Multivariate Scheme Research Articles

High-speed hardware architecture for implementations of multivariate signature generations on FPGAs

Multivariate signature belongs to Multivariate-Quadratic-Equations Public Key Cryptography (MPKC), which is secure to quantum computer attacks. Compared with RSA and ECC, it is required to speed up multivariate signature implementations. A high-speed hardware architecture for signature generations of a multivariate scheme is proposed in this paper. The main computations of signature generations of multivariate schemes are additions, multiplications, inversions, and solving systems of linear equations (LSEs) in a finite field. Thus, we improve the finite field multiplications via using composite field expression and design a finite field inversion via using binary trees. Besides, we improve solving LSEs in a finite field based on a variant algorithm of Gauss-Jordan elimination and use the XOR gates to compute additions. We implement the high-speed hardware architecture based on the above improvements on an Altera Stratix Field-Programmable Gate Array (FPGA), which shows that it takes only 90 clock cycles and 0.9 μs to generate a multivariate signature. The comparison shows that the hardware architecture is much faster than other implementations.

Towards data storage in cryptographic systems: an efficient hardware architecture based on multivariate scheme for secure storage applications

Secure storage devices are a good example of the Internet of Things (IoTs), which require secure access control mechanisms via using the prominent pubic key cryptographic systems, e.g. RSA, ECC and related systems. However, quantum computer is in a position to attack RSA, ECC and other signature algorithms adopted by many storage devices. Therefore, storage security is facing severe threats. In this paper, an efficient hardware architecture based on multivariate scheme for storage devices is proposed. Multivariate scheme belongs to multivariate public key cryptography (MPKC), which uses affine transformations and central map transformations during cryptographic operations. The advantage of adopting multivariate scheme is that multivariate scheme is immune to quantum computer attack. The efficient hardware architecture is composed of processor module, cryptographic module, storage module, display module, power module, keyboard module, export module and terminal module (off-chip). We implement the hardware architecture on the methodology of TSMC-0.18 μm standard cell CMOS Application Specific Integrated Circuit. The implementation results show that the architecture based on multivariate scheme is very efficient and well suit for storage devices.

Plackett-Burman and Box-Behnken designs as chemometric tools for micro-determination of l-Ornithine

Plackett-Burman (PB) and Box-Behnken (BB) screening and response surface factorial designs were used to evaluate spectrophotometric and spectrofluorimetric approaches for the determination of l-Ornithine (ORN) as per se and in dietary supplements. Both approaches were based on the derivatization of the primary amino group of ORN via Hantzsch condensation reaction producing yellow coloured adducts (dihydrolutidine derivative). The reaction product was determined spectrophotometrically (method A) at λmax=327nm and spectrofluorimetrically (method B) at 480nm (λem) after excitation at 325nm (λex). A multivariate scheme was tailored to investigate the process numerical variables; reaction temperature, heating time, reagent volume, and pH implementing PB as a screening design followed by BB as an optimization strategy. Categorical factors including diluting solvent and sequence of addition were kept invariable. Responses of the reaction systems were the maximum absorbance (Y1) and maximum fluorescence intensity (Y2), correspondingly. Quality tools as well as ANOVA testing, before and after response transformation were used to decide upon the substantial variables. Following the optimization of reaction variables using desirability plots, calibration graphs were found to be rectilinear in the range of 6–14μg/mL and 0.4–1.2μg/mL for methods A and B, respectively. Both methods proved to be sensitive with detection limits (DL) of 337 and 85ng/mL, and quantitation limits (QL) of 1086 and 283ng/mL, for methods A and B, respectively. An interference study was performed using potential foreign species. No significant interference effect was observed on any of the proposed procedures. System performance was addressed following ICH guidelines and considering parameters such as linearity, detection and quantification limits, accuracy and precision, robustness and specificity.

Measurmenet of inflation forecast targeting. A proposal of methodological solution

The paper offers a methodological solution to measuring infl ation forecast targeting (IFT). IFT is a central bank’s procedure which consists in: producing its own infl ation forecast, revealing its results and making interest rates decisions on the basis of the forecast. Infl ation forecast targeting is implemented, to some extent, by any central bank implementing infl ation targeting framework. It facilitates the forward-looking attitude of the central banker. This forward-lookingness facilitates the expectations shaping and goals achievement. The literature on IFT is broad but it does not cover a concise framework for IFT analysis and measurement. The following contribution fi lls in this gap. It presents the IFT index. The idea of its elaboration is in line with any monetary policy qualitative aspects measurement: 5 aspects of IFT are distinguished, each of which having a system of points attribution described. These aspects are: forecasting methodology, declaration on the forecast role in the monetary policy, forecast publication, actual central bank’s practice and ex post forecast evaluation. Actual central bank’s practice covers the assessment of the central bank’s decision compatibility with the message of the forecast. The multivariate scheme of assessment which considers central bank’s fl exibility in the decision making process – guaranteed by the framework of the infl ation targeting – is elaborated. The other IFT aspects’ evaluations consist in the analysis of the information revealed. The IFT can be divided into two sub indices: one covering only the forecasting system transparency and the other one – for decisions compatibility check. The IFT index and sub-indices are universal in the sense of considering the IFT broadly on the basis of various central bank’s practice. The index can be used to cross-country and over time comparison – such an example is also presented in the paper. It also makes it possible to verify the hypotheses on the central banks’ consistency in implementing IFT – measured by IFT index – and the central banks’ performance.

