEGDA) polymer, mechanical properties have been greatly improved, and the storage modulus increased by nearly 20‐fold compared to that of pure polymer. After debinding/sintering of composites by high temperature treatment, zeolite porosity is preserved with good adsorption capacities. Some step‐like object with 3D structure is also obtained by an original 3D printing technique noted “layer by layer molding/curing.” Hence, as a great breakthrough, this work is expected to lead to a valuable development in the field of photopolymerization in quite highly filled composites. This also will expand their potential applications for 3D printing in the fields of high‐performance lightweight materials and adsorption, and opens the door to a new process of zeolite shaping.

The design of keyed hash function based on CNN-MD structure

In this paper, we propose a new chaotic neural network called the Merkle–Damgaard (CNN-MD) iterative structure, which we designed from the perspective of resisting attacks, such as length extension, second original image, Joux multiple collision, and intermediate encounter attacks. A new chaotic hash function based on the CNN-MD is presented. First, CNN is used as the compression function of CNN-MD, which ensures both the unidirectionality of data compression and the sensitivity of the hash value. Furthermore, the multi-input single-output construction of the CNN can achieve highly efficient data compression. In addition, in the process of group encryption of data, we use the value padding of coupled lattice mapping to improve the alignment of the plaintext of the MD algorithm. This new alignment can enhance the hash function’s collision resistance and the CNN-MD sensitivity. Experimental and theoretical analyses show that our proposed hashing algorithm can resist attacks, such as second original image, brute force, and multiple collision attacks, better than MD5, SHA-1, and SHA-2 algorithms. In terms of speed, our proposed hashing algorithm can successfully handle existing commercial scenarios better than MD5, SHA-1, SHA-3, and other methods. Therefore, the hash algorithm proposed in this study can be applied to existing e-commerce scenarios.

Reinforcement Learning Configuration Interaction.

Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. We explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.

A single-branch impedance compression network (icn) optimized by particle swarm optimization algorithm for rf energy harvesting system

Most previous works regarding the impedance compression network ICNs reported in the literature consist of dual branches to carry out the compression functionalities. Although this type of circuits are efficient, they are complex and have large sizes. This work proposes a complex impedance compression network with only a single branch for wireless power transfer (WPT) circuits, operating at 2.4GHz under a wide input power range and a variable load. The design size is about 4x4.2mm2 which thereby, produces a simple, and small design. The simulation results demonstrate that the improvement in efficiency is by 10%, as compared to a design without ICN. The improvement can increased by utilizing the particle swarm optimization PSO algorithm where the best dimensions of the ICN components will be selected relying on the suitable fitness function in the PSO algorithm to the given desired goals. The impedance matching is also enhanced in which it stands behind the conversion efficiency increment. The circuit size is very slightly increased and it becomes 4.2x4.3mm2. The final design has a conversion efficiency about 76%, with more than 15% improvement to a design with only a matching circuit or without using the automation techniques. Eventually, results show that the enhancement is obtained not only the designated input power but also along a range of the input power.

Embedded Mobile Video Surveillance Service System

in the analysis of mobile end-user video surveillance service, it is necessary to pay attention to the system application of video surveillance service media under protocol wireless network according to the effective three-terminal mode plan. Through the analysis of system mode, the protocol component standard under hardware compression function is adjusted, the fusion of embedded service and the application of embedded system are paid attention to, through the operation of open source media library, the planning of TCP//RTCP protocol is paid attention to, and the actual problems are analyzed, and the control standard mode of bitter war platform is studied.