Параметрический анализ параболической антенны космического аппарата с многовариантной схемой подкрепления

Параметрический анализ параболической антенны космического аппарата с многовариантной схемой подкрепления

Optimal Cost-Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load

Most of the existing fault detection methods rarely consider the cost-optimal maintenance policy. A novel multivariate Bayesian control approach is proposed, which enables the implementation of early fault detection for a helicopter gearbox with cost minimization maintenance policy under varying load. A continuous time hidden semi-Markov model (HSMM) is employed to describe the stochastic relationship between the unobservable states and observable observations of the gear system. Explicit expressions for the remaining useful life prediction are derived using HSMM. Considering the maintenance cost in fault detection, the multivariate Bayesian control scheme based on HSMM is developed; the objective is to minimize the long-run expected average cost per unit time. An effective computational algorithm in the semi-Markov decision process (SMDP) framework is designed to obtain the optimal control limit. A comparison with the multivariate Bayesian control chart based on hidden Markov model (HMM) and the traditional age-based replacement policy is given, which illustrates the effectiveness of the proposed approach.

CyclicSRP - A Multivariate Encryption Scheme with a Partially Cyclic Public Key

CyclicSRP - A Multivariate Encryption Scheme with a Partially Cyclic Public Key

Current State of Multivariate Cryptography

A review of the current state of multivariate public-key cryptosystems compares and contrasts the most promising multivariate schemes in digital signatures and public-key encryption as well as their security.

Robust Decentralized Nonlinear Control for a Twin Rotor MIMO System.

This article presents the design of a novel decentralized nonlinear multivariate control scheme for an underactuated, nonlinear and multivariate laboratory helicopter denominated the twin rotor MIMO system (TRMS). The TRMS is characterized by a coupling effect between rotor dynamics and the body of the model, which is due to the action-reaction principle originated in the acceleration and deceleration of the motor-propeller groups. The proposed controller is composed of two nested loops that are utilized to achieve stabilization and precise trajectory tracking tasks for the controlled position of the generalized coordinates of the TRMS. The nonlinear internal loop is used to control the electrical dynamics of the platform, and the nonlinear external loop allows the platform to be perfectly stabilized and positioned in space. Finally, we illustrate the theoretical control developments with a set of experiments in order to verify the effectiveness of the proposed nonlinear decentralized feedback controller, in which a comparative study with other controllers is performed, illustrating the excellent performance of the proposed robust decentralized control scheme in both stabilization and trajectory tracking tasks.

A Headway to QoS on Traffic Prediction over VANETs using RRSCM Statistical Classifier

In this paper, a novel throughput measurement forecast model is recommended for VANETs. The model is based on a statistical technique adopted and deployed over a high speed IP network traffic. Network traffic would always experience more QoS (Quality of Service) issues such as jitter, delay, packet loss and degradation due to very low bit rate codification too. Despite of all such dictated issues the traffic throughput is to be predicted with at most accuracy using a proposed multivariate analysis scheme represented as a RRSCM (Refined Regression Statistical Classifier Model) that optimizes parting parameters. Henceforth, the focus is towards the measurement methodology that estimates the traffic parameters that triggers to predict the accurate traffic and extemporize the QoS for the end-users. Finally, the proposed RRSCM classification model’s end-results are compared with the ANN (Artificial Neural Network) classification model to showcase its better act on the projected model

Regularity of non-stationary subdivision: a matrix approach

In this paper, we study scalar multivariate non-stationary subdivision schemes with integer dilation matrix M and present a unifying, general approach for checking their convergence and for determining their Hölder regularity (latter in the case M = mI, m ge 2). The combination of the concepts of asymptotic similarity and approximate sum rules allows us to link stationary and non-stationary settings and to employ recent advances in methods for exact computation of the joint spectral radius. As an application, we prove a recent conjecture by Dyn et al. on the Hölder regularity of the generalized Daubechies wavelets. We illustrate our results with several examples.

Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests

In this paper, we develop a statistical monitoring scheme for multivariate count data. In many applications involving multivariate count data, individual variables are not only correlated to each other, but also over-dispersed. Traditional statistical monitoring methods for multivariate count data that assume simple statistical models fail to fit the data collected when the underlying process is under normal working state, also referred to as the in-control state. Therefore, we propose a monitoring scheme which is based on the Poisson–multivariate Gaussian mixed model. Although such models are quite flexible, efficient statistical monitoring schemes for such models have not been developed. In this paper, we develop likelihood ratio test-based monitoring schemes that are shown to be superior to standard multivariate statistical monitoring schemes. The key challenge in developing likelihood ratio test for the Poisson–multivariate Gaussian mixed models is that the likelihood function can only be calculated by multidimensional numerical integration. We tackle this issue using an approximation of this complex likelihood function.