Accurate and efficient image segmentation and bias correction model based on entropy function and level sets

With the development of society, image segmentation plays a pivotal role in real-world life. However, the images we obtain are often distorted or contaminated by noise and shade, causing low construct and weak boundaries. In this paper, we propose a new model suitable for segmenting and correcting images with inhomogeneous intensity simultaneously. According to the characteristics of the entropy function, we use it as the coefficient of the global and local terms of the energy functional, related to the intensity of the image. Most importantly, the compression function is added to control the global item and the local item in the same range, reducing parameters to be tuned. In addition, our model can be extended to the multi-objective model and the colour model to deal with a more difficult situation. Furthermore, by utilizing the split Bregman method to solve the energy functional, we reduce computational cost and maintain the convergence of the algorithm. A large number of experimental results demonstrate the superiority of our model over previous models. Our model has promising performance for the segmentation and correction of inhomogeneous medical images and colour images.

EXPLICITLY MODELING PERMANENT SET AND ANISOTROPY PROPERTY INDUCED BY STRESS SOFTENING FOR RUBBER-LIKE MATERIALS

Stress softening, known as the Mullins effect, is observed in rubber-like materials after initial loading cycles. Experimental observations have shown that the Mullins effect leads to a permanent set and induces anisotropic properties. A multi-axial, compressible strain energy function based on the Hencky strain is proposed to account for the stress softening and simulate the permanent set and anisotropic properties by introducing two variables, separately characterizing the permanent set and anisotropic properties, which are dependent on dissipation. A comprehensive, explicit shape function is proposed by using the spherical coordinate system to make the model effective in arbitrary direction. The new model exhibits isotropic properties when it not yet induced stress softening, and becomes anisotropy when the Mullins effect occurs. The residual strain increases and reach a fixed value as the loading-unloading cycles proceeds, and the anisotropy becomes more obvious. The simulation results are shown good accord with the classical experimental data and successfully forecast the permanent set and anisotropic properties induced by stress softening.

Refocusing of Moving Ships in Squint SAR Images Based on Spectrum Orthogonalization

Moving ship refocusing is challenging because the target motion parameters are unknown. Moreover, moving ships in squint synthetic aperture radar (SAR) images obtained by the back-projection (BP) algorithm usually suffer from geometric deformation and spectrum winding. Therefore, a spectrum-orthogonalization algorithm that refocuses moving ships in squint SAR images is presented. First, “squint minimization” is introduced to correct the spectrum by two spectrum compression functions: one to align the spectrum centers and another to translate the inclined spectrum into orthogonalized form. Then, the precise analytic function of the two-dimensional (2D) wavenumber spectrum is derived to obtain the phase error. Finally, motion compensation is performed in the two-dimensional wavenumber domain after the motion parameter is estimated by maximizing the image sharpness. This method has low computational complexity because it lacks interpolation and can be implemented by the inverse fast Fourier translation (IFFT) and fast Fourier translation (FFT). Processing results of simulation experiments and the GaoFen-3 squint SAR data validate the effectiveness of this method.

Application of SeDeM Expert System in the development of novel directly compressible co-processed excipients via co-processing

BackgroundComputer-aided formulation design is gaining fantastic attention in chemical engineering of high functionality pharmaceutical materials for dosage form manufacture. To accelerate development of novel formulations in a quality-by-design perspective, SeDeM Expert System preformulation algorithm was developed as a tool for the design of solid drug delivery systems and for prediction of direct compression manufacturability of solid formulations. This research aims to integrate SeDeM Expert System into particle engineering design space of co-processing of solid excipients to develop novel composites with optimum direct compression propensity, using corn starch and microcrystalline cellulose powders as model primary excipients.ResultThe data and information generated from the expert system have elucidated the bulk-level characteristics of the primary excipients, enabled computation of the optimum co-processing ratio of the ingredients, and validated the impact of co-processing on material functionality. The experimental flowability (7.78±0.17), compressibility functions (5.16±0.14), parameter profile (0.92), and parametric profile index (6.72±0.27) of the engineered composites, were within the acceptable thresholds. With a reliability constant of 0.961, the net direct compression propensity of the composites expressed as Good Compression Index (6.46±0.26) was superior to that of the primary excipients, but comparable to reference co-processed materials, StarLac® (6.44±0.14) and MicroceLac®100 (6.58±0.03).ConclusionApplication of SeDeM Expert System in particle engineering via co-processing has provided an accelerated upstream proactive mechanism for designing directly compressible co-processed excipients in a quality-by-design fashion. A four-stage systematic methodology of co-processing of solid excipients was postulated. Stage I entails the characterization of CMAs of both defective and corrective excipients, and elucidation of their physicomechanical limitations using SeDeM diagrams. Stage II involves computation of loading capacity of the corrective excipient using dilution potential equation. Stage III entails the selection of co-processing technique based on desired Critical Material Attributes as revealed by the information obtained from Stage I. Stage IV evaluates the impact of co-processing by monitoring the critical behavior of the engineered composites with a decision on either to accept or reject the product.

Modeling the effects of dynamic range compression on signals in noise.

Hearing aids use dynamic range compression (DRC), a form of automatic gain control, to make quiet sounds louder and loud sounds quieter. Compression can improve listening comfort, but it can also cause unwanted distortion in noisy environments. It has been widely reported that DRC performs poorly in noise, but there has been little mathematical analysis of these noise-induced distortion effects. This work introduces a mathematical model to study the behavior of DRC in noise. By making simplifying assumptions about the signal envelopes, we define an effective compression function that models the compression applied to one signal in the presence of another. Using the properties of concave functions, we prove results about DRC that have been previously observed experimentally: that the effective compression applied to each sound in a mixture is weaker than it would have been for the signal alone; that uncorrelated signal envelopes become negatively correlated when compressed as a mixture; and that compression can reduce the long-term signal-to-noise ratio in certain conditions. These theoretical results are supported by software experiments using recorded speech signals.

JCB Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”

<i>JCB</i> Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”

Indirect reduction technique via Nice knot for transverse fracture of patella

To assess the outcomes in indirect reduction technique via Nice knot for transverse patellar fractures. The clinical data of 25 patients with transverse patellar fractures meeting the inclusion criteria between January 2017 and December 2018 were retrospectively analyzed. The patients were divided into trial group ( n=13) and control group ( n=12) according to different intraoperative reduction methods. No significant difference was found in gender, age, affected side, cause of fracture, classification, or the time from injury to operation between the two groups ( P>0.05). In the trial group, No.2 suture was used to cross the quadriceps tendon and patellar tendon to construct the Nice knot, then the suture was tightened to make the distal and proximal fracture segments contact in an indirect reduction pattern. Depend on Nice knot's sliding compression and self-stabilizing function, the suture mesh created an anterior tension band as a temporary fixation. In the control group, Weber's clamp was used to hold the fracture segments directly and fixed temporarily. After reduction, terminal fixation was conducted using a titanium Kirschner wire with titanium cable in both groups. The operation time, intraoperative blood loss, follow-up time, fracture healing time, and complications were recorded and compared in the two groups. At last follow-up, the knee function was evaluated according to the Böstman scoring criteria for efficacy in patellar fractures. The operation time in the trial group was significantly shorter than that in the control group ( t=-2.165, P=0.041). There was no significant difference of intraoperative blood loss between the two groups ( t=0.514, P=0.612). The incisions of the two groups healed by first intention. All the patients were followed up 12-16 months, with an average of 14.4 months, no significant difference was found in the follow-up time between the two groups ( t=-0.309, P=0.760). One patient in the control group developed soft tissue irritation symptoms at 1 day after operation, and no special treatment was given, the symptoms disappeared at 2 months after operation. The fractures of the two groups healed at the 12-week follow-up. During the follow-up, there was no complication such as loosening and fracture of titanium cables and tendon tissue calcification. At last follow-up, the Böstman score presented no significant difference between the two groups ( t=-0.086, P=0.932). In the trial group, an 80-year-old female patient was evaluated as good (score, 27) due to atrophy of the quadriceps femoris, leg weakness, and affected stair climbing, and the rest 24 patients were all evaluated as excellent. The indirect reduction with Nice knot can shorten the operation time in the treatment of transverse patellar fractures, and obtain good effectiveness.