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

AbstractThe cloud-detection procedure developed by McNally and Watts (MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual (MMR) scheme for both simulated and real Advanced Infrared Sounder (AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR gener...

Best Linear Classification Rule for Multivariate Interval Screened Data

In classification analysis, the target variable is often in practice defined by an underlying multivariate interval screening scheme. This engenders the problem of properly characterizing the screened populations as well as that of obtaining a classification procedure. Such problems paved the way for the development of yet another linear classification procedure and the incorporation of a class of skew-elliptical distributions for describing evolutions in the populations. To render the linear procedure effective, this article considers derivation and properties of the classification procedure as well as efficient estimation. The procedure is illustrated in applications to real and simulation data.

The multivariate Horner scheme revisited

We compare two variants of a multivariate Horner scheme for polynomials of a certain total degree with respect to computational effort and backward error.

A multivariate public key cryptographic scheme

This paper presents a multivariate public key cryptographic scheme over a finite field with odd prime characteristic. The idea of embedding and layering is manifested in its construction. The security of the scheme is analyzed in detail, and this paper indicates that the scheme can withstand the up to date differential cryptanalysis. We give heuristic arguments to show that this scheme resists all known attacks.

A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence

A time series generator is presented, employing a robust three-level multivariate scheme for stochastic simulation of correlated processes. It preserves the essential statistical characteristics of historical data at three time scales (annual, monthly, daily), using a disaggregation approach. It also reproduces key properties of hydrometeorological and geophysical processes, namely the long-term persistence (Hurst–Kolmogorov behaviour), the periodicity and intermittency. Its efficiency is illustrated through two case studies in Greece. The first aims to generate monthly runoff and rainfall data at three reservoirs of the hydrosystem of Athens. The second involves the generation of daily rainfall for flood simulation at five rain gauges. In the first emphasis is given to long-term persistence – a dominant characteristic in the management of large-scale hydrosystems, comprising reservoirs with carry-over storage capacity. In the second we highlight to the consistent representation of intermittency and asymmetry of daily rainfall, and the distribution of annual daily maxima.

Alexithymia and alcohol dependence: Do different subtypes manifest different emotion regulations?

Objective: Alcohol abusers are vulnerable to emotional disabilities. Studies have shown that alexithymia is significantly associated with alcohol dependence and many other phenotypic descriptors. Given the high phenotypic heterogeneity that characterizes the alcohol dependence syndrome, the study aims to assess alexithymia features according to a multivariate classification scheme of alcoholism – the Lesch Alcoholism Typology (LAT). Methods: A total of 274 alcohol dependence outpatients (DSM diagnosis) were recruited from the Alcoholism Unit of the Psychiatric Service of Santa Maria University Hospital. After a detailed clinical observation, subjects were evaluated with the alcoholism classification system (LAT) and self-rated alexithymia with the Toronto Alexithymic Scale – 20 items (TAS-20). Results: Data showed that almost half of the alcohol-dependent individuals were defined as alexithymic cases (48.5%). “Organic” alcohol-dependent patients (type IV of Lesch classification) presented higher scores in TAS-20 when compared to other subtypes, even after controlling for potential confounding factors. Furthermore, the TAS-20 scores were related to educational level and family history of alcoholism. Conclusions: Results indicate that the alexithymia construct is an important psychological dimension in the characterization of alcohol dependence, especially the alcohol-dependent phenotypes that are more likely to present co-existing organic complications.

Subdivision schemes, network flows and linear optimization

We link regularity and smoothness analysis of multivariate vector subdivision schemes with network flow theory and with special linear optimization problems. This connection allows us to prove the existence of what we call optimal difference masks that posseses crucial properties unifying the regularity analysis of univariate and multivariate subdivision schemes. We also provide efficient optimization algorithms for construction of such optimal masks. Integrality of the corresponding optimal values leads to purely analytic proofs of Ck?regularity of subdivision.

Cryptanalysis of an extended MQ signature system HTTM

Since Multivatriate Quadratic (MQ)-based Public Key Cryptosystem (MPKC) has been one of the hot research fields of post-quantum cryptography, it becomes important to analyze the security of new MPKC schemes. Wang et al. proposed a novel multivariate signature scheme with Hash-based Tame Transformation and Minus (HTTM) in 2011. For this extended MQ-based signature, we can transform it into an SFLASH variant by splitting and merging HT transformation, and solve an equivalent private key corresponding to the public key of HTTM. Thus, the adversary can forge legitimate signature for any message by using this equivalent private key.