Nanomechanical Molecular Mass Sensing Using Suspended Microchannel Resonators.

In this work we study the different phenomena taking place when a hydrostatic pressure is applied in the inner fluid of a suspended microchannel resonator. Additionally to pressure-induced stiffness terms, we have theoretically predicted and experimentally demonstrated that the pressure also induces mass effects which depend on both the applied pressure and the fluid properties. We have used these phenomena to characterize the frequency response of the device as a function of the fluid compressibility and molecular masses of different fluids ranging from liquids to gases. The proposed device in this work can measure the mass density of an unknown liquid sample with a resolution of 0.7 µg/mL and perform gas mixtures characterization by measuring its average molecular mass with a resolution of 0.01 atomic mass units.

Ground moving target indication and parameter estimation algorithm using deramp space-time adaptive processing

Deramp space-time adaptive processing (Deramp-STAP) algorithm has been validated in its effectiveness in reducing the required channels to enable ground moving target indication (GMTI) for multi-channel synthetic aperture radar (SAR) systems. However, the parameter estimation for the moving target, including the critical radial velocity, is not accurate enough, and the radial velocity is always approximated by the equivalent velocity obtained by the Deramp operation. For the indication and imaging of ground moving targets, the accurate estimation of the radial velocity is of importance to improve the signal-clutter-noise-ratio of STAP and estimate the initial azimuth position of the moving target. A method for GMTI and the corresponding parameter estimation using Deramp-STAP is proposed. Compared with the traditional Deramp-STAP algorithms, the proposed algorithm utilizes the equivalent velocity estimated by Radon transform to remove the linear range migration before STAP and figure out where the moving target is located in the azimuth frequency domain. To estimate the radial velocity more accurately, the fast Fourier transform along the spatial direction is performed on the pixel where the moving target is located in the azimuth frequency domain after the Deramp operation, and then the radial velocity can be obtained through the spatial spectrum analysis. Finally, the estimated radial velocity is applied to construct the azimuth compression function for the moving target imaging, and then the initial azimuth position can be estimated. Several simulation experiments have been implemented to validate the effectiveness of the proposed algorithm.

Radial kernelized regressive merkle–damgård cryptographic hash blockchain for secure data transmission with IoT sensor node

The concept of the Internet of Things (IoT) has drawn noteworthy attention to use the collected data anywhere in the world. Wireless Sensor Node (WSN) is integrated into the IoT for data collection. The IoT is very intense towards the security investigation. Patient data is medical information about an individual patient. Patient data contains their past and current health, treatment history, lifestyle options and genetic data. The patient data security is a most important issue in IoT. When a collected data get transmitted from the sensor node to the server it theft or altered by various intruders and it may cause a lack of security. In order to address these issues, in this research work, a novel technique called Radial Kernelized Regressive Merkle–Damgard Cryptographic Hash Blockchain (RKRMDCHB) technique is introduced to improve secure data transmission. The wireless sensor nodes are deployed to sense and monitor the data. At first, the sensing data are collected for secure transmission to the server. The RKRMDCHB technique uses the Radial Basis Kernelized Regression Function to perform the data classification. The regression function analyzes the input data and is categorized into various types of classes based on the radial basis kernel function. The classified data is given to the data block of the blockchain for secure transmission. The RKRMDCHB technique uses the Merkle–Damgard Cryptographic technique to generate the hash for each input data with the help of the one-way compression function. The data in the form of a hash is transmitted to the server through the internet. As a result, the RKRMDCHB technique ensures security and confidentiality to preserve the data and provide better communication. Experimental assessment is carried out on certain factors such as data confidentiality rate, data integrity, and processing time, with respect to a number of data sensed from the sensor device. The results demonstrate that the proposed RKRMDCHB technique provides an efficient solution for secure data transmission while preserving sensitive information against potential threats.

Indifferentiable hash functions in the standard model

Indifferentiability of iterated hash functions is seen as evidence that there are no structural flaws in the iteration structure of the algorithm. However, it is often overlooked that such considerations only hold in the random oracle model and do not give any guarantee in the standard model. In this article, we show the following separation result: there is a hash function that is indifferentiable from a random oracle, but is totally insecure in the standard model. In particular, we show that it does not satisfy collision or multicollision-resistance, second preimage-resistance or preimage-resistance for any family of compression functions. Therefore, at least in theory, hash function indifferentiability does not guarantee the structural integrity of the hash algorithm in the standard model. Results in the random oracle model are not affected.

Latent heat method to detect melting and freezing of metals at megabar pressures

The high-pressure melting curves of metals provide simple and useful tests for theories of melting, as well as important constraints for the modeling of planetary interiors. Here, we present an experimental technique that reveals the latent heat of fusion of a metal sample compressed inside a diamond anvil cell. The technique combines microsecond-timescale pulsed electrical heating with an internally-heated diamond anvil cell for the first time. Further, we use the technique to measure the melting curve of platinum to the highest pressure measured to date. Melting temperature increases from $\sim 3000$ K at 34 GPa to $\sim 4500$ K at 107 GPa, thermodynamic conditions that are between the steep and shallow experimental melting curves reported previously. The melting curve is a linear function of compression over the 0 to 20% range of compression studied here, allowing a good fit to the Kraut-Kennedy empirical model with fit parameter $C=6.0$.

The Role of Intermittent Pneumatic Compression in the Treatment of Lower Extremity Chronic Wounds

Intermittent pneumatic compression devices (IPC) are often used as noninvasive adjuncts in patients with lymphedema, and more recently with venous stasis disease, to promote flow and reduce the adverse effects of interstitial edema associated with both disorders. We will be focusing on lower extremity wounds associated with venous and/or lymphatic disease, the combination often referred to as “lymphophlebitic” disease, and the treatment effect of IPC on this disease process and its sequelae. The function and purpose of pneumatic compression is closely examined along with a variety of pneumatic compression devices that currently exist in the market. A thorough review of the literature was conducted to evaluate the utility of intermittent pneumatic compression in the treatment of lower extremity venous stasis ulcers. Additionally, the author describes personal experience with the use of pneumatic compression on 10 patients with venous stasis ulcers at a single center. There is significant data supporting the use of IPC in patients with lymphophlebitic disease. Overall, ideal patient selection may be crucial. Previous data has shown that patients with high body mass index (&gt;33 kg/m2) and poor functional status (walking less than 200m a day) are related to poor ulcer healing. Therefore, a study that looks primarily at this group (as our small quality assurance [QA] project did) may show increased benefit in this population. It is clear that IPC is of benefit to some patient cohorts with lymphophlebitic disease. This advanced therapy would help patients who have failure of their calf muscle pump and an inability to improve it through other means. However, it is only part of an algorithm that includes: direct wound bed management, moisture control, possible primary venous disease intervention, physical therapy, weight loss, and improved nutrition.

Autoencoder networks extract latent variables and encode these variables in their connectomes

Advances in electron microscopy and data processing techniques are leading to increasingly large and complete microscale connectomes. At the same time, advances in artificial neural networks have produced model systems that perform comparably rich computations with perfectly specified connectivity. This raises an exciting scientific opportunity for the study of both biological and artificial neural networks: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that – even with well constrained neural dynamics – there are in principle many different connectomes that could support a given computation. Here, we define a tractable setting in which the problem of inferring circuit function from circuit connectivity can be analyzed in detail: the function of input compression and reconstruction, in an autoencoder network with a single hidden layer. Here, in general there is substantial ambiguity in the weights that can produce the same circuit function, because largely arbitrary changes to input weights can be undone by applying the inverse modifications to the output weights. However, we use mathematical arguments and simulations to show that adding simple, biologically motivated regularization of connectivity resolves this ambiguity in an interesting way: weights are constrained such that the latent variable structure underlying the inputs can be extracted from the weights by using nonlinear dimensionality reduction methods